milvus/internal/core/unittest/test_indexing.cpp
xige-16 515d0369de
Support string type in segcore (#16546)
Signed-off-by: xige-16 <xi.ge@zilliz.com>
Co-authored-by: dragondriver <jiquan.long@zilliz.com>

Co-authored-by: dragondriver <jiquan.long@zilliz.com>
2022-04-29 13:35:49 +08:00

334 lines
13 KiB
C++

// Copyright (C) 2019-2020 Zilliz. All rights reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance
// with the License. You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software distributed under the License
// is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
// or implied. See the License for the specific language governing permissions and limitations under the License
#include <gtest/gtest.h>
#include <iostream>
#include <random>
#include <string>
#include <vector>
#include "faiss/utils/distances.h"
#include "knowhere/index/vector_index/IndexIVF.h"
#include "knowhere/index/vector_offset_index/IndexIVF_NM.h"
#include "query/SearchBruteForce.h"
#include "segcore/Reduce.h"
#include "test_utils/DataGen.h"
#include "test_utils/Timer.h"
using namespace milvus;
using namespace milvus::segcore;
namespace {
template <int DIM>
auto
generate_data(int N) {
std::vector<float> raw_data;
std::vector<uint64_t> timestamps;
std::vector<int64_t> uids;
std::default_random_engine er(42);
std::uniform_real_distribution<> distribution(0.0, 1.0);
std::default_random_engine ei(42);
for (int i = 0; i < N; ++i) {
uids.push_back(10 * N + i);
timestamps.push_back(0);
// append vec
std::vector<float> vec(DIM);
for (auto& x : vec) {
x = distribution(er);
}
raw_data.insert(raw_data.end(), std::begin(vec), std::end(vec));
}
return std::make_tuple(raw_data, timestamps, uids);
}
} // namespace
TEST(Indexing, SmartBruteForce) {
constexpr int N = 100000;
constexpr int DIM = 16;
constexpr int TOPK = 10;
auto [raw_data, timestamps, uids] = generate_data<DIM>(N);
auto total_count = DIM * TOPK;
auto raw = (const float*)raw_data.data();
AssertInfo(raw, "wtf");
constexpr int64_t queries = 3;
auto heap = faiss::float_maxheap_array_t{};
auto query_data = raw;
std::vector<int64_t> final_uids(total_count, -1);
std::vector<float> final_dis(total_count, std::numeric_limits<float>::max());
for (int beg = 0; beg < N; beg += TestChunkSize) {
std::vector<int64_t> buf_uids(total_count, -1);
std::vector<float> buf_dis(total_count, std::numeric_limits<float>::max());
faiss::float_maxheap_array_t buf = {queries, TOPK, buf_uids.data(), buf_dis.data()};
auto end = beg + TestChunkSize;
if (end > N) {
end = N;
}
auto nsize = end - beg;
auto src_data = raw + beg * DIM;
faiss::knn_L2sqr(query_data, src_data, DIM, queries, nsize, &buf, nullptr);
for (auto& x : buf_uids) {
x = uids[x + beg];
}
merge_into(queries, TOPK, final_dis.data(), final_uids.data(), buf_dis.data(), buf_uids.data());
}
for (int qn = 0; qn < queries; ++qn) {
for (int kn = 0; kn < TOPK; ++kn) {
auto index = qn * TOPK + kn;
std::cout << final_uids[index] << "->" << final_dis[index] << std::endl;
}
std::cout << std::endl;
}
}
TEST(Indexing, Naive) {
constexpr int N = 10000;
constexpr int DIM = 16;
constexpr int TOPK = 10;
auto [raw_data, timestamps, uids] = generate_data<DIM>(N);
auto index = knowhere::VecIndexFactory::GetInstance().CreateVecIndex(knowhere::IndexEnum::INDEX_FAISS_IVFPQ,
knowhere::IndexMode::MODE_CPU);
auto conf = knowhere::Config{
{knowhere::meta::DIM, DIM},
{knowhere::meta::TOPK, TOPK},
{knowhere::IndexParams::nlist, 100},
{knowhere::IndexParams::nprobe, 4},
{knowhere::IndexParams::m, 4},
{knowhere::IndexParams::nbits, 8},
{knowhere::Metric::TYPE, knowhere::Metric::L2},
{knowhere::meta::DEVICEID, 0},
};
// auto ds = knowhere::GenDataset(N, DIM, raw_data.data());
// auto ds2 = knowhere::GenDatasetWithIds(N / 2, DIM, raw_data.data() +
// sizeof(float[DIM]) * N / 2, uids.data() + N / 2);
// NOTE: you must train first and then add
// index->Train(ds, conf);
// index->Train(ds2, conf);
// index->AddWithoutIds(ds, conf);
// index->Add(ds2, conf);
std::vector<knowhere::DatasetPtr> datasets;
std::vector<std::vector<float>> ftrashs;
auto raw = raw_data.data();
for (int beg = 0; beg < N; beg += TestChunkSize) {
auto end = beg + TestChunkSize;
if (end > N) {
end = N;
}
std::vector<float> ft(raw + DIM * beg, raw + DIM * end);
auto ds = knowhere::GenDataset(end - beg, DIM, ft.data());
datasets.push_back(ds);
ftrashs.push_back(std::move(ft));
// // NOTE: you must train first and then add
// index->Train(ds, conf);
// index->Add(ds, conf);
}
for (auto& ds : datasets) {
index->Train(ds, conf);
}
for (auto& ds : datasets) {
index->AddWithoutIds(ds, conf);
}
auto bitmap = BitsetType(N, false);
// exclude the first
for (int i = 0; i < N / 2; ++i) {
bitmap.set(i);
}
// index->SetBlacklist(bitmap);
BitsetView view = bitmap;
auto query_ds = knowhere::GenDataset(1, DIM, raw_data.data());
auto final = index->Query(query_ds, conf, view);
auto ids = final->Get<idx_t*>(knowhere::meta::IDS);
auto distances = final->Get<float*>(knowhere::meta::DISTANCE);
for (int i = 0; i < TOPK; ++i) {
if (ids[i] < N / 2) {
std::cout << "WRONG: ";
}
std::cout << ids[i] << "->" << distances[i] << std::endl;
}
}
TEST(Indexing, IVFFlat) {
constexpr int N = 100000;
constexpr int NQ = 10;
constexpr int DIM = 16;
constexpr int TOPK = 5;
constexpr int NLIST = 128;
constexpr int NPROBE = 16;
Timer timer;
auto [raw_data, timestamps, uids] = generate_data<DIM>(N);
std::cout << "generate data: " << timer.get_step_seconds() << " seconds" << std::endl;
auto indexing = std::make_shared<knowhere::IVF>();
auto conf = knowhere::Config{{knowhere::meta::DIM, DIM},
{knowhere::meta::TOPK, TOPK},
{knowhere::IndexParams::nlist, NLIST},
{knowhere::IndexParams::nprobe, NPROBE},
{knowhere::Metric::TYPE, knowhere::Metric::L2},
{knowhere::meta::DEVICEID, 0}};
auto database = knowhere::GenDataset(N, DIM, raw_data.data());
std::cout << "init ivf " << timer.get_step_seconds() << " seconds" << std::endl;
indexing->Train(database, conf);
std::cout << "train ivf " << timer.get_step_seconds() << " seconds" << std::endl;
indexing->AddWithoutIds(database, conf);
std::cout << "insert ivf " << timer.get_step_seconds() << " seconds" << std::endl;
EXPECT_EQ(indexing->Count(), N);
EXPECT_EQ(indexing->Dim(), DIM);
auto dataset = knowhere::GenDataset(NQ, DIM, raw_data.data() + DIM * 4200);
auto result = indexing->Query(dataset, conf, nullptr);
std::cout << "query ivf " << timer.get_step_seconds() << " seconds" << std::endl;
auto ids = result->Get<int64_t*>(knowhere::meta::IDS);
auto dis = result->Get<float*>(knowhere::meta::DISTANCE);
for (int i = 0; i < std::min(NQ * TOPK, 100); ++i) {
std::cout << ids[i] << "->" << dis[i] << std::endl;
}
}
TEST(Indexing, IVFFlatNM) {
constexpr int N = 100000;
constexpr int NQ = 10;
constexpr int DIM = 16;
constexpr int TOPK = 5;
constexpr int NLIST = 128;
constexpr int NPROBE = 16;
Timer timer;
auto [raw_data, timestamps, uids] = generate_data<DIM>(N);
std::cout << "generate data: " << timer.get_step_seconds() << " seconds" << std::endl;
auto indexing = std::make_shared<knowhere::IVF_NM>();
auto conf = knowhere::Config{{knowhere::meta::DIM, DIM},
{knowhere::meta::TOPK, TOPK},
{knowhere::IndexParams::nlist, NLIST},
{knowhere::IndexParams::nprobe, NPROBE},
{knowhere::Metric::TYPE, knowhere::Metric::L2},
{knowhere::meta::DEVICEID, 0}};
auto database = knowhere::GenDataset(N, DIM, raw_data.data());
std::cout << "init ivf_nm " << timer.get_step_seconds() << " seconds" << std::endl;
indexing->Train(database, conf);
std::cout << "train ivf_nm " << timer.get_step_seconds() << " seconds" << std::endl;
indexing->AddWithoutIds(database, conf);
std::cout << "insert ivf_nm " << timer.get_step_seconds() << " seconds" << std::endl;
indexing->SetIndexSize(NQ * DIM * sizeof(float));
knowhere::BinarySet bs = indexing->Serialize(conf);
knowhere::BinaryPtr bptr = std::make_shared<knowhere::Binary>();
bptr->data = std::shared_ptr<uint8_t[]>((uint8_t*)raw_data.data(), [&](uint8_t*) {});
bptr->size = DIM * N * sizeof(float);
bs.Append(RAW_DATA, bptr);
indexing->Load(bs);
EXPECT_EQ(indexing->Count(), N);
EXPECT_EQ(indexing->Dim(), DIM);
auto dataset = knowhere::GenDataset(NQ, DIM, raw_data.data() + DIM * 4200);
auto result = indexing->Query(dataset, conf, nullptr);
std::cout << "query ivf_nm " << timer.get_step_seconds() << " seconds" << std::endl;
auto ids = result->Get<int64_t*>(knowhere::meta::IDS);
auto dis = result->Get<float*>(knowhere::meta::DISTANCE);
for (int i = 0; i < std::min(NQ * TOPK, 100); ++i) {
std::cout << ids[i] << "->" << dis[i] << std::endl;
}
}
TEST(Indexing, BinaryBruteForce) {
int64_t N = 100000;
int64_t num_queries = 10;
int64_t topk = 5;
int64_t round_decimal = 3;
int64_t dim = 8192;
auto result_count = topk * num_queries;
auto schema = std::make_shared<Schema>();
auto vec_fid = schema->AddDebugField("vecbin", DataType::VECTOR_BINARY, dim, MetricType::METRIC_Jaccard);
auto i64_fid = schema->AddDebugField("age", DataType::INT64);
auto dataset = DataGen(schema, N, 10);
auto bin_vec = dataset.get_col<uint8_t>(vec_fid);
auto query_data = 1024 * dim / 8 + bin_vec.data();
query::dataset::SearchDataset search_dataset{
faiss::MetricType::METRIC_Jaccard, //
num_queries, //
topk, //
round_decimal,
dim, //
query_data //
};
auto sub_result = query::BinarySearchBruteForce(search_dataset, bin_vec.data(), N, nullptr);
SearchResult sr;
sr.num_queries_ = num_queries;
sr.topk_ = topk;
sr.seg_offsets_ = std::move(sub_result.mutable_seg_offsets());
sr.distances_ = std::move(sub_result.mutable_distances());
auto json = SearchResultToJson(sr);
std::cout << json.dump(2);
#ifdef __linux__
auto ref = json::parse(R"(
[
[
[ "1024->0.000000", "48942->0.642000", "18494->0.644000", "68225->0.644000", "93557->0.644000" ],
[ "1025->0.000000", "73557->0.641000", "53086->0.643000", "9737->0.643000", "62855->0.644000" ],
[ "1026->0.000000", "62904->0.644000", "46758->0.644000", "57969->0.645000", "98113->0.646000" ],
[ "1027->0.000000", "92446->0.638000", "96034->0.640000", "92129->0.644000", "45887->0.644000" ],
[ "1028->0.000000", "22992->0.643000", "73903->0.644000", "19969->0.645000", "65178->0.645000" ],
[ "1029->0.000000", "19776->0.641000", "15166->0.642000", "85470->0.642000", "16730->0.643000" ],
[ "1030->0.000000", "55939->0.640000", "84253->0.643000", "31958->0.644000", "11667->0.646000" ],
[ "1031->0.000000", "89536->0.637000", "61622->0.638000", "9275->0.639000", "91403->0.640000" ],
[ "1032->0.000000", "69504->0.642000", "23414->0.644000", "48770->0.645000", "23231->0.645000" ],
[ "1033->0.000000", "33540->0.636000", "25310->0.640000", "18576->0.640000", "73729->0.642000" ]
]
]
)");
#else // for mac
auto ref = json::parse(R"(
[
[
[ "1024->0.000000", "59169->0.645000", "98548->0.646000", "3356->0.646000", "90373->0.647000" ],
[ "1025->0.000000", "61245->0.638000", "95271->0.639000", "31087->0.639000", "31549->0.640000" ],
[ "1026->0.000000", "65225->0.648000", "35750->0.648000", "14971->0.649000", "75385->0.649000" ],
[ "1027->0.000000", "70158->0.640000", "27076->0.640000", "3407->0.641000", "59527->0.641000" ],
[ "1028->0.000000", "45757->0.645000", "3356->0.645000", "77230->0.646000", "28690->0.647000" ],
[ "1029->0.000000", "13291->0.642000", "24960->0.643000", "83770->0.643000", "88244->0.643000" ],
[ "1030->0.000000", "96807->0.641000", "39920->0.643000", "62943->0.644000", "12603->0.644000" ],
[ "1031->0.000000", "65769->0.648000", "60493->0.648000", "48738->0.648000", "4353->0.648000" ],
[ "1032->0.000000", "57827->0.637000", "8213->0.638000", "22221->0.639000", "23328->0.640000" ],
[ "1033->0.000000", "676->0.645000", "91430->0.646000", "85353->0.646000", "6014->0.646000" ]
]
]
)");
#endif
auto json_str = json.dump(2);
auto ref_str = ref.dump(2);
ASSERT_EQ(json_str, ref_str);
}