Class faiss::gpu::GpuIndexBinaryFlat

class faiss::gpu::GpuIndexBinaryFlat : public faiss::IndexBinary

A GPU version of IndexBinaryFlat for brute-force comparison of bit vectors via Hamming distance

Public Types

using idx_t = Index::idx_t

all indices are this type

using component_t = uint8_t
using distance_t = int32_t

Public Functions

GpuIndexBinaryFlat(GpuResourcesProvider *resources, const faiss::IndexBinaryFlat *index, GpuIndexBinaryFlatConfig config = GpuIndexBinaryFlatConfig())

Construct from a pre-existing faiss::IndexBinaryFlat instance, copying data over to the given GPU

GpuIndexBinaryFlat(GpuResourcesProvider *resources, int dims, GpuIndexBinaryFlatConfig config = GpuIndexBinaryFlatConfig())

Construct an empty instance that can be added to.

~GpuIndexBinaryFlat() override
int getDevice() const

Returns the device that this index is resident on.

std::shared_ptr<GpuResources> getResources()

Returns a reference to our GpuResources object that manages memory, stream and handle resources on the GPU

void copyFrom(const faiss::IndexBinaryFlat *index)

Initialize ourselves from the given CPU index; will overwrite all data in ourselves

void copyTo(faiss::IndexBinaryFlat *index) const

Copy ourselves to the given CPU index; will overwrite all data in the index instance

virtual void add(faiss::IndexBinary::idx_t n, const uint8_t *x) override

Add n vectors of dimension d to the index.

Vectors are implicitly assigned labels ntotal .. ntotal + n - 1

Parameters

x – input matrix, size n * d / 8

virtual void reset() override

Removes all elements from the database.

virtual void search(faiss::IndexBinary::idx_t n, const uint8_t *x, faiss::IndexBinary::idx_t k, int32_t *distances, faiss::IndexBinary::idx_t *labels) const override

Query n vectors of dimension d to the index.

return at most k vectors. If there are not enough results for a query, the result array is padded with -1s.

Parameters
  • x – input vectors to search, size n * d / 8

  • labels – output labels of the NNs, size n*k

  • distances – output pairwise distances, size n*k

virtual void reconstruct(faiss::IndexBinary::idx_t key, uint8_t *recons) const override

Reconstruct a stored vector.

This function may not be defined for some indexes.

Parameters
  • key – id of the vector to reconstruct

  • recons – reconstucted vector (size d / 8)

virtual void train(idx_t n, const uint8_t *x)

Perform training on a representative set of vectors.

Parameters
  • n – nb of training vectors

  • x – training vecors, size n * d / 8

virtual void add_with_ids(idx_t n, const uint8_t *x, const idx_t *xids)

Same as add, but stores xids instead of sequential ids.

The default implementation fails with an assertion, as it is not supported by all indexes.

Parameters

xids – if non-null, ids to store for the vectors (size n)

virtual void range_search(idx_t n, const uint8_t *x, int radius, RangeSearchResult *result) const

Query n vectors of dimension d to the index.

return all vectors with distance < radius. Note that many indexes do not implement the range_search (only the k-NN search is mandatory). The distances are converted to float to reuse the RangeSearchResult structure, but they are integer. By convention, only distances < radius (strict comparison) are returned, ie. radius = 0 does not return any result and 1 returns only exact same vectors.

Parameters
  • x – input vectors to search, size n * d / 8

  • radius – search radius

  • result – result table

void assign(idx_t n, const uint8_t *x, idx_t *labels, idx_t k = 1) const

Return the indexes of the k vectors closest to the query x.

This function is identical to search but only returns labels of neighbors.

Parameters
  • x – input vectors to search, size n * d / 8

  • labels – output labels of the NNs, size n*k

virtual size_t remove_ids(const IDSelector &sel)

Removes IDs from the index. Not supported by all indexes.

virtual void reconstruct_n(idx_t i0, idx_t ni, uint8_t *recons) const

Reconstruct vectors i0 to i0 + ni - 1.

This function may not be defined for some indexes.

Parameters

recons – reconstucted vectors (size ni * d / 8)

virtual void search_and_reconstruct(idx_t n, const uint8_t *x, idx_t k, int32_t *distances, idx_t *labels, uint8_t *recons) const

Similar to search, but also reconstructs the stored vectors (or an approximation in the case of lossy coding) for the search results.

If there are not enough results for a query, the resulting array is padded with -1s.

Parameters

recons – reconstructed vectors size (n, k, d)

void display() const

Display the actual class name and some more info.

Public Members

int d

vector dimension

int code_size

number of bytes per vector ( = d / 8 )

idx_t ntotal

total nb of indexed vectors

bool verbose

verbosity level

bool is_trained

set if the Index does not require training, or if training is done already

MetricType metric_type

type of metric this index uses for search

Protected Functions

void searchFromCpuPaged_(int n, const uint8_t *x, int k, int32_t *outDistancesData, int *outIndicesData) const

Called from search when the input data is on the CPU; potentially allows for pinned memory usage

void searchNonPaged_(int n, const uint8_t *x, int k, int32_t *outDistancesData, int *outIndicesData) const

Protected Attributes

std::shared_ptr<GpuResources> resources_

Manages streans, cuBLAS handles and scratch memory for devices.

const GpuIndexBinaryFlatConfig binaryFlatConfig_

Configuration options.

std::unique_ptr<BinaryFlatIndex> data_

Holds our GPU data containing the list of vectors.