v1.7.4

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