Summary:
When I reconstruct with by_residual turned off, the distance was greatly increased.
This is because the reconstruct_from_offset function did not check if the by_residual option was off.
I fix this bug with simple if statement.
(like this https://github.com/facebookresearch/faiss/blob/main/faiss/IndexIVFPQ.cpp#L365)
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2298
Reviewed By: alexanderguzhva
Differential Revision: D35746566
Pulled By: mdouze
fbshipit-source-id: 50f98c7cc97c7936507573fe41b65a79ecdbc4ca
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2255
The `DistanceComputer` object is derived from an Index (obtained with `get_distance_computer()`). It maintains a current query and quickly computes distances from that query to any item in the database. This is useful, eg. for the IndexHNSW and IndexNSG that rely on query-to-point comparisons in the datasets.
This diff introduces the `FlatCodesDistanceComputer`, that inherits from `DistanceComputer` for Flat indexes. In addition to the distance-to-item function, it adds a `distance_to_code` that computes the distance from any code to the current query, even if it is not stored in the index.
This is implemented for all FlatCode indexes (IndexFlat, IndexPQ, IndexScalarQuantizer and IndexAdditiveQuantizer).
In the process, the two classes were extracted to their own header file `impl/DistanceComputer.h`
Reviewed By: beauby
Differential Revision: D34863609
fbshipit-source-id: 39d8c66475e55c3223c4a6a210827aa48bca292d
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2132
This diff adds the class IndexFlatCodes that becomes the parent of all "flat" encodings.
IndexPQ
IndexFlat
IndexAdditiveQuantizer
IndexScalarQuantizer
IndexLSH
Index2Layer
The other changes are:
- for IndexFlat, there is no vector<float> with the data anymore. It is replaced with a `get_xb()` function. This broke quite a few external codes, that this diff also attempts to fix.
- I/O functions needed to be adapted. This is done without changing the I/O format for any index.
- added a small contrib function to get the data from the IndexFlat
- the functionality has been made uniform, for example remove_ids and add are now in the parent class.
Eventually, we may support generic storage for flat indexes, similar to `InvertedLists`, eg to memmap the data, but this will again require a big change.
Reviewed By: wickedfoo
Differential Revision: D32646769
fbshipit-source-id: 04a1659173fd51b130ae45d345176b72183cae40
Summary:
PCA whitening implies to multiply eigenvectors with 1/sqrt(singular values of convariance matrix). The singular values are sometimes 0 (because the vector subspace is not full-rank) or negative (because of numerical issues).
Therefore, this diff adds an epsilon to the denominator above (default 0).
Reviewed By: edpizzi
Differential Revision: D31725075
fbshipit-source-id: dae68bda9f7452220785d76e30ce4b2ac8582413
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/1972
This fixes a few issues that I ran into + adds tests:
- range_search_max_results with IP search
- a few missing downcasts for VectorTRansforms
- ResultHeap supports max IP search
Reviewed By: wickedfoo
Differential Revision: D29525093
fbshipit-source-id: d4ff0aff1d83af9717ff1aaa2fe3cda7b53019a3
Summary:
This diff includes:
- progressive dimension k-means.
- the ResidualQuantizer object
- GpuProgressiveDimIndexFactory so that it can be trained on GPU
- corresponding tests
- reference Python implementation of the same in scripts/matthijs/LCC_encoding
Reviewed By: wickedfoo
Differential Revision: D27608029
fbshipit-source-id: 9a8cf3310c8439a93641961ca8b042941f0f4249
Summary:
After initial positive feedback to the idea in https://github.com/facebookresearch/faiss/issues/1741 from mdouze, here are the patches
I currently have as a basis for discussion.
Matthijs suggests to not bother with the deprecation warnings at all, which is fine for me
as well, though I would normally still advocate to provide users with _some_ advance notice
before removing parts of an interface.
Fixes https://github.com/facebookresearch/faiss/issues/1741
PS. The deprecation warning is only shown once per session (per class)
PPS. I have tested in https://github.com/conda-forge/faiss-split-feedstock/pull/32 that the respective
classes remain available both through `import faiss` and `from faiss import *`.
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/1742
Reviewed By: mdouze
Differential Revision: D26978886
Pulled By: beauby
fbshipit-source-id: b52e2b5b5b0117af7cd95ef5df3128e9914633ad
Summary:
As discussed in https://github.com/facebookresearch/faiss/issues/685, I'm going to add an NSG index to faiss. This PR which adds an NNDescent index is the first step as I commented [here ](https://github.com/facebookresearch/faiss/issues/685#issuecomment-760608431).
**Changes:**
1. Add an `IndexNNDescent` and an `IndexNNDescentFlat` which allow users to construct a KNN graph on a million scale dataset using CPU and search NN on it. The implementation part is put under `faiss/impl`.
2. Add compilation entries to `CMakeLists.txt` for C++ and `swigfaiss.swig` for Python. `IndexNNDescentFlat` could be directly called by users in C++ and Python.
3. `VisitedTable` struct in `HNSW.h` is moved into `AuxIndexStructures.h`.
3. Add a demo `demo_nndescent.cpp` to demonstrate the effectiveness.
**TODO**
1. Support index factor.
2. Implement `IndexNNDescentPQ` and `IndexNNDescentSQ`
3. More comments in the code.
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/1654
Test Plan:
buck test //faiss/tests/:test_index_accuracy -- TestNNDescent
buck test //faiss/tests/:test_build_blocks -- TestNNDescentKNNG
Reviewed By: wickedfoo
Differential Revision: D26309716
Pulled By: mdouze
fbshipit-source-id: 2abade9708d29023f8bccbf77143e8eea14f66c4
Summary:
IndexPQ and IndexIVFPQ implementations with AVX shuffle instructions.
The training and computing of the codes does not change wrt. the original PQ versions but the code layout is "packed" so that it can be used efficiently by the SIMD computation kernels.
The main changes are:
- new IndexPQFastScan and IndexIVFPQFastScan objects
- simdib.h for an abstraction above the AVX2 intrinsics
- BlockInvertedLists for invlists that are 32-byte aligned and where codes are not sequential
- pq4_fast_scan.h/.cpp: for packing codes and look-up tables + optmized distance comptuation kernels
- simd_result_hander.h: SIMD version of result collection in heaps / reservoirs
Misc changes:
- added contrib.inspect_tools to access fields in C++ objects
- moved .h and .cpp code for inverted lists to an invlists/ subdirectory, and made a .h/.cpp for InvertedListsIOHook
- added a new inverted lists type with 32-byte aligned codes (for consumption by SIMD)
- moved Windows-specific intrinsics to platfrom_macros.h
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/1542
Test Plan:
```
buck test mode/opt -j 4 //faiss/tests/:test_fast_scan_ivf //faiss/tests/:test_fast_scan
buck test mode/opt //faiss/manifold/...
```
Reviewed By: wickedfoo
Differential Revision: D25175439
Pulled By: mdouze
fbshipit-source-id: ad1a40c0df8c10f4b364bdec7172e43d71b56c34
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/1531
vector_to_array assumes that long is 64 bit. Fix this and test it.
Reviewed By: wickedfoo
Differential Revision: D25022363
fbshipit-source-id: f51f723d590d71ee5ef39e3f86ef69426df833fa
Summary:
This diff streamlines the code that collects results for brute force distance computations for the L2 / IP and range search / knn search combinations.
It introduces a `ResultHandler` template class that abstracts what happens with the computed distances and ids. In addition to the heap result handler and the range search result handler, it introduces a reservoir result handler that improves the search speed for large k (>=100).
Benchmark results (https://fb.quip.com/y0g1ACLEqJXx#OCaACA2Gm45) show that on small datasets (10k) search is 10-50% faster (improvements are larger for small k). There is room for improvement in the reservoir implementation, whose implementation is quite naive currently, but the diff is already useful in its current form.
Experiments on precomputed db vector norms for L2 distance computations were not very concluding performance-wise, so the implementation is removed from IndexFlatL2.
This diff also removes IndexL2BaseShift, which was never used.
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/1502
Test Plan:
```
buck test //faiss/tests/:test_product_quantizer
buck test //faiss/tests/:test_index -- TestIndexFlat
```
Reviewed By: wickedfoo
Differential Revision: D24705464
Pulled By: mdouze
fbshipit-source-id: 270e10b19f3c89ed7b607ec30549aca0ac5027fe
Summary: When an INNER_PRODUCT index is used for clustering, higher objective is better, so when redoing clusterings the highest objective should be retained (not the lowest). This diff fixes this and adds a test.
Reviewed By: wickedfoo
Differential Revision: D24701894
fbshipit-source-id: b9ec224cf8f4ffdfd2b8540ce37da43386a27b7a
Summary:
`long` is 32 bits on windows and so is the default int type for numpy (eg. the one used for `np.arange`).
This diff explicitly specifies 64-bit ints for all occurrences where it matters.
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/1381
Reviewed By: wickedfoo
Differential Revision: D23371232
Pulled By: mdouze
fbshipit-source-id: 220262cd70ee70379f83de93561a4eae71c94b04
Bugfixes:
- slow scanning of inverted lists (#836).
Features:
- add basic support for 6 new metrics in CPU `IndexFlat` and `IndexHNSW` (#848);
- add support for `IndexIDMap`/`IndexIDMap2` with binary indexes (#780).
Misc:
- throw python exception for OOM (#758);
- make `DistanceComputer` available for all random access indexes;
- gradually moving from `long` to `int64_t` for portability.
Changelog:
- changed license: BSD+Patents -> MIT
- propagates exceptions raised in sub-indexes of IndexShards and IndexReplicas
- support for searching several inverted lists in parallel (parallel_mode != 0)
- better support for PQ codes where nbit != 8 or 16
- IVFSpectralHash implementation: spectral hash codes inside an IVF
- 6-bit per component scalar quantizer (4 and 8 bit were already supported)
- combinations of inverted lists: HStackInvertedLists and VStackInvertedLists
- configurable number of threads for OnDiskInvertedLists prefetching (including 0=no prefetch)
- more test and demo code compatible with Python 3 (print with parentheses)
- refactored benchmark code: data loading is now in a single file
Facebook sync (Mar 2019)
- MatrixStats object
- option to round coordinates during k-means optimization
- alternative option for search in HNSW
- moved stats and imbalance_factor of IndexIVF to InvertedLists object
- range search for IVFScalarQuantizer
- direct unit8 codec in ScalarQuantizer
- renamed IndexProxy to IndexReplicas and moved to main Faiss
- better support for PQ code assignment with external index
- support for IMI2x16 (4B virtual centroids!)
- support for k = 2048 search on GPU (instead of 1024)
- most CUDA mem alloc failures throw exceptions instead of terminating on an assertion
- support for renaming an ondisk invertedlists
- interrupt computations with ctrl-C in python
Features:
- automatic tracking of C++ references in Python
- non-intel platforms supported -- some functions optimized for ARM
- override nprobe for concurrent searches
- support for floating-point quantizers in binary indexes
Bug fixes:
- no more segfaults in python (I know it's the same as the first feature but it's important!)
- fix GpuIndexIVFFlat issues for float32 with 64 / 128 dims
- fix sharding of flat indexes on GPU with index_cpu_to_gpu_multiple
* Refactors Makefiles and add configure script.
* Give MKL higher priority in configure script.
* Clean up Linux example makefile.inc.
* Cleanup makefile.inc examples.
* Fix python clean Makefile target.
* Regen swig wrappers.
* Remove useless CUDAFLAGS variable.
* Fix python linking flags.
* Separate compile and link phase in python makefile.
* Add macro to look for swig.
* Add CUDA check in configure script.
* Cleanup make depend targets.
* Cleanup CUDA flags.
* Fix linking flags.
* Fix python GPU linking.
* Remove useless flags from python gpu module linking.
* Add check for cuda libs.
* Cleanup GPU targets.
* Clean up test target.
* Add cpu/gpu targets to python makefile.
* Clean up tutorial Makefile.
* Remove stale OS var from example makefiles.
* Clean up cuda example flags.
* moved most FAISS_ASSERT calls to C++ exceptions, and adjusted
memory allocation to avoid mem leaks
* added an IndexIVFScalarQuantizer type that offers an
intermediate compression between IVFFlat and IVFPQ
* support removal of indices in IndexIDMap / IndexFlat combination
* various fixes in GPU code
spectaclehong
Matthijs Douze
H. Vetinari
Check Deng
Lucas Hosseini
Lucas Hosseini
Ailing
Soumith Chintala