CAGRA#

CAGRA is a graph-based nearest neighbors algorithm that was built from the ground up for GPU acceleration. CAGRA demonstrates state-of-the art index build and query performance for both small- and large-batch sized search.

Index build parameters#

class cuvs.neighbors.cagra.IndexParams( metric='sqeuclidean', *, intermediate_graph_degree=128, graph_degree=64, build_algo='ivf_pq', nn_descent_niter=20, compression=None, )#

Parameters to build index for CAGRA nearest neighbor search

Parameters:

metricstring denoting the metric type, default=”sqeuclidean”

Valid values for metric: [“sqeuclidean”, “inner_product”], where

sqeuclidean is the euclidean distance without the square root operation, i.e.: distance(a,b) = sum_i (a_i - b_i)^2
inner_product distance is defined as distance(a, b) = sum_i a_i * b_i.

intermediate_graph_degreeint, default = 128

graph_degreeint, default = 64

build_algo: string denoting the graph building algorithm to use, default = “ivf_pq”

Valid values for algo: [“ivf_pq”, “nn_descent”], where

ivf_pq will use the IVF-PQ algorithm for building the knn graph
nn_descent (experimental) will use the NN-Descent algorithm for building the knn graph. It is expected to be generally faster than ivf_pq.

compression: CompressionParams, optional

If compression is desired should be a CompressionParams object. If None compression will be disabled.

Attributes:

build_algo
compression: compression: object
graph_degree
intermediate_graph_degree
metric
nn_descent_niter

compression#: compression: object

Index search parameters#

class cuvs.neighbors.cagra.SearchParams( max_queries=0, *, itopk_size=64, max_iterations=0, algo='auto', team_size=0, search_width=1, min_iterations=0, thread_block_size=0, hashmap_mode='auto', hashmap_min_bitlen=0, hashmap_max_fill_rate=0.5, num_random_samplings=1, rand_xor_mask=0x128394, )#

CAGRA search parameters

Parameters:

max_queries: int, default = 0

Maximum number of queries to search at the same time (batch size). Auto select when 0.

itopk_size: int, default = 64

Number of intermediate search results retained during the search. This is the main knob to adjust trade off between accuracy and search speed. Higher values improve the search accuracy.

max_iterations: int, default = 0

Upper limit of search iterations. Auto select when 0.

algo: string denoting the search algorithm to use, default = “auto”

Valid values for algo: [“auto”, “single_cta”, “multi_cta”], where

auto will automatically select the best value based on query size
single_cta is better when query contains larger number of vectors (e.g >10)
multi_cta is better when query contains only a few vectors

team_size: int, default = 0

Number of threads used to calculate a single distance. 4, 8, 16, or 32.

search_width: int, default = 1

Number of graph nodes to select as the starting point for the search in each iteration.

min_iterations: int, default = 0

Lower limit of search iterations.

thread_block_size: int, default = 0

Thread block size. 0, 64, 128, 256, 512, 1024. Auto selection when 0.

hashmap_mode: string denoting the type of hash map to use.

It’s usually better to allow the algorithm to select this value, default = “auto”. Valid values for hashmap_mode: [“auto”, “small”, “hash”], where

auto will automatically select the best value based on algo

small will use the small shared memory hash table with resetting.

hash will use a single hash table in global memory.

hashmap_min_bitlen: int, default = 0

Upper limit of hashmap fill rate. More than 0.1, less than 0.9.

hashmap_max_fill_rate: float, default = 0.5

Upper limit of hashmap fill rate. More than 0.1, less than 0.9.

num_random_samplings: int, default = 1

Number of iterations of initial random seed node selection. 1 or more.

rand_xor_mask: int, default = 0x128394

Bit mask used for initial random seed node selection.

Attributes:

algo
hashmap_max_fill_rate
hashmap_min_bitlen
hashmap_mode
itopk_size
max_iterations
max_queries
min_iterations
num_random_samplings
rand_xor_mask
search_width
team_size
thread_block_size

Index#

class cuvs.neighbors.cagra.Index#

CAGRA index object. This object stores the trained CAGRA index state which can be used to perform nearest neighbors searches.

Attributes:

dim
trained

Index build#

cuvs.neighbors.cagra.build(IndexParams index_params, dataset, resources=None)[source]#

Build the CAGRA index from the dataset for efficient search.

The build performs two different steps- first an intermediate knn-graph is constructed, then it’s optimized it to create the final graph. The index_params object controls the node degree of these graphs.

It is required that both the dataset and the optimized graph fit the GPU memory.

The following distance metrics are supported:

L2
InnerProduct

Parameters:

index_paramsIndexParams object
datasetCUDA array interface compliant matrix shape (n_samples, dim): Supported dtype [float, int8, uint8]
resourcesOptional cuVS Resource handle for reusing CUDA resources.: If Resources aren’t supplied, CUDA resources will be allocated inside this function and synchronized before the function exits. If resources are supplied, you will need to explicitly synchronize yourself by calling resources.sync() before accessing the output.

Returns:

index: cuvs.cagra.Index

Examples

>>> import cupy as cp
>>> from cuvs.neighbors import cagra
>>> n_samples = 50000
>>> n_features = 50
>>> n_queries = 1000
>>> k = 10
>>> dataset = cp.random.random_sample((n_samples, n_features),
...                                   dtype=cp.float32)
>>> build_params = cagra.IndexParams(metric="sqeuclidean")
>>> index = cagra.build(build_params, dataset)
>>> distances, neighbors = cagra.search(cagra.SearchParams(),
...                                      index, dataset,
...                                      k)
>>> distances = cp.asarray(distances)
>>> neighbors = cp.asarray(neighbors)

Index search#

cuvs.neighbors.cagra.search( SearchParams search_params, Index index, queries, k, neighbors=None, distances=None, resources=None, )[source]#

Find the k nearest neighbors for each query.

Parameters:

search_paramsSearchParams
indexIndex: Trained CAGRA index.
queriesCUDA array interface compliant matrix shape (n_samples, dim): Supported dtype [float, int8, uint8]
kint: The number of neighbors.
neighborsOptional CUDA array interface compliant matrix shape: (n_queries, k), dtype int64_t. If supplied, neighbor indices will be written here in-place. (default None)
distancesOptional CUDA array interface compliant matrix shape: (n_queries, k) If supplied, the distances to the neighbors will be written here in-place. (default None)
resourcesOptional cuVS Resource handle for reusing CUDA resources.: If Resources aren’t supplied, CUDA resources will be allocated inside this function and synchronized before the function exits. If resources are supplied, you will need to explicitly synchronize yourself by calling resources.sync() before accessing the output.

Examples

>>> import cupy as cp
>>> from cuvs.neighbors import cagra
>>> n_samples = 50000
>>> n_features = 50
>>> n_queries = 1000
>>> dataset = cp.random.random_sample((n_samples, n_features),
...                                   dtype=cp.float32)
>>> # Build index
>>> index = cagra.build(cagra.IndexParams(), dataset)
>>> # Search using the built index
>>> queries = cp.random.random_sample((n_queries, n_features),
...                                   dtype=cp.float32)
>>> k = 10
>>> search_params = cagra.SearchParams(
...     max_queries=100,
...     itopk_size=64
... )
>>> # Using a pooling allocator reduces overhead of temporary array
>>> # creation during search. This is useful if multiple searches
>>> # are performed with same query size.
>>> distances, neighbors = cagra.search(search_params, index, queries,
...                                     k)
>>> neighbors = cp.asarray(neighbors)
>>> distances = cp.asarray(distances)

Index save#

cuvs.neighbors.cagra.save(filename, Index index, bool include_dataset=True, resources=None)[source]#

Saves the index to a file.

Saving / loading the index is experimental. The serialization format is subject to change.

Parameters:

filenamestring: Name of the file.
indexIndex: Trained CAGRA index.
include_datasetbool: Whether or not to write out the dataset along with the index. Including the dataset in the serialized index will use extra disk space, and might not be desired if you already have a copy of the dataset on disk. If this option is set to false, you will have to call index.update_dataset(dataset) after loading the index.
resourcesOptional cuVS Resource handle for reusing CUDA resources.: If Resources aren’t supplied, CUDA resources will be allocated inside this function and synchronized before the function exits. If resources are supplied, you will need to explicitly synchronize yourself by calling resources.sync() before accessing the output.

Examples

>>> import cupy as cp
>>> from cuvs.neighbors import cagra
>>> n_samples = 50000
>>> n_features = 50
>>> dataset = cp.random.random_sample((n_samples, n_features),
...                                   dtype=cp.float32)
>>> # Build index
>>> index = cagra.build(cagra.IndexParams(), dataset)
>>> # Serialize and deserialize the cagra index built
>>> cagra.save("my_index.bin", index)
>>> index_loaded = cagra.load("my_index.bin")

Index load#

cuvs.neighbors.cagra.load(filename, resources=None)[source]#

Loads index from file.

Saving / loading the index is experimental. The serialization format is subject to change, therefore loading an index saved with a previous version of cuvs is not guaranteed to work.

Parameters:

filenamestring: Name of the file.
resourcesOptional cuVS Resource handle for reusing CUDA resources.: If Resources aren’t supplied, CUDA resources will be allocated inside this function and synchronized before the function exits. If resources are supplied, you will need to explicitly synchronize yourself by calling resources.sync() before accessing the output.

Returns:

indexIndex