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IndexHNSW.h
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IndexHNSW.h
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/**
* Copyright (c) Facebook, Inc. and its affiliates.
*
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
*/
// -*- c++ -*-
#pragma once
#include <vector>
#include <faiss/impl/HNSW.h>
#include <faiss/IndexFlat.h>
#include <faiss/IndexPQ.h>
#include <faiss/IndexScalarQuantizer.h>
#include <faiss/utils/utils.h>
namespace faiss {
struct IndexHNSW;
struct ReconstructFromNeighbors {
typedef Index::idx_t idx_t;
typedef HNSW::storage_idx_t storage_idx_t;
const IndexHNSW & index;
size_t M; // number of neighbors
size_t k; // number of codebook entries
size_t nsq; // number of subvectors
size_t code_size;
int k_reorder; // nb to reorder. -1 = all
std::vector<float> codebook; // size nsq * k * (M + 1)
std::vector<uint8_t> codes; // size ntotal * code_size
size_t ntotal;
size_t d, dsub; // derived values
explicit ReconstructFromNeighbors(const IndexHNSW& index,
size_t k=256, size_t nsq=1);
/// codes must be added in the correct order and the IndexHNSW
/// must be populated and sorted
void add_codes(size_t n, const float *x);
size_t compute_distances(size_t n, const idx_t *shortlist,
const float *query, float *distances) const;
/// called by add_codes
void estimate_code(const float *x, storage_idx_t i, uint8_t *code) const;
/// called by compute_distances
void reconstruct(storage_idx_t i, float *x, float *tmp) const;
void reconstruct_n(storage_idx_t n0, storage_idx_t ni, float *x) const;
/// get the M+1 -by-d table for neighbor coordinates for vector i
void get_neighbor_table(storage_idx_t i, float *out) const;
};
/** The HNSW index is a normal random-access index with a HNSW
* link structure built on top */
struct IndexHNSW : Index {
typedef HNSW::storage_idx_t storage_idx_t;
// the link strcuture
HNSW hnsw;
// the sequential storage
bool own_fields;
Index *storage;
ReconstructFromNeighbors *reconstruct_from_neighbors;
explicit IndexHNSW (int d = 0, int M = 32);
explicit IndexHNSW (Index *storage, int M = 32);
~IndexHNSW() override;
void add(idx_t n, const float *x) override;
/// Trains the storage if needed
void train(idx_t n, const float* x) override;
/// entry point for search
void search (idx_t n, const float *x, idx_t k,
float *distances, idx_t *labels) const override;
void reconstruct(idx_t key, float* recons) const override;
void reset () override;
void shrink_level_0_neighbors(int size);
/** Perform search only on level 0, given the starting points for
* each vertex.
*
* @param search_type 1:perform one search per nprobe, 2: enqueue
* all entry points
*/
void search_level_0(idx_t n, const float *x, idx_t k,
const storage_idx_t *nearest, const float *nearest_d,
float *distances, idx_t *labels, int nprobe = 1,
int search_type = 1) const;
/// alternative graph building
void init_level_0_from_knngraph(
int k, const float *D, const idx_t *I);
/// alternative graph building
void init_level_0_from_entry_points(
int npt, const storage_idx_t *points,
const storage_idx_t *nearests);
// reorder links from nearest to farthest
void reorder_links();
void link_singletons();
};
/** Flat index topped with with a HNSW structure to access elements
* more efficiently.
*/
struct IndexHNSWFlat : IndexHNSW {
IndexHNSWFlat();
IndexHNSWFlat(int d, int M);
};
/** PQ index topped with with a HNSW structure to access elements
* more efficiently.
*/
struct IndexHNSWPQ : IndexHNSW {
IndexHNSWPQ();
IndexHNSWPQ(int d, int pq_m, int M);
void train(idx_t n, const float* x) override;
};
/** SQ index topped with with a HNSW structure to access elements
* more efficiently.
*/
struct IndexHNSWSQ : IndexHNSW {
IndexHNSWSQ();
IndexHNSWSQ(int d, ScalarQuantizer::QuantizerType qtype, int M);
};
/** 2-level code structure with fast random access
*/
struct IndexHNSW2Level : IndexHNSW {
IndexHNSW2Level();
IndexHNSW2Level(Index *quantizer, size_t nlist, int m_pq, int M);
void flip_to_ivf();
/// entry point for search
void search (idx_t n, const float *x, idx_t k,
float *distances, idx_t *labels) const override;
};
} // namespace faiss