add some statistics and refactor

5 files changed, 145 insertions, 60 deletions

diff --git a/eval/src/tests/ann/nns-l2.h b/eval/src/tests/ann/nns-l2.h
index cfa5fed704f..dcad5f1bda6 100644
--- a/eval/src/tests/ann/nns-l2.h
+++ b/eval/src/tests/ann/nns-l2.h
@@ -1,8 +1,36 @@
 // Copyright 2019 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
 
 #pragma once
+#include <string.h>
 #include <vespa/vespalib/hwaccelrated/iaccelrated.h>
 
+template <typename T, size_t VLEN>
+static double hw_l2_sq_dist(const T * af, const T * bf, size_t sz)
+{   
+    constexpr const size_t OpsPerV = VLEN/sizeof(T);
+    typedef T V __attribute__ ((vector_size (VLEN), aligned(VLEN)));
+
+    const V * a = reinterpret_cast<const V *>(af);
+    const V * b = reinterpret_cast<const V *>(bf);
+
+    V tmp_diff;
+    V tmp_squa;
+    V tmp_sum;
+    memset(&tmp_sum, 0, sizeof(tmp_sum)); 
+
+    const size_t numOps = sz/OpsPerV;
+    for (size_t i = 0; i < numOps; ++i) {
+        tmp_diff = a[i] - b[i];
+        tmp_squa = tmp_diff * tmp_diff;
+        tmp_sum += tmp_squa;
+    }
+    double sum = 0;
+    for (size_t i = 0; i < OpsPerV; ++i) {
+        sum += tmp_sum[i];
+    }
+    return sum;
+}
+
 template <typename FltType = float>
 struct L2DistCalc {
     vespalib::hwaccelrated::IAccelrated::UP _hw;
@@ -11,7 +39,10 @@ struct L2DistCalc {
 
     using Arr = vespalib::ArrayRef<FltType>;
     using ConstArr = vespalib::ConstArrayRef<FltType>;
-    
+
+    double product(const FltType *v1, const FltType *v2, size_t sz) {
+        return _hw->dotProduct(v1, v2, sz);
+    }
     double product(ConstArr v1, ConstArr v2) {
         const FltType *p1 = v1.begin();
         const FltType *p2 = v2.begin();
@@ -28,9 +59,7 @@ struct L2DistCalc {
         return l2sq(tmp);
     }
     double l2sq_dist(ConstArr v1, ConstArr v2) {
-        std::vector<FltType> tmp;
-        tmp.resize(v1.size());
-        return l2sq_dist(v1, v2, Arr(tmp));
+        return hw_l2_sq_dist<FltType, 32>(v1.cbegin(), v2.cbegin(), v1.size());
     }
 };
 
diff --git a/eval/src/tests/ann/nns.h b/eval/src/tests/ann/nns.h
index 2e6666309bd..79c1aac4379 100644
--- a/eval/src/tests/ann/nns.h
+++ b/eval/src/tests/ann/nns.h
@@ -6,23 +6,28 @@
 #include "nns-l2.h"
 #include <memory>
 
+struct SqDist {
+    double distance;
+    explicit SqDist(double d) : distance(d) {}
+};
+
 struct NnsHit {
     uint32_t docid;
-    double sqDistance;
-    NnsHit(uint32_t di, double sqD)
-        : docid(di), sqDistance(sqD) {}
+    SqDist sq;
+    NnsHit(uint32_t di, SqDist sqD)
+        : docid(di), sq(sqD) {}
 };
 struct NnsHitComparatorLessDistance {
     bool operator() (const NnsHit &lhs, const NnsHit& rhs) const {
-        if (lhs.sqDistance > rhs.sqDistance) return false;
-        if (lhs.sqDistance < rhs.sqDistance) return true;
+        if (lhs.sq.distance > rhs.sq.distance) return false;
+        if (lhs.sq.distance < rhs.sq.distance) return true;
         return (lhs.docid > rhs.docid);
     }
 };
 struct NnsHitComparatorGreaterDistance {
     bool operator() (const NnsHit &lhs, const NnsHit& rhs) const {
-        if (lhs.sqDistance < rhs.sqDistance) return false;
-        if (lhs.sqDistance > rhs.sqDistance) return true;
+        if (lhs.sq.distance < rhs.sq.distance) return false;
+        if (lhs.sq.distance > rhs.sq.distance) return true;
         return (lhs.docid > rhs.docid);
     }
 };
@@ -58,3 +63,7 @@ make_annoy_nns(uint32_t numDims, const DocVectorAccess<float> &dva);
 extern
 std::unique_ptr<NNS<float>>
 make_rplsh_nns(uint32_t numDims, const DocVectorAccess<float> &dva);
+
+extern
+std::unique_ptr<NNS<float>>
+make_hnsw_nns(uint32_t numDims, const DocVectorAccess<float> &dva);
diff --git a/eval/src/tests/ann/sift_benchmark.cpp b/eval/src/tests/ann/sift_benchmark.cpp
index f64351166c1..451d4e1ba50 100644
--- a/eval/src/tests/ann/sift_benchmark.cpp
+++ b/eval/src/tests/ann/sift_benchmark.cpp
@@ -10,7 +10,7 @@
 #include <chrono>
 
 #define NUM_DIMS 128
-#define NUM_DOCS 1000000
+#define NUM_DOCS 250000
 #define NUM_Q 1000
 
 #include "doc_vector_access.h"
@@ -19,6 +19,7 @@
 #include "for-sift-top-k.h"
 
 std::vector<TopK> bruteforceResults;
+std::vector<float> tmp_v(NUM_DIMS);
 
 struct PointVector {
     float v[NUM_DIMS];
@@ -26,11 +27,17 @@ struct PointVector {
     operator ConstArr() const { return ConstArr(v, NUM_DIMS); }
 };
 
-static PointVector *generatedQueries =
-    (PointVector *) malloc(NUM_Q * sizeof(PointVector));
+static PointVector *aligned_alloc(size_t num) {
+    char *mem = (char *)malloc(num * sizeof(PointVector) + 512);
+    mem += 512;
+    size_t val = (size_t)mem;
+    size_t unalign = val % 512;
+    mem -= unalign;
+    return (PointVector *)mem;
+}
 
-static PointVector *generatedDocs =
-    (PointVector *) malloc(NUM_DOCS * sizeof(PointVector));
+static PointVector *generatedQueries = aligned_alloc(NUM_Q);
+static PointVector *generatedDocs = aligned_alloc(NUM_DOCS);
 
 struct DocVectorAdapter : public DocVectorAccess<float>
 {
@@ -42,7 +49,7 @@ struct DocVectorAdapter : public DocVectorAccess<float>
 
 double computeDistance(const PointVector &query, uint32_t docid) {
     const PointVector &docvector = generatedDocs[docid];
-    return l2distCalc.l2sq_dist(query, docvector);
+    return l2distCalc.l2sq_dist(query, docvector, tmp_v);
 }
 
 void read_queries(std::string fn) {
@@ -137,7 +144,6 @@ public:
 TopK bruteforce_nns(const PointVector &query) {
     TopK result;
     BfHitHeap heap(result.K);
-    std::vector<float> tmp_v(NUM_DIMS);
     for (uint32_t docid = 0; docid < NUM_DOCS; ++docid) {
         const PointVector &docvector = generatedDocs[docid];
         double d = l2distCalc.l2sq_dist(query, docvector, tmp_v);
@@ -173,6 +179,7 @@ void verifyBF(uint32_t qid) {
 
 TEST("require that brute force works") {
     TimePoint bef = std::chrono::steady_clock::now();
+    bruteforceResults.reserve(NUM_Q);
     for (uint32_t cnt = 0; cnt < NUM_Q; ++cnt) {
         const PointVector &query = generatedQueries[cnt];
         bruteforceResults.emplace_back(bruteforce_nns(query));
@@ -193,7 +200,7 @@ TopK find_with_nns(uint32_t sk, NNS_API &nns, uint32_t 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].sqDistance);
+        result.hits[i] = Hit(rv[i].docid, rv[i].sq.distance);
     }
     return result;
 }
@@ -207,6 +214,10 @@ void verify_nns_quality(uint32_t sk, NNS_API &nns, uint32_t qid) {
     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);
     }
@@ -223,57 +234,48 @@ void verify_nns_quality(uint32_t sk, NNS_API &nns, uint32_t qid) {
     }
 }
 
-TEST("require that Locality Sensitive Hashing mostly works") {
+void benchmark_nns(const char *name, NNS_API &nns, std::vector<uint32_t> sk_list) {
+    fprintf(stderr, "trying %s indexing...\n", name);
     TimePoint bef = std::chrono::steady_clock::now();
-    DocVectorAdapter adapter;
-    std::unique_ptr<NNS_API> nns = make_rplsh_nns(NUM_DIMS, adapter);
     for (uint32_t i = 0; i < NUM_DOCS; ++i) {
-        nns->addDoc(i);
+        nns.addDoc(i);
     }
     fprintf(stderr, "added %u documents...\n", NUM_DOCS);
+    find_with_nns(1, nns, 0);
     TimePoint aft = std::chrono::steady_clock::now();
-    fprintf(stderr, "build RPLSH index: %.3f ms\n", to_ms(aft - bef));
+    fprintf(stderr, "build %s index: %.3f ms\n", name, to_ms(aft - bef));
 
-    for (uint32_t search_k : { 200, 1000 }) {
+    for (uint32_t search_k : sk_list) {
         bef = std::chrono::steady_clock::now();
         for (int cnt = 0; cnt < NUM_Q; ++cnt) {
-            find_with_nns(search_k, *nns, cnt);
+            find_with_nns(search_k, nns, cnt);
         }
         aft = std::chrono::steady_clock::now();
-        fprintf(stderr, "timing for RPLSH search_k=%u: %.3f ms = %.3f ms per query\n",
-                search_k, to_ms(aft - bef), to_ms(aft - bef)/NUM_Q);
+        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);
         for (int cnt = 0; cnt < NUM_Q; ++cnt) {
-            verify_nns_quality(search_k, *nns, cnt);
+            verify_nns_quality(search_k, nns, cnt);
         }
     }
 }
 
-TEST("require that Indexing via NNS api mostly works") {
-    fprintf(stderr, "trying indexing...\n");
-    TimePoint bef = std::chrono::steady_clock::now();
+
+#if 1
+TEST("require that Locality Sensitive Hashing mostly works") {
     DocVectorAdapter adapter;
-    std::unique_ptr<NNS_API> nns = make_annoy_nns(NUM_DIMS, adapter);
-    for (uint32_t i = 0; i < NUM_DOCS; ++i) {
-        nns->addDoc(i);
-    }
-    fprintf(stderr, "added %u documents...\n", NUM_DOCS);
-    nns->topK(1, adapter.get(0), 1);
-    TimePoint aft = std::chrono::steady_clock::now();
-    fprintf(stderr, "build annoy index: %.3f ms\n", to_ms(aft - bef));
+    std::unique_ptr<NNS_API> nns = make_rplsh_nns(NUM_DIMS, adapter);
+    benchmark_nns("RPLSH", *nns, { 200, 1000 });
+}
+#endif
 
-    for (uint32_t search_k : { 10000, 20000 }) {
-        bef = std::chrono::steady_clock::now();
-        for (int cnt = 0; cnt < NUM_Q; ++cnt) {
-            find_with_nns(search_k, *nns, cnt);
-        }
-        aft = std::chrono::steady_clock::now();
-        fprintf(stderr, "timing for index search_k=%u: %.3f ms = %.3f ms per query\n",
-                search_k, to_ms(aft - bef), to_ms(aft - bef)/NUM_Q);
-        for (int cnt = 0; cnt < NUM_Q; ++cnt) {
-            verify_nns_quality(search_k, *nns, cnt);
-        }
-    }
+#if 1
+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
+
 
 int main(int argc, char **argv) {
     TEST_MASTER.init(__FILE__);
diff --git a/eval/src/tests/ann/xp-annoy-nns.cpp b/eval/src/tests/ann/xp-annoy-nns.cpp
index e5661c0c044..45392084c80 100644
--- a/eval/src/tests/ann/xp-annoy-nns.cpp
+++ b/eval/src/tests/ann/xp-annoy-nns.cpp
@@ -11,6 +11,12 @@ using V = vespalib::ConstArrayRef<float>;
 class AnnoyLikeNns;
 struct Node;
 
+static uint64_t plane_dist_cnt = 0;
+static uint64_t w_cen_dist_cnt = 0;
+static uint64_t leaf_split_cnt = 0;
+static uint64_t find_top_k_cnt = 0;
+static uint64_t find_cand_cnt = 0;
+
 using QueueNode = std::pair<double, Node *>;
 using NodeQueue = std::priority_queue<QueueNode>;
 
@@ -20,6 +26,7 @@ struct Node {
     virtual Node *addDoc(uint32_t docid, V vector, AnnoyLikeNns &meta) = 0;
     virtual int remove(uint32_t docid, V vector) = 0;
     virtual void findCandidates(std::set<uint32_t> &cands, V vector, NodeQueue &queue, double minDist) const = 0;
+    virtual void stats(std::vector<uint32_t> &depths) = 0;
 };
 
 struct LeafNode : public Node {
@@ -32,6 +39,7 @@ struct LeafNode : public Node {
     void findCandidates(std::set<uint32_t> &cands, V vector, NodeQueue &queue, double minDist) const override;
 
     Node *split(AnnoyLikeNns &meta);
+    virtual void stats(std::vector<uint32_t> &depths) override { depths.push_back(1); }
 };
 
 struct SplitNode : public Node {
@@ -48,6 +56,12 @@ struct SplitNode : public Node {
     void findCandidates(std::set<uint32_t> &cands, V vector, NodeQueue &queue, double minDist) const override;
 
     double planeDistance(V vector) const;
+    virtual void stats(std::vector<uint32_t> &depths) override {
+        size_t i = depths.size();
+        leftChildren->stats(depths);
+        rightChildren->stats(depths);
+        while (i < depths.size()) { ++depths[i++]; }
+    }
 };
 
 class AnnoyLikeNns : public NNS<float>
@@ -67,7 +81,10 @@ public:
         }
     }
 
+    void dumpStats();
+
     ~AnnoyLikeNns() {
+        dumpStats();
         for (Node *root : _roots) {
             delete root;
         }
@@ -97,11 +114,9 @@ public:
 double
 SplitNode::planeDistance(V vector) const
 {
+    ++plane_dist_cnt;
     assert(vector.size() == hyperPlane.size());
-    double dp = 0.0;
-    for (size_t i = 0; i < vector.size(); ++i) {
-        dp += vector[i] * hyperPlane[i];
-    }
+    double dp = l2distCalc.product(&vector[0], &hyperPlane[0], vector.size());
     return dp - offsetFromOrigo;
 }
 
@@ -167,6 +182,7 @@ struct WeightedCentroid {
         return r;
     }
     double weightedDistance(V vector) {
+        ++w_cen_dist_cnt;
         size_t sz = vector.size();
         for (size_t i = 0; i < sz; ++i) {
             tmp_vector[i] = vector[i] * cnt;
@@ -179,6 +195,7 @@ struct WeightedCentroid {
 Node *
 LeafNode::split(AnnoyLikeNns &meta)
 {
+    ++leaf_split_cnt;
     uint32_t dims = meta.dims();
     uint32_t retries = 3;
 retry:
@@ -232,6 +249,8 @@ retry:
 
     std::vector<uint32_t> leftDs;
     std::vector<uint32_t> rightDs;
+    leftDs.reserve(128);
+    rightDs.reserve(128);
 
     for (uint32_t docid : docids) {
         V vector = meta.getVector(docid);
@@ -260,9 +279,9 @@ retry:
 #endif
 
     LeafNode *newRightNode = new LeafNode();
-    newRightNode->docids = rightDs;
+    newRightNode->docids = std::move(rightDs);
     s->rightChildren = newRightNode;
-    this->docids = leftDs;
+    this->docids = std::move(leftDs);
     s->leftChildren = this;
     return s;
 }
@@ -327,6 +346,7 @@ SplitNode::findCandidates(std::set<uint32_t> &, V vector, NodeQueue &queue, doub
 std::vector<NnsHit>
 AnnoyLikeNns::topK(uint32_t k, Vector vector, uint32_t search_k)
 {
+    ++find_top_k_cnt;
     std::vector<float> tmp;
     tmp.resize(_numDims);
     vespalib::ArrayRef<float> tmpArr(tmp);
@@ -347,6 +367,7 @@ AnnoyLikeNns::topK(uint32_t k, Vector vector, uint32_t search_k)
         Node *n = top.second;
         queue.pop();
         n->findCandidates(candidates, vector, queue, md);
+        ++find_cand_cnt;
     }
 #if 0
     while (queue.size() > 0) {
@@ -357,7 +378,7 @@ AnnoyLikeNns::topK(uint32_t k, Vector vector, uint32_t search_k)
 #endif
     for (uint32_t docid : candidates) {
         double dist = l2distCalc.l2sq_dist(vector, _dva.get(docid), tmpArr);
-        NnsHit hit(docid, dist);
+        NnsHit hit(docid, SqDist(dist));
         r.push_back(hit);
     }
     std::sort(r.begin(), r.end(), NnsHitComparatorLessDistance());
@@ -365,6 +386,30 @@ AnnoyLikeNns::topK(uint32_t k, Vector vector, uint32_t search_k)
     return r;
 }
 
+void
+AnnoyLikeNns::dumpStats() {
+    fprintf(stderr, "stats for AnnoyLikeNns:\n");
+    fprintf(stderr, "planeDistance() calls: %zu\n", plane_dist_cnt);
+    fprintf(stderr, "weightedDistance() calls: %zu\n", w_cen_dist_cnt);
+    fprintf(stderr, "leaf split() calls: %zu\n", leaf_split_cnt);
+    fprintf(stderr, "topK() calls: %zu\n", find_top_k_cnt);
+    fprintf(stderr, "findCandidates() calls: %zu\n", find_cand_cnt);
+    std::vector<uint32_t> depths;
+    _roots[0]->stats(depths);
+    std::vector<uint32_t> counts;
+    for (uint32_t deep : depths) {
+        while (counts.size() <= deep) counts.push_back(0);
+        counts[deep]++;
+    }
+    fprintf(stderr, "depths for %zu leaves [\n", depths.size());
+    for (uint32_t deep = 0; deep < counts.size(); ++deep) {
+        if (counts[deep] > 0) {
+            fprintf(stderr, "%u deep count %u\n", deep, counts[deep]);
+        }
+    }
+    fprintf(stderr, "]\n");
+}
+
 std::unique_ptr<NNS<float>>
 make_annoy_nns(uint32_t numDims, const DocVectorAccess<float> &dva)
 {
diff --git a/eval/src/tests/ann/xp-lsh-nns.cpp b/eval/src/tests/ann/xp-lsh-nns.cpp
index 285985167c0..0ea119a9c70 100644
--- a/eval/src/tests/ann/xp-lsh-nns.cpp
+++ b/eval/src/tests/ann/xp-lsh-nns.cpp
@@ -231,7 +231,7 @@ RpLshNns::topK(uint32_t k, Vector vector, uint32_t search_k)
     std::vector<LshHit> best = heap.bestLshHits();
     size_t numHits = std::min((size_t)k, best.size());
     for (size_t i = 0; i < numHits; ++i) {
-        result.emplace_back(best[i].docid, best[i].distance);
+        result.emplace_back(best[i].docid, SqDist(best[i].distance));
     }
     return result;
 }