Summary:
mdouze Please let me know if any additional unit tests are needed
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/3444
Reviewed By: algoriddle
Differential Revision: D57665641
Pulled By: mdouze
fbshipit-source-id: 9bec91306a1c31ea4f1f1d726c9d60ac6415fdfc
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/3455
Code quality control by reducing the number of prints
Reviewed By: junjieqi
Differential Revision: D57502194
fbshipit-source-id: a6cd65ed4cc49590ce73d2978d41b640b5259c17
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/3190
This diff adds more result handlers in order to expose them externally.
This enables range search for HSNW and Fast Scan, and nprobe parameter support for FastScan.
Reviewed By: pemazare
Differential Revision: D52547384
fbshipit-source-id: 271da5ffea6411df3d8e50641abade18bd7b774b
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2986
A NaN vector is a vector with at least one NaN (not-a-number) entry.
After discussion in the Faiss team we decided that:
- training should throw an exception on NaN vectors
- added NaN vectors should be ignored (never returned)
- searched NaN vectors should return only -1s
This diff implements this for a few common index types + adds relevant tests.
Reviewed By: algoriddle
Differential Revision: D48031390
fbshipit-source-id: 99e7786582e91950e3a53c1d8bcffdd00b6afd24
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2860
Optimized range search function where the GPU computes by default and falls back on gpu for queries where there are too many results.
Parallelize the CPU to GPU cloning, it seems to work.
Support range_search_preassigned in Python
Fix long-standing issue with SWIG exposed functions that did not release the GIL (in particular the MapLong2Long).
Adds a MapInt64ToInt64 that is more efficient than MapLong2Long.
Reviewed By: algoriddle
Differential Revision: D45672301
fbshipit-source-id: 2e77397c40083818584dbafa5427149359a2abfd
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2618
The Faiss tests run in dev mode are very slow
The PQ polysemous training is particularly sensitive to this with the default settings.
This diff adds a "np" suffix to two PQ factory strings to disable polysemous training. The tests that are detected as flaky because they occasionally time out.
Reviewed By: alexanderguzhva
Differential Revision: D41955699
fbshipit-source-id: b1e0382a0142a3ed28b498c5ea6f5499de2c1b3f
Summary:
Adds:
- a sparse update function to the heaps
- bucket sort functions
- an IndexRandom index to serve as a dummy coarse quantizer for testing
Reviewed By: algoriddle
Differential Revision: D41804055
fbshipit-source-id: 9402b31c37c367aa8554271d8c88bc93cc1e2bda
Summary:
IndexHNSW has a deadlock in the add() method, which is fixed by
temporarily releasing the lock on the current element while updating
its neighbors' adjacency lists.
This bug concerns multi-threaded insertion only, and seems to manifest
itself only with certain OpenMP configurations.
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2143
Reviewed By: mdouze
Differential Revision: D32919041
Pulled By: beauby
fbshipit-source-id: e515541c1b22bfcb79d29c0bde1843e63f5175fb
Summary:
The results returned by `NSG::search` are already sorted. Calling `maxheap_reorder` will make the results unorder.
Fixed https://github.com/facebookresearch/faiss/issues/2081.
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2086
Test Plan: buck test //faiss/tests/:test_index -- test_order
Reviewed By: beauby
Differential Revision: D32593924
Pulled By: mdouze
fbshipit-source-id: 794b94681610657bd2f305f7e3d6cd5d25c6bdba
Summary:
This diff fixes a serious bug in the range search implementation.
During range search in a flat index, (exhaustive_L2sqr_seq and exhaustive_inner_product_seq) when running in multiple threads, the per-thread results are collected into RangeSearchPartialResult structures.
When the computation is finished, they are aggregated into a RangeSearchResult. In the previous version of the code, this loop was nested into a second loop that is used to check for KeyboardInterrupts. Thus, at each iteration, the results were overwritten.
The fix removes the outer loop. It is most likely useless anyways because the sequential code is called only for a small number of queries, for a larger number the BLAS version is used.
Reviewed By: wickedfoo
Differential Revision: D28486415
fbshipit-source-id: 89a52b17f6ca1ef68fc5e758f0e5a44d0df9fe38
Summary:
## Description:
This diff implemented Navigating Spreading-out Graph (NSG) which accepts a KNN graph as input.
Here is the interface of building an NSG graph:
``` c++
void IndexNSG::build(idx_t n, const float *x, idx_t *knn_graph, int GK);
```
where `GK` is the nb of neighbors per node and `knn_graph[i * GK + j]` is the j-th neighbor of node i.
The `add` method is not implemented yet.
The unit tests could be found in `tests/test_nsg.cpp`.
mdouze beauby Maybe I need some advice on how to design the interface and support python.
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/1707
Test Plan: buck test //faiss/tests/:test_index -- TestNSG
Reviewed By: beauby
Differential Revision: D26748498
Pulled By: mdouze
fbshipit-source-id: 3280f705fb1b5f9c8cc5efeba63b904c3b832544
Summary: Checking for invalid parameters (number of nearest neighbors and number of probes where applicable) in the indices and throwing. Along with unit tests.
Reviewed By: wickedfoo
Differential Revision: D26582467
fbshipit-source-id: e345635d2f0f44ddcecc3f3314b2b9113359a787
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:
This diff fixes https://github.com/facebookresearch/faiss/issues/1412
There were various inconsistencies in how the shard and replica wrappers updated their internal state as the sub-indices were updated. This makes the two container classes work in the same way with similar synchronization functionality.
Reviewed By: beauby
Differential Revision: D23974186
fbshipit-source-id: c688c0c9124f823e4239aa2ff617b007b4564859
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
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
