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AutoTune.cpp
/**
* Copyright (c) 2015-present, Facebook, Inc.
* All rights reserved.
*
* This source code is licensed under the CC-by-NC license found in the
* LICENSE file in the root directory of this source tree.
*/
/* Copyright 2004-present Facebook. All Rights Reserved.
implementation of Hyper-parameter auto-tuning
*/
#include "AutoTune.h"
#include "FaissAssert.h"
#include "utils.h"
#include "IndexFlat.h"
#include "VectorTransform.h"
#include "IndexLSH.h"
#include "IndexPQ.h"
#include "IndexIVF.h"
#include "IndexIVFPQ.h"
#include "MetaIndexes.h"
namespace faiss {
AutoTuneCriterion::AutoTuneCriterion (idx_t nq, idx_t nnn):
nq (nq), nnn (nnn), gt_nnn (0)
{}
void AutoTuneCriterion::set_groundtruth (
int gt_nnn, const float *gt_D_in, const idx_t *gt_I_in)
{
this->gt_nnn = gt_nnn;
if (gt_D_in) { // allow null for this, as it is often not used
gt_D.resize (nq * gt_nnn);
memcpy (gt_D.data(), gt_D_in, sizeof (gt_D[0]) * nq * gt_nnn);
}
gt_I.resize (nq * gt_nnn);
memcpy (gt_I.data(), gt_I_in, sizeof (gt_I[0]) * nq * gt_nnn);
}
OneRecallAtRCriterion::OneRecallAtRCriterion (idx_t nq, idx_t R):
AutoTuneCriterion(nq, R), R(R)
{}
double OneRecallAtRCriterion::evaluate (const float *D, const idx_t *I) const
{
FAISS_ASSERT ((gt_I.size() == gt_nnn * nq && gt_nnn >= 1 && nnn >= R) ||
!"gound truth not initialized");
idx_t n_ok = 0;
for (idx_t q = 0; q < nq; q++) {
idx_t gt_nn = gt_I [q * gt_nnn];
const idx_t *I_line = I + q * nnn;
for (int i = 0; i < R; i++) {
if (I_line[i] == gt_nn) {
n_ok++;
break;
}
}
}
return n_ok / double (nq);
}
IntersectionCriterion::IntersectionCriterion (idx_t nq, idx_t R):
AutoTuneCriterion(nq, R), R(R)
{}
double IntersectionCriterion::evaluate (const float *D, const idx_t *I) const
{
FAISS_ASSERT ((gt_I.size() == gt_nnn * nq && gt_nnn >= R && nnn >= R) ||
!"gound truth not initialized");
long n_ok = 0;
#pragma omp parallel for reduction(+: n_ok)
for (idx_t q = 0; q < nq; q++) {
n_ok += ranklist_intersection_size (
R, &gt_I [q * gt_nnn],
R, I + q * nnn);
}
return n_ok / double (nq * R);
}
/***************************************************************
* OperatingPoints
***************************************************************/
OperatingPoints::OperatingPoints ()
{
clear();
}
void OperatingPoints::clear ()
{
all_pts.clear();
optimal_pts.clear();
/// default point: doing nothing gives 0 performance and takes 0 time
OperatingPoint op = {0, 0, "", -1};
optimal_pts.push_back(op);
}
/// add a performance measure
bool OperatingPoints::add (double perf, double t, const std::string & key,
size_t cno)
{
OperatingPoint op = {perf, t, key, long(cno)};
all_pts.push_back (op);
if (perf == 0) {
return false; // no method for 0 accuracy is faster than doing nothing
}
std::vector<OperatingPoint> & a = optimal_pts;
if (perf > a.back().perf) {
// keep unconditionally
a.push_back (op);
} else if (perf == a.back().perf) {
if (t < a.back ().t) {
a.back() = op;
} else {
return false;
}
} else {
int i;
// stricto sensu this should be a bissection
for (i = 0; i < a.size(); i++) {
if (a[i].perf >= perf) break;
}
assert (i < a.size());
if (t < a[i].t) {
if (a[i].perf == perf) {
a[i] = op;
} else {
a.insert (a.begin() + i, op);
}
} else {
return false;
}
}
{ // remove non-optimal points from array
int i = a.size() - 1;
while (i > 0) {
if (a[i].t < a[i - 1].t)
a.erase (a.begin() + (i - 1));
i--;
}
}
return true;
}
int OperatingPoints::merge_with (const OperatingPoints &other,
const std::string & prefix)
{
int n_add = 0;
for (int i = 0; i < other.all_pts.size(); i++) {
const OperatingPoint & op = other.all_pts[i];
if (add (op.perf, op.t, prefix + op.key, op.cno))
n_add++;
}
return n_add;
}
/// get time required to obtain a given performance measure
double OperatingPoints::t_for_perf (double perf) const
{
const std::vector<OperatingPoint> & a = optimal_pts;
if (perf > a.back().perf) return 1e50;
int i0 = -1, i1 = a.size() - 1;
while (i0 + 1 < i1) {
int imed = (i0 + i1 + 1) / 2;
if (a[imed].perf < perf) i0 = imed;
else i1 = imed;
}
return a[i1].t;
}
void OperatingPoints::all_to_gnuplot (const char *fname) const
{
FILE *f = fopen(fname, "w");
if (!f) {
fprintf (stderr, "cannot open %s", fname);
perror("");
abort();
}
for (int i = 0; i < all_pts.size(); i++) {
const OperatingPoint & op = all_pts[i];
fprintf (f, "%g %g %s\n", op.perf, op.t, op.key.c_str());
}
fclose(f);
}
void OperatingPoints::optimal_to_gnuplot (const char *fname) const
{
FILE *f = fopen(fname, "w");
if (!f) {
fprintf (stderr, "cannot open %s", fname);
perror("");
abort();
}
double prev_perf = 0.0;
for (int i = 0; i < optimal_pts.size(); i++) {
const OperatingPoint & op = optimal_pts[i];
fprintf (f, "%g %g\n", prev_perf, op.t);
fprintf (f, "%g %g %s\n", op.perf, op.t, op.key.c_str());
prev_perf = op.perf;
}
fclose(f);
}
void OperatingPoints::display (bool only_optimal) const
{
const std::vector<OperatingPoint> &pts =
only_optimal ? optimal_pts : all_pts;
printf("Tested %ld operating points, %ld ones are optimal:\n",
all_pts.size(), optimal_pts.size());
for (int i = 0; i < pts.size(); i++) {
const OperatingPoint & op = pts[i];
const char *star = "";
if (!only_optimal) {
for (int j = 0; j < optimal_pts.size(); j++) {
if (op.cno == optimal_pts[j].cno) {
star = "*";
break;
}
}
}
printf ("cno=%ld key=%s perf=%.4f t=%.3f %s\n",
op.cno, op.key.c_str(), op.perf, op.t, star);
}
}
/***************************************************************
* ParameterSpace
***************************************************************/
ParameterSpace::ParameterSpace ():
verbose (1), n_experiments (500),
batchsize (1<<30), thread_over_batches (false)
{
}
/* not keeping this constructor as inheritors will call the parent
initialize()
*/
#if 0
ParameterSpace::ParameterSpace (Index *index):
verbose (1), n_experiments (500),
batchsize (1<<30), thread_over_batches (false)
{
initialize(index);
}
#endif
size_t ParameterSpace::n_combinations () const
{
size_t n = 1;
for (int i = 0; i < parameter_ranges.size(); i++)
n *= parameter_ranges[i].values.size();
return n;
}
/// get string representation of the combination
std::string ParameterSpace::combination_name (size_t cno) const {
char buf[1000], *wp = buf;
*wp = 0;
for (int i = 0; i < parameter_ranges.size(); i++) {
const ParameterRange & pr = parameter_ranges[i];
size_t j = cno % pr.values.size();
cno /= pr.values.size();
wp += snprintf (
wp, buf + 1000 - wp, "%s%s=%g", i == 0 ? "" : ",",
pr.name.c_str(), pr.values[j]);
}
return std::string (buf);
}
bool ParameterSpace::combination_ge (size_t c1, size_t c2) const
{
for (int i = 0; i < parameter_ranges.size(); i++) {
int nval = parameter_ranges[i].values.size();
size_t j1 = c1 % nval;
size_t j2 = c2 % nval;
if (!(j1 >= j2)) return false;
c1 /= nval;
c2 /= nval;
}
return true;
}
#define DC(classname) \
const classname *ix = dynamic_cast<const classname *>(index)
static void init_pq_ParameterRange (const ProductQuantizer & pq,
ParameterRange & pr)
{
if (pq.code_size % 4 == 0) {
// Polysemous not supported for code sizes that are not a
// multiple of 4
for (int i = 2; i <= pq.code_size * 8 / 2; i+= 2)
pr.values.push_back(i);
}
pr.values.push_back (pq.code_size * 8);
}
ParameterRange &ParameterSpace::add_range(const char * name)
{
parameter_ranges.push_back (ParameterRange ());
parameter_ranges.back ().name = name;
return parameter_ranges.back ();
}
/// initialize with reasonable parameters for the index
void ParameterSpace::initialize (const Index * index)
{
if (DC (IndexPreTransform)) {
index = ix->index;
}
if (DC (IndexRefineFlat)) {
ParameterRange & pr = add_range("k_factor_rf");
for (int i = 0; i <= 6; i++) {
pr.values.push_back (1 << i);
}
index = ix->base_index;
}
if (DC (IndexPreTransform)) {
index = ix->index;
}
if (DC (IndexIVF)) {
ParameterRange & pr = add_range("nprobe");
for (int i = 0; i < 13; i++) {
size_t nprobe = 1 << i;
if (nprobe >= ix->nlist) break;
pr.values.push_back (nprobe);
}
}
if (DC (IndexPQ)) {
ParameterRange & pr = add_range("ht");
init_pq_ParameterRange (ix->pq, pr);
}
if (DC (IndexIVFPQ)) {
ParameterRange & pr = add_range("ht");
init_pq_ParameterRange (ix->pq, pr);
const MultiIndexQuantizer *miq =
dynamic_cast<const MultiIndexQuantizer *> (ix->quantizer);
if (miq) {
ParameterRange & pr_max_codes = add_range("max_codes");
for (int i = 8; i < 20; i++) {
pr_max_codes.values.push_back (1 << i);
}
pr_max_codes.values.push_back (1.0 / 0.0);
}
}
if (DC (IndexIVFPQR)) {
assert (ix);
ParameterRange & pr = add_range("k_factor");
for (int i = 0; i <= 6; i++) {
pr.values.push_back (1 << i);
}
}
}
#undef DC
// non-const version
#define DC(classname) classname *ix = dynamic_cast<classname *>(index)
/// set a combination of parameters on an index
void ParameterSpace::set_index_parameters (Index *index, size_t cno) const
{
for (int i = 0; i < parameter_ranges.size(); i++) {
const ParameterRange & pr = parameter_ranges[i];
size_t j = cno % pr.values.size();
cno /= pr.values.size();
double val = pr.values [j];
set_index_parameter (index, pr.name, val);
}
}
/// set a combination of parameters on an index
void ParameterSpace::set_index_parameters (
Index *index, const char *description_in) const
{
char description[strlen(description_in) + 1];
char *ptr;
memcpy (description, description_in, strlen(description_in) + 1);
for (char *tok = strtok_r (description, " ,", &ptr);
tok;
tok = strtok_r (nullptr, " ,", &ptr)) {
char name[100];
double val;
FAISS_ASSERT (sscanf (tok, "%100[^=]=%lf", name, &val) == 2);
set_index_parameter (index, name, val);
}
}
void ParameterSpace::set_index_parameter (
Index * index, const std::string & name, double val) const
{
if (verbose > 1)
printf(" set %s=%g\n", name.c_str(), val);
if (name == "verbose") {
index->verbose = int(val);
}
if (DC (IndexPreTransform)) {
index = ix->index;
}
if (name == "verbose") {
index->verbose = int(val);
}
if (DC (IndexRefineFlat)) {
if (name == "k_factor_rf") {
ix->k_factor = int(val);
return;
}
index = ix->base_index;
}
if (DC (IndexPreTransform)) {
index = ix->index;
}
if (name == "verbose") {
index->verbose = int(val);
return; // last verbose that we could find
}
if (name == "nprobe") {
DC(IndexIVF);
ix->nprobe = int(val);
} else if (name == "ht") {
if (DC (IndexPQ)) {
if (val >= ix->pq.code_size * 8) {
ix->search_type = IndexPQ::ST_PQ;
} else {
ix->search_type = IndexPQ::ST_polysemous;
ix->polysemous_ht = int(val);
}
} else if (DC (IndexIVFPQ)) {
if (val >= ix->pq.code_size * 8) {
ix->polysemous_ht = 0;
} else {
ix->polysemous_ht = int(val);
}
}
} else if (name == "k_factor") {
DC (IndexIVFPQR);
ix->k_factor = val;
} else if (name == "max_codes") {
DC (IndexIVFPQ);
ix->max_codes = finite(val) ? size_t(val) : 0;
} else {
fprintf(stderr,
"ParameterSpace::set_index_parameter:"
"could not set parameter %s\n",
name.c_str());
}
}
void ParameterSpace::display () const
{
printf ("ParameterSpace, %ld parameters, %ld combinations:\n",
parameter_ranges.size (), n_combinations ());
for (int i = 0; i < parameter_ranges.size(); i++) {
const ParameterRange & pr = parameter_ranges[i];
printf (" %s: ", pr.name.c_str ());
char sep = '[';
for (int j = 0; j < pr.values.size(); j++) {
printf ("%c %g", sep, pr.values [j]);
sep = ',';
}
printf ("]\n");
}
}
void ParameterSpace::update_bounds (size_t cno, const OperatingPoint & op,
double *upper_bound_perf,
double *lower_bound_t) const
{
if (combination_ge (cno, op.cno)) {
if (op.t > *lower_bound_t) *lower_bound_t = op.t;
}
if (combination_ge (op.cno, cno)) {
if (op.perf < *upper_bound_perf) *upper_bound_perf = op.perf;
}
}
void ParameterSpace::explore (Index *index,
size_t nq, const float *xq,
const AutoTuneCriterion & crit,
OperatingPoints * ops) const
{
FAISS_ASSERT (nq == crit.nq ||
!"criterion does not have the same nb of queries");
size_t n_comb = n_combinations ();
if (n_experiments == 0) {
for (size_t cno = 0; cno < n_comb; cno++) {
set_index_parameters (index, cno);
std::vector<Index::idx_t> I(nq * crit.nnn);
std::vector<float> D(nq * crit.nnn);
double t0 = getmillisecs ();
index->search (nq, xq, crit.nnn, D.data(), I.data());
double t_search = (getmillisecs() - t0) / 1e3;
double perf = crit.evaluate (D.data(), I.data());
bool keep = ops->add (perf, t_search, combination_name (cno), cno);
if (verbose)
printf(" %ld/%ld: %s perf=%.3f t=%.3f s %s\n", cno, n_comb,
combination_name (cno).c_str(), perf, t_search,
keep ? "*" : "");
}
return;
}
int n_exp = n_experiments;
if (n_exp > n_comb) n_exp = n_comb;
FAISS_ASSERT (n_comb == 1 || n_exp > 2);
std::vector<int> perm (n_comb);
// make sure the slowest and fastest experiment are run
perm[0] = 0;
if (n_comb > 1) {
perm[1] = n_comb - 1;
rand_perm (&perm[2], n_comb - 2, 1234);
for (int i = 2; i < perm.size(); i++) perm[i] ++;
}
for (size_t xp = 0; xp < n_exp; xp++) {
size_t cno = perm[xp];
if (verbose)
printf(" %ld/%d: cno=%ld %s ", xp, n_exp, cno,
combination_name (cno).c_str());
{
double lower_bound_t = 0.0;
double upper_bound_perf = 1.0;
for (int i = 0; i < ops->all_pts.size(); i++) {
update_bounds (cno, ops->all_pts[i],
&upper_bound_perf, &lower_bound_t);
}
double best_t = ops->t_for_perf (upper_bound_perf);
if (verbose)
printf ("bounds [perf<=%.3f t>=%.3f] %s",
upper_bound_perf, lower_bound_t,
best_t <= lower_bound_t ? "skip\n" : "");
if (best_t <= lower_bound_t) continue;
}
set_index_parameters (index, cno);
std::vector<Index::idx_t> I(nq * crit.nnn);
std::vector<float> D(nq * crit.nnn);
double t0 = getmillisecs ();
if (thread_over_batches) {
#pragma omp parallel for
for (size_t q0 = 0; q0 < nq; q0 += batchsize) {
size_t q1 = q0 + batchsize;
if (q1 > nq) q1 = nq;
index->search (q1 - q0, xq + q0 * index->d,
crit.nnn,
D.data() + q0 * crit.nnn,
I.data() + q0 * crit.nnn);
}
} else {
for (size_t q0 = 0; q0 < nq; q0 += batchsize) {
size_t q1 = q0 + batchsize;
if (q1 > nq) q1 = nq;
index->search (q1 - q0, xq + q0 * index->d,
crit.nnn,
D.data() + q0 * crit.nnn,
I.data() + q0 * crit.nnn);
}
}
double t_search = (getmillisecs() - t0) / 1e3;
double perf = crit.evaluate (D.data(), I.data());
bool keep = ops->add (perf, t_search, combination_name (cno), cno);
if (verbose)
printf(" perf %.3f t %.3f %s\n", perf, t_search,
keep ? "*" : "");
}
}
/***************************************************************
* index_factory
***************************************************************/
Index *index_factory (int d, const char *description_in, MetricType metric)
{
VectorTransform *vt = nullptr;
Index *coarse_quantizer = nullptr;
Index *index = nullptr;
bool add_idmap = false;
bool make_IndexRefineFlat = false;
char description[strlen(description_in) + 1];
char *ptr;
memcpy (description, description_in, strlen(description_in) + 1);
int ncentroids = -1;
for (char *tok = strtok_r (description, " ,", &ptr);
tok;
tok = strtok_r (nullptr, " ,", &ptr)) {
int d_out, opq_M, nbit, M, M2;
VectorTransform *vt_1 = nullptr;
Index *coarse_quantizer_1 = nullptr;
Index *index_1 = nullptr;
// VectorTransforms
if (sscanf (tok, "PCA%d", &d_out) == 1) {
vt_1 = new PCAMatrix (d, d_out);
d = d_out;
} else if (sscanf (tok, "PCAR%d", &d_out) == 1) {
vt_1 = new PCAMatrix (d, d_out, 0, true);
d = d_out;
} else if (sscanf (tok, "OPQ%d_%d", &opq_M, &d_out) == 2) {
vt_1 = new OPQMatrix (d, opq_M, d_out);
d = d_out;
} else if (sscanf (tok, "OPQ%d", &opq_M) == 1) {
vt_1 = new OPQMatrix (d, opq_M);
// coarse quantizers
} else if (sscanf (tok, "IVF%d", &ncentroids) == 1) {
if (metric == METRIC_L2) {
coarse_quantizer_1 = new IndexFlatL2 (d);
} else { // if (metric == METRIC_IP)
coarse_quantizer_1 = new IndexFlatIP (d);
}
} else if (sscanf (tok, "IMI2x%d", &nbit) == 1) {
FAISS_ASSERT(metric == METRIC_L2 ||
!"MultiIndex not implemented for inner prod search");
coarse_quantizer_1 = new MultiIndexQuantizer (d, 2, nbit);
ncentroids = 1 << (2 * nbit);
} else if (strcmp(tok, "IDMap") == 0) {
add_idmap = true;
// IVFs
} else if (strcmp (tok, "Flat") == 0) {
if (coarse_quantizer) {
// if there was an IVF in front, then it is an IVFFlat
IndexIVF *index_ivf = new IndexIVFFlat (
coarse_quantizer, d, ncentroids, metric);
index_ivf->quantizer_trains_alone =
dynamic_cast< MultiIndexQuantizer*>(coarse_quantizer)
!= nullptr;
index_ivf->cp.spherical = metric == METRIC_INNER_PRODUCT;
index_ivf->own_fields = true;
index_1 = index_ivf;
} else {
index_1 = new IndexFlat (d, metric);
if (add_idmap) {
IndexIDMap *idmap = new IndexIDMap(index_1);
idmap->own_fields = true;
index_1 = idmap;
add_idmap = false;
}
}
} else if (sscanf (tok, "PQ%d+%d", &M, &M2) == 2) {
FAISS_ASSERT(coarse_quantizer ||
!"PQ with + works only with an IVF");
FAISS_ASSERT(metric == METRIC_L2 ||
!"IVFPQR not implemented for inner product search");
IndexIVFPQR *index_ivf = new IndexIVFPQR (
coarse_quantizer, d, ncentroids, M, 8, M2, 8);
index_ivf->quantizer_trains_alone =
dynamic_cast< MultiIndexQuantizer*>(coarse_quantizer)
!= nullptr;
index_ivf->own_fields = true;
index_1 = index_ivf;
} else if (sscanf (tok, "PQ%d", &M) == 1) {
if (coarse_quantizer) {
IndexIVFPQ *index_ivf = new IndexIVFPQ (
coarse_quantizer, d, ncentroids, M, 8);
index_ivf->quantizer_trains_alone =
dynamic_cast< MultiIndexQuantizer*>(coarse_quantizer)
!= nullptr;
index_ivf->metric_type = metric;
index_ivf->cp.spherical = metric == METRIC_INNER_PRODUCT;
index_ivf->own_fields = true;
index_ivf->do_polysemous_training = true;
index_1 = index_ivf;
} else {
IndexPQ *index_pq = new IndexPQ (d, M, 8, metric);
index_pq->do_polysemous_training = true;
index_1 = index_pq;
if (add_idmap) {
IndexIDMap *idmap = new IndexIDMap(index_1);
idmap->own_fields = true;
index_1 = idmap;
add_idmap = false;
}
}
} else if (strcmp (tok, "RFlat") == 0) {
make_IndexRefineFlat = true;
} else {
fprintf (stderr, "could not parse token \"%s\" in %s\n",
tok, description_in);
FAISS_ASSERT (!"parse error");
}
if (vt_1) {
FAISS_ASSERT (!vt || !"cannot apply two VectorTransforms");
vt = vt_1;
}
if (coarse_quantizer_1) {
FAISS_ASSERT (!coarse_quantizer ||
!"cannot have 2 coarse quantizers");
coarse_quantizer = coarse_quantizer_1;
}
if (index_1) {
FAISS_ASSERT (!index || !"cannot have 2 indexes");
index = index_1;
}
}
if (add_idmap) {
fprintf(stderr, "index_factory: WARNING: "
"IDMap option not used\n");
}
if (vt) {
IndexPreTransform *index_pt = new IndexPreTransform (vt, index);
index_pt->own_fields = true;
index = index_pt;
}
if (make_IndexRefineFlat) {
IndexRefineFlat *index_rf = new IndexRefineFlat (index);
index_rf->own_fields = true;
index = index_rf;
}
return index;
}
}; // namespace faiss
faiss::ParameterSpace::explore
void explore(Index *index, size_t nq, const float *xq, const AutoTuneCriterion &crit, OperatingPoints *ops) const
Definition: AutoTune.cpp:512
faiss::IndexIVF
Definition: IndexIVF.h:46
faiss::ParameterSpace::parameter_ranges
std::vector< ParameterRange > parameter_ranges
all tunable parameters
Definition: AutoTune.h:135
faiss::OperatingPoint::key
std::string key
key that identifies this op pt
Definition: AutoTune.h:90
faiss::IndexFlat
Definition: IndexFlat.h:24
faiss::OperatingPoint::cno
long cno
integer identifer
Definition: AutoTune.h:91
faiss::IndexIVFPQR
Definition: IndexIVFPQ.h:189
faiss::IndexPQ::do_polysemous_training
bool do_polysemous_training
false = standard PQ
Definition: IndexPQ.h:71
faiss::OperatingPoints::clear
void clear()
Definition: AutoTune.cpp:101
faiss::OperatingPoint
Definition: AutoTune.h:87
faiss::OperatingPoints::display
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easy-to-read output
Definition: AutoTune.cpp:220
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Definition: IndexPQ.h:29
faiss::OperatingPoint::perf
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performance measure (output of a Criterion)
Definition: AutoTune.h:88
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Definition: AutoTune.h:94
faiss::OperatingPoints::t_for_perf
double t_for_perf(double perf) const
get time required to obtain a given performance measure
Definition: AutoTune.cpp:173
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nb of NNs that the query should request
Definition: AutoTune.h:30
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just pass over the trainset to quantizer
Definition: IndexIVF.h:51
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Definition: VectorTransform.h:136
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Definition: IndexFlat.h:81
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virtual double evaluate(const float *D, const idx_t *I) const override
Definition: AutoTune.cpp:78
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bool add(double perf, double t, const std::string &key, size_t cno=0)
add a performance measure. Return whether it is an optimal point
Definition: AutoTune.cpp:111
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Definition: VectorTransform.h:188
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bool do_polysemous_training
reorder PQ centroids after training?
Definition: IndexIVFPQ.h:36
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size_t batchsize
maximum number of queries to submit at a time.
Definition: AutoTune.h:146
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virtual double evaluate(const float *D, const idx_t *I) const =0
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Definition: VectorTransform.h:238
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Definition: MetaIndexes.h:26
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nb of queries this criterion is evaluated on
Definition: AutoTune.h:29
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Definition: IndexIVF.h:113
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std::vector< OperatingPoint > optimal_pts
optimal operating points, sorted by perf
Definition: AutoTune.h:99
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Definition: IndexFlat.h:75
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void set_groundtruth(int gt_nnn, const float *gt_D_in, const idx_t *gt_I_in)
Definition: AutoTune.cpp:37
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ParameterRange & add_range(const char *name)
add a new parameter
Definition: AutoTune.cpp:321
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Definition: IndexFlat.h:105
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nb of GT NNs required to evaluate crterion
Definition: AutoTune.h:31
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void all_to_gnuplot(const char *fname) const
output to a format easy to digest by gnuplot
Definition: AutoTune.cpp:187
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should the base index be deallocated?
Definition: IndexFlat.h:112
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int d
vector dimension
Definition: Index.h:66
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size_t code_size
byte per indexed vector
Definition: ProductQuantizer.h:35
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Definition: IndexIVFPQ.h:30
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ClusteringParameters cp
to override default clustering params
Definition: IndexIVF.h:54
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bool thread_over_batches
Definition: AutoTune.h:150
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whether object owns the quantizer
Definition: IndexIVF.h:52
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Definition: AutoTune.h:96
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size_t ranklist_intersection_size(size_t k1, const long *v1, size_t k2, const long *v2_in)
Definition: utils.cpp:1394
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Definition: AutoTune.h:27
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bool verbose
verbosity level
Definition: Index.h:68
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double getmillisecs()
ms elapsed since some arbitrary epoch
Definition: utils.cpp:71
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std::vector< float > gt_D
Ground-truth distances (size nq * gt_nnn)
Definition: AutoTune.h:33
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std::string combination_name(size_t cno) const
get string representation of the combination
Definition: AutoTune.cpp:276
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virtual void search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels) const =0
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Definition: IndexPQ.h:137
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void update_bounds(size_t cno, const OperatingPoint &op, double *upper_bound_perf, double *lower_bound_t) const
Definition: AutoTune.cpp:498
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Definition: VectorTransform.h:29
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! the sub-index
Definition: VectorTransform.h:243
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virtual void initialize(const Index *index)
initialize with reasonable parameters for the index
Definition: AutoTune.cpp:330
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verbosity during exploration
Definition: AutoTune.h:140
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int merge_with(const OperatingPoints &other, const std::string &prefix="")
add operating points from other to this, with a prefix to the keys
Definition: AutoTune.cpp:158
faiss::ParameterSpace::set_index_parameter
virtual void set_index_parameter(Index *index, const std::string &name, double val) const
set one of the parameters
Definition: AutoTune.cpp:419
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size_t n_combinations() const
nb of combinations, = product of values sizes
Definition: AutoTune.cpp:267
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MetricType metric_type
type of metric this index uses for search
Definition: Index.h:74
faiss::ParameterSpace::set_index_parameters
void set_index_parameters(Index *index, size_t cno) const
set a combination of parameters on an index
Definition: AutoTune.cpp:388
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Definition: Index.h:61
faiss::IndexPQ::ST_PQ
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Definition: IndexPQ.h:78
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void display() const
print a description on stdout
Definition: AutoTune.cpp:480
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HE filter (using ht) + PQ combination.
Definition: IndexPQ.h:82
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Definition: ProductQuantizer.h:26
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bool combination_ge(size_t c1, size_t c2) const
returns whether combinations c1 &gt;= c2 in the tuple sense
Definition: AutoTune.cpp:291
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bool spherical
do we want normalized centroids?
Definition: Clustering.h:31
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possible values of a parameter, sorted from least to most expensive/accurate
Definition: AutoTune.h:126
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Definition: AutoTune.cpp:623
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int n_experiments
nb of experiments during optimization (0 = try all combinations)
Definition: AutoTune.h:143
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Ground-truth indexes (size nq * gt_nnn)
Definition: AutoTune.h:34
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corresponding execution time (ms)
Definition: AutoTune.h:89
faiss::MetricType
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Some algorithms support both an inner product vetsion and a L2 search version.
Definition: Index.h:44
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bool own_fields
! the sub-index
Definition: MetaIndexes.h:28