Class faiss::gpu::GpuIndexIVF

class faiss::gpu::GpuIndexIVF : public faiss::gpu::GpuIndex 

Subclassed by faiss::gpu::GpuIndexIVFFlat, faiss::gpu::GpuIndexIVFPQ, faiss::gpu::GpuIndexIVFScalarQuantizer

Public Types

using idx_t = int64_t: all indices are this type

using component_t = float

using distance_t = float

Public Functions

GpuIndexIVF(GpuResourcesProvider *provider, int dims, faiss::MetricType metric, float metricArg, int nlist, GpuIndexIVFConfig config = GpuIndexIVFConfig())

~GpuIndexIVF() override

void copyFrom(const faiss::IndexIVF *index): Copy what we need from the CPU equivalent.

void copyTo(faiss::IndexIVF *index) const: Copy what we have to the CPU equivalent.

int getNumLists() const: Returns the number of inverted lists we’re managing.

virtual int getListLength(int listId) const = 0: Returns the number of vectors present in a particular inverted list.

virtual std::vector<uint8_t> getListVectorData(int listId, bool gpuFormat = false) const = 0: Return the encoded vector data contained in a particular inverted list, for debugging purposes. If gpuFormat is true, the data is returned as it is encoded in the GPU-side representation. Otherwise, it is converted to the CPU format. compliant format, while the native GPU format may differ.

virtual std::vector<Index::idx_t> getListIndices(int listId) const = 0: Return the vector indices contained in a particular inverted list, for debugging purposes.

GpuIndexFlat *getQuantizer(): Return the quantizer we’re using.

void setNumProbes(int nprobe): Sets the number of list probes per query.

int getNumProbes() const: Returns our current number of list probes per query.

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 setMinPagingSize(size_t size): Set the minimum data size for searches (in MiB) for which we use CPU -> GPU paging

size_t getMinPagingSize() const: Returns the current minimum data size for paged searches.

virtual void add(Index::idx_t, const float *x) override: x can be resident on the CPU or any GPU; copies are performed as needed Handles paged adds if the add set is too large; calls addInternal_

virtual void add_with_ids(Index::idx_t n, const float *x, const Index::idx_t *ids) override: x and ids can be resident on the CPU or any GPU; copies are performed as needed Handles paged adds if the add set is too large; calls addInternal_

virtual void assign(Index::idx_t n, const float *x, Index::idx_t *labels, Index::idx_t k = 1) const override: x and labels can be resident on the CPU or any GPU; copies are performed as needed

virtual void search(Index::idx_t n, const float *x, Index::idx_t k, float *distances, Index::idx_t *labels) const override: x, distances and labels can be resident on the CPU or any GPU; copies are performed as needed

virtual void compute_residual(const float *x, float *residual, Index::idx_t key) const override: Overridden to force GPU indices to provide their own GPU-friendly implementation

virtual void compute_residual_n(Index::idx_t n, const float *xs, float *residuals, const Index::idx_t *keys) const override: Overridden to force GPU indices to provide their own GPU-friendly implementation

virtual void train(idx_t n, const float *x)

Perform training on a representative set of vectors

Parameters

n – nb of training vectors
x – training vecors, size n * d

virtual void range_search(idx_t n, const float *x, float 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).

Parameters

x – input vectors to search, size n * d
radius – search radius
result – result table

virtual void reset() = 0: removes all elements from the database.

virtual size_t remove_ids(const IDSelector &sel): removes IDs from the index. Not supported by all indexes. Returns the number of elements removed.

virtual void reconstruct(idx_t key, float *recons) const

Reconstruct a stored vector (or an approximation if lossy coding)

this function may not be defined for some indexes

Parameters

key – id of the vector to reconstruct
recons – reconstucted vector (size d)

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

Reconstruct vectors i0 to i0 + ni - 1

this function may not be defined for some indexes

Parameters: recons – reconstucted vector (size ni * d)

virtual void search_and_reconstruct(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels, float *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 arrays is padded with -1s.

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

virtual DistanceComputer *get_distance_computer() const

Get a DistanceComputer (defined in AuxIndexStructures) object for this kind of index.

DistanceComputer is implemented for indexes that support random access of their vectors.

virtual size_t sa_code_size() const: size of the produced codes in bytes

virtual void sa_encode(idx_t n, const float *x, uint8_t *bytes) const

encode a set of vectors

Parameters

n – number of vectors
x – input vectors, size n * d
bytes – output encoded vectors, size n * sa_code_size()

virtual void sa_decode(idx_t n, const uint8_t *bytes, float *x) const

decode a set of vectors

Parameters

n – number of vectors
bytes – input encoded vectors, size n * sa_code_size()
x – output vectors, size n * d

Public Members

ClusteringParameters cp: Exposing this like the CPU version for manipulation.

int nlist: Exposing this like the CPU version for query.

int nprobe: Exposing this like the CPU version for manipulation.

GpuIndexFlat *quantizer: Exposeing this like the CPU version for query.

int d: vector dimension

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

float metric_arg: argument of the metric type

Protected Functions

virtual bool addImplRequiresIDs_() const override: Does addImpl_ require IDs? If so, and no IDs are provided, we will generate them sequentially based on the order in which the IDs are added

void trainQuantizer_(Index::idx_t n, const float *x)

void copyFrom(const faiss::Index *index): Copy what we need from the CPU equivalent.

void copyTo(faiss::Index *index) const: Copy what we have to the CPU equivalent.

virtual void addImpl_(int n, const float *x, const Index::idx_t *ids) = 0: Overridden to actually perform the add All data is guaranteed to be resident on our device

virtual void searchImpl_(int n, const float *x, int k, float *distances, Index::idx_t *labels) const = 0: Overridden to actually perform the search All data is guaranteed to be resident on our device

Protected Attributes

const GpuIndexIVFConfig ivfConfig_: Our configuration options.

std::shared_ptr<GpuResources> resources_: Manages streams, cuBLAS handles and scratch memory for devices.

const GpuIndexConfig config_: Our configuration options.

size_t minPagedSize_: Size above which we page copies from the CPU to GPU.

Private Functions

void init_(): Shared initialization functions.