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/**
* Copyright (c) 2015-present, Facebook, Inc.
* All rights reserved.
*
* This source code is licensed under the BSD+Patents license found in the
* LICENSE file in the root directory of this source tree.
*/
// -*- c++ -*-
#ifndef FAISS_INDEX_PQ_H
#define FAISS_INDEX_PQ_H
#include <stdint.h>
#include <vector>
#include "Index.h"
#include "ProductQuantizer.h"
#include "PolysemousTraining.h"
namespace faiss {
/** Index based on a product quantizer. Stored vectors are
* approximated by PQ codes. */
struct IndexPQ: Index {
/// The product quantizer used to encode the vectors
ProductQuantizer pq;
/// Codes. Size ntotal * pq.code_size
std::vector<uint8_t> codes;
/** Constructor.
*
* @param d dimensionality of the input vectors
* @param M number of subquantizers
* @param nbits number of bit per subvector index
*/
IndexPQ (int d, ///< dimensionality of the input vectors
size_t M, ///< number of subquantizers
size_t nbits, ///< number of bit per subvector index
MetricType metric = METRIC_L2);
IndexPQ ();
void train(idx_t n, const float* x) override;
void add(idx_t n, const float* x) override;
void search(
idx_t n,
const float* x,
idx_t k,
float* distances,
idx_t* labels) const override;
void reset() override;
void reconstruct_n(idx_t i0, idx_t ni, float* recons) const override;
void reconstruct(idx_t key, float* recons) const override;
long remove_ids(const IDSelector& sel) override;
/******************************************************
* Polysemous codes implementation
******************************************************/
bool do_polysemous_training; ///< false = standard PQ
/// parameters used for the polysemous training
PolysemousTraining polysemous_training;
/// how to perform the search in search_core
enum Search_type_t {
ST_PQ, ///< asymmetric product quantizer (default)
ST_HE, ///< Hamming distance on codes
ST_generalized_HE, ///< nb of same codes
ST_SDC, ///< symmetric product quantizer (SDC)
ST_polysemous, ///< HE filter (using ht) + PQ combination
ST_polysemous_generalize, ///< Filter on generalized Hamming
};
Search_type_t search_type;
// just encode the sign of the components, instead of using the PQ encoder
// used only for the queries
bool encode_signs;
/// Hamming threshold used for polysemy
int polysemous_ht;
// actual polysemous search
void search_core_polysemous (idx_t n, const float *x, idx_t k,
float *distances, idx_t *labels) const;
/// prepare query for a polysemous search, but instead of
/// computing the result, just get the histogram of Hamming
/// distances. May be computed on a provided dataset if xb != NULL
/// @param dist_histogram (M * nbits + 1)
void hamming_distance_histogram (idx_t n, const float *x,
idx_t nb, const float *xb,
long *dist_histogram);
/** compute pairwise distances between queries and database
*
* @param n nb of query vectors
* @param x query vector, size n * d
* @param dis output distances, size n * ntotal
*/
void hamming_distance_table (idx_t n, const float *x,
int32_t *dis) const;
};
/// statistics are robust to internal threading, but not if
/// IndexPQ::search is called by multiple threads
struct IndexPQStats {
size_t nq; // nb of queries run
size_t ncode; // nb of codes visited
size_t n_hamming_pass; // nb of passed Hamming distance tests (for polysemy)
IndexPQStats () {reset (); }
void reset ();
};
extern IndexPQStats indexPQ_stats;
/** Quantizer where centroids are virtual: they are the Cartesian
* product of sub-centroids. */
struct MultiIndexQuantizer: Index {
ProductQuantizer pq;
MultiIndexQuantizer (int d, ///< dimension of the input vectors
size_t M, ///< number of subquantizers
size_t nbits); ///< number of bit per subvector index
void train(idx_t n, const float* x) override;
void search(
idx_t n, const float* x, idx_t k,
float* distances, idx_t* labels) const override;
/// add and reset will crash at runtime
void add(idx_t n, const float* x) override;
void reset() override;
MultiIndexQuantizer () {}
void reconstruct(idx_t key, float* recons) const override;
};
/** MultiIndexQuantizer where the PQ assignmnet is performed by sub-indexes
*/
struct MultiIndexQuantizer2: MultiIndexQuantizer {
/// M Indexes on d / M dimensions
std::vector<Index*> assign_indexes;
bool own_fields;
MultiIndexQuantizer2 (
int d, size_t M, size_t nbits,
Index **indexes);
MultiIndexQuantizer2 (
int d, size_t nbits,
Index *assign_index_0,
Index *assign_index_1);
void train(idx_t n, const float* x) override;
void search(
idx_t n, const float* x, idx_t k,
float* distances, idx_t* labels) const override;
};
} // namespace faiss
#endif