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Diffstat (limited to 'eval/src/tests/ann/gist_benchmark.cpp')
-rw-r--r-- | eval/src/tests/ann/gist_benchmark.cpp | 295 |
1 files changed, 295 insertions, 0 deletions
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 <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 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<TopK> bruteforceResults; + +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 reinterpret_cast<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); +} + +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 < 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<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 < 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<float>; + +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<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); + } +} + +#include "quality-nns.h" + +template <typename FUNC> +void bm_nns_simple(const char *name, FUNC creator, std::vector<uint32_t> sk_list) { + std::unique_ptr<NNS_API> 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 <typename FUNC> +void benchmark_nns(const char *name, FUNC creator, std::vector<uint32_t> 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); +} |