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Increment to next release, v1.15.1 (#5645)

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Summary:
- Increment version to 1.15.1
- Update CHANGELOG.md with commits since v1.15.0 (57 commits)
- Bump version in CMakeLists.txt, setup.py, Index.h, INSTALL.md

Pull Request resolved: https://github.com/facebookresearch/faiss/pull/5645

Test Plan:
- [ ] CI passes
- [ ] CHANGELOG entries are correctly categorized

Reviewed By: mnorris11

Differential Revision: D120264788

Pulled By: alibeklfc

fbshipit-source-id: d95d68c9eb0c7c5b06aaeab7d8d0015b99d23145
This commit is contained in:
Alibek Zhakubayev
2026-09-15 20:42:13 -07:00
committed by meta-codesync[bot]
parent 3b3711c112
commit d63faeaf5a
5 changed files with 79 additions and 11 deletions
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CHANGELOG.md
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@@ -3,6 +3,73 @@ All notable changes to this project will be documented in this file.
## [Unreleased] ## [Unreleased]
## [1.15.1] - 2026-09-15
Added
- 4fdbc9dc18adbfef3cd410a2446aa4c757320a8a Support ZeroCopyIOReader for IndexRaBitQ deserialization (#5629)
- b5a14632a6f2bfdc824c86d683b9d6e9205c2604 Run the eight SIMD kernel tests that no build executed (#5620)
- e6bd1d252caa5b9315a04390269d382845984ae2 Add AVX512 VPOPCNT dynamic dispatch level (#5531)
- 7f4cedda9c384de652c7bd5690db0f808d668216 Add staged RaBitQ storage to HNSW (#5526)
- cfbf4ec5ff68d0bca4701d0b24b8a0736e110932 SVS: allow option to disable vector storing (#5525)
- cda4f6e7133c629e502c2ea9bc51e0ef51847ab1 Expose `IndexBinaryIVF.set_direct_map_type` via the C API (#5451)
- 256980cb6ea6610e4b0edf497a038086df8bbfce Add SVE low-dimensional nearest fast path and spherical SuperKMeans support (#5530)
- 3a716ead1a3c4562fe6e96d982d26789c58e19ed Add Windows ARM64 support to CI (#5544)
- 7059eaf7da7eddda62e71367e684d4bdedd7f94f Add AVX512 bitplane kernel for multi-bit RaBitQ inner product (#5522)
- a424dcb809fd725c44dd976d9063febd4837d16a faiss HNSW: add opt-in deterministic lock-free graph build (faiss::hnsw_deterministic_build) (#5486)
- da3191e754b49ca67b6503a9262e96ce5a3d8ed8 IDSelectorWithContext: extend context hook to IVFPQ/PQR, multibit RaBitQ, and Panorama scanners (#5491)
- edddbc998e858f16330f9b226dfd72f73d4d1d46 IDSelectorWithContext: scan-context hook for locality-aware selectors (#5490)
Changed
- 3bb48643ab8ea9e50162b354d2463270acd6ed9c Pin faiss-gpu-cuvs RAPIDS deps to the 26.06 build series (#5643)
- 6bbb068ad8dab612b84304400846da1b14539085 Measure 20-byte Hamming codes eight at a time (#5587)
- 3c7042f17a467c38d99f490ef39e4dfa615dc510 Let the binary Hamming paths reach the VPOPCNT kernels
- 9f993087b07e83f026248addb01dd99e99b15a3e Name the SIMD level masks after what they hold (#5600)
- e2cf9e1ebf24c14b888fa69eee96713aa6d64b1a Accept ragged code sizes in faiss::hammings(), and fix the vectorization axis for wide codes (#5520)
- 545550f6a27031c2269881d97237fd6a5bef15df Use unittest assertions and f-strings in test_index_composite (#5571)
- af9eeffcf70d7795107c2b0511bdba507ac45339 Reserve cs vector capacity in hammings_knn_mc_impl to avoid reallocations (#5548)
- 317a207bcacfcfa695d98289c3f4ea562b782837 ARM SVE optimization for L2 distance kernels adapted to new simd_impl structure (#5098)
- 2027d3b33144dbf19674b540ec0e31b2f842b1cc Make cuVS dispatch opt-in rather than build-implied (#5529)
- a39e2606887bbacc6c8f54e783c4b42da00cf4b3 Fold multi-GPU CAGRA build into train(), delete trainMultiGpu (#5521)
- 1f93154314afbef210f0ebebeab840da22f9ec7d Revert D114685755: Fold multi-GPU CAGRA build into train(), delete trainMultiGpu
- 6644dcfa5576221b1a4417fd766a8b2af71d869e Fold multi-GPU CAGRA build into train(), delete trainMultiGpu (#5500)
- 80a16564f86530dbf0bfaf96c2b71feffeb5093f Enable Faiss dynamic dispatch for OVIS and VeST fbpkgs (#5515)
- 2688c34f942c8462d97c8632c720a53653456270 Parallelize the k-means++ D^2 seeding update (#5457)
- 76c67b7b736ca80c2b3ea29528434d6583adc88b Update default Panorama batch size to 1024 (#5441)
- 3b50babc5914570576b09da043f64dfd7d62a075 Optimize RVV batch-4 distance kernels (#5469)
Fixed
- 7c3b55c01b8418dc044881625a260fceae9c712b Make Panorama bounds checks overflow-safe (#5616)
- fb2d57a3121c073828d2ed7d04ccf09d8bea771c Fix leak and unbounded lists[] in OnDisk invlist deserialization (#5560)
- fc13c81fb8b22f76e4c2c7bd2f027bd049b9a207 Stop aarch64 falling back to SIMDLevel::NONE in faiss dispatch (#5572)
- 00928e3f7628b8288c17cfece5ee9600a8287f56 Fix ROCm CI: build in a dedicated conda env instead of the runner's broken base (#5545)
- 1e5780fbbfbe191f3ed3517f93ace8301239e9b2 Fix OOB read in `Panorama::reconstruct` via `IndexPreTransform` (T287094917) (#5562)
- 2ed4c106e9fb9686e7727e5daf8ad6ad1e164109 Fix: Windows SIMD detection (#5497)
- b4c66ba6031d1f659163e43430f8ce745b0dbe79 Fix reverse_index_factory/get_code_size round-trip for HNSW, IMI and IVFPQR (#5564)
- c0084f5cd2534236acda05cf7b8e0fc8062ddec3 Clamp EDEN unbiased L2 distances to be non-negative (#5569)
- 6457e941f5f4808303bb480679cb78df0df51934 Contain DirectoryArchiver entries within the extraction root (#5561)
- ac60182fc009476c91a2788e639191d1f624157f RaBitQ: Fix zero residual sign convention (#5553)
- 1a0cfa64e015923d5009d8c7d917aa591e9eac9a Apply the deserialization byte limit to SVS IVF (#5568)
- 718002f7cf269f3f3752dde04a4cc3f8eabb0050 Fix distance conversion in IndexBinaryFromFloat for non-L2 metrics (#5549)
- 02ea14372c9983f3eaa15698512180dc62ea234d Fix range_search_max_results to respect all similarity metrics (#5533)
- bd7087453e6d2262b6e6c76ab0cfa1e2dfeddebf fix(faiss): use is_similarity_metric for IndexShardsIVF merge (#5541)
- 561e6ecdf3337e32542b82c735bfaf83d9e3afaa Merge shard results by metric type rather than assuming similarity (#5509)
- 613e0acac507c7593ff14dee2c6204a83a539acb Fix IndexIDMap range search parameters (#5536)
- 8b01f308f09cce7f259a1b8978f82c90fcd54c48 Validate IndexScalarQuantizer codes buffer size on deserialization (#5540)
- 2832ac3f671f83617087789691c79670ee57c728 Fix empty-index BLAS search results (#5528)
- e135a1a667a1cfd326faece69d33cf9708a97a7c Fix OSX arm64 nightly: bump libopenblas from 0.3.33 to 0.3.34 (#5538)
- 920a631851cc7fefe0645c9af81071bb7b2c1237 Don't raise HNSW entry_point to a point that isn't linked yet (#5524)
- e2f9ccaccc38b4cc3b65c1c70cb076bb639ef8ca Fix SQtqmse codebook scale: fold 1/sqrt(d) into Lloyd-Max table (#5517)
- 151452ec94b0df219820b120a365c5396f63bef0 Fix SIMD level detection in Faiss (#5507)
- 02dbb4e42f49aa2ce57cd78977b0e47a11f7cb91 Fix data race in NNDescent::update() (#5413)
- a9ecee221dd3887b3af886b68c7598d8553a5acd Reject AdditiveQuantizer with zero-bit code size on deserialization (#5506)
- 4de3bbb6017910ecb3db1d3a9086318c517f6e39 Faiss OSS nightly autofix (#5505)
- b5ca016ded0921401b532bfd256c2f636406df38 Fix IndexBinaryIVF reconstruct_n and search_and_reconstruct byte stride (#5463)
- b3942aba5aa318f39e04d36baa00cb118098dc74 Avoid signed int overflow in VStackInvertedLists binary search (#5482)
Deprecated
- a74867cf56a73234ad5d957e40c35c9616d4d273 faiss HNSW: delete the flag and the lock-based graph build (#5611)
- 5702cafcb75359780cefbc5d50b512ac2e5cfde7 Remove dead TurboQuant projection state (#5618)
## [1.15.0] - 2026-07-31 ## [1.15.0] - 2026-07-31
Added Added
@@ -1412,7 +1479,8 @@ by conda install -c pytorch faiss-gpu cudatoolkit=10.0.
- C bindings. - C bindings.
- Extended tutorial to GPU indices. - Extended tutorial to GPU indices.
[Unreleased]: https://github.com/facebookresearch/faiss/compare/v1.15.0...HEAD [Unreleased]: https://github.com/facebookresearch/faiss/compare/v1.15.1...HEAD
[1.15.1]: https://github.com/facebookresearch/faiss/compare/v1.15.0...v1.15.1
[1.15.0]: https://github.com/facebookresearch/faiss/compare/v1.14.3...v1.15.0 [1.15.0]: https://github.com/facebookresearch/faiss/compare/v1.14.3...v1.15.0
[1.14.3]: https://github.com/facebookresearch/faiss/compare/v1.14.2...v1.14.3 [1.14.3]: https://github.com/facebookresearch/faiss/compare/v1.14.2...v1.14.3
[1.14.2]: https://github.com/facebookresearch/faiss/compare/v1.14.1...v1.14.2 [1.14.2]: https://github.com/facebookresearch/faiss/compare/v1.14.1...v1.14.2
CMakeLists.txt
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@@ -74,7 +74,7 @@ if(FAISS_ENABLE_CUVS)
endif() endif()
project(faiss project(faiss
VERSION 1.15.0 VERSION 1.15.1
DESCRIPTION "A library for efficient similarity search and clustering of dense vectors." DESCRIPTION "A library for efficient similarity search and clustering of dense vectors."
HOMEPAGE_URL "https://github.com/facebookresearch/faiss" HOMEPAGE_URL "https://github.com/facebookresearch/faiss"
LANGUAGES ${FAISS_LANGUAGES}) LANGUAGES ${FAISS_LANGUAGES})
INSTALL.md
+7 -7
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@@ -12,13 +12,13 @@ To install the latest stable release:
``` shell ``` shell
# CPU-only version # CPU-only version
$ conda install -c pytorch -c conda-forge faiss-cpu=1.15.0 $ conda install -c pytorch -c conda-forge faiss-cpu=1.15.1
# GPU(+CPU) version # GPU(+CPU) version
$ conda install -c pytorch -c nvidia -c conda-forge faiss-gpu=1.15.0 $ conda install -c pytorch -c nvidia -c conda-forge faiss-gpu=1.15.1
# GPU(+CPU) version with NVIDIA cuVS # GPU(+CPU) version with NVIDIA cuVS
$ conda install -c pytorch -c nvidia -c rapidsai -c conda-forge libnvjitlink faiss-gpu-cuvs=1.15.0 $ conda install -c pytorch -c nvidia -c rapidsai -c conda-forge libnvjitlink faiss-gpu-cuvs=1.15.1
# GPU(+CPU) version using AMD ROCm not yet available # GPU(+CPU) version using AMD ROCm not yet available
``` ```
@@ -34,15 +34,15 @@ If you prefer [Pixi](https://pixi.sh/latest/), you can use the same channels and
``` shell ``` shell
# CPU-only version # CPU-only version
$ pixi init -c pytorch -c conda-forge $ pixi init -c pytorch -c conda-forge
$ pixi add faiss-cpu=1.15.0 $ pixi add faiss-cpu=1.15.1
# GPU(+CPU) version # GPU(+CPU) version
$ pixi init -c pytorch -c nvidia -c conda-forge $ pixi init -c pytorch -c nvidia -c conda-forge
$ pixi add faiss-gpu=1.15.0 $ pixi add faiss-gpu=1.15.1
# GPU(+CPU) version with NVIDIA cuVS # GPU(+CPU) version with NVIDIA cuVS
$ pixi init -c pytorch -c nvidia -c rapidsai -c conda-forge $ pixi init -c pytorch -c nvidia -c rapidsai -c conda-forge
$ pixi add libnvjitlink faiss-gpu-cuvs=1.15.0 $ pixi add libnvjitlink faiss-gpu-cuvs=1.15.1
``` ```
Pixi resolves packages from the listed Conda channels, so the same channel requirements described above still apply. Pixi resolves packages from the listed Conda channels, so the same channel requirements described above still apply.
@@ -54,7 +54,7 @@ Nightly pre-release packages can be installed as follows:
$ conda install -c pytorch/label/nightly -c conda-forge faiss-cpu $ conda install -c pytorch/label/nightly -c conda-forge faiss-cpu
# GPU(+CPU) version # GPU(+CPU) version
$ conda install -c pytorch/label/nightly -c nvidia -c conda-forge faiss-gpu=1.15.0 $ conda install -c pytorch/label/nightly -c nvidia -c conda-forge faiss-gpu=1.15.1
# GPU(+CPU) version with NVIDIA cuVS (package built with CUDA 13.2) # GPU(+CPU) version with NVIDIA cuVS (package built with CUDA 13.2)
conda install -c pytorch -c rapidsai -c rapidsai-nightly -c conda-forge -c nvidia pytorch/label/nightly::faiss-gpu-cuvs 'cuda-version=13.2' conda install -c pytorch -c rapidsai -c rapidsai-nightly -c conda-forge -c nvidia pytorch/label/nightly::faiss-gpu-cuvs 'cuda-version=13.2'
faiss/Index.h
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@@ -17,7 +17,7 @@
#define FAISS_VERSION_MAJOR 1 #define FAISS_VERSION_MAJOR 1
#define FAISS_VERSION_MINOR 15 #define FAISS_VERSION_MINOR 15
#define FAISS_VERSION_PATCH 0 #define FAISS_VERSION_PATCH 1
// Macro to combine the version components into a single string // Macro to combine the version components into a single string
#ifndef FAISS_STRINGIFY #ifndef FAISS_STRINGIFY
faiss/python/setup.py
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@@ -119,7 +119,7 @@ are implemented on the GPU. It is developed by Facebook AI Research.
""" """
setup( setup(
name="faiss", name="faiss",
version="1.15.0", version="1.15.1",
description=( description=(
"A library for efficient similarity search and clustering of dense " "A library for efficient similarity search and clustering of dense "
"vectors" "vectors"