Summary:
IVF GPU indexes now support CPU quantizers.
In order for the the auto-tuning to support accessing the parameters of the quantizers, GpuAutoTune needs to be adapted.
Reviewed By: algoriddle
Differential Revision: D42633348
fbshipit-source-id: ef472855aa882ccde9d878ae09782204045e38c5
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2638
This diff is a more streamlined way of searching IVF indexes with precomputed clusters.
This will be used for experiments with hybrid CPU / GPU search.
Reviewed By: algoriddle
Differential Revision: D41301032
fbshipit-source-id: a1d645fd0f2bf806454dfd04971edc0a6200d20d
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2647
The `max_codes` search parameter for IVF indexes limits the number of distance computations that are performed.
Previously, the number of distance computations could exceed max_codes because inverted lists were scanned completely.
This diff changed this to scan the beginning of the last inverted list to reach `max_codes` exactly.
Reviewed By: alexanderguzhva
Differential Revision: D42367593
fbshipit-source-id: 67c88b93a407ab271397e913c5fa17104f4274c3
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2650
LocalSearchQuantizer::icm_encode_step was optimized:
* The loop orders were changes
* Memory allocations were optimized
* Different order for accessing 'binaries' tensor, because it is symmetric
* SIMD for the lookup of the best code.
Results are unchanged.
Also, fixes incorrect test in test_local_search_quantizer.py
Reviewed By: mdouze
Differential Revision: D42352124
fbshipit-source-id: bf7e349f2123e6ee99e0776cf15ad5fc1cf2439a
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2651
Modify approximate heap facility to maintain the correct order as if the input elements were pushed into the heap sequentially
Reviewed By: mdouze
Differential Revision: D42373532
fbshipit-source-id: 477dc8acd2567157e2b99076a566326821021c8c
Summary:
In ```cmp_with_scann.py```, we will save npy file for base and query vector file and gt file. However, we will only do this while the lib is faiss, if we directly run this script with scann lib it will complain that file does not exsit.
Therefore, the code should be refactored to save npy file from the beginning so that nothing will go wrong.
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2573
Reviewed By: mdouze
Differential Revision: D42338435
Pulled By: algoriddle
fbshipit-source-id: 9227f95e1ff79f5329f6206a0cb7ca169185fdb3
Summary:
There are two different names for the same thing and name inconsistency between the function declaration and definition which uses `M2` in the function declaration and uses `nsq` in the function definition. It may cause confusion, so it's better to unify them.
Signed-off-by: zh Wang <rekind133@outlook.com>
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2542
Reviewed By: mdouze
Differential Revision: D42338110
Pulled By: algoriddle
fbshipit-source-id: d386e00fb8d8904051dd676bae1e7f4702a172d9
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2633
The core idea.
Instead of putting every element of the dataset into MaxHeap, split the dataset into buckets and let every bucket track elements min-1, min-2 or min-3 distances.
Applied to ResidualQuantizer class for vector codec purposes.
An example
```
rq.approx_topk_mode = faiss.APPROX_TOPK_BUCKETS_B16_D2
```
Reviewed By: mdouze
Differential Revision: D42044398
fbshipit-source-id: 43169026476650442806a31d1c1aa2d5d5028e65
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2625
This diff introduces a new abstraction for the code layouts that are not simply flat one after another.
The packed codes are assumed to be packed together in fixed-size blocks. Hence, code `#i` is stored at offset `i % nvec` of block `floor(i / nvec)`. Each block has size `block_size`.
The `CodePacker` object takes care of the translation between packed and flat codes. The packing / unpacking functions are virtual functions now, but they could as well be inlined for performance.
The `CodePacker` object makes it possible to do manipulations onarrays of codes (including inverted lists) in a uniform way, for example merging / adding / updating / removing / converting to&from CPU.
In this diff, the only non-trivial CodePacker implemnted is for the FastScan code. The new functionality supported is merging IVFFastScan indexes.
Reviewed By: alexanderguzhva
Differential Revision: D42072972
fbshipit-source-id: d1f8bdbcf7ab0f454b5d9c37ba2720fd191833d0
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2626
Improves the performance of fused kernels used in exhaustive_L2sqr_blas() call. The code parameters are tweaked to utilize concurrently working ALU and LOAD/STORE units in a single CPU core.
The parameters values were tweaked for AVX2 and AVX512 kernels, but ARM ones remained unchanged because there was no way for me to benchmark the changes. Please feel free to alter ones if you have access to an ARM machine.
Reviewed By: mdouze
Differential Revision: D42079875
fbshipit-source-id: f1f7dc1759dbad57827d1c1b1b2b399322b33df0
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2631
The pytorch in fbcode complains about `storage()` saying it is deprecated and we need to move to UntypedStorage `_storage()`, while github CI is using an older version of pytorch where `_storage()` doesn't exist.
As it is only a warning not an error in fbcode, revert to the old form, but we'll likely have to change to `_storage()` eventually.
Reviewed By: alexanderguzhva
Differential Revision: D42107029
fbshipit-source-id: 699c15932e6ae48cd1c60ebb7212dcd9b47626f6
Summary:
This diff fixes four separate issues:
- Using the pytorch bridge produces the following deprecation warning. We switch to `_storage()` instead.
```
torch_utils.py:51: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor._storage() instead of tensor.storage()
x.storage().data_ptr() + x.storage_offset() * 4)
```
- The `storage_offset` for certain types was wrong, but this would only affect torch tensors that were a view into a storage that didn't begin at the beginning.
- The `reconstruct_n` numpy pytorch bridge function allowed passing `-1` for `ni` which indicated that all vectors should be reconstructed. The torch bridge didn't follow this and throw an error:
```
TypeError: torch_replacement_reconstruct_n() missing 2 required positional arguments: 'n0' and 'ni'
```
- Choosing values in the range (1024, 2048] for `k` or `nprobe` were broken in D37777979; this is now fixed again.
Reviewed By: alexanderguzhva
Differential Revision: D42041239
fbshipit-source-id: c7d9b4aba63db8ac73e271c8ef34e231002963d9
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2620
Reduce the number of points to match the number of clusters needed for every index, so that a clustering procedure would skip doing actual clustering.
Reviewed By: mdouze
Differential Revision: D41964901
fbshipit-source-id: 8be8b3fda8f07a66b18b85072e1f745483cdd956
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:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2567
Intuitively, it should be easier to handle big-batch searches because all distance computations for a set of queries can be done locally within each inverted list.
This benchmark implements this in pure python (but should be close to optimal in terms of speed), on CPU for IndexIVFFlat, IndexIVFPQ and IndexIVFScalarQuantizer. GPU is also supported.
The results are not systematically better, see https://docs.google.com/document/d/1d3YuV8uN7hut6aOATCOMx8Ut-QEl_oRnJdPgDBRF1QA/edit?usp=sharing
Reviewed By: algoriddle
Differential Revision: D41098338
fbshipit-source-id: 479e471b0d541f242d420f581775d57b708a61b8
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:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2611
Moves the invlist splitting code so that it can be used independently from the IndexIVF.
Add a simple test for the splitting code.
Fix a bug in the IndexShards implementation.
Reviewed By: alexanderguzhva
Differential Revision: D41807025
fbshipit-source-id: 3f53afc5f81744343597bdfcfa90daa4f324a673
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2582
A few more or less cosmetic improvements
* Index::idx_t was in the Index object, which does not make much sense, this diff moves it to faiss::idx_t
* replace multiprocessing.dummy with multiprocessing.pool
* add Alexandr as a core contributor of Faiss in the README ;-)
```
for i in $( find . -name \*.cu -o -name \*.cuh -o -name \*.h -o -name \*.cpp ) ; do
sed -i s/Index::idx_t/idx_t/ $i
done
```
For the fbcode deps:
```
for i in $( fbgs Index::idx_t --exclude fbcode/faiss -l ) ; do
sed -i s/Index::idx_t/idx_t/ $i
done
```
Reviewed By: algoriddle
Differential Revision: D41437507
fbshipit-source-id: 8300f2a3ae97cace6172f3f14a9be3a83999fb89
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2598
Significantly speeds up sa_encode() for RQ and PRQ both LUT=0 and LUT=1 versions by preallocating the needed buffers.
Reviewed By: mdouze
Differential Revision: D41320670
fbshipit-source-id: fa0bbe251013def2c961eb9d19f8408630831e9e
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2594
Bumping `mkl` to 2021 and installing `mkl-devel` in the build environment to fix the Windows nightly build.
Reviewed By: mlomeli1
Differential Revision: D41534391
fbshipit-source-id: 7c681f530a1efe649cd176135a23ebb0fb44d70f
Summary:
`IndexIVFPQ` doesn't seem to depend on single-byte code words, and
encoding / decoding is abstracted away out of it.
There is some reachable logic that supports only `pq.nbits == 8`
(polysemous training is the only one I found), but those are themselves
are gated, and missing support is an implementation detail (also, the
existing `<=` is wrong even if the intention was to gate on polysemous
training support).
Reviewed By: luciang
Differential Revision: D41483414
fbshipit-source-id: 06f471c25293e01242d7bab37ff54e709edc710b
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2584
The `bfKnn` C++ function and `knn_gpu` Python functions for running brute-force k-NN on the GPU did not have a way to specify the GPU device on which the search should run, as it simply used the current thread-local `cudaGetDevice(...)` setting in the CUDA runtime API.
This is unlike the GPU index classes which takes a device argument in the index config struct. Now, both the C++ and Python interface to bfKnn have an optional argument to specify the device.
Default behavior is the current behavior; if the `device` is -1 then the current CUDA thread-local device is used, otherwise we perform the work on the desired device.
Reviewed By: mdouze
Differential Revision: D41448254
fbshipit-source-id: a63c68c12edbe4d725b9fc2a749d5dc935574e12
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2539
Adding `clone_index()` for `IndexRefine` and `IndexRefineFlat` and several others index types.
https://github.com/facebookresearch/faiss/issues/2517
Note the change in `index_factory.cpp`, `RFlat` now constructs an `IndexRefineFlat`.
Reviewed By: mdouze
Differential Revision: D40511409
fbshipit-source-id: e55852eacb1f7d08be8c18005a82802939b4a6d9
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2568
Add a fused kernel for exhaustive_L2sqr_blas() call that combines a computation of dot product and the search for the nearest centroid. As a result, no temporary dot product values are written and read in RAM.
Speeds up the training of PQx[1] indices for dsub = 1, 2, 4, 8, and the effect is higher for higher values of [1]. AVX512 version provides additional overloads for dsub = 12, 16.
The speedup is also beneficial for higher values of pq.cp.max_points_per_centroid (which is 256 by default).
Speeds up IVFPQ training as well.
AVX512 kernel is not enabled, but I've seen it speeding up the training TWICE versus AVX2 version. So, please feel free to use it by enabling AVX512 manually.
Reviewed By: mdouze
Differential Revision: D41166766
fbshipit-source-id: 443014e2e59396b3a90b9171fec8c8191052bcf4
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2572
This is a fix to https://github.com/facebookresearch/faiss/issues/2561
namely, GpuIndexIVFFlat didn't work for query batch sizes larger than 65536, due to that being the maximum grid Y dimension.
This code fixes the IVFFlat code to instead perform a grid loop over the queries as needed for >65536, and in other cases which are not currently violated by large batch queries but we're simply passing in some array size as a grid Y parameter, I added asserts to catch this for the future.
Added two tests for IVFPQ and IVFFlat for the large batch case. The IVFPQ large batch test passed before, but IVFFlat reproduced the same issue as seen in GH issue 2561.
Reviewed By: alexanderguzhva
Differential Revision: D41184878
fbshipit-source-id: 92a87bd095319d6dcd73c76ba9044c019b8ca71c
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/2562
Introduce a table of transposed centroids in ProductQuantizer that significantly speeds up ProductQuantizer::compute_codes() call for certain PQ parameters, so speeds up search queries.
* ::sync_tranposed_centroids() call is used to fill the table
* ::clear_transposed_centroids() call clear the table, so that the original baseline code is used for ::compute_codes()
Reviewed By: mdouze
Differential Revision: D40763338
fbshipit-source-id: 87b40e5dd2f8c3cadeb94c1cd9e8a4a5b6ffa97d
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2533
Implements merge_from for IndexIDMap[2] and IndexPreTransform. In the process, split off IndexIDMap to their own .h/.cpp files.
Reviewed By: alexanderguzhva
Differential Revision: D40420373
fbshipit-source-id: 1570a460706dd3fbc1447f9fcc0e2721eab869bb
Summary:
previously, range_search on IVFFastScan crashed because it used the range_search implem in IndexIVF that tries to obtain an InvertedListsScanner which raises an exception that is not propagated properly in openmp to python.
This diff just throws an exception right away.
Reviewed By: mlomeli1
Differential Revision: D40853406
fbshipit-source-id: e594a3af682b79868233e32a94aea80579378fc0
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2552
The `conda inspect` commands in the `test` section fail without `conda-build` in the `test` environment.
Reviewed By: mlomeli1
Differential Revision: D40793051
fbshipit-source-id: 184418cfa8d0efd6af6b0c806f7bddbeba176732
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2550
This diff contains two fixes:
-`GpuIndex::search_and_reconstruct` was implemented in D37777979 (the default `faiss::Index` implementation never worked on GPU if given GPU input data), but the number of vectors passed to reconstruct was wrong in that diff. This fixes that, and includes a test for `search_and_reconstruct` as well.
-`GpuFlatIndex::reserve` only worked properly if you were calling `add` afterwards. If not, then this would potentially leave the index in a bad state. This bug has existed since 2016 in GPU Faiss.
Also implemented a test for a massive `GpuIndexFlat` index (more than 4 GB of data). Proper implementation of large (>2 GB) indexes via 64 bit indexing arithmetic will be done in a followup diff which touches most of the GPU code.
Reviewed By: alexanderguzhva
Differential Revision: D40765397
fbshipit-source-id: 7eb4368e7588aea144bc5bcc53fd11b1e70f33ea
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2544
Don't use #pragma once for the include headers.
Reviewed By: rahulg
Differential Revision: D40544318
fbshipit-source-id: 129e6de27d569fd46ccc460a262de3b991f568bc
