292 lines
8.9 KiB
C++
292 lines
8.9 KiB
C++
/**
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* Copyright (c) 2015-present, Facebook, Inc.
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* All rights reserved.
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*
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* This source code is licensed under the CC-by-NC license found in the
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* LICENSE file in the root directory of this source tree.
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*/
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// Copyright 2004-present Facebook. All Rights Reserved.
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// -*- c++ -*-
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#ifndef FAISS_INDEX_IVFPQ_H
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#define FAISS_INDEX_IVFPQ_H
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#include <vector>
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#include "IndexIVF.h"
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#include "IndexPQ.h"
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namespace faiss {
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/** Inverted file with Product Quantizer encoding. Each residual
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* vector is encoded as a product quantizer code.
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*/
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struct IndexIVFPQ: IndexIVF {
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bool by_residual; ///< Encode residual or plain vector?
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int use_precomputed_table; ///< if by_residual, build precompute tables
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size_t code_size; ///< code size per vector in bytes
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ProductQuantizer pq; ///< produces the codes
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bool do_polysemous_training; ///< reorder PQ centroids after training?
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PolysemousTraining *polysemous_training; ///< if NULL, use default
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// search-time parameters
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size_t scan_table_threshold; ///< use table computation or on-the-fly?
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size_t max_codes; ///< max nb of codes to visit to do a query
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int polysemous_ht; ///< Hamming thresh for polysemous filtering
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std::vector < std::vector<uint8_t> > codes; // binary codes, size nlist
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/// if use_precompute_table
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/// size nlist * pq.M * pq.ksub
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std::vector <float> precomputed_table;
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IndexIVFPQ (
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Index * quantizer, size_t d, size_t nlist,
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size_t M, size_t nbits_per_idx);
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void add_with_ids(idx_t n, const float* x, const long* xids = nullptr)
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override;
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/// same as add_core, also:
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/// - output 2nd level residuals if residuals_2 != NULL
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/// - use precomputed list numbers if precomputed_idx != NULL
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void add_core_o (idx_t n, const float *x,
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const long *xids, float *residuals_2,
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const long *precomputed_idx = nullptr);
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void search(
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idx_t n,
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const float* x,
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idx_t k,
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float* distances,
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idx_t* labels) const override;
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void reset() override;
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long remove_ids(const IDSelector& sel) override;
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/// trains the product quantizer
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void train_residual(idx_t n, const float* x) override;
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/// same as train_residual, also output 2nd level residuals
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void train_residual_o (idx_t n, const float *x, float *residuals_2);
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/** Reconstruct a subset of the indexed vectors
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*
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* @param i0 first vector to reconstruct
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* @param ni nb of vectors to reconstruct
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* @param recons output array of reconstructed vectors, size ni * d
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*/
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void reconstruct_n(idx_t i0, idx_t ni, float* recons) const override;
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void reconstruct(idx_t key, float* recons) const override;
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/** Find exact duplicates in the dataset.
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*
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* the duplicates are returned in pre-allocated arrays (see the
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* max sizes).
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*
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* @params lims limits between groups of duplicates
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* (max size ntotal / 2 + 1)
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* @params ids ids[lims[i]] : ids[lims[i+1]-1] is a group of
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* duplicates (max size ntotal)
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* @return n number of groups found
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*/
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size_t find_duplicates (idx_t *ids, size_t *lims) const;
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// map a vector to a binary code knowning the index
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void encode (long key, const float * x, uint8_t * code) const;
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/** Encode multiple vectors
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*
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* @param n nb vectors to encode
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* @param keys posting list ids for those vectors (size n)
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* @param x vectors (size n * d)
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* @param codes output codes (size n * code_size)
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* @param compute_keys if false, assume keys are precomputed,
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* otherwise compute them
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*/
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void encode_multiple (size_t n, long *keys,
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const float * x, uint8_t * codes,
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bool compute_keys = false) const;
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/// inverse of encode_multiple
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void decode_multiple (size_t n, const long *keys,
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const uint8_t * xcodes, float * x) const;
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/** search a set of vectors, that are pre-quantized by the IVF
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* quantizer. Fill in the corresponding heaps with the query
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* results.
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*
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* @param nx nb of vectors to query
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* @param qx query vectors, size nx * d
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* @param keys coarse quantization indices, size nx * nprobe
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* @param coarse_dis
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* distances to coarse centroids, size nx * nprobe
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* @param res heaps for all the results, gives the nprobe
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* @param store_pairs store inv list index + inv list offset
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* instead in upper/lower 32 bit of result,
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* instead of ids (used for reranking).
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*/
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virtual void search_knn_with_key (
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size_t nx,
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const float * qx,
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const long * keys,
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const float * coarse_dis,
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float_maxheap_array_t* res,
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bool store_pairs = false) const;
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/// build precomputed table
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void precompute_table ();
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/// used to implement merging
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void merge_from_residuals(IndexIVF& other) override;
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/** copy a subset of the entries index to the other index
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*
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* if subset_type == 0: copies ids in [a1, a2)
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* if subset_type == 1: copies ids if id % a1 == a2
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*/
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void copy_subset_to (IndexIVFPQ & other, int subset_type,
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long a1, long a2) const;
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IndexIVFPQ ();
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};
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/// statistics are robust to internal threading, but not if
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/// IndexIVFPQ::search is called by multiple threads
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struct IndexIVFPQStats {
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size_t nq; // nb of queries run
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size_t nlist; // nb of inverted lists scanned
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size_t ncode; // nb of codes visited
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size_t nrefine; // nb of refines (IVFPQR)
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size_t n_hamming_pass;
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// nb of passed Hamming distance tests (for polysemous)
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// timings measured with the CPU RTC
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// on all threads
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size_t assign_cycles;
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size_t search_cycles;
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size_t refine_cycles; // only for IVFPQR
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// single thread (double-counted with search_cycles)
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size_t init_query_cycles;
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size_t init_list_cycles;
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size_t scan_cycles;
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size_t heap_cycles;
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IndexIVFPQStats () {reset (); }
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void reset ();
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};
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// global var that collects them all
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extern IndexIVFPQStats indexIVFPQ_stats;
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/** Index with an additional level of PQ refinement */
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struct IndexIVFPQR: IndexIVFPQ {
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ProductQuantizer refine_pq; ///< 3rd level quantizer
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std::vector <uint8_t> refine_codes; ///< corresponding codes
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/// factor between k requested in search and the k requested from the IVFPQ
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float k_factor;
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IndexIVFPQR (
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Index * quantizer, size_t d, size_t nlist,
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size_t M, size_t nbits_per_idx,
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size_t M_refine, size_t nbits_per_idx_refine);
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void reset() override;
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long remove_ids(const IDSelector& sel) override;
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/// trains the two product quantizers
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void train_residual(idx_t n, const float* x) override;
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void add_with_ids(idx_t n, const float* x, const long* xids) override;
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/// same as add_with_ids, but optionally use the precomputed list ids
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void add_core (idx_t n, const float *x, const long *xids,
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const long *precomputed_idx = nullptr);
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void reconstruct_n(idx_t i0, idx_t ni, float* recons) const override;
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void search(
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idx_t n,
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const float* x,
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idx_t k,
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float* distances,
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idx_t* labels) const override;
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void merge_from_residuals(IndexIVF& other) override;
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IndexIVFPQR();
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};
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/** Index with 32-bit ids and flat tables. Must be constructed from an
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* exisiting IndexIVFPQ. Cannot be copy-constructed/assigned. The
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* actual data is stored in the compact_* tables, the ids and codes
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* tables are not used. */
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struct IndexIVFPQCompact: IndexIVFPQ {
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explicit IndexIVFPQCompact (const IndexIVFPQ &other);
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/// how were the compact tables allocated?
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enum Alloc_type_t {
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Alloc_type_none, ///< alloc from outside
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Alloc_type_new, ///< was allocated with new
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Alloc_type_mmap ///< was mmapped
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};
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Alloc_type_t alloc_type;
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uint32_t *limits; ///< size nlist + 1
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uint32_t *compact_ids; ///< size ntotal
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uint8_t *compact_codes; ///< size ntotal * code_size
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// file and buffer this was mmapped (will be unmapped when object
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// is deleted)
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char * mmap_buffer;
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long mmap_length;
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void search_knn_with_key(
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size_t nx,
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const float* qx,
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const long* keys,
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const float* coarse_dis,
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float_maxheap_array_t* res,
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bool store_pairs = false) const override;
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/// the three following functions will fail at runtime
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void add(idx_t, const float*) override;
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void reset() override;
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void train(idx_t, const float*) override;
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~IndexIVFPQCompact() override;
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IndexIVFPQCompact ();
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};
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} // namespace faiss
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#endif
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