Struct faiss::IndexIVFPQFastScan

struct faiss::IndexIVFPQFastScan : public faiss::IndexIVF

Fast scan version of IVFPQ. Works for 4-bit PQ for now.

The codes in the inverted lists are not stored sequentially but grouped in blocks of size bbs. This makes it possible to very quickly compute distances with SIMD instructions.

Implementations (implem): 0: auto-select implementation (default) 1: orig’s search, re-implemented 2: orig’s search, re-ordered by invlist 10: optimizer int16 search, collect results in heap, no qbs 11: idem, collect results in reservoir 12: optimizer int16 search, collect results in heap, uses qbs 13: idem, collect results in reservoir

Public Types

using idx_t = int64_t

all indices are this type

using component_t = float
using distance_t = float

Public Functions

IndexIVFPQFastScan(Index *quantizer, size_t d, size_t nlist, size_t M, size_t nbits_per_idx, MetricType metric = METRIC_L2, int bbs = 32)
IndexIVFPQFastScan()
explicit IndexIVFPQFastScan(const IndexIVFPQ &orig, int bbs = 32)
virtual void train_residual(idx_t n, const float *x) override

Sub-classes that encode the residuals can train their encoders here does nothing by default

void precompute_table()

build precomputed table, possibly updating use_precomputed_table

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

same as the regular IVFPQ encoder. The codes are not reorganized by blocks a that point

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

default implementation that calls encode_vectors

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

assign the vectors, then call search_preassign

void compute_LUT(size_t n, const float *x, const idx_t *coarse_ids, const float *coarse_dis, AlignedTable<float> &dis_tables, AlignedTable<float> &biases) const
void compute_LUT_uint8(size_t n, const float *x, const idx_t *coarse_ids, const float *coarse_dis, AlignedTable<uint8_t> &dis_tables, AlignedTable<uint16_t> &biases, float *normalizers) const
template<bool is_max>
void search_dispatch_implem(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels) const
template<class C>
void search_implem_1(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels) const
template<class C>
void search_implem_2(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels) const
template<class C>
void search_implem_10(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels, int impl, size_t *ndis_out, size_t *nlist_out) const
template<class C>
void search_implem_12(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels, int impl, size_t *ndis_out, size_t *nlist_out) const
virtual void reset() override

removes all elements from the database.

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

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

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

Calls add_with_ids with NULL ids.

virtual void add_core(idx_t n, const float *x, const idx_t *xids, const idx_t *precomputed_idx)

Implementation of vector addition where the vector assignments are predefined. The default implementation hands over the code extraction to encode_vectors.

Parameters

precomputed_idx – quantization indices for the input vectors (size n)

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

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 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

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)

virtual void range_search(idx_t n, const float *x, float radius, RangeSearchResult *result) 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
  • x – input vectors to search, size n * d

  • radius – search radius

  • result – result table

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
virtual InvertedListScanner *get_InvertedListScanner(bool store_pairs = false) const

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

The default search implementation uses this to compute the distances

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 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)

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

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 size_t remove_ids(const IDSelector &sel) override

Dataset manipulation functions.

void check_compatible_for_merge(const IndexIVF &other) const

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

virtual void merge_from(IndexIVF &other, idx_t add_id)

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 copy_subset_to(IndexIVF &other, int subset_type, idx_t a1, idx_t a2) const

copy a subset of the entries index to the other index

if subset_type == 0: copies ids in [a1, a2) if subset_type == 1: copies ids if id % a1 == a2 if subset_type == 2: copies inverted lists such that a1 elements are left before and a2 elements are after

inline size_t get_list_size(size_t list_no) const
void make_direct_map(bool new_maintain_direct_map = true)

intialize 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

Parameters
  • n – number of vectors

  • x – input vectors, size n * d

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

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

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

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.

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

encode 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 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(Index::idx_t list_no, uint8_t *code) const
Index::idx_t decode_listno(const uint8_t *code) const

Public Members

bool by_residual

Encode residual or plain vector?

ProductQuantizer pq

produces the codes

int bbs
size_t M2
int use_precomputed_table = 0

precomputed tables management

AlignedTable<float> precomputed_table

if use_precompute_table size (nlist, pq.M, pq.ksub)

int implem = 0
int skip = 0
int qbs = 0
size_t qbs2 = 0
InvertedLists *orig_invlists = nullptr

orig’s inverted lists (for debugging)

InvertedLists *invlists

Access to the actual data.

bool own_invlists
size_t code_size

code size per vector in bytes

size_t nprobe

number of probes at query time

size_t max_codes

max nb of codes to visit to do a query

int parallel_mode

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()

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

Index *quantizer

quantizer that maps vectors to inverted lists

size_t nlist

number of possible key values

char quantizer_trains_alone

= 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

whether object owns the quantizer (false by default)

ClusteringParameters cp

to override default clustering params

Index *clustering_index

to override index used during clustering