From cc3c709d6278ebd699d4f4c67f8f769c9b6fa177 Mon Sep 17 00:00:00 2001 From: Arne Juul Date: Wed, 12 Feb 2020 10:30:39 +0000 Subject: add and verify filter option split out common subroutines --- eval/src/tests/ann/CMakeLists.txt | 10 ++ eval/src/tests/ann/find-with-nns.h | 12 ++ eval/src/tests/ann/for-sift-top-k.h | 2 +- eval/src/tests/ann/gist_benchmark.cpp | 295 +++++++++++++++++++++++++++++++++ eval/src/tests/ann/nns.h | 26 +++ eval/src/tests/ann/quality-nns.h | 42 +++++ eval/src/tests/ann/remove-bm.cpp | 258 ++++++++-------------------- eval/src/tests/ann/sift_benchmark.cpp | 193 ++++++++++++++------- eval/src/tests/ann/verify-top-k.h | 27 +++ eval/src/tests/ann/xp-annoy-nns.cpp | 58 +++++++ eval/src/tests/ann/xp-hnsw-wrap.cpp | 28 ++++ eval/src/tests/ann/xp-hnswlike-nns.cpp | 121 ++++++++++++-- eval/src/tests/ann/xp-lsh-nns.cpp | 40 +++++ 13 files changed, 853 insertions(+), 259 deletions(-) create mode 100644 eval/src/tests/ann/find-with-nns.h create mode 100644 eval/src/tests/ann/gist_benchmark.cpp create mode 100644 eval/src/tests/ann/quality-nns.h create mode 100644 eval/src/tests/ann/verify-top-k.h (limited to 'eval') diff --git a/eval/src/tests/ann/CMakeLists.txt b/eval/src/tests/ann/CMakeLists.txt index 52b4d675d9c..34babf1412f 100644 --- a/eval/src/tests/ann/CMakeLists.txt +++ b/eval/src/tests/ann/CMakeLists.txt @@ -10,6 +10,16 @@ vespa_add_executable(eval_sift_benchmark_app vespaeval ) +vespa_add_executable(eval_gist_benchmark_app + SOURCES + gist_benchmark.cpp + xp-annoy-nns.cpp + xp-hnswlike-nns.cpp + xp-lsh-nns.cpp + DEPENDS + vespaeval +) + vespa_add_executable(eval_remove_bm_app SOURCES remove-bm.cpp diff --git a/eval/src/tests/ann/find-with-nns.h b/eval/src/tests/ann/find-with-nns.h new file mode 100644 index 00000000000..3481b403f86 --- /dev/null +++ b/eval/src/tests/ann/find-with-nns.h @@ -0,0 +1,12 @@ +// Copyright 2020 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. + +TopK find_with_nns(uint32_t sk, NNS_API &nns, uint32_t qid) { + TopK result; + const PointVector &qv = generatedQueries[qid]; + vespalib::ConstArrayRef 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; +} diff --git a/eval/src/tests/ann/for-sift-top-k.h b/eval/src/tests/ann/for-sift-top-k.h index ba91cb2aebc..8a659a507bc 100644 --- a/eval/src/tests/ann/for-sift-top-k.h +++ b/eval/src/tests/ann/for-sift-top-k.h @@ -6,7 +6,7 @@ struct TopK { static constexpr size_t K = 100; Hit hits[K]; - size_t recall(const TopK &other) { + size_t recall(const TopK &other) const { size_t overlap = 0; size_t i = 0; size_t j = 0; diff --git a/eval/src/tests/ann/gist_benchmark.cpp b/eval/src/tests/ann/gist_benchmark.cpp new file mode 100644 index 00000000000..45559fc2557 --- /dev/null +++ b/eval/src/tests/ann/gist_benchmark.cpp @@ -0,0 +1,295 @@ +// Copyright 2020 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. + +#include +#include +#include +#include +#include +#include +#include +#include + +#define NUM_DIMS 960 +#define NUM_DOCS 200000 +#define NUM_REACH 10000 +#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 bruteforceResults; + +struct PointVector { + float v[NUM_DIMS]; + using ConstArr = vespalib::ConstArrayRef; + 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 reinterpret_cast(mem); +} + +static PointVector *generatedQueries = aligned_alloc(NUM_Q); +static PointVector *generatedDocs = aligned_alloc(NUM_DOCS); + +struct DocVectorAdapter : public DocVectorAccess +{ + vespalib::ConstArrayRef 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); +} + +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 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 _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 bestHits() { + std::vector 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 < NUM_DOCS; ++docid) { + const PointVector &docvector = generatedDocs[docid]; + double d = l2distCalc.l2sq_dist(query, docvector); + Hit h(docid, d); + heap.maybe_use(h); + } + std::vector 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 all_c2; + for (uint32_t i = 0; i < NUM_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() != NUM_DOCS) return; + std::sort(all_c2.begin(), all_c2.end()); + for (uint32_t idx : { 1, 3, 10, 30, 100, 300, 1000, 3000, NUM_DOCS/2, NUM_DOCS-1}) { + fprintf(stderr, "c2-factor[%u] = %.3f\n", idx, all_c2[idx]); + } +} + +using NNS_API = NNS; + +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); + } +} + +#include "find-with-nns.h" +#include "verify-top-k.h" + +void timing_nns(const char *name, NNS_API &nns, std::vector 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); + } +} + +#include "quality-nns.h" + +template +void bm_nns_simple(const char *name, FUNC creator, std::vector sk_list) { + std::unique_ptr nnsp = creator(); + NNS_API &nns = *nnsp; + fprintf(stderr, "trying %s indexing...\n", name); + TimePoint bef = std::chrono::steady_clock::now(); + for (uint32_t i = 0; i < NUM_DOCS; ++i) { + nns.addDoc(i); + } + TimePoint aft = std::chrono::steady_clock::now(); + fprintf(stderr, "build %s index with %u docs: %.3f ms\n", name, NUM_DOCS, to_ms(aft - bef)); + timing_nns(name, nns, sk_list); + fprintf(stderr, "Quality for %s [A] clean build with %u documents:\n", name, NUM_DOCS); + quality_nns(nns, sk_list); +} + +template +void benchmark_nns(const char *name, FUNC creator, std::vector sk_list) { + bm_nns_simple(name, creator, sk_list); +} + +#if 0 +TEST("require that Locality Sensitive Hashing mostly works") { + DocVectorAdapter adapter; + auto creator = [&adapter]() { return make_rplsh_nns(NUM_DIMS, adapter); }; + benchmark_nns("RPLSH", creator, { 200, 1000 }); +} +#endif + +#if 0 +TEST("require that Annoy via NNS api mostly works") { + DocVectorAdapter adapter; + auto creator = [&adapter]() { return make_annoy_nns(NUM_DIMS, adapter); }; + benchmark_nns("Annoy", creator, { 8000, 10000 }); +} +#endif + +#if 1 +TEST("require that HNSW via NNS api mostly works") { + DocVectorAdapter adapter; + auto creator = [&adapter]() { return make_hnsw_nns(NUM_DIMS, adapter); }; + benchmark_nns("HNSW-like", creator, { 100, 150, 200 }); +} +#endif + +#if 0 +TEST("require that HNSW wrapped api mostly works") { + DocVectorAdapter adapter; + auto creator = [&adapter]() { return make_hnsw_wrap(NUM_DIMS, adapter); }; + benchmark_nns("HNSW-wrap", creator, { 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); +} diff --git a/eval/src/tests/ann/nns.h b/eval/src/tests/ann/nns.h index ffe2882188e..ef3e4b5d69c 100644 --- a/eval/src/tests/ann/nns.h +++ b/eval/src/tests/ann/nns.h @@ -37,6 +37,31 @@ struct NnsHitComparatorLessDocid { } }; +class BitVector { +private: + std::vector _bits; +public: + BitVector(size_t sz) : _bits((sz+63)/64) {} + BitVector& setBit(size_t idx) { + uint64_t mask = 1; + mask <<= (idx%64); + _bits[idx/64] |= mask; + return *this; + } + bool isSet(size_t idx) const { + uint64_t mask = 1; + mask <<= (idx%64); + uint64_t word = _bits[idx/64]; + return (word & mask) != 0; + } + BitVector& clearBit(size_t idx) { + uint64_t mask = 1; + mask <<= (idx%64); + _bits[idx/64] &= ~mask; + return *this; + } +}; + template class NNS { @@ -50,6 +75,7 @@ public: using Vector = vespalib::ConstArrayRef; virtual std::vector topK(uint32_t k, Vector vector, uint32_t search_k) = 0; + virtual std::vector topKfilter(uint32_t k, Vector vector, uint32_t search_k, const BitVector &blacklist) = 0; virtual ~NNS() {} protected: uint32_t _numDims; diff --git a/eval/src/tests/ann/quality-nns.h b/eval/src/tests/ann/quality-nns.h new file mode 100644 index 00000000000..9ac37f0ef04 --- /dev/null +++ b/eval/src/tests/ann/quality-nns.h @@ -0,0 +1,42 @@ +// Copyright 2020 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. + +bool reach_with_nns_k(NNS_API &nns, uint32_t docid, uint32_t k) { + const PointVector &qv = generatedDocs[docid]; + vespalib::ConstArrayRef query(qv.v, NUM_DIMS); + auto rv = nns.topK(k, query, k); + if (rv.size() != k) { + fprintf(stderr, "Result/K=%u from query for %u is %zu hits\n", + k, docid, rv.size()); + return false; + } + if (rv[0].docid != docid) { + if (rv[0].sq.distance != 0.0) + fprintf(stderr, "Expected/K=%u to find %u but got %u with sq distance %.3f\n", + k, docid, rv[0].docid, rv[0].sq.distance); + } + return (rv[0].docid == docid || rv[0].sq.distance == 0.0); +} + +void quality_nns(NNS_API &nns, std::vector sk_list) { + for (uint32_t search_k : sk_list) { + double sum_recall = 0; + for (int cnt = 0; cnt < NUM_Q; ++cnt) { + sum_recall += verify_nns_quality(search_k, nns, cnt); + } + fprintf(stderr, "Overall average recall: %.2f\n", sum_recall / NUM_Q); + } + for (uint32_t search_k : { 1, 10, 100, 1000 }) { + TimePoint bef = std::chrono::steady_clock::now(); + uint32_t reached = 0; + for (uint32_t i = 0; i < NUM_REACH; ++i) { + uint32_t target = i * (NUM_DOCS / NUM_REACH); + if (reach_with_nns_k(nns, target, search_k)) ++reached; + } + fprintf(stderr, "Could reach %u of %u documents with k=%u\n", + reached, NUM_REACH, search_k); + TimePoint aft = std::chrono::steady_clock::now(); + fprintf(stderr, "reach time k=%u: %.3f ms = %.3f ms/q\n", + search_k, to_ms(aft - bef), to_ms(aft - bef)/NUM_REACH); + if (reached == NUM_REACH) break; + } +} diff --git a/eval/src/tests/ann/remove-bm.cpp b/eval/src/tests/ann/remove-bm.cpp index be010552ab8..005f3804af9 100644 --- a/eval/src/tests/ann/remove-bm.cpp +++ b/eval/src/tests/ann/remove-bm.cpp @@ -13,6 +13,7 @@ #define NUM_DOCS 250000 #define NUM_DOCS_REMOVE 50000 #define EFFECTIVE_DOCS (NUM_DOCS - NUM_DOCS_REMOVE) +#define NUM_REACH 10000 #define NUM_Q 1000 #include "doc_vector_access.h" @@ -30,10 +31,10 @@ struct PointVector { }; static PointVector *aligned_alloc(size_t num) { - size_t sz = num * sizeof(PointVector); - double mega_bytes = sz / (1024.0*1024.0); + 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(sz + 512); + char *mem = (char *)malloc(num_bytes + 512); mem += 512; size_t val = (size_t)mem; size_t unalign = val % 512; @@ -221,83 +222,8 @@ TEST("require that brute force works") { } } -bool reach_with_nns_1(NNS_API &nns, uint32_t docid) { - const PointVector &qv = generatedDocs[docid]; - vespalib::ConstArrayRef 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 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 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 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); -} +#include "find-with-nns.h" +#include "verify-top-k.h" void timing_nns(const char *name, NNS_API &nns, std::vector sk_list) { for (uint32_t search_k : sk_list) { @@ -311,64 +237,22 @@ void timing_nns(const char *name, NNS_API &nns, std::vector sk_list) { } } -void quality_nns(NNS_API &nns, std::vector 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); -} +#include "quality-nns.h" -void benchmark_nns(const char *name, NNS_API &nns, std::vector sk_list) { +template +void bm_nns_simple(const char *name, FUNC creator, std::vector sk_list) { + std::unique_ptr nnsp = creator(); + NNS_API &nns = *nnsp; 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); + fprintf(stderr, "Quality for %s [A] 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); @@ -379,111 +263,115 @@ void benchmark_nns(const char *name, NNS_API &nns, std::vector sk_list 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); + fprintf(stderr, "Quality for %s [B] remove-damaged build with %u documents:\n", name, EFFECTIVE_DOCS); quality_nns(nns, sk_list); -#endif +} -#if 0 +template +void bm_nns_remove_old(const char *name, FUNC creator, std::vector sk_list) { + std::unique_ptr nnsp = creator(); + NNS_API &nns = *nnsp; 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; ++i) { nns.addDoc(i); } + for (uint32_t i = 0; i < NUM_DOCS_REMOVE; ++i) { + nns.removeDoc(EFFECTIVE_DOCS + 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); + fprintf(stderr, "Quality for %s [C] remove-oldest 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(); +template +void bm_nns_interleave(const char *name, FUNC creator, std::vector sk_list) { + std::unique_ptr nnsp = creator(); + NNS_API &nns = *nnsp; + TimePoint 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 < 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(addSecond + i); + nns.addDoc(EFFECTIVE_DOCS - NUM_DOCS_REMOVE + 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)); - + 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 with %u documents some churn:\n", name, EFFECTIVE_DOCS); + fprintf(stderr, "Quality for %s [D] realistic build with %u documents:\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); +template +void bm_nns_remove_old_add_new(const char *name, FUNC creator, std::vector sk_list) { + std::unique_ptr nnsp = creator(); + NNS_API &nns = *nnsp; + 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); } - 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)); - + for (uint32_t i = 0; i < NUM_DOCS_REMOVE; ++i) { + nns.removeDoc(EFFECTIVE_DOCS + i); + } + for (uint32_t i = 0; i < NUM_DOCS_REMOVE; ++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 with %u documents full churn:\n", name, EFFECTIVE_DOCS); + fprintf(stderr, "Quality for %s [E] remove old, add new build with %u documents:\n", name, EFFECTIVE_DOCS); quality_nns(nns, sk_list); -#endif +} + +template +void benchmark_nns(const char *name, FUNC creator, std::vector sk_list) { + bm_nns_simple(name, creator, sk_list); + bm_nns_remove_old(name, creator, sk_list); + bm_nns_interleave(name, creator, sk_list); + bm_nns_remove_old_add_new(name, creator, sk_list); } #if 0 TEST("require that Locality Sensitive Hashing mostly works") { DocVectorAdapter adapter; - std::unique_ptr nns = make_rplsh_nns(NUM_DIMS, adapter); - benchmark_nns("RPLSH", *nns, { 200, 1000 }); + auto creator = [&adapter]() { return make_rplsh_nns(NUM_DIMS, adapter); }; + benchmark_nns("RPLSH", creator, { 200, 1000 }); } #endif #if 0 TEST("require that Annoy via NNS api mostly works") { DocVectorAdapter adapter; - std::unique_ptr nns = make_annoy_nns(NUM_DIMS, adapter); - benchmark_nns("Annoy", *nns, { 8000, 10000 }); + auto creator = [&adapter]() { return make_annoy_nns(NUM_DIMS, adapter); }; + benchmark_nns("Annoy", creator, { 8000, 10000 }); } #endif #if 1 TEST("require that HNSW via NNS api mostly works") { DocVectorAdapter adapter; - std::unique_ptr nns = make_hnsw_nns(NUM_DIMS, adapter); - benchmark_nns("HNSW-like", *nns, { 100, 150, 200 }); + auto creator = [&adapter]() { return make_hnsw_nns(NUM_DIMS, adapter); }; + benchmark_nns("HNSW-like", creator, { 100, 150, 200 }); } #endif #if 0 TEST("require that HNSW wrapped api mostly works") { DocVectorAdapter adapter; - std::unique_ptr nns = make_hnsw_wrap(NUM_DIMS, adapter); - benchmark_nns("HNSW-wrap", *nns, { 100, 150, 200 }); + auto creator = [&adapter]() { return make_hnsw_wrap(NUM_DIMS, adapter); }; + benchmark_nns("HNSW-wrap", creator, { 100, 150, 200 }); } #endif diff --git a/eval/src/tests/ann/sift_benchmark.cpp b/eval/src/tests/ann/sift_benchmark.cpp index 022c9404f5d..5f3c16e127d 100644 --- a/eval/src/tests/ann/sift_benchmark.cpp +++ b/eval/src/tests/ann/sift_benchmark.cpp @@ -13,14 +13,15 @@ #define NUM_DIMS 128 #define NUM_DOCS 1000000 #define NUM_Q 1000 +#define NUM_REACH 10000 #include "doc_vector_access.h" #include "nns.h" #include "for-sift-hit.h" #include "for-sift-top-k.h" +#include "std-random.h" std::vector bruteforceResults; -std::vector tmp_v(NUM_DIMS); struct PointVector { float v[NUM_DIMS]; @@ -29,10 +30,10 @@ struct PointVector { }; static PointVector *aligned_alloc(size_t num) { - size_t sz = num * sizeof(PointVector); - double mega_bytes = sz / (1024.0*1024.0); + 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(sz + 512); + char *mem = (char *)malloc(num_bytes + 512); mem += 512; size_t val = (size_t)mem; size_t unalign = val % 512; @@ -53,7 +54,7 @@ struct DocVectorAdapter : public DocVectorAccess double computeDistance(const PointVector &query, uint32_t docid) { const PointVector &docvector = generatedDocs[docid]; - return l2distCalc.l2sq_dist(query, docvector, tmp_v); + return l2distCalc.l2sq_dist(query, docvector); } void read_queries(std::string fn) { @@ -151,7 +152,7 @@ TopK bruteforce_nns(const PointVector &query) { BfHitHeap heap(result.K); for (uint32_t docid = 0; docid < NUM_DOCS; ++docid) { const PointVector &docvector = generatedDocs[docid]; - double d = l2distCalc.l2sq_dist(query, docvector, tmp_v); + double d = l2distCalc.l2sq_dist(query, docvector); Hit h(docid, d); heap.maybe_use(h); } @@ -162,24 +163,58 @@ TopK bruteforce_nns(const PointVector &query) { return result; } +TopK bruteforce_nns_filter(const PointVector &query, const BitVector &blacklist) { + TopK result; + BfHitHeap heap(result.K); + for (uint32_t docid = 0; docid < NUM_DOCS; ++docid) { + if (blacklist.isSet(docid)) continue; + const PointVector &docvector = generatedDocs[docid]; + double d = l2distCalc.l2sq_dist(query, docvector); + Hit h(docid, d); + heap.maybe_use(h); + } + std::vector best = heap.bestHits(); + EXPECT_EQUAL(best.size(), result.K); + 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 all_c2; for (uint32_t i = 0; i < NUM_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) all_c2.push_back(dist / min_distance); } - if (all_c2.size() != NUM_DOCS) return; - std::sort(all_c2.begin(), all_c2.end()); - for (uint32_t idx : { 1, 3, 10, 30, 100, 300, 1000, 3000, NUM_DOCS/2, NUM_DOCS-1}) { - fprintf(stderr, "c2-factor[%u] = %.3f\n", idx, all_c2[idx]); +} + +void timing_bf_filter(int percent) +{ + BitVector blacklist(NUM_DOCS); + RndGen rnd; + for (uint32_t idx = 0; idx < NUM_DOCS; ++idx) { + if (rnd.nextUniform() < 0.01 * percent) { + blacklist.setBit(idx); + } else { + blacklist.clearBit(idx); + } + } + TimePoint bef = std::chrono::steady_clock::now(); + for (int cnt = 0; cnt < NUM_Q; ++cnt) { + const PointVector &qv = generatedQueries[cnt]; + auto res = bruteforce_nns_filter(qv, blacklist); + EXPECT_TRUE(res.hits[res.K - 1].distance > 0.0); } + TimePoint aft = std::chrono::steady_clock::now(); + fprintf(stderr, "timing for bruteforce filter %d %%: %.3f ms = %.3f ms/q\n", + percent, to_ms(aft - bef), to_ms(aft - bef)/NUM_Q); } TEST("require that brute force works") { @@ -195,52 +230,90 @@ TEST("require that brute force works") { for (int cnt = 0; cnt < NUM_Q; cnt = (cnt+1)*2) { verifyBF(cnt); } +#if 1 + for (uint32_t filter_percent : { 0, 1, 10, 50, 90, 95, 99 }) { + timing_bf_filter(filter_percent); + } +#endif } using NNS_API = NNS; -TopK find_with_nns(uint32_t sk, NNS_API &nns, uint32_t qid) { - TopK result; +size_t search_with_filter(uint32_t sk, NNS_API &nns, uint32_t qid, + const BitVector &blacklist) +{ const PointVector &qv = generatedQueries[qid]; vespalib::ConstArrayRef 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); + auto rv = nns.topKfilter(100, query, sk, blacklist); + return rv.size(); +} + +#include "find-with-nns.h" +#include "verify-top-k.h" + +void verify_with_filter(uint32_t sk, NNS_API &nns, uint32_t qid, + const BitVector &blacklist) +{ + const PointVector &qv = generatedQueries[qid]; + auto expected = bruteforce_nns_filter(qv, blacklist); + vespalib::ConstArrayRef query(qv.v, NUM_DIMS); + auto rv = nns.topKfilter(expected.K, query, sk, blacklist); + TopK actual; + for (size_t i = 0; i < actual.K; ++i) { + actual.hits[i] = Hit(rv[i].docid, rv[i].sq.distance); } - return result; + verify_top_k(expected, actual, sk, qid); } -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); +void timing_nns_filter(const char *name, NNS_API &nns, + std::vector sk_list, int percent) +{ + BitVector blacklist(NUM_DOCS); + RndGen rnd; + for (uint32_t idx = 0; idx < NUM_DOCS; ++idx) { + if (rnd.nextUniform() < 0.01 * percent) { + blacklist.setBit(idx); + } else { + blacklist.clearBit(idx); + } + } + for (uint32_t search_k : sk_list) { + TimePoint bef = std::chrono::steady_clock::now(); + for (int cnt = 0; cnt < NUM_Q; ++cnt) { + uint32_t nh = search_with_filter(search_k, nns, cnt, blacklist); + EXPECT_EQUAL(nh, 100u); + } + TimePoint aft = std::chrono::steady_clock::now(); + fprintf(stderr, "timing for %s filter %d %% search_k=%u: %.3f ms = %.3f ms/q\n", + name, percent, search_k, to_ms(aft - bef), to_ms(aft - bef)/NUM_Q); +#if 0 + fprintf(stderr, "Quality check for %s filter %d %%:\n", name, percent); + for (int cnt = 0; cnt < NUM_Q; ++cnt) { + verify_with_filter(search_k, nns, cnt, blacklist); } - sum_error += factor; - c_factor = std::max(c_factor, factor); +#endif } - 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); - if (qid == 6) { - for (size_t i = 0; i < 10; ++i) { - fprintf(stderr, "topk[%zu] BF{%u %.3f} index{%u %.3f}\n", - i, - perfect.hits[i].docid, perfect.hits[i].distance, - result.hits[i].docid, result.hits[i].distance); +} + +void timing_nns(const char *name, NNS_API &nns, std::vector 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 benchmark_nns(const char *name, NNS_API &nns, std::vector sk_list) { +#include "quality-nns.h" + +template +void benchmark_nns(const char *name, FUNC creator, std::vector sk_list) { fprintf(stderr, "trying %s indexing...\n", name); + std::unique_ptr nnsp = creator(); + NNS_API &nns = *nnsp; TimePoint bef = std::chrono::steady_clock::now(); for (uint32_t i = 0; i < NUM_DOCS; ++i) { nns.addDoc(i); @@ -250,50 +323,44 @@ void benchmark_nns(const char *name, NNS_API &nns, std::vector sk_list TimePoint aft = std::chrono::steady_clock::now(); fprintf(stderr, "build %s index: %.3f ms\n", name, to_ms(aft - bef)); - 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); - } - 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); - for (int cnt = 0; cnt < NUM_Q; ++cnt) { - verify_nns_quality(search_k, nns, cnt); - } + fprintf(stderr, "Timings for %s :\n", name); + timing_nns(name, nns, sk_list); + for (uint32_t filter_percent : { 0, 1, 10, 50, 90, 95, 99 }) { + timing_nns_filter(name, nns, sk_list, filter_percent); } + fprintf(stderr, "Quality for %s :\n", name); + quality_nns(nns, sk_list); } - -#if 1 +#if 0 TEST("require that Locality Sensitive Hashing mostly works") { DocVectorAdapter adapter; - std::unique_ptr nns = make_rplsh_nns(NUM_DIMS, adapter); - benchmark_nns("RPLSH", *nns, { 200, 1000 }); + auto creator = [&adapter]() { return make_rplsh_nns(NUM_DIMS, adapter); }; + benchmark_nns("RPLSH", creator, { 200, 1000 }); } #endif #if 1 TEST("require that Annoy via NNS api mostly works") { DocVectorAdapter adapter; - std::unique_ptr nns = make_annoy_nns(NUM_DIMS, adapter); - benchmark_nns("Annoy", *nns, { 8000, 10000 }); + auto creator = [&adapter]() { return make_annoy_nns(NUM_DIMS, adapter); }; + benchmark_nns("Annoy", creator, { 8000, 10000 }); } #endif #if 1 TEST("require that HNSW via NNS api mostly works") { DocVectorAdapter adapter; - std::unique_ptr nns = make_hnsw_nns(NUM_DIMS, adapter); - benchmark_nns("HNSW-like", *nns, { 100, 150, 200 }); + auto creator = [&adapter]() { return make_hnsw_nns(NUM_DIMS, adapter); }; + benchmark_nns("HNSW-like", creator, { 100, 150, 200 }); } #endif #if 0 TEST("require that HNSW wrapped api mostly works") { DocVectorAdapter adapter; - std::unique_ptr nns = make_hnsw_wrap(NUM_DIMS, adapter); - benchmark_nns("HNSW-wrap", *nns, { 100, 150, 200 }); + auto creator = [&adapter]() { return make_hnsw_wrap(NUM_DIMS, adapter); }; + benchmark_nns("HNSW-wrap", creator, { 100, 150, 200 }); } #endif diff --git a/eval/src/tests/ann/verify-top-k.h b/eval/src/tests/ann/verify-top-k.h new file mode 100644 index 00000000000..220c273d017 --- /dev/null +++ b/eval/src/tests/ann/verify-top-k.h @@ -0,0 +1,27 @@ +// Copyright 2020 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. + +int verify_top_k(const TopK &perfect, const TopK &result, uint32_t sk, uint32_t 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); + return recall; +} + +int verify_nns_quality(uint32_t sk, NNS_API &nns, uint32_t qid) { + TopK perfect = bruteforceResults[qid]; + TopK result = find_with_nns(sk, nns, qid); + return verify_top_k(perfect, result, sk, qid); +} diff --git a/eval/src/tests/ann/xp-annoy-nns.cpp b/eval/src/tests/ann/xp-annoy-nns.cpp index f022aae5974..213e583d95a 100644 --- a/eval/src/tests/ann/xp-annoy-nns.cpp +++ b/eval/src/tests/ann/xp-annoy-nns.cpp @@ -27,6 +27,7 @@ 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 &cands, V vector, NodeQueue &queue, double minDist) const = 0; + virtual void filterCandidates(std::set &cands, V vector, NodeQueue &queue, double minDist, const BitVector &blacklist) const = 0; virtual void stats(std::vector &depths) = 0; }; @@ -38,6 +39,7 @@ struct LeafNode : public Node { Node *addDoc(uint32_t docid, V vector, AnnoyLikeNns &meta) override; int remove(uint32_t docid, V vector) override; void findCandidates(std::set &cands, V vector, NodeQueue &queue, double minDist) const override; + void filterCandidates(std::set &cands, V vector, NodeQueue &queue, double minDist, const BitVector &blacklist) const override; Node *split(AnnoyLikeNns &meta); virtual void stats(std::vector &depths) override { depths.push_back(1); } @@ -55,6 +57,7 @@ struct SplitNode : public Node { Node *addDoc(uint32_t docid, V vector, AnnoyLikeNns &meta) override; int remove(uint32_t docid, V vector) override; void findCandidates(std::set &cands, V vector, NodeQueue &queue, double minDist) const override; + void filterCandidates(std::set &cands, V vector, NodeQueue &queue, double minDist, const BitVector &blacklist) const override; double planeDistance(V vector) const; virtual void stats(std::vector &depths) override { @@ -106,6 +109,8 @@ public: } std::vector topK(uint32_t k, Vector vector, uint32_t search_k) override; + std::vector topKfilter(uint32_t k, Vector vector, uint32_t search_k, const BitVector &bitvector) override; + V getVector(uint32_t docid) const { return _dva.get(docid); } double uniformRnd() { return _rndGen.nextUniform(); } uint32_t dims() const { return _numDims; } @@ -304,6 +309,16 @@ LeafNode::findCandidates(std::set &cands, V, NodeQueue &, double) cons } } +void +LeafNode::filterCandidates(std::set &cands, V, NodeQueue &, double, const BitVector &blacklist) const +{ + for (uint32_t d : docids) { + if (blacklist.isSet(d)) continue; + cands.insert(d); + } +} + + SplitNode::~SplitNode() { delete leftChildren; @@ -344,6 +359,15 @@ SplitNode::findCandidates(std::set &, V vector, NodeQueue &queue, doub queue.push(std::make_pair(std::min(d, minDist), rightChildren)); } +void +SplitNode::filterCandidates(std::set &, V vector, NodeQueue &queue, double minDist, const BitVector &) const +{ + double d = planeDistance(vector); + // fprintf(stderr, "push 2 nodes dist %g\n", d); + queue.push(std::make_pair(std::min(-d, minDist), leftChildren)); + queue.push(std::make_pair(std::min(d, minDist), rightChildren)); +} + std::vector AnnoyLikeNns::topK(uint32_t k, Vector vector, uint32_t search_k) { @@ -387,6 +411,40 @@ AnnoyLikeNns::topK(uint32_t k, Vector vector, uint32_t search_k) return r; } +std::vector +AnnoyLikeNns::topKfilter(uint32_t k, Vector vector, uint32_t search_k, const BitVector &blacklist) +{ + ++find_top_k_cnt; + std::vector r; + r.reserve(k); + std::set candidates; + NodeQueue queue; + for (Node *root : _roots) { + double dist = std::numeric_limits::max(); + queue.push(std::make_pair(dist, root)); + } + while ((candidates.size() < std::max(k, search_k)) && (queue.size() > 0)) { + const QueueNode& top = queue.top(); + double md = top.first; + // fprintf(stderr, "find candidates: node with min distance %g\n", md); + Node *n = top.second; + queue.pop(); + n->filterCandidates(candidates, vector, queue, md, blacklist); + ++find_cand_cnt; + } + for (uint32_t docid : candidates) { + if (blacklist.isSet(docid)) continue; + double dist = l2distCalc.l2sq_dist(vector, _dva.get(docid)); + NnsHit hit(docid, SqDist(dist)); + r.push_back(hit); + } + std::sort(r.begin(), r.end(), NnsHitComparatorLessDistance()); + while (r.size() > k) r.pop_back(); + return r; +} + + + void AnnoyLikeNns::dumpStats() { fprintf(stderr, "stats for AnnoyLikeNns:\n"); diff --git a/eval/src/tests/ann/xp-hnsw-wrap.cpp b/eval/src/tests/ann/xp-hnsw-wrap.cpp index 3eb01142dcd..45c7a974254 100644 --- a/eval/src/tests/ann/xp-hnsw-wrap.cpp +++ b/eval/src/tests/ann/xp-hnsw-wrap.cpp @@ -46,6 +46,34 @@ public: } return result; } + + std::vector topKfilter(uint32_t k, Vector vector, uint32_t search_k, const BitVector &blacklist) override { + std::vector reversed; + uint32_t adjusted_k = k+4; + uint32_t adjusted_sk = search_k+4; + for (int retry = 0; (retry < 5) && (reversed.size() < k); ++retry) { + reversed.clear(); + _hnsw.setEf(adjusted_sk); + auto priQ = _hnsw.searchKnn(vector.cbegin(), adjusted_k); + while (! priQ.empty()) { + auto pair = priQ.top(); + if (! blacklist.isSet(pair.second)) { + reversed.emplace_back(pair.second, SqDist(pair.first)); + } + priQ.pop(); + } + double got = 1 + reversed.size(); + double factor = 1.25 * k / got; + adjusted_k *= factor; + adjusted_sk *= factor; + } + std::vector result; + while (result.size() < k && !reversed.empty()) { + result.push_back(reversed.back()); + reversed.pop_back(); + } + return result; + } }; std::unique_ptr> diff --git a/eval/src/tests/ann/xp-hnswlike-nns.cpp b/eval/src/tests/ann/xp-hnswlike-nns.cpp index 5cdbdd8efa3..90fc0fe2e92 100644 --- a/eval/src/tests/ann/xp-hnswlike-nns.cpp +++ b/eval/src/tests/ann/xp-hnswlike-nns.cpp @@ -32,6 +32,11 @@ static size_t distcalls_heuristic; static size_t distcalls_shrink; static size_t distcalls_refill; static size_t refill_needed_calls; +static size_t shrink_needed_calls; +static size_t disconnected_weak_links; +static size_t disconnected_for_symmetry; +static size_t select_n_full; +static size_t select_n_partial; struct LinkList : std::vector { @@ -76,6 +81,7 @@ struct VisitedSet ptr = (Mark *)malloc(size * sizeof(Mark)); curval = -1; sz = size; + clear(); } void clear() { ++curval; @@ -99,8 +105,9 @@ struct VisitedSetPool VisitedSet &get(size_t size) { if (size > lastUsed->sz) { lastUsed = std::make_unique(size*2); + } else { + lastUsed->clear(); } - lastUsed->clear(); return *lastUsed; } }; @@ -214,6 +221,10 @@ public: void search_layer(Vector vector, FurthestPriQ &w, uint32_t ef, uint32_t searchLevel); + void search_layer_with_filter(Vector vector, FurthestPriQ &w, + uint32_t ef, uint32_t searchLevel, + const BitVector &blacklist); + bool haveCloserDistance(HnswHit e, const LinkList &r) const { for (uint32_t prevId : r) { double dist = distance(e.docid, prevId); @@ -278,6 +289,10 @@ public: fprintf(stderr, "distance calls for refill: %zu is %.3f per op\n", distcalls_refill, distcalls_refill / div); fprintf(stderr, "distance calls for other: %zu is %.3f per op\n", distcalls_other, distcalls_other / div); fprintf(stderr, "refill needed calls: %zu is %.3f per op\n", refill_needed_calls, refill_needed_calls / div); + fprintf(stderr, "shrink needed calls: %zu is %.3f per op\n", shrink_needed_calls, shrink_needed_calls / div); + fprintf(stderr, "disconnected weak links: %zu is %.3f per op\n", disconnected_weak_links, disconnected_weak_links / div); + fprintf(stderr, "disconnected for symmetry: %zu is %.3f per op\n", disconnected_for_symmetry, disconnected_for_symmetry / div); + fprintf(stderr, "select neighbors: partial %zu vs full %zu\n", select_n_partial, select_n_full); } } @@ -315,10 +330,19 @@ public: LinkList lostLinks; LinkList oldLinks = links; links = remove_weakest(distances, maxLinks, lostLinks); +#define KEEP_SYM +#ifdef KEEP_SYM for (uint32_t lost_id : lostLinks) { + ++disconnected_for_symmetry; remove_link_from(lost_id, shrink_id, level); + } +#define DO_REFILL_AFTER_KEEP_SYM +#ifdef DO_REFILL_AFTER_KEEP_SYM + for (uint32_t lost_id : lostLinks) { refill_ifneeded(lost_id, oldLinks, level); } +#endif +#endif } void each_shrink_ifneeded(const LinkList &neighbors, uint32_t level); @@ -337,7 +361,9 @@ public: } remove_link_from(n_id, docid, level); } - for (uint32_t n_id : my_links) { + while (! my_links.empty()) { + uint32_t n_id = my_links.back(); + my_links.pop_back(); refill_ifneeded(n_id, my_links, level); } } @@ -363,12 +389,12 @@ public: ++distcalls_other; HnswHit entryPoint(_entryId, SqDist(entryDist)); int searchLevel = _entryLevel; - FurthestPriQ w; - w.push(entryPoint); while (searchLevel > 0) { - search_layer(vector, w, std::min(k, search_k), searchLevel); + entryPoint = search_layer_simple(vector, entryPoint, searchLevel); --searchLevel; } + FurthestPriQ w; + w.push(entryPoint); search_layer(vector, w, std::max(k, search_k), 0); while (w.size() > k) { w.pop(); @@ -381,8 +407,11 @@ public: } return result; } + + std::vector topKfilter(uint32_t k, Vector vector, uint32_t search_k, const BitVector &blacklist) override; }; + double HnswLikeNns::distance(Vector v, uint32_t b) const { @@ -390,12 +419,40 @@ HnswLikeNns::distance(Vector v, uint32_t b) const return l2distCalc.l2sq_dist(v, w); } +std::vector +HnswLikeNns::topKfilter(uint32_t k, Vector vector, uint32_t search_k, const BitVector &blacklist) +{ + std::vector result; + if (_entryLevel < 0) return result; + double entryDist = distance(vector, _entryId); + ++distcalls_other; + HnswHit entryPoint(_entryId, SqDist(entryDist)); + int searchLevel = _entryLevel; + while (searchLevel > 0) { + entryPoint = search_layer_simple(vector, entryPoint, searchLevel); + --searchLevel; + } + FurthestPriQ w; + w.push(entryPoint); + search_layer_with_filter(vector, w, std::max(k, search_k), 0, blacklist); + NearestList tmp = w.steal(); + std::sort(tmp.begin(), tmp.end(), LesserDist()); + result.reserve(std::min((size_t)k, tmp.size())); + for (const auto & hit : tmp) { + if (blacklist.isSet(hit.docid)) continue; + result.emplace_back(hit.docid, SqDist(hit.dist)); + if (result.size() == k) break; + } + return result; +} + void HnswLikeNns::each_shrink_ifneeded(const LinkList &neighbors, uint32_t level) { uint32_t maxLinks = (level > 0) ? _M : (2 * _M); for (uint32_t old_id : neighbors) { LinkList &oldLinks = getLinkList(old_id, level); if (oldLinks.size() > maxLinks) { + ++shrink_needed_calls; shrink_links(old_id, maxLinks, level); } } @@ -437,6 +494,44 @@ HnswLikeNns::search_layer(Vector vector, FurthestPriQ &w, return; } +void +HnswLikeNns::search_layer_with_filter(Vector vector, FurthestPriQ &w, + uint32_t ef, uint32_t searchLevel, + const BitVector &blacklist) +{ + NearestPriQ candidates; + VisitedSet &visited = _visitedSetPool.get(_nodes.size()); + + for (const HnswHit & entry : w.peek()) { + candidates.push(entry); + visited.mark(entry.docid); + if (blacklist.isSet(entry.docid)) ++ef; + } + double limd = std::numeric_limits::max(); + while (! candidates.empty()) { + HnswHit cand = candidates.top(); + if (cand.dist > limd) { + break; + } + candidates.pop(); + for (uint32_t e_id : getLinkList(cand.docid, searchLevel)) { + if (visited.isMarked(e_id)) continue; + visited.mark(e_id); + double e_dist = distance(vector, e_id); + ++distcalls_search_layer; + if (e_dist < limd) { + candidates.emplace(e_id, SqDist(e_dist)); + if (blacklist.isSet(e_id)) continue; + w.emplace(e_id, SqDist(e_dist)); + if (w.size() > ef) { + w.pop(); + limd = w.top().dist; + } + } + } + } +} + LinkList HnswLikeNns::remove_weakest(const NearestList &neighbors, uint32_t curMax, LinkList &lost) const { @@ -458,13 +553,13 @@ HnswLikeNns::remove_weakest(const NearestList &neighbors, uint32_t curMax, LinkL return result; } +#define NO_BACKFILL #ifdef NO_BACKFILL LinkList HnswLikeNns::select_neighbors(const NearestList &neighbors, uint32_t curMax) const { LinkList result; result.reserve(curMax+1); - bool needFiltering = (neighbors.size() > curMax); NearestPriQ w; for (const auto & entry : neighbors) { w.push(entry); @@ -472,12 +567,16 @@ HnswLikeNns::select_neighbors(const NearestList &neighbors, uint32_t curMax) con while (! w.empty()) { HnswHit e = w.top(); w.pop(); - if (needFiltering && haveCloserDistance(e, result)) { + if (haveCloserDistance(e, result)) { continue; } result.push_back(e.docid); - if (result.size() == curMax) return result; + if (result.size() == curMax) { + ++select_n_full; + return result; + } } + ++select_n_partial; return result; } #else @@ -502,10 +601,10 @@ HnswLikeNns::select_neighbors(const NearestList &neighbors, uint32_t curMax) con result.push_back(e.docid); if (result.size() == curMax) return result; } - if (result.size() * 4 < curMax) { + if (result.size() * 4 < _M) { for (uint32_t fill_id : backfill) { result.push_back(fill_id); - if (result.size() * 4 >= curMax) break; + if (result.size() * 2 >= _M) break; } } return result; @@ -576,7 +675,9 @@ HnswLikeNns::dumpStats() const { for (uint32_t n_id : link_list) { const LinkList &neigh_list = getLinkList(n_id, 0); if (! neigh_list.has_link_to(id)) { +#ifdef KEEP_SYM fprintf(stderr, "BAD: %u has link to neighbor %u, but backlink is missing\n", id, n_id); +#endif all_sym = false; } } diff --git a/eval/src/tests/ann/xp-lsh-nns.cpp b/eval/src/tests/ann/xp-lsh-nns.cpp index 0ea119a9c70..c028a07a9d7 100644 --- a/eval/src/tests/ann/xp-lsh-nns.cpp +++ b/eval/src/tests/ann/xp-lsh-nns.cpp @@ -118,6 +118,7 @@ public: } } std::vector topK(uint32_t k, Vector vector, uint32_t search_k) override; + std::vector topKfilter(uint32_t k, Vector vector, uint32_t search_k, const BitVector &bitvector) override; V getVector(uint32_t docid) const { return _dva.get(docid); } double uniformRnd() { return _rndGen.nextUniform(); } @@ -195,6 +196,45 @@ public: } }; +std::vector +RpLshNns::topKfilter(uint32_t k, Vector vector, uint32_t search_k, const BitVector &blacklist) +{ + std::vector result; + result.reserve(k); + + std::vector tmp(_numDims); + vespalib::ArrayRef tmpArr(tmp); + + LsMaskHash query_hash = mask_hash_from_pv(vector, _transformationMatrix); + LshHitHeap heap(std::max(k, search_k)); + int limit_hash_dist = 99999; + int skipCnt = 0; + int fullCnt = 0; + int whdcCnt = 0; + size_t docidLimit = _generated_doc_hashes.size(); + for (uint32_t docid = 0; docid < docidLimit; ++docid) { + if (blacklist.isSet(docid)) continue; + int hd = hash_dist(query_hash, _generated_doc_hashes[docid]); + if (hd <= limit_hash_dist) { + ++fullCnt; + double dist = l2distCalc.l2sq_dist(vector, _dva.get(docid), tmpArr); + LshHit h(docid, dist, hd); + if (heap.maybe_use(h)) { + ++whdcCnt; + limit_hash_dist = heap.limitHashDistance(); + } + } else { + ++skipCnt; + } + } + std::vector 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, SqDist(best[i].distance)); + } + return result; +} + std::vector RpLshNns::topK(uint32_t k, Vector vector, uint32_t search_k) { -- cgit v1.2.3