Struct faiss::IndexIVFPQR

struct IndexIVFPQR : public faiss::IndexIVFPQ

Index with an additional level of PQ refinement

Public Types

using component_t = float
using distance_t = float

Public Functions

IndexIVFPQR(Index *quantizer, size_t d, size_t nlist, size_t M, size_t nbits_per_idx, size_t M_refine, size_t nbits_per_idx_refine)
virtual void reset() override

removes all elements from the database.

virtual size_t remove_ids(const IDSelector &sel) override

Dataset manipulation functions.

virtual void train_encoder(idx_t n, const float *x, const idx_t *assign) override

trains the two product quantizers

virtual idx_t train_encoder_num_vectors() const override

can be redefined by subclasses to indicate how many training vectors they need

virtual void add_with_ids(idx_t n, const float *x, const idx_t *xids) override

default implementation that calls encode_vectors

virtual void add_core(idx_t n, const float *x, const idx_t *xids, const idx_t *precomputed_idx, void *inverted_list_context = nullptr) override

same as add_with_ids, but optionally use the precomputed list ids

virtual void reconstruct_from_offset(int64_t list_no, int64_t offset, float *recons) const override

Reconstruct a vector given the location in terms of (inv list index + inv list offset) instead of the id.

Useful for reconstructing when the direct_map is not maintained and the inv list offset is computed by search_preassigned() with store_pairs set.

virtual void merge_from(Index &otherIndex, idx_t add_id) override

moves the entries from another dataset to self. On output, other is empty. add_id is added to all moved ids (for sequential ids, this would be this->ntotal)

virtual void search_preassigned(idx_t n, const float *x, idx_t k, const idx_t *assign, const float *centroid_dis, float *distances, idx_t *labels, bool store_pairs, const IVFSearchParameters *params = nullptr, IndexIVFStats *stats = nullptr) const override

search a set of vectors, that are pre-quantized by the IVF quantizer. Fill in the corresponding heaps with the query results. The default implementation uses InvertedListScanners to do the search.

Parameters:

  • n – nb of vectors to query

  • x – query vectors, size nx * d

  • assign – coarse quantization indices, size nx * nprobe

  • centroid_dis – distances to coarse centroids, size nx * nprobe

  • distance – output distances, size n * k

  • labels – output labels, size n * k

  • store_pairs – store inv list index + inv list offset instead in upper/lower 32 bit of result, instead of ids (used for reranking).

  • params – used to override the object’s search parameters

  • stats – search stats to be updated (can be null)

IndexIVFPQR()
virtual void encode_vectors(idx_t n, const float *x, const idx_t *list_nos, uint8_t *codes, bool include_listnos = false) const override

Encodes a set of vectors as they would appear in the inverted lists

Parameters:

  • list_nos – inverted list ids as returned by the quantizer (size n). -1s are ignored.

  • codes – output codes, size n * code_size

  • include_listno – include the list ids in the code (in this case add ceil(log8(nlist)) to the code size)

virtual void sa_decode(idx_t n, const uint8_t *bytes, float *x) const override

decode a set of vectors

Parameters:

  • n – number of vectors

  • bytes – input encoded vectors, size n * sa_code_size()

  • x – output vectors, size n * d

void add_core_o(idx_t n, const float *x, const idx_t *xids, float *residuals_2, const idx_t *precomputed_idx = nullptr, void *inverted_list_context = nullptr)

same as add_core, also:

  • output 2nd level residuals if residuals_2 != NULL

  • accepts precomputed_idx = nullptr

size_t find_duplicates(idx_t *ids, size_t *lims) const

Find exact duplicates in the dataset.

the duplicates are returned in pre-allocated arrays (see the max sizes).

Parameters:

  • lims – limits between groups of duplicates (max size ntotal / 2 + 1)

  • ids – ids[lims[i]] : ids[lims[i+1]-1] is a group of duplicates (max size ntotal)

Returns:

n number of groups found

void encode(idx_t key, const float *x, uint8_t *code) const
void encode_multiple(size_t n, idx_t *keys, const float *x, uint8_t *codes, bool compute_keys = false) const

Encode multiple vectors

Parameters:

  • n – nb vectors to encode

  • keys – posting list ids for those vectors (size n)

  • x – vectors (size n * d)

  • codes – output codes (size n * code_size)

  • compute_keys – if false, assume keys are precomputed, otherwise compute them

void decode_multiple(size_t n, const idx_t *keys, const uint8_t *xcodes, float *x) const

inverse of encode_multiple

virtual InvertedListScanner *get_InvertedListScanner(bool store_pairs, const IDSelector *sel) const override

Get a scanner for this index (store_pairs means ignore labels)

The default search implementation uses this to compute the distances

void precompute_table()

build precomputed table

virtual void train(idx_t n, const float *x) override

Trains the quantizer and calls train_encoder to train sub-quantizers.

virtual void add(idx_t n, const float *x) override

Calls add_with_ids with NULL ids.

virtual void add_sa_codes(idx_t n, const uint8_t *codes, const idx_t *xids) override

Add vectors that are computed with the standalone codec

Parameters:

  • codes – codes to add size n * sa_code_size()

  • xids – corresponding ids, size n

virtual void range_search_preassigned(idx_t nx, const float *x, float radius, const idx_t *keys, const float *coarse_dis, RangeSearchResult *result, bool store_pairs = false, const IVFSearchParameters *params = nullptr, IndexIVFStats *stats = nullptr) const override

Range search a set of vectors, that are pre-quantized by the IVF quantizer. Fill in the RangeSearchResults results. The default implementation uses InvertedListScanners to do the search.

Parameters:

  • n – nb of vectors to query

  • x – query vectors, size nx * d

  • assign – coarse quantization indices, size nx * nprobe

  • centroid_dis – distances to coarse centroids, size nx * nprobe

  • result – Output results

  • store_pairs – store inv list index + inv list offset instead in upper/lower 32 bit of result, instead of ids (used for reranking).

  • params – used to override the object’s search parameters

  • stats – search stats to be updated (can be null)

virtual void search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels, const SearchParameters *params = nullptr) const override

assign the vectors, then call search_preassign

virtual void range_search(idx_t n, const float *x, float radius, RangeSearchResult *result, const SearchParameters *params = nullptr) const override

query n vectors of dimension d to the index.

return all vectors with distance < radius. Note that many indexes do not implement the range_search (only the k-NN search is mandatory).

Parameters:

  • n – number of vectors

  • x – input vectors to search, size n * d

  • radius – search radius

  • result – result table

virtual void reconstruct(idx_t key, float *recons) const override

reconstruct a vector. Works only if maintain_direct_map is set to 1 or 2

virtual void update_vectors(int nv, const idx_t *idx, const float *v)

Update a subset of vectors.

The index must have a direct_map

Parameters:

  • nv – nb of vectors to update

  • idx – vector indices to update, size nv

  • v – vectors of new values, size nv*d

virtual void reconstruct_n(idx_t i0, idx_t ni, float *recons) const override

Reconstruct a subset of the indexed vectors.

Overrides default implementation to bypass reconstruct() which requires direct_map to be maintained.

Parameters:

  • i0 – first vector to reconstruct

  • ni – nb of vectors to reconstruct

  • recons – output array of reconstructed vectors, size ni * d

virtual void search_and_reconstruct(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels, float *recons, const SearchParameters *params = nullptr) const override

Similar to search, but also reconstructs the stored vectors (or an approximation in the case of lossy coding) for the search results.

Overrides default implementation to avoid having to maintain direct_map and instead fetch the code offsets through the store_pairs flag in search_preassigned().

Parameters:

recons – reconstructed vectors size (n, k, d)

void search_and_return_codes(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels, uint8_t *recons, bool include_listno = false, const SearchParameters *params = nullptr) const

Similar to search, but also returns the codes corresponding to the stored vectors for the search results.

Parameters:

  • codes – codes (n, k, code_size)

  • include_listno – include the list ids in the code (in this case add ceil(log8(nlist)) to the code size)

virtual void check_compatible_for_merge(const Index &otherIndex) const override

check that the two indexes are compatible (ie, they are trained in the same way and have the same parameters). Otherwise throw.

virtual CodePacker *get_CodePacker() const
virtual void copy_subset_to(IndexIVF &other, InvertedLists::subset_type_t subset_type, idx_t a1, idx_t a2) const

copy a subset of the entries index to the other index see Invlists::copy_subset_to for the meaning of subset_type

inline size_t get_list_size(size_t list_no) const
bool check_ids_sorted() const

are the ids sorted?

void make_direct_map(bool new_maintain_direct_map = true)

initialize a direct map

Parameters:

new_maintain_direct_map – if true, create a direct map, else clear it

void set_direct_map_type(DirectMap::Type type)
void replace_invlists(InvertedLists *il, bool own = false)

replace the inverted lists, old one is deallocated if own_invlists

virtual size_t sa_code_size() const override

size of the produced codes in bytes

virtual void sa_encode(idx_t n, const float *x, uint8_t *bytes) const override

encode a set of vectors sa_encode will call encode_vector with include_listno=true

Parameters:

  • n – nb of vectors to encode

  • x – the vectors to encode

  • bytes – output array for the codes

Returns:

nb of bytes written to codes

virtual void assign(idx_t n, const float *x, idx_t *labels, idx_t k = 1) const

return the indexes of the k vectors closest to the query x.

This function is identical as search but only return labels of neighbors.

Parameters:

  • n – number of vectors

  • x – input vectors to search, size n * d

  • labels – output labels of the NNs, size n*k

  • k – number of nearest neighbours

virtual void reconstruct_batch(idx_t n, const idx_t *keys, float *recons) const

Reconstruct several stored vectors (or an approximation if lossy coding)

this function may not be defined for some indexes

Parameters:

  • n – number of vectors to reconstruct

  • keys – ids of the vectors to reconstruct (size n)

  • recons – reconstucted vector (size n * d)

virtual void compute_residual(const float *x, float *residual, idx_t key) const

Computes a residual vector after indexing encoding.

The residual vector is the difference between a vector and the reconstruction that can be decoded from its representation in the index. The residual can be used for multiple-stage indexing methods, like IndexIVF’s methods.

Parameters:

  • x – input vector, size d

  • residual – output residual vector, size d

  • key – encoded index, as returned by search and assign

virtual void compute_residual_n(idx_t n, const float *xs, float *residuals, const idx_t *keys) const

Computes a residual vector after indexing encoding (batch form). Equivalent to calling compute_residual for each vector.

The residual vector is the difference between a vector and the reconstruction that can be decoded from its representation in the index. The residual can be used for multiple-stage indexing methods, like IndexIVF’s methods.

Parameters:

  • n – number of vectors

  • xs – input vectors, size (n x d)

  • residuals – output residual vectors, size (n x d)

  • keys – encoded index, as returned by search and assign

virtual DistanceComputer *get_distance_computer() const

Get a DistanceComputer (defined in AuxIndexStructures) object for this kind of index.

DistanceComputer is implemented for indexes that support random access of their vectors.

void train_q1(size_t n, const float *x, bool verbose, MetricType metric_type)

Trains the quantizer and calls train_residual to train sub-quantizers.

size_t coarse_code_size() const

compute the number of bytes required to store list ids

void encode_listno(idx_t list_no, uint8_t *code) const
idx_t decode_listno(const uint8_t *code) const

Public Members

ProductQuantizer refine_pq

3rd level quantizer

std::vector<uint8_t> refine_codes

corresponding codes

float k_factor

factor between k requested in search and the k requested from the IVFPQ

ProductQuantizer pq

produces the codes

bool do_polysemous_training

reorder PQ centroids after training?

PolysemousTraining *polysemous_training

if NULL, use default

size_t scan_table_threshold

use table computation or on-the-fly?

int polysemous_ht

Hamming thresh for polysemous filtering.

int use_precomputed_table

Precompute table that speed up query preprocessing at some memory cost (used only for by_residual with L2 metric)

AlignedTable<float> precomputed_table

if use_precompute_table size nlist * pq.M * pq.ksub

InvertedLists *invlists = nullptr

Access to the actual data.

bool own_invlists = false
size_t code_size = 0

code size per vector in bytes

int parallel_mode = 0

Parallel mode determines how queries are parallelized with OpenMP

0 (default): split over queries 1: parallelize over inverted lists 2: parallelize over both 3: split over queries with a finer granularity

PARALLEL_MODE_NO_HEAP_INIT: binary or with the previous to prevent the heap to be initialized and finalized

const int PARALLEL_MODE_NO_HEAP_INIT = 1024
DirectMap direct_map

optional map that maps back ids to invlist entries. This enables reconstruct()

bool by_residual = true

do the codes in the invlists encode the vectors relative to the centroids?

int d

vector dimension

idx_t ntotal

total nb of indexed vectors

bool verbose

verbosity level

bool is_trained

set if the Index does not require training, or if training is done already

MetricType metric_type

type of metric this index uses for search

float metric_arg

argument of the metric type

size_t nprobe = 1

number of probes at query time

size_t max_codes = 0

max nb of codes to visit to do a query

Index *quantizer = nullptr

quantizer that maps vectors to inverted lists

size_t nlist = 0

number of inverted lists

char quantizer_trains_alone = 0

= 0: use the quantizer as index in a kmeans training = 1: just pass on the training set to the train() of the quantizer = 2: kmeans training on a flat index + add the centroids to the quantizer

bool own_fields = false

whether object owns the quantizer

ClusteringParameters cp

to override default clustering params

Index *clustering_index = nullptr

to override index used during clustering