Selection#

This page provides C++ class references for the publicly-exposed elements of the cuvs/selection package.

Select-K#

#include <cuvs/selection/select_k.hpp>

namespace cuvs::selection

group select_k

Functions

void select_k(raft::resources const &handle, raft::device_matrix_view<const float, int64_t, raft::row_major> in_val, std::optional<raft::device_matrix_view<const int64_t, int64_t, raft::row_major>> in_idx, raft::device_matrix_view<float, int64_t, raft::row_major> out_val, raft::device_matrix_view<int64_t, int64_t, raft::row_major> out_idx, bool select_min, bool sorted = false, SelectAlgo algo = SelectAlgo::kAuto, std::optional<raft::device_vector_view<const int64_t, int64_t>> len_i = std::nullopt)#

Select k smallest or largest key/values from each row in the input data.

If you think of the input data in_val as a row-major matrix with len columns and batch_size rows, then this function selects k smallest/largest values in each row and fills in the row-major matrix out_val of size (batch_size, k).

Example usage 

using namespace raft;
// get a 2D row-major array of values to search through
auto in_values = {... input device_matrix_view<const float, int64_t, row_major> ...}
// prepare output arrays
auto out_extents = make_extents<int64_t>(in_values.extent(0), k);
auto out_values  = make_device_mdarray<float>(handle, out_extents);
auto out_indices = make_device_mdarray<int64_t>(handle, out_extents);
// search `k` smallest values in each row
cuvs::selection::select_k(
  handle, in_values, std::nullopt, out_values.view(), out_indices.view(), true);

Parameters:

  • handle – [in] container of reusable resources

  • in_val – [in] inputs values [batch_size, len]; these are compared and selected.

  • in_idx – [in] optional input payload [batch_size, len]; typically, these are indices of the corresponding in_val. If in_idx is std::nullopt, a contiguous array 0...len-1 is implied.

  • out_val – [out] output values [batch_size, k]; the k smallest/largest values from each row of the in_val.

  • out_idx – [out] output payload (e.g. indices) [batch_size, k]; the payload selected together with out_val.

  • select_min – [in] whether to select k smallest (true) or largest (false) keys.

  • sorted – [in] whether to make sure selected pairs are sorted by value

  • algo – [in] the selection algorithm to use

  • len_i – [in] optional array of size (batch_size) providing lengths for each individual row

void select_k(raft::resources const &handle, raft::device_matrix_view<const float, int64_t, raft::row_major> in_val, std::optional<raft::device_matrix_view<const uint32_t, int64_t, raft::row_major>> in_idx, raft::device_matrix_view<float, int64_t, raft::row_major> out_val, raft::device_matrix_view<uint32_t, int64_t, raft::row_major> out_idx, bool select_min, bool sorted = false, SelectAlgo algo = SelectAlgo::kAuto, std::optional<raft::device_vector_view<const uint32_t, int64_t>> len_i = std::nullopt)#

Select k smallest or largest key/values from each row in the input data.

If you think of the input data in_val as a row-major matrix with len columns and batch_size rows, then this function selects k smallest/largest values in each row and fills in the row-major matrix out_val of size (batch_size, k).

Example usage 

using namespace raft;
// get a 2D row-major array of values to search through
auto in_values = {... input device_matrix_view<const float, int64_t, row_major> ...}
// prepare output arrays
auto out_extents = make_extents<int64_t>(in_values.extent(0), k);
auto out_values  = make_device_mdarray<float>(handle, out_extents);
auto out_indices = make_device_mdarray<uint32_t>(handle, out_extents);
// search `k` smallest values in each row
cuvs::selection::select_k(
  handle, in_values, std::nullopt, out_values.view(), out_indices.view(), true);

Parameters:

  • handle – [in] container of reusable resources

  • in_val – [in] inputs values [batch_size, len]; these are compared and selected.

  • in_idx – [in] optional input payload [batch_size, len]; typically, these are indices of the corresponding in_val. If in_idx is std::nullopt, a contiguous array 0...len-1 is implied.

  • out_val – [out] output values [batch_size, k]; the k smallest/largest values from each row of the in_val.

  • out_idx – [out] output payload (e.g. indices) [batch_size, k]; the payload selected together with out_val.

  • select_min – [in] whether to select k smallest (true) or largest (false) keys.

  • sorted – [in] whether to make sure selected pairs are sorted by value

  • algo – [in] the selection algorithm to use

  • len_i – [in] optional array of size (batch_size) providing lengths for each individual row

void select_k(raft::resources const &handle, raft::device_matrix_view<const half, int64_t, raft::row_major> in_val, std::optional<raft::device_matrix_view<const uint32_t, int64_t, raft::row_major>> in_idx, raft::device_matrix_view<half, int64_t, raft::row_major> out_val, raft::device_matrix_view<uint32_t, int64_t, raft::row_major> out_idx, bool select_min, bool sorted = false, SelectAlgo algo = SelectAlgo::kAuto, std::optional<raft::device_vector_view<const uint32_t, int64_t>> len_i = std::nullopt)#

Select k smallest or largest key/values from each row in the input data.

If you think of the input data in_val as a row-major matrix with len columns and batch_size rows, then this function selects k smallest/largest values in each row and fills in the row-major matrix out_val of size (batch_size, k).

Example usage 

using namespace raft;
// get a 2D row-major array of values to search through
auto in_values = {... input device_matrix_view<const half, int64_t, row_major> ...}
// prepare output arrays
auto out_extents = make_extents<int64_t>(in_values.extent(0), k);
auto out_values  = make_device_mdarray<half>(handle, out_extents);
auto out_indices = make_device_mdarray<uint32_t>(handle, out_extents);
// search `k` smallest values in each row
cuvs::selection::select_k(
  handle, in_values, std::nullopt, out_values.view(), out_indices.view(), true);

Parameters:

  • handle – [in] container of reusable resources

  • in_val – [in] inputs values [batch_size, len]; these are compared and selected.

  • in_idx – [in] optional input payload [batch_size, len]; typically, these are indices of the corresponding in_val. If in_idx is std::nullopt, a contiguous array 0...len-1 is implied.

  • out_val – [out] output values [batch_size, k]; the k smallest/largest values from each row of the in_val.

  • out_idx – [out] output payload (e.g. indices) [batch_size, k]; the payload selected together with out_val.

  • select_min – [in] whether to select k smallest (true) or largest (false) keys.

  • sorted – [in] whether to make sure selected pairs are sorted by value

  • algo – [in] the selection algorithm to use

  • len_i – [in] optional array of size (batch_size) providing lengths for each individual row

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