All-neighbors#

All-neighbors allows building an approximate all-neighbors knn graph. Given a full dataset, it finds nearest neighbors for all the training vectors in the dataset.

#include <cuvs/neighbors/all_neighbors.hpp>

namespace cuvs::neighbors::all_neighbors

All neighbors knn graph build parameters#

using GraphBuildParams = std::variant<graph_build_params::ivf_pq_params, graph_build_params::nn_descent_params>#
struct all_neighbors_params#
#include <all_neighbors.hpp>

Parameters used to build an all-neighbors graph (find nearest neighbors for all the training vectors). For scalability, the all-neighbors graph construction algorithm partitions a set of training vectors into overlapping clusters, computes a local knn graph on each cluster, and merges the local graphs into a single global graph. Device memory usage and accuracy can be configured by changing the overlap_factor and n_clusters. The algorithm used to build each local graph is also configurable.

Public Members

GraphBuildParams graph_build_params#

Parameters for knn graph building algorithm Approximate nearest neighbors methods are used to build the knn graph. Currently supported options are ‘IVF-PQ’ and ‘NN Descent’. IVF-PQ is more accurate, but slower compared to NN Descent.

Set ivf_pq_params, or nn_descent_params to select the graph build algorithm and control their parameters.

all_neighbors::index_params params;
// 1. Choose IVF-PQ algorithm
params.graph_build_params = all_neighbors::graph_build_params::ivf_pq_params{};

// 2. Choose NN Descent algorithm for kNN graph construction
params.graph_build_params = all_neighbors::graph_build_params::nn_descent_params{};
size_t overlap_factor = 2#

Number of nearest clusters each data point will be assigned to in the batching algorithm. Start with overlap_factor = 2 and gradually increase (2->3->4 …) for better accuracy at the cost of device memory usage.

size_t n_clusters = 1#

Number of total clusters (aka batches) to split the data into. If set to 1, algorithm creates an all-neighbors graph without batching. Start with n_clusters = 4 and increase (4 → 8 → 16…) for less device memory usage at the cost of accuracy. This is independent from overlap_factor as long as overlap_factor < n_clusters.

The ratio of overlap_factor / n_clusters determines device memory usage. Approximately (overlap_factor / n_clusters) * num_rows_in_entire_data number of rows will be put on device memory at once. E.g. between (overlap_factor / n_clusters) = 2/10 and 2/20, the latter will use less device memory.

Larger overlap_factor results in better accuracy of the final all-neighbors knn graph. E.g. While using similar device memory, (overlap_factor / n_clusters) = 4/20 will have better accuracy than 2/10 at the cost of performance.

cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded#

Metric used.

Build#

void build(
const raft::resources &handle,
const all_neighbors_params &params,
raft::host_matrix_view<const float, int64_t, row_major> dataset,
raft::device_matrix_view<int64_t, int64_t, row_major> indices,
std::optional<raft::device_matrix_view<float, int64_t, row_major>> distances = std::nullopt
)#

Builds an approximate all-neighbors knn graph (find nearest neighbors for all the training vectors)

Usage example: 

using namespace cuvs::neighbors;
// use default index parameters
all_neighbors::all_neighbors_params params;
auto indices = raft::make_device_matrix<int64_t, int64_t>(handle, n_row, k);
auto distances = raft::make_device_matrix<float, int64_t>(handle, n_row, k);
all_neighbors::build(res, params, dataset, indices.view(), distances.view());

Parameters:

  • handle – [in] raft::resources is an object mangaging resources

  • params – [in] an instance of all_neighbors::all_neighbors_params that are parameters to build all-neighbors knn graph

  • dataset – [in] raft::host_matrix_view input dataset expected to be located in host memory

  • indices – [out] nearest neighbor indices of shape [n_row x k]

  • distances – [out] nearest neighbor distances [n_row x k]

void build(
const raft::resources &handle,
const all_neighbors_params &params,
raft::device_matrix_view<const float, int64_t, row_major> dataset,
raft::device_matrix_view<int64_t, int64_t, row_major> indices,
std::optional<raft::device_matrix_view<float, int64_t, row_major>> distances = std::nullopt
)#

Builds an approximate all-neighbors knn graph (find nearest neighbors for all the training vectors) params.n_clusters should be 1 for data on device. To use a larger params.n_clusters for efficient device memory usage, put data on host RAM.

Usage example: 

using namespace cuvs::neighbors;
// use default index parameters
all_neighbors::all_neighbors_params params;
auto indices = raft::make_device_matrix<int64_t, int64_t>(handle, n_row, k);
auto distances = raft::make_device_matrix<float, int64_t>(handle, n_row, k);
all_neighbors::build(res, params, dataset, indices.view(), distances.view());

Parameters:

  • handle – [in] raft::resources is an object mangaging resources

  • params – [in] an instance of all_neighbors::all_neighbors_params that are parameters to build all-neighbors knn graph

  • dataset – [in] raft::device_matrix_view input dataset expected to be located in device memory

  • indices – [out] nearest neighbor indices of shape [n_row x k]

  • distances – [out] nearest neighbor distances [n_row x k]