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Fix typos in demos, benchs, and other directories (#4709)

Browse Source 
Summary: Pull Request resolved: https://github.com/facebookresearch/faiss/pull/4709

Reviewed By: limqiying

Differential Revision: D86571400

fbshipit-source-id: ac60662cf7a1939b900b89830273ee3d40f87e9c
This commit is contained in:
Subhadeep Karan
2025-12-05 15:44:35 -08:00
committed by meta-codesync[bot]
parent 89dd5a7b4e
commit 3d4d59fc3b
36 changed files with 115 additions and 116 deletions
Download Patch File Download Diff File Expand all files Collapse all files
CHANGELOG.md
+9 -9
View File
@@ -386,12 +386,12 @@ Changed
- Improve naming due to codemod (#4063)
- Improve naming due to codemod (#4064)
- Improve naming due to codemod (#4065)
- separare the github build into two conditions (#4066)
- separate the github build into two conditions (#4066)
- Improve naming due to codemod (#4070)
- improve naming due to codemod (#4067)
- improve naming due to codemod (#4071)
- improve naming due to codemod (#4072)
- fix nightily build (#4080)
- fix nightly build (#4080)
- Change github action workflows name (#4083)
- Resolve Packaging Issues (#4044)
- Update __init__.py (#4086)
@@ -474,7 +474,7 @@ Deprecated
- faster hnsw CPU index training (#3822)
- Some small improvements. (#3692)
- First attempt at LSH matching with nbits (#3679)
- Set verbosoe before train (#3619)
- Set verbose before train (#3619)
- Remove duplicate NegativeDistanceComputer instances (#3450)
- interrupt for NNDescent (#3432)
- Get rid of redundant instructions in ScalarQuantizer (#3430)
@@ -486,11 +486,11 @@ Deprecated
- Skip HNSWPQ sdc init with new io flag (#3250)
### Fixed
- FIx a bug for a non-simdlib code of ResidualQuantizer (#3868)
- Fix a bug for a non-simdlib code of ResidualQuantizer (#3868)
- assign_index should default to null (#3855)
- Fix an incorrectly counted the number of computed distances for HNSW (#3840)
- Fix incorrectly counted the number of computed distances for HNSW (#3840)
- Add error for overflowing nbits during PQ construction (#3833)
- Fix radius search with HSNW and IP (#3698)
- Fix radius search with HNSW and IP (#3698)
- fix algorithm of spreading vectors over shards (#3374)
- Fix IndexBinary.assign Python method (#3384)
- Few fixes in bench_fw to enable IndexFromCodec (#3383)
@@ -558,7 +558,7 @@ Deprecated
- Improved ResidualQuantizer vector encoding (pooling memory allocations, avoid r/w to a temporary buffer)
### Fixed
- HSNW bug fixed which improves the recall rate! Special thanks to zh Wang @hhy3 for this.
- HNSW bug fixed which improves the recall rate! Special thanks to zh Wang @hhy3 for this.
- Faiss GPU IVF large query batch fix
- Faiss + Torch fixes, re-enable k = 2048
- Fix the number of distance computations to match max_codes parameter
@@ -599,7 +599,7 @@ Deprecated
- Implementation of Local Search Quantization (by @KinglittleQ)
### Changed
- The order of xb an xq was different between `faiss.knn` and `faiss.knn_gpu`.
- The order of xb and xq was different between `faiss.knn` and `faiss.knn_gpu`.
Also the metric argument was called distance_type.
- The typed vectors (LongVector, LongLongVector, etc.) of the SWIG interface have
been deprecated. They have been replaced with Int32Vector, Int64Vector, etc. (by h-vetinari)
@@ -730,7 +730,7 @@ propagated.
- Support for IMI2x16 (4B virtual centroids).
- Support for k = 2048 search on GPU (instead of 1024).
- Support for renaming an ondisk invertedlists.
- Support for nterrupting computations with interrupt signal (ctrl-C) in python.
- Support for interrupting computations with interrupt signal (ctrl-C) in python.
- Simplified build system (with --with-cuda/--with-cuda-arch options).
### Changed
benchs/bench_all_ivf/parse_bench_all_ivf.py
+1 -1
View File
@@ -136,7 +136,7 @@ def collect_results_for(db='deep1M', prefix="autotune."):
print("FAIL %s" % e)
res = np.zeros((2, 0))
except Exception as e:
print("PARSE ERROR " + e)
print("PARSE ERROR " + str(e))
res = np.zeros((2, 0))
else:
print(len(res), "results")
benchs/bench_cppcontrib_sa_decode.cpp
+64 -64
View File
@@ -221,9 +221,9 @@ static void verifyIndex2LevelDecoder(
// generate a random order of points
std::uniform_int_distribution<uint64_t> un(0, n - 1);
std::vector<uint64_t> pointIncidesToDecode(nIterations * n, 0);
std::vector<uint64_t> pointIndicesToDecode(nIterations * n, 0);
for (uint64_t i = 0; i < nIterations * n; i++) {
pointIncidesToDecode[i] = un(rng);
pointIndicesToDecode[i] = un(rng);
}
{
@@ -234,7 +234,7 @@ static void verifyIndex2LevelDecoder(
StopWatch swFaiss;
for (uint64_t iter = 0; iter < nIterations; iter++) {
for (uint64_t i = 0; i < n; i++) {
const auto pointIdx = pointIncidesToDecode[i + iter * n];
const auto pointIdx = pointIndicesToDecode[i + iter * n];
index->sa_decode(
1,
encodedData.data() + pointIdx * codeSize,
@@ -247,7 +247,7 @@ static void verifyIndex2LevelDecoder(
StopWatch swKernel;
for (uint64_t iter = 0; iter < nIterations; iter++) {
for (uint64_t i = 0; i < n; i++) {
const auto pointIdx = pointIncidesToDecode[i + iter * n];
const auto pointIdx = pointIndicesToDecode[i + iter * n];
T::store(
pqCoarseCentroidsQ,
pqFineCentroidsQ,
@@ -289,7 +289,7 @@ static void verifyIndex2LevelDecoder(
for (uint64_t i = 0; i < n; i++) {
for (uint64_t iter = 0; iter < nIterations; iter++) {
const auto pointIdx =
pointIncidesToDecode[i * nIterations + iter];
pointIndicesToDecode[i * nIterations + iter];
const auto weight = weights[i * nIterations + iter];
index->sa_decode(
1,
@@ -307,7 +307,7 @@ static void verifyIndex2LevelDecoder(
for (uint64_t i = 0; i < n; i++) {
for (uint64_t iter = 0; iter < nIterations; iter++) {
const auto pointIdx =
pointIncidesToDecode[i * nIterations + iter];
pointIndicesToDecode[i * nIterations + iter];
const auto weight = weights[i * nIterations + iter];
T::accum(
pqCoarseCentroidsQ,
@@ -331,10 +331,10 @@ static void verifyIndex2LevelDecoder(
for (uint64_t i = 0; i < n; i++) {
for (uint64_t iter = 0; iter < nIterations; iter += 2) {
const auto pointIdx0 =
pointIncidesToDecode[i * nIterations + iter + 0];
pointIndicesToDecode[i * nIterations + iter + 0];
const auto weight0 = weights[i * nIterations + iter + 0];
const auto pointIdx1 =
pointIncidesToDecode[i * nIterations + iter + 1];
pointIndicesToDecode[i * nIterations + iter + 1];
const auto weight1 = weights[i * nIterations + iter + 1];
T::accum(
pqCoarseCentroidsQ,
@@ -360,10 +360,10 @@ static void verifyIndex2LevelDecoder(
for (uint64_t i = 0; i < n; i++) {
for (uint64_t iter = 0; iter < nIterations; iter += 2) {
const auto pointIdx0 =
pointIncidesToDecode[i * nIterations + iter + 0];
pointIndicesToDecode[i * nIterations + iter + 0];
const auto weight0 = weights[i * nIterations + iter + 0];
const auto pointIdx1 =
pointIncidesToDecode[i * nIterations + iter + 1];
pointIndicesToDecode[i * nIterations + iter + 1];
const auto weight1 = weights[i * nIterations + iter + 1];
T::accum(
pqCoarseCentroidsQ,
@@ -391,13 +391,13 @@ static void verifyIndex2LevelDecoder(
for (uint64_t i = 0; i < n; i++) {
for (uint64_t iter = 0; iter < nIterations; iter += 3) {
const auto pointIdx0 =
pointIncidesToDecode[i * nIterations + iter + 0];
pointIndicesToDecode[i * nIterations + iter + 0];
const auto weight0 = weights[i * nIterations + iter + 0];
const auto pointIdx1 =
pointIncidesToDecode[i * nIterations + iter + 1];
pointIndicesToDecode[i * nIterations + iter + 1];
const auto weight1 = weights[i * nIterations + iter + 1];
const auto pointIdx2 =
pointIncidesToDecode[i * nIterations + iter + 2];
pointIndicesToDecode[i * nIterations + iter + 2];
const auto weight2 = weights[i * nIterations + iter + 2];
T::accum(
pqCoarseCentroidsQ,
@@ -425,13 +425,13 @@ static void verifyIndex2LevelDecoder(
for (uint64_t i = 0; i < n; i++) {
for (uint64_t iter = 0; iter < nIterations; iter += 3) {
const auto pointIdx0 =
pointIncidesToDecode[i * nIterations + iter + 0];
pointIndicesToDecode[i * nIterations + iter + 0];
const auto weight0 = weights[i * nIterations + iter + 0];
const auto pointIdx1 =
pointIncidesToDecode[i * nIterations + iter + 1];
pointIndicesToDecode[i * nIterations + iter + 1];
const auto weight1 = weights[i * nIterations + iter + 1];
const auto pointIdx2 =
pointIncidesToDecode[i * nIterations + iter + 2];
pointIndicesToDecode[i * nIterations + iter + 2];
const auto weight2 = weights[i * nIterations + iter + 2];
T::accum(
pqCoarseCentroidsQ,
@@ -532,9 +532,9 @@ static void verifyMinMaxIndex2LevelDecoder(
// generate a random order of points
std::uniform_int_distribution<uint64_t> un(0, n - 1);
std::vector<uint64_t> pointIncidesToDecode(nIterations * n, 0);
std::vector<uint64_t> pointIndicesToDecode(nIterations * n, 0);
for (uint64_t i = 0; i < nIterations * n; i++) {
pointIncidesToDecode[i] = un(rng);
pointIndicesToDecode[i] = un(rng);
}
{
@@ -545,7 +545,7 @@ static void verifyMinMaxIndex2LevelDecoder(
StopWatch swFaiss;
for (uint64_t iter = 0; iter < nIterations; iter++) {
for (uint64_t i = 0; i < n; i++) {
const auto pointIdx = pointIncidesToDecode[i + iter * n];
const auto pointIdx = pointIndicesToDecode[i + iter * n];
index->sa_decode(
1,
encodedData.data() + pointIdx * codeSize,
@@ -558,7 +558,7 @@ static void verifyMinMaxIndex2LevelDecoder(
StopWatch swKernel;
for (uint64_t iter = 0; iter < nIterations; iter++) {
for (uint64_t i = 0; i < n; i++) {
const auto pointIdx = pointIncidesToDecode[i + iter * n];
const auto pointIdx = pointIndicesToDecode[i + iter * n];
T::store(
pqCoarseCentroidsQ,
pqFineCentroidsQ,
@@ -601,7 +601,7 @@ static void verifyMinMaxIndex2LevelDecoder(
for (uint64_t i = 0; i < n; i++) {
for (uint64_t iter = 0; iter < nIterations; iter++) {
const auto pointIdx =
pointIncidesToDecode[i * nIterations + iter];
pointIndicesToDecode[i * nIterations + iter];
const auto weight = weights[i * nIterations + iter];
index->sa_decode(
1,
@@ -620,7 +620,7 @@ static void verifyMinMaxIndex2LevelDecoder(
float outputAccumMin = 0;
for (uint64_t iter = 0; iter < nIterations; iter++) {
const auto pointIdx =
pointIncidesToDecode[i * nIterations + iter];
pointIndicesToDecode[i * nIterations + iter];
const auto weight = weights[i * nIterations + iter];
T::accum(
pqCoarseCentroidsQ,
@@ -649,10 +649,10 @@ static void verifyMinMaxIndex2LevelDecoder(
float outputAccumMin = 0;
for (uint64_t iter = 0; iter < nIterations; iter += 2) {
const auto pointIdx0 =
pointIncidesToDecode[i * nIterations + iter + 0];
pointIndicesToDecode[i * nIterations + iter + 0];
const auto weight0 = weights[i * nIterations + iter + 0];
const auto pointIdx1 =
pointIncidesToDecode[i * nIterations + iter + 1];
pointIndicesToDecode[i * nIterations + iter + 1];
const auto weight1 = weights[i * nIterations + iter + 1];
T::accum(
pqCoarseCentroidsQ,
@@ -683,10 +683,10 @@ static void verifyMinMaxIndex2LevelDecoder(
float outputAccumMin = 0;
for (uint64_t iter = 0; iter < nIterations; iter += 2) {
const auto pointIdx0 =
pointIncidesToDecode[i * nIterations + iter + 0];
pointIndicesToDecode[i * nIterations + iter + 0];
const auto weight0 = weights[i * nIterations + iter + 0];
const auto pointIdx1 =
pointIncidesToDecode[i * nIterations + iter + 1];
pointIndicesToDecode[i * nIterations + iter + 1];
const auto weight1 = weights[i * nIterations + iter + 1];
T::accum(
pqCoarseCentroidsQ,
@@ -719,13 +719,13 @@ static void verifyMinMaxIndex2LevelDecoder(
float outputAccumMin = 0;
for (uint64_t iter = 0; iter < nIterations; iter += 3) {
const auto pointIdx0 =
pointIncidesToDecode[i * nIterations + iter + 0];
pointIndicesToDecode[i * nIterations + iter + 0];
const auto weight0 = weights[i * nIterations + iter + 0];
const auto pointIdx1 =
pointIncidesToDecode[i * nIterations + iter + 1];
pointIndicesToDecode[i * nIterations + iter + 1];
const auto weight1 = weights[i * nIterations + iter + 1];
const auto pointIdx2 =
pointIncidesToDecode[i * nIterations + iter + 2];
pointIndicesToDecode[i * nIterations + iter + 2];
const auto weight2 = weights[i * nIterations + iter + 2];
T::accum(
pqCoarseCentroidsQ,
@@ -758,13 +758,13 @@ static void verifyMinMaxIndex2LevelDecoder(
float outputAccumMin = 0;
for (uint64_t iter = 0; iter < nIterations; iter += 3) {
const auto pointIdx0 =
pointIncidesToDecode[i * nIterations + iter + 0];
pointIndicesToDecode[i * nIterations + iter + 0];
const auto weight0 = weights[i * nIterations + iter + 0];
const auto pointIdx1 =
pointIncidesToDecode[i * nIterations + iter + 1];
pointIndicesToDecode[i * nIterations + iter + 1];
const auto weight1 = weights[i * nIterations + iter + 1];
const auto pointIdx2 =
pointIncidesToDecode[i * nIterations + iter + 2];
pointIndicesToDecode[i * nIterations + iter + 2];
const auto weight2 = weights[i * nIterations + iter + 2];
T::accum(
pqCoarseCentroidsQ,
@@ -859,9 +859,9 @@ static void verifyIndexPQDecoder(
// generate a random order of points
std::uniform_int_distribution<uint64_t> un(0, n - 1);
std::vector<uint64_t> pointIncidesToDecode(nIterations * n, 0);
std::vector<uint64_t> pointIndicesToDecode(nIterations * n, 0);
for (uint64_t i = 0; i < nIterations * n; i++) {
pointIncidesToDecode[i] = un(rng);
pointIndicesToDecode[i] = un(rng);
}
{
@@ -872,7 +872,7 @@ static void verifyIndexPQDecoder(
StopWatch swFaiss;
for (uint64_t iter = 0; iter < nIterations; iter++) {
for (uint64_t i = 0; i < n; i++) {
const auto pointIdx = pointIncidesToDecode[i + iter * n];
const auto pointIdx = pointIndicesToDecode[i + iter * n];
index->sa_decode(
1,
encodedData.data() + pointIdx * codeSize,
@@ -885,7 +885,7 @@ static void verifyIndexPQDecoder(
StopWatch swKernel;
for (uint64_t iter = 0; iter < nIterations; iter++) {
for (uint64_t i = 0; i < n; i++) {
const auto pointIdx = pointIncidesToDecode[i + iter * n];
const auto pointIdx = pointIndicesToDecode[i + iter * n];
T::store(
pqFineCentroidsQ,
encodedData.data() + pointIdx * codeSize,
@@ -927,7 +927,7 @@ static void verifyIndexPQDecoder(
for (uint64_t i = 0; i < n; i++) {
for (uint64_t iter = 0; iter < nIterations; iter++) {
const auto pointIdx =
pointIncidesToDecode[i * nIterations + iter];
pointIndicesToDecode[i * nIterations + iter];
const auto weight = weights[i * nIterations + iter];
index->sa_decode(
1,
@@ -945,7 +945,7 @@ static void verifyIndexPQDecoder(
for (uint64_t i = 0; i < n; i++) {
for (uint64_t iter = 0; iter < nIterations; iter++) {
const auto pointIdx =
pointIncidesToDecode[i * nIterations + iter];
pointIndicesToDecode[i * nIterations + iter];
const auto weight = weights[i * nIterations + iter];
T::accum(
pqFineCentroidsQ,
@@ -968,10 +968,10 @@ static void verifyIndexPQDecoder(
for (uint64_t i = 0; i < n; i++) {
for (uint64_t iter = 0; iter < nIterations; iter += 2) {
const auto pointIdx0 =
pointIncidesToDecode[i * nIterations + iter + 0];
pointIndicesToDecode[i * nIterations + iter + 0];
const auto weight0 = weights[i * nIterations + iter + 0];
const auto pointIdx1 =
pointIncidesToDecode[i * nIterations + iter + 1];
pointIndicesToDecode[i * nIterations + iter + 1];
const auto weight1 = weights[i * nIterations + iter + 1];
T::accum(
pqFineCentroidsQ,
@@ -996,10 +996,10 @@ static void verifyIndexPQDecoder(
for (uint64_t i = 0; i < n; i++) {
for (uint64_t iter = 0; iter < nIterations; iter += 2) {
const auto pointIdx0 =
pointIncidesToDecode[i * nIterations + iter + 0];
pointIndicesToDecode[i * nIterations + iter + 0];
const auto weight0 = weights[i * nIterations + iter + 0];
const auto pointIdx1 =
pointIncidesToDecode[i * nIterations + iter + 1];
pointIndicesToDecode[i * nIterations + iter + 1];
const auto weight1 = weights[i * nIterations + iter + 1];
T::accum(
pqFineCentroidsQ,
@@ -1025,13 +1025,13 @@ static void verifyIndexPQDecoder(
for (uint64_t i = 0; i < n; i++) {
for (uint64_t iter = 0; iter < nIterations; iter += 3) {
const auto pointIdx0 =
pointIncidesToDecode[i * nIterations + iter + 0];
pointIndicesToDecode[i * nIterations + iter + 0];
const auto weight0 = weights[i * nIterations + iter + 0];
const auto pointIdx1 =
pointIncidesToDecode[i * nIterations + iter + 1];
pointIndicesToDecode[i * nIterations + iter + 1];
const auto weight1 = weights[i * nIterations + iter + 1];
const auto pointIdx2 =
pointIncidesToDecode[i * nIterations + iter + 2];
pointIndicesToDecode[i * nIterations + iter + 2];
const auto weight2 = weights[i * nIterations + iter + 2];
T::accum(
pqFineCentroidsQ,
@@ -1058,13 +1058,13 @@ static void verifyIndexPQDecoder(
for (uint64_t i = 0; i < n; i++) {
for (uint64_t iter = 0; iter < nIterations; iter += 3) {
const auto pointIdx0 =
pointIncidesToDecode[i * nIterations + iter + 0];
pointIndicesToDecode[i * nIterations + iter + 0];
const auto weight0 = weights[i * nIterations + iter + 0];
const auto pointIdx1 =
pointIncidesToDecode[i * nIterations + iter + 1];
pointIndicesToDecode[i * nIterations + iter + 1];
const auto weight1 = weights[i * nIterations + iter + 1];
const auto pointIdx2 =
pointIncidesToDecode[i * nIterations + iter + 2];
pointIndicesToDecode[i * nIterations + iter + 2];
const auto weight2 = weights[i * nIterations + iter + 2];
T::accum(
pqFineCentroidsQ,
@@ -1157,9 +1157,9 @@ static void verifyMinMaxIndexPQDecoder(
// generate a random order of points
std::uniform_int_distribution<uint64_t> un(0, n - 1);
std::vector<uint64_t> pointIncidesToDecode(nIterations * n, 0);
std::vector<uint64_t> pointIndicesToDecode(nIterations * n, 0);
for (uint64_t i = 0; i < nIterations * n; i++) {
pointIncidesToDecode[i] = un(rng);
pointIndicesToDecode[i] = un(rng);
}
{
@@ -1170,7 +1170,7 @@ static void verifyMinMaxIndexPQDecoder(
StopWatch swFaiss;
for (uint64_t iter = 0; iter < nIterations; iter++) {
for (uint64_t i = 0; i < n; i++) {
const auto pointIdx = pointIncidesToDecode[i + iter * n];
const auto pointIdx = pointIndicesToDecode[i + iter * n];
index->sa_decode(
1,
encodedData.data() + pointIdx * codeSize,
@@ -1183,7 +1183,7 @@ static void verifyMinMaxIndexPQDecoder(
StopWatch swKernel;
for (uint64_t iter = 0; iter < nIterations; iter++) {
for (uint64_t i = 0; i < n; i++) {
const auto pointIdx = pointIncidesToDecode[i + iter * n];
const auto pointIdx = pointIndicesToDecode[i + iter * n];
T::store(
pqFineCentroidsQ,
encodedData.data() + pointIdx * codeSize,
@@ -1225,7 +1225,7 @@ static void verifyMinMaxIndexPQDecoder(
for (uint64_t i = 0; i < n; i++) {
for (uint64_t iter = 0; iter < nIterations; iter++) {
const auto pointIdx =
pointIncidesToDecode[i * nIterations + iter];
pointIndicesToDecode[i * nIterations + iter];
const auto weight = weights[i * nIterations + iter];
index->sa_decode(
1,
@@ -1244,7 +1244,7 @@ static void verifyMinMaxIndexPQDecoder(
float outputAccumMin = 0;
for (uint64_t iter = 0; iter < nIterations; iter++) {
const auto pointIdx =
pointIncidesToDecode[i * nIterations + iter];
pointIndicesToDecode[i * nIterations + iter];
const auto weight = weights[i * nIterations + iter];
T::accum(
pqFineCentroidsQ,
@@ -1272,10 +1272,10 @@ static void verifyMinMaxIndexPQDecoder(
float outputAccumMin = 0;
for (uint64_t iter = 0; iter < nIterations; iter += 2) {
const auto pointIdx0 =
pointIncidesToDecode[i * nIterations + iter + 0];
pointIndicesToDecode[i * nIterations + iter + 0];
const auto weight0 = weights[i * nIterations + iter + 0];
const auto pointIdx1 =
pointIncidesToDecode[i * nIterations + iter + 1];
pointIndicesToDecode[i * nIterations + iter + 1];
const auto weight1 = weights[i * nIterations + iter + 1];
T::accum(
pqFineCentroidsQ,
@@ -1305,10 +1305,10 @@ static void verifyMinMaxIndexPQDecoder(
float outputAccumMin = 0;
for (uint64_t iter = 0; iter < nIterations; iter += 2) {
const auto pointIdx0 =
pointIncidesToDecode[i * nIterations + iter + 0];
pointIndicesToDecode[i * nIterations + iter + 0];
const auto weight0 = weights[i * nIterations + iter + 0];
const auto pointIdx1 =
pointIncidesToDecode[i * nIterations + iter + 1];
pointIndicesToDecode[i * nIterations + iter + 1];
const auto weight1 = weights[i * nIterations + iter + 1];
T::accum(
pqFineCentroidsQ,
@@ -1339,13 +1339,13 @@ static void verifyMinMaxIndexPQDecoder(
float outputAccumMin = 0;
for (uint64_t iter = 0; iter < nIterations; iter += 3) {
const auto pointIdx0 =
pointIncidesToDecode[i * nIterations + iter + 0];
pointIndicesToDecode[i * nIterations + iter + 0];
const auto weight0 = weights[i * nIterations + iter + 0];
const auto pointIdx1 =
pointIncidesToDecode[i * nIterations + iter + 1];
pointIndicesToDecode[i * nIterations + iter + 1];
const auto weight1 = weights[i * nIterations + iter + 1];
const auto pointIdx2 =
pointIncidesToDecode[i * nIterations + iter + 2];
pointIndicesToDecode[i * nIterations + iter + 2];
const auto weight2 = weights[i * nIterations + iter + 2];
T::accum(
pqFineCentroidsQ,
@@ -1377,13 +1377,13 @@ static void verifyMinMaxIndexPQDecoder(
float outputAccumMin = 0;
for (uint64_t iter = 0; iter < nIterations; iter += 3) {
const auto pointIdx0 =
pointIncidesToDecode[i * nIterations + iter + 0];
pointIndicesToDecode[i * nIterations + iter + 0];
const auto weight0 = weights[i * nIterations + iter + 0];
const auto pointIdx1 =
pointIncidesToDecode[i * nIterations + iter + 1];
pointIndicesToDecode[i * nIterations + iter + 1];
const auto weight1 = weights[i * nIterations + iter + 1];
const auto pointIdx2 =
pointIncidesToDecode[i * nIterations + iter + 2];
pointIndicesToDecode[i * nIterations + iter + 2];
const auto weight2 = weights[i * nIterations + iter + 2];
T::accum(
pqFineCentroidsQ,
benchs/bench_fw/descriptors.py
+1 -1
View File
@@ -326,7 +326,7 @@ class IndexDescriptor(IndexBaseDescriptor):
self.flat_desc_name = self.codec_desc.flat_name() + self.database_desc.get_filename(prefix=IndexDescriptor.FILENAME_PREFIX)
return self.flat_desc_name
# alias is used to refer when index is uploaded to blobstore and refered again
# alias is used to refer when index is uploaded to blobstore and referred again
def alias(self, benchmark_io: BenchmarkIO):
if hasattr(benchmark_io, "bucket"):
return IndexDescriptor(desc_name=self.get_name(), bucket=benchmark_io.bucket, path=self.get_path(benchmark_io), d=self.d, metric=self.metric)
benchs/bench_fw/index.py
+1 -1
View File
@@ -558,7 +558,7 @@ class IndexBase:
"construction_params": self.get_construction_params(),
"search_params": search_parameters,
}
logger.info("range_seach: end")
logger.info("range_search: end")
return lims, D, I, R, P, None
benchs/bench_gpu_1bn.py
+4 -4
View File
@@ -166,7 +166,7 @@ def rate_limited_imap(f, l):
class IdentPreproc:
"""a pre-processor is either a faiss.VectorTransform or an IndentPreproc"""
"""a pre-processor is either a faiss.VectorTransform or an IdentPreproc"""
def __init__(self, d):
self.d_in = self.d_out = d
@@ -229,7 +229,7 @@ elif dbname == 'Deep1B':
xb = mmap_fvecs('deep1b/base.fvecs')
xq = mmap_fvecs('deep1b/deep1B_queries.fvecs')
xt = mmap_fvecs('deep1b/learn.fvecs')
# deep1B's train is is outrageously big
# deep1B's train is outrageously big
xt = xt[:10 * 1000 * 1000]
gt_I = ivecs_read('deep1b/deep1B_groundtruth.ivecs')
@@ -740,11 +740,11 @@ def eval_dataset(index, preproc):
print("1-R@%d: %.4f" % (rank, nok / float(nq)), end=' ')
print()
if I_fname:
I_fname_i = I_fname % I
I_fname_i = I_fname % nprobe
print("storing", I_fname_i)
np.save(I, I_fname_i)
if D_fname:
D_fname_i = I_fname % I
D_fname_i = D_fname % nprobe
print("storing", D_fname_i)
np.save(D, D_fname_i)
benchs/bench_hybrid_cpu_gpu.py
+3 -3
View File
@@ -282,7 +282,7 @@ def main():
group = parser.add_argument_group('dataset options')
aa('--nq', type=int, default=int(10e5),
help="nb queries (queries will be duplicated if below that number")
help="nb queries (queries will be duplicated if below that number)")
aa('--db', default='bigann10M', help='dataset')
group = parser.add_argument_group('index options')
@@ -311,7 +311,7 @@ def main():
help="how to search"
)
aa('--ivf_quant_nlist', type=int, default=1024,
help="nb of invlists for IVF quantizer")
help="nb of inverted lists for IVF quantizer")
aa('--batch_size', type=int, default=-1,
help="batch size for tiled CPU / GPU computation (-1= no tiling)")
aa('--n_autotune', type=int, default=300,
@@ -540,7 +540,7 @@ def main():
op.all_experiments = []
op.platform = {
"loadavg": open("/proc/loadavg", "r").readlines(),
"procesor": [l for l in open("/proc/cpuinfo") if "model name" in l][0],
"processor": [l for l in open("/proc/cpuinfo") if "model name" in l][0],
"GPU": list(os.popen("nvidia-smi", "r")),
"mem": open("/proc/meminfo", "r").readlines(),
"pid": os.getpid()
benchs/bench_ivf_flat_panorama.py
+1
View File
@@ -93,6 +93,7 @@ eval_and_plot(f"IVF{nlist},Flat")
eval_and_plot(f"PCA{d},IVF{nlist},FlatPanorama{nlevels}")
plt.title("IVF Flat Indexes on GIST1M")
plt.title("Indices on GIST1M")
plt.xlabel(f"Recall@{k}")
plt.ylabel("QPS")
plt.yscale("log")
benchs/bench_partition.py
+1 -1
View File
@@ -59,7 +59,7 @@ def do_partition(n, qin, maxval=65536, seed=123, id_type='int64'):
print(
f"times {times.mean():.3f} µs (± {times.std():.4f} µs) nerr={nerr} "
f"bissect {stats.bissect_cycles / 1e6:.3f} Mcy "
f"bisect {stats.bissect_cycles / 1e6:.3f} Mcy "
f"compress {stats.compress_cycles / 1e6:.3f} Mcy"
)
benchs/bench_polysemous_1bn.py
+1 -1
View File
@@ -69,7 +69,7 @@ elif dbname == 'Deep1B':
xb = mmap_fvecs('deep1b/base.fvecs')
xq = mmap_fvecs('deep1b/deep1B_queries.fvecs')
xt = mmap_fvecs('deep1b/learn.fvecs')
# deep1B's train is is outrageously big
# deep1B's train is outrageously big
xt = xt[:10 * 1000 * 1000]
gt = ivecs_read('deep1b/deep1B_groundtruth.ivecs')
benchs/bench_pq_tables.py
+1 -1
View File
@@ -73,5 +73,5 @@ for have_threads in False, True:
res = run_bench(M * dsub, dsub, nbit, metric)
allres.append(res)
allres = np.array(allres)
print("formated result:")
print("formatted result:")
print(format_tab(allres))
benchs/distributed_ondisk/combined_index.py
-1
View File
@@ -136,7 +136,6 @@ class CombinedIndex:
for idx in self.indexes:
il = faiss.downcast_InvertedLists(
faiss.extract_index_ivf(idx).invlists)
il.prefetch_nthread
il.prefetch_nthread = nt
def set_omp_num_threads(self, nt):
benchs/distributed_ondisk/run_on_cluster.bash
+2 -2
View File
@@ -42,11 +42,11 @@ elif [ $todo == test_kmeans_1 ]; then
--k $k --gpu -1
elif [ $todo == test_kmeans_2 ]; then
# distrbuted run, with one local server per GPU
# distributed run, with one local server per GPU
ngpu=$( echo /dev/nvidia? | wc -w )
baseport=12012
# kill background porcesses on output of this script
# kill background processes on output of this script
trap 'kill -HUP 0' 0
hostports=''
demos/demo_auto_tune.py
+1 -1
View File
@@ -101,7 +101,7 @@ use_gpu = False
if use_gpu:
# if this fails, it means that the GPU version was not comp
# if this fails, it means that the GPU version was not compiled
assert faiss.StandardGpuResources, \
"Faiss was not compiled with GPU support, or loading _swigfaiss_gpu.so failed"
res = faiss.StandardGpuResources()
demos/demo_distributed_kmeans_torch.py
+1 -1
View File
@@ -113,7 +113,7 @@ class DatasetAssignDistributedGPU(clustering.DatasetAssign):
torch.distributed.reduce(sum_per_centroid, 0)
if rank == 0:
# gather deos not support tensors of different sizes
# gather does not support tensors of different sizes
# should be implemented with point-to-point communication
assert np.all(self.sizes == self.sizes[0])
device = self.device
demos/demo_imi_flat.cpp
+2 -2
View File
@@ -63,14 +63,14 @@ int main() {
ncentroids,
nb);
// the coarse quantizer should not be dealloced before the index
// the coarse quantizer should not be deallocated before the index
// 4 = nb of bytes per code (d must be a multiple of this)
// 8 = nb of bits per sub-code (almost always 8)
faiss::MetricType metric = faiss::METRIC_L2; // can be METRIC_INNER_PRODUCT
faiss::IndexIVFFlat index(&coarse_quantizer, d, ncentroids, metric);
index.quantizer_trains_alone = true;
// define the number of probes. 2048 is for high-dim, overkilled in practice
// define the number of probes. 2048 is for high-dim, overkill in practice
// Use 4-1024 depending on the trade-off speed accuracy that you want
index.nprobe = 2048;
demos/demo_imi_pq.cpp
+1 -1
View File
@@ -74,7 +74,7 @@ int main() {
ncentroids,
nb);
// the coarse quantizer should not be dealloced before the index
// the coarse quantizer should not be deallocated before the index
// 4 = nb of bytes per code (d must be a multiple of this)
// 8 = nb of bits per sub-code (almost always 8)
faiss::IndexIVFPQ index(
demos/demo_ivfpq_indexing.cpp
+1 -1
View File
@@ -41,7 +41,7 @@ int main() {
// a reasonable number of centroids to index nb vectors
int ncentroids = int(4 * sqrt(nb));
// the coarse quantizer should not be dealloced before the index
// the coarse quantizer should not be deallocated before the index
// 4 = nb of bytes per code (d must be a multiple of this)
// 8 = nb of bits per sub-code (almost always 8)
faiss::IndexIVFPQ index(&coarse_quantizer, d, ncentroids, 4, 8);
demos/demo_residual_quantizer.cpp
+1 -1
View File
@@ -76,7 +76,7 @@ int main() {
faiss::ResidualQuantizer rq(
d, 7, 6, faiss::AdditiveQuantizer::ST_norm_qint8);
// do cheap an inaccurate training
// do cheap and inaccurate training
rq.cp.niter = 5;
rq.max_beam_size = 5;
rq.train_type = 0;
demos/demo_sift1M.cpp
+1 -1
View File
@@ -23,7 +23,7 @@
*
* http://corpus-texmex.irisa.fr/
*
* and unzip it to the sudirectory sift1M.
* and unzip it to the subdirectory sift1M.
**/
/*****************************************************
demos/offline_ivf/README.md
+1 -2
View File
@@ -48,5 +48,4 @@ b. A new dataset needs to be prepared (step 1) before passing it to the query ve
6. We can always run the consistency-check for sanity checks!
`python run.py --command consistency_check--config config_ssnpp.yaml --xb ssnpp_1B`
`python run.py --command consistency_check --config config_ssnpp.yaml --xb ssnpp_1B`
demos/offline_ivf/offline_ivf.py
+1 -1
View File
@@ -284,7 +284,7 @@ class OfflineIVF:
logging.info(jj)
assert (
jj <= self.shard_size
), f"jj {jj} and shard_zide {self.shard_size}"
), f"jj {jj} and shard_size {self.shard_size}"
if jj == self.shard_size:
break
logging.info(f"writing {sfn}...")
demos/rocksdb_ivf/README.md
+1 -1
View File
@@ -19,5 +19,5 @@ make -C build -j$(nproc)
```
cd ~/faiss/demos/rocksdb_ivf/build
./rocksdb_ivf test_db
./demo_rocksdb_ivf test_db
```
faiss/gpu/utils/blockselect/BlockSelectFloatF512.cu
+1 -1
View File
@@ -12,5 +12,5 @@ namespace gpu {
BLOCK_SELECT_IMPL(float, false, 512, 8);
}
} // namespace gpu
} // namespace faiss
faiss/gpu/utils/blockselect/BlockSelectFloatT1024.cu
+1 -1
View File
@@ -12,5 +12,5 @@ namespace gpu {
BLOCK_SELECT_IMPL(float, true, 1024, 8);
}
} // namespace gpu
} // namespace faiss
faiss/gpu/utils/blockselect/BlockSelectFloatT512.cu
+1 -1
View File
@@ -12,5 +12,5 @@ namespace gpu {
BLOCK_SELECT_IMPL(float, true, 512, 8);
}
} // namespace gpu
} // namespace faiss
faiss/gpu/utils/warpselect/WarpSelectFloatF512.cu
+1 -1
View File
@@ -12,5 +12,5 @@ namespace gpu {
WARP_SELECT_IMPL(float, false, 512, 8);
}
} // namespace gpu
} // namespace faiss
faiss/gpu/utils/warpselect/WarpSelectFloatT1024.cu
+1 -1
View File
@@ -12,5 +12,5 @@ namespace gpu {
WARP_SELECT_IMPL(float, true, 1024, 8);
}
} // namespace gpu
} // namespace faiss
faiss/gpu/utils/warpselect/WarpSelectFloatT512.cu
+1 -1
View File
@@ -12,5 +12,5 @@ namespace gpu {
WARP_SELECT_IMPL(float, true, 512, 8);
}
} // namespace gpu
} // namespace faiss
faiss/impl/IDSelector.h
+3 -3
View File
@@ -131,7 +131,7 @@ struct IDSelectorAll : IDSelector {
virtual ~IDSelectorAll() {}
};
/// does an AND operation on the the two given IDSelector's is_membership
/// does an AND operation on the two given IDSelector's is_membership
/// results.
struct IDSelectorAnd : IDSelector {
const IDSelector* lhs;
@@ -144,7 +144,7 @@ struct IDSelectorAnd : IDSelector {
virtual ~IDSelectorAnd() {}
};
/// does an OR operation on the the two given IDSelector's is_membership
/// does an OR operation on the two given IDSelector's is_membership
/// results.
struct IDSelectorOr : IDSelector {
const IDSelector* lhs;
@@ -157,7 +157,7 @@ struct IDSelectorOr : IDSelector {
virtual ~IDSelectorOr() {}
};
/// does an XOR operation on the the two given IDSelector's is_membership
/// does an XOR operation on the two given IDSelector's is_membership
/// results.
struct IDSelectorXOr : IDSelector {
const IDSelector* lhs;
faiss/invlists/BlockInvertedLists.h
+1 -1
View File
@@ -20,7 +20,7 @@ struct IDSelector;
/** Inverted Lists that are organized by blocks.
*
* Different from the regular inverted lists, the codes are organized by blocks
* of size block_size bytes that reprsent a set of n_per_block. Therefore, code
* of size block_size bytes that represent a set of n_per_block. Therefore, code
* allocations are always rounded up to block_size bytes. The codes are also
* aligned on 32-byte boundaries for use with SIMD.
*
faiss/invlists/DirectMap.cpp
+1 -1
View File
@@ -53,7 +53,7 @@ void DirectMap::set_type(
for (long ofs = 0; ofs < list_size; ofs++) {
FAISS_THROW_IF_NOT_MSG(
0 <= idlist[ofs] && idlist[ofs] < ntotal,
"direct map supported only for seuquential ids");
"direct map supported only for sequential ids");
array[idlist[ofs]] = lo_build(key, ofs);
}
} else if (new_type == Hashtable) {
faiss/invlists/InvertedLists.cpp
+1 -1
View File
@@ -229,7 +229,7 @@ bool InvertedLists::is_empty(size_t list_no, void* inverted_list_context)
}
}
// implemnent iterator on top of get_codes / get_ids
// implement iterator on top of get_codes / get_ids
namespace {
struct CodeArrayIterator : InvertedListsIterator {
faiss/invlists/OnDiskInvertedLists.cpp
+1 -1
View File
@@ -372,7 +372,7 @@ OnDiskInvertedLists::~OnDiskInvertedLists() {
if (ptr != nullptr) {
int err = munmap(ptr, totsize);
if (err != 0) {
fprintf(stderr, "mumap error: %s", strerror(errno));
fprintf(stderr, "munmap error: %s", strerror(errno));
}
}
delete locks;
faiss/invlists/OnDiskInvertedLists.h
+1 -1
View File
@@ -121,7 +121,7 @@ struct OnDiskInvertedLists : InvertedLists {
LockLevels* locks;
// encapsulates the threads that are busy prefeteching
// encapsulates the threads that are busy prefetching
struct OngoingPrefetch;
OngoingPrefetch* pf;
int prefetch_nthread;
tutorial/cpp/7-PQFastScan.cpp
+1 -1
View File
@@ -72,4 +72,4 @@ int main() {
delete[] xq;
return 0;
} // namespace facebook::detail
}