This GitHub repository includes the source code for the SIGMOD paper:
Conglong Li, Minjia Zhang, David G. Andersen, and Yuxiong He. Improving Approximate Nearest Neighbor Search through Learned Adaptive Early Termination. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (SIGMOD), 2020.
@INPROCEEDINGS{li2020ann,
AUTHOR = "Li, Conglong and Zhang, Minjia and Andersen, David G. and He, Yuxiong",
TITLE = "{Improving Approximate Nearest Neighbor Search through Learned Adaptive Early Termination}",
BOOKTITLE = "{Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (SIGMOD)}",
YEAR = {2020} }
Our implementation is based on Faiss version 1.5.2 and LightGBM version 2.3.1. See INSTALL.md for details about changes to the source code and installation process. See learned_termination README for details about how to reproduce our experiments.
We follow the Faiss and LightGBM repositories' MIT license.
This project is currently not active. We are providing this code "as-is", with limited support. If you have questions, please email Conglong Li at conglong.li@microsoft.com.
Below is the original Faiss documentation:
Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU. It is developed by Facebook AI Research.
NEW: version 1.5.2 (2018-05-27) the license was relaxed to MIT from BSD+Patents. Read LICENSE for details.
NEW: version 1.5.0 (2018-12-19) GPU binary flat index and binary HNSW index
NEW: version 1.4.0 (2018-08-30) no more crashes in pure Python code
NEW: version 1.3.0 (2018-07-12) support for binary indexes
NEW: latest commit (2018-02-22) supports on-disk storage of inverted indexes, see demos/demo_ondisk_ivf.py
NEW: latest commit (2018-01-09) includes an implementation of the HNSW indexing method, see benchs/bench_hnsw.py
NEW: there is now a Facebook public discussion group for Faiss users at https://www.facebook.com/groups/faissusers/
NEW: on 2017-07-30, the license on Faiss was relaxed to BSD from CC-BY-NC. Read LICENSE for details.
Faiss contains several methods for similarity search. It assumes that the instances are represented as vectors and are identified by an integer, and that the vectors can be compared with L2 distances or dot products. Vectors that are similar to a query vector are those that have the lowest L2 distance or the highest dot product with the query vector. It also supports cosine similarity, since this is a dot product on normalized vectors.
Most of the methods, like those based on binary vectors and compact quantization codes, solely use a compressed representation of the vectors and do not require to keep the original vectors. This generally comes at the cost of a less precise search but these methods can scale to billions of vectors in main memory on a single server.
The GPU implementation can accept input from either CPU or GPU memory. On a server with GPUs, the GPU indexes can be used a drop-in replacement for the CPU indexes (e.g., replace IndexFlatL2 with GpuIndexFlatL2) and copies to/from GPU memory are handled automatically. Results will be faster however if both input and output remain resident on the GPU. Both single and multi-GPU usage is supported.
The library is mostly implemented in C++, with optional GPU support provided via CUDA, and an optional Python interface. The CPU version requires a BLAS library. It compiles with a Makefile and can be packaged in a docker image. See INSTALL.md for details.
Faiss is built around an index type that stores a set of vectors, and provides a function to search in them with L2 and/or dot product vector comparison. Some index types are simple baselines, such as exact search. Most of the available indexing structures correspond to various trade-offs with respect to
- search time
- search quality
- memory used per index vector
- training time
- need for external data for unsupervised training
The optional GPU implementation provides what is likely (as of March 2017) the fastest exact and approximate (compressed-domain) nearest neighbor search implementation for high-dimensional vectors, fastest Lloyd's k-means, and fastest small k-selection algorithm known. The implementation is detailed here.
The following are entry points for documentation:
- the full documentation, including a tutorial, a FAQ and a troubleshooting section can be found on the wiki page
- the doxygen documentation gives per-class information
- to reproduce results from our research papers, Polysemous codes and Billion-scale similarity search with GPUs, refer to the benchmarks README. For Link and code: Fast indexing with graphs and compact regression codes, see the link_and_code README
The main authors of Faiss are:
- Hervé Jégou initiated the Faiss project and wrote its first implementation
- Matthijs Douze implemented most of the CPU Faiss
- Jeff Johnson implemented all of the GPU Faiss
- Lucas Hosseini implemented the binary indexes
Reference to cite when you use Faiss in a research paper:
@article{JDH17,
title={Billion-scale similarity search with GPUs},
author={Johnson, Jeff and Douze, Matthijs and J{\'e}gou, Herv{\'e}},
journal={arXiv preprint arXiv:1702.08734},
year={2017}
}
For public discussion of Faiss or for questions, there is a Facebook public discussion group at https://www.facebook.com/groups/faissusers/
We monitor the issues page of the repository. You can report bugs, ask questions, etc.
Faiss is MIT-licensed.