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Prepare for v1.7.4 release (#2820)
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Summary:
Updated the changelog with features since last release, see 1.7.3_release...main for details. Please comment if you want to highlight anything that I've missed.

Pull Request resolved: #2820

Reviewed By: mdouze

Differential Revision: D44922916

Pulled By: mlomeli1

fbshipit-source-id: db16754698af4dd0fb8dddff7ec9885170a3d5c4
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mlomeli1 authored and facebook-github-bot committed Apr 19, 2023
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37 changes: 36 additions & 1 deletion CHANGELOG.md
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Expand Up @@ -9,6 +9,40 @@ We try to indicate most contributions here with the contributor names who are no
the Facebook Faiss team. Feel free to add entries here if you submit a PR.

## [Unreleased]
## [1.7.4] - 2023-04-12
### Added
- Added big batch IVF search for conducting efficient search with big batches of queries
- Checkpointing in big batch search support
- Precomputed centroids support
- Support for iterable inverted lists for eg. key value stores
- 64-bit indexing arithmetic support in FAISS GPU
- IndexIVFShards now handle IVF indexes with a common quantizer
- Jaccard distance support
- CodePacker for non-contiguous code layouts
- Approximate evaluation of top-k distances for ResidualQuantizer and IndexBinaryFlat
- Added support for 12-bit PQ / IVFPQ fine quantizer decoders for standalone vector codecs (faiss/cppcontrib)
- Conda packages for osx-arm64 (Apple M1) and linux-aarch64 (ARM64) architectures
- Support for Python 3.10

### Removed
- CUDA 10 is no longer supported in precompiled packages
- Removed Python 3.7 support for precompiled packages
- Removed constraint for using fine quantizer with no greater than 8 bits for IVFPQ, for example, now it is possible to use IVF256,PQ10x12 for a CPU index

### Changed
- Various performance optimizations for PQ / IVFPQ for AVX2 and ARM for training (fused distance+nearest kernel), search (faster kernels for distance_to_code() and scan_list_*()) and vector encoding
- A magnitude faster CPU code for LSQ/PLSQ training and vector encoding (reworked code)
- Performance improvements for Hamming Code computations for AVX2 and ARM (reworked code)
- Improved auto-vectorization support for IP and L2 distance computations (better handling of pragmas)
- 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.
- 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
- Fix decoding of large fast_scan blocks


## [1.7.3] - 2022-11-3
### Added
Expand Down Expand Up @@ -224,7 +258,8 @@ by conda install -c pytorch faiss-gpu cudatoolkit=10.0.
- C bindings.
- Extended tutorial to GPU indices.

[Unreleased]: https://github.com/facebookresearch/faiss/compare/v1.7.2...HEAD
[Unreleased]: https://github.com/facebookresearch/faiss/compare/v1.7.4...HEAD
[1.7.4]: https://github.com/facebookresearch/faiss/compare/v1.7.3...v1.7.4
[1.7.3]: https://github.com/facebookresearch/faiss/compare/v1.7.2...v1.7.3
[1.7.2]: https://github.com/facebookresearch/faiss/compare/v1.7.1...v1.7.2
[1.7.1]: https://github.com/facebookresearch/faiss/compare/v1.7.0...v1.7.1
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2 changes: 1 addition & 1 deletion CMakeLists.txt
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Expand Up @@ -7,7 +7,7 @@
cmake_minimum_required(VERSION 3.23.1 FATAL_ERROR)

project(faiss
VERSION 1.7.3
VERSION 1.7.4
DESCRIPTION "A library for efficient similarity search and clustering of dense vectors."
HOMEPAGE_URL "https://github.com/facebookresearch/faiss"
LANGUAGES CXX)
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2 changes: 1 addition & 1 deletion faiss/Index.h
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Expand Up @@ -18,7 +18,7 @@

#define FAISS_VERSION_MAJOR 1
#define FAISS_VERSION_MINOR 7
#define FAISS_VERSION_PATCH 3
#define FAISS_VERSION_PATCH 4

/**
* @namespace faiss
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2 changes: 1 addition & 1 deletion faiss/python/setup.py
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Expand Up @@ -53,7 +53,7 @@
"""
setup(
name='faiss',
version='1.7.3',
version='1.7.4',
description='A library for efficient similarity search and clustering of dense vectors',
long_description=long_description,
url='https://github.com/facebookresearch/faiss',
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