Struct faiss::gpu::GpuIndexCagra
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struct GpuIndexCagra : public faiss::gpu::GpuIndex
Public Types
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using component_t = float
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using distance_t = float
Public Functions
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GpuIndexCagra(GpuResourcesProvider *provider, int dims, faiss::MetricType metric = faiss::METRIC_L2, GpuIndexCagraConfig config = GpuIndexCagraConfig())
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virtual void train(idx_t n, const float *x) override
Trains CAGRA based on the given vector data.
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void copyFrom(const faiss::IndexHNSWCagra *index)
Initialize ourselves from the given CPU index; will overwrite all data in ourselves
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void copyTo(faiss::IndexHNSWCagra *index) const
Copy ourselves to the given CPU index; will overwrite all data in the index instance
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virtual void reset() override
removes all elements from the database.
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int getDevice() const
Returns the device that this index is resident on.
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std::shared_ptr<GpuResources> getResources()
Returns a reference to our GpuResources object that manages memory, stream and handle resources on the GPU
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void setMinPagingSize(size_t size)
Set the minimum data size for searches (in MiB) for which we use CPU -> GPU paging
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size_t getMinPagingSize() const
Returns the current minimum data size for paged searches.
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virtual void add(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_
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virtual void add_with_ids(idx_t n, const float *x, const idx_t *ids) override
x
andids
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_
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virtual void assign(idx_t n, const float *x, idx_t *labels, idx_t k = 1) const override
x
andlabels
can be resident on the CPU or any GPU; copies are performed as needed
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virtual void search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels, const SearchParameters *params = nullptr) const override
x
,distances
andlabels
can be resident on the CPU or any GPU; copies are performed as needed
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virtual void search_and_reconstruct(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels, float *recons, const SearchParameters *params = nullptr) const override
x
,distances
andlabels
andrecons
can be resident on the CPU or any GPU; copies are performed as needed
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virtual void compute_residual(const float *x, float *residual, idx_t key) const override
Overridden to force GPU indices to provide their own GPU-friendly implementation
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virtual void compute_residual_n(idx_t n, const float *xs, float *residuals, const idx_t *keys) const override
Overridden to force GPU indices to provide their own GPU-friendly implementation
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virtual void range_search(idx_t n, const float *x, float radius, RangeSearchResult *result, const SearchParameters *params = nullptr) 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:
n – number of vectors
x – input vectors to search, size n * d
radius – search radius
result – result table
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virtual size_t remove_ids(const IDSelector &sel)
removes IDs from the index. Not supported by all indexes. Returns the number of elements removed.
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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)
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virtual void reconstruct_batch(idx_t n, const idx_t *keys, float *recons) const
Reconstruct several stored vectors (or an approximation if lossy coding)
this function may not be defined for some indexes
- Parameters:
n – number of vectors to reconstruct
keys – ids of the vectors to reconstruct (size n)
recons – reconstucted vector (size n * d)
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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:
i0 – index of the first vector in the sequence
ni – number of vectors in the sequence
recons – reconstucted vector (size ni * d)
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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.
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virtual size_t sa_code_size() const
size of the produced codes in bytes
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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()
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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
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virtual void merge_from(Index &otherIndex, idx_t add_id = 0)
moves the entries from another dataset to self. On output, other is empty. add_id is added to all moved ids (for sequential ids, this would be this->ntotal)
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virtual void check_compatible_for_merge(const Index &otherIndex) const
check that the two indexes are compatible (ie, they are trained in the same way and have the same parameters). Otherwise throw.
Public Members
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int d
vector dimension
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idx_t ntotal
total nb of indexed vectors
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bool verbose
verbosity level
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bool is_trained
set if the Index does not require training, or if training is done already
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MetricType metric_type
type of metric this index uses for search
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float metric_arg
argument of the metric type
Protected Functions
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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
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virtual void addImpl_(idx_t n, const float *x, const idx_t *ids) override
Overridden to actually perform the add All data is guaranteed to be resident on our device
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virtual void searchImpl_(idx_t n, const float *x, int k, float *distances, idx_t *labels, const SearchParameters *search_params) const override
Called from GpuIndex for search.
Protected Attributes
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const GpuIndexCagraConfig cagraConfig_
Our configuration options.
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std::shared_ptr<RaftCagra> index_
Instance that we own; contains the inverted lists.
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std::shared_ptr<GpuResources> resources_
Manages streams, cuBLAS handles and scratch memory for devices.
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const GpuIndexConfig config_
Our configuration options.
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size_t minPagedSize_
Size above which we page copies from the CPU to GPU.
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using component_t = float