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:
This PR adds the features that AbdelrahmanElmeniawy worked on during his internship and the speedups by alexanderguzhva in the CHANGELOG, ahead of the 1.7.3 release
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2564
Reviewed By: algoriddle
Differential Revision: D41119343
Pulled By: mlomeli1
fbshipit-source-id: b41ce354440dea2a6f8f214bf6654ff453ef10e7
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2509
Adds support for:
- IDSelector for Flat and SQ
- search_type in SearchParametersPQ
- IDSelectors implemented in Python (slow but good for testing)
Start optimization of IDSelectorRange and IDSelectorArray for IndexFlat and IDSelectorRange for IndexIVF
Reviewed By: alexanderguzhva
Differential Revision: D40037795
fbshipit-source-id: 61e01acb43c6aa39fea2c3b67a8bba9072383b74
Summary:
Work in progress.
This PR is going to implement the following search methods for ProductAdditiveQuantizer, including index factory and I/O:
- [x] IndexProductAdditiveQuantizer
- [x] IndexIVFProductAdditiveQuantizer
- [x] IndexProductAdditiveQuantizerFastScan
- [x] IndexIVFProductAdditiveQuantizerFastScan
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2336
Test Plan:
buck test //faiss/tests/:test_fast_scan
buck test //faiss/tests/:test_fast_scan_ivf
buck test //faiss/tests/:test_local_search_quantizer
buck test //faiss/tests/:test_residual_quantizer
Reviewed By: alexanderguzhva
Differential Revision: D37172745
Pulled By: mdouze
fbshipit-source-id: 6ff18bfc462525478c90cd42e21805ab8605bd0f
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/2217
This diff introduces a new Faiss contrib module that contains:
- generic k-means implemented in python (was in distributed_ondisk)
- the two-level clustering code, including a simple function that runs it on a Faiss IVF index.
- sparse clustering code (new)
The main idea is that that code is often re-used so better have it in contrib.
Reviewed By: beauby
Differential Revision: D34170932
fbshipit-source-id: cc297cc56d241b5ef421500ed410d8e2be0f1b77
Summary:
This diff implemented non-uniform quantization of vector norms in additive quantizers. index_factory and I/O are supported.
index_factory: `XXX_Ncqint{nbits}` where `nbits` is the number of bits to quantize vector norm.
For 8 bits code, it is almost the same as 8-bit uniform quantization. It will slightly improve the accuracy if the code size is less than 8 bits.
```
RQ4x8_Nqint8: R@1 0.1116
RQ4x8_Ncqint8: R@1 0.1117
RQ4x8_Nqint4: R@1 0.0901
RQ4x8_Ncqint4: R@1 0.0989
```
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2037
Test Plan:
buck test //faiss/tests/:test_clustering -- TestClustering1D
buck test //faiss/tests/:test_lsq -- test_index_accuracy_cqint
buck test //faiss/tests/:test_residual_quantizer -- test_norm_cqint
buck test //faiss/tests/:test_residual_quantizer -- test_search_L2
Reviewed By: beauby
Differential Revision: D31083476
Pulled By: mdouze
fbshipit-source-id: f34c3dafc4eb1c6f44a63e68137158911aa4a2f4
Summary:
## Description
This PR added support for LSQ on GPU. Only the encoding part is running on GPU and the others are still running on CPU.
Multi-GPU is also supported.
## Usage
``` python
lsq = faiss.LocalSearchQuantizer(d, M, nbits)
ngpus = faiss.get_num_gpus()
lsq.icm_encoder_factory = faiss.GpuIcmEncoderFactory(ngpus) # we use all gpus
lsq.train(xt)
codes = lsq.compute_codes(xb)
decoded = lsq.decode(codes)
```
## Performance on SIFT1M
On 1 GPU:
```
===== lsq-gpu:
mean square error = 17337.878528
training time: 40.9857234954834 s
encoding time: 27.12640070915222 s
```
On 2 GPUs:
```
===== lsq-gpu:
mean square error = 17364.658176
training time: 25.832106113433838 s
encoding time: 14.879548072814941 s
```
On CPU:
```
===== lsq:
mean square error = 17305.880576
training time: 152.57522344589233 s
encoding time: 110.01779270172119 s
```
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/1978
Test Plan: buck test mode/dev-nosan //faiss/gpu/test/:test_gpu_index_py -- TestLSQIcmEncoder
Reviewed By: wickedfoo
Differential Revision: D29609763
Pulled By: mdouze
fbshipit-source-id: b6ffa2a3c02bf696a4e52348132affa0dd838870
Summary:
This is required for the renaming of the default branch from `master` to `main`, in accordance with the new Facebook OSS guidelines.
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2029
Reviewed By: mdouze
Differential Revision: D30672862
Pulled By: beauby
fbshipit-source-id: 0b6458a4ff02a12aae14cf94057e85fdcbcbff96
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/1906
This PR implemented LSQ/LSQ++, a vector quantization technique described in the following two papers:
1. Revisiting additive quantization
2. LSQ++: Lower running time and higher recall in multi-codebook quantization
Here is a benchmark running on SIFT1M for 64 bits encoding:
```
===== lsq:
mean square error = 17335.390208
training time: 312.729779958725 s
encoding time: 244.6277096271515 s
===== pq:
mean square error = 23743.004672
training time: 1.1610801219940186 s
encoding time: 2.636141061782837 s
===== rq:
mean square error = 20999.737344
training time: 31.813055515289307 s
encoding time: 307.51959800720215 s
```
Changes:
1. Add LocalSearchQuantizer object
2. Fix an out of memory bug in ResidualQuantizer
3. Add a benchmark for evaluating quantizers
4. Add tests for LocalSearchQuantizer
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/1862
Test Plan:
```
buck test //faiss/tests/:test_lsq
buck run mode/opt //faiss/benchs/:bench_quantizer -- lsq pq rq
```
Reviewed By: beauby
Differential Revision: D28376369
Pulled By: mdouze
fbshipit-source-id: 2a394d38bf75b9de0a1c2cd6faddf7dd362a6fa8
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:
## 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: add getstate / setstate to serialize indexes. Seems to work properly with object ownership etc.
Reviewed By: wickedfoo
Differential Revision: D26521228
fbshipit-source-id: ebbe08cfe2c15af2aa5b7ea1fc1bf87546066c23
