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/*
* SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION.
* SPDX-License-Identifier: Apache-2.0
*/
use crate::distance_type::DistanceType;
use crate::error::{check_cuvs, Result};
use std::fmt;
use std::io::{stderr, Write};
pub use ffi::cuvsIvfPqCodebookGen;
pub use ffi::cuvsIvfPqListLayout;
pub struct IndexParams(pub ffi::cuvsIvfPqIndexParams_t);
impl IndexParams {
/// Returns a new IndexParams
pub fn new() -> Result<IndexParams> {
unsafe {
let mut params = std::mem::MaybeUninit::<ffi::cuvsIvfPqIndexParams_t>::uninit();
check_cuvs(ffi::cuvsIvfPqIndexParamsCreate(params.as_mut_ptr()))?;
Ok(IndexParams(params.assume_init()))
}
}
/// The number of clusters used in the coarse quantizer.
pub fn set_n_lists(self, n_lists: u32) -> IndexParams {
unsafe {
(*self.0).n_lists = n_lists;
}
self
}
/// DistanceType to use for building the index
pub fn set_metric(self, metric: DistanceType) -> IndexParams {
unsafe {
(*self.0).metric = metric;
}
self
}
/// The number of iterations searching for kmeans centers during index building.
pub fn set_metric_arg(self, metric_arg: f32) -> IndexParams {
unsafe {
(*self.0).metric_arg = metric_arg;
}
self
}
/// The number of iterations searching for kmeans centers during index building.
pub fn set_kmeans_n_iters(self, kmeans_n_iters: u32) -> IndexParams {
unsafe {
(*self.0).kmeans_n_iters = kmeans_n_iters;
}
self
}
/// If kmeans_trainset_fraction is less than 1, then the dataset is
/// subsampled, and only n_samples * kmeans_trainset_fraction rows
/// are used for training.
pub fn set_kmeans_trainset_fraction(self, kmeans_trainset_fraction: f64) -> IndexParams {
unsafe {
(*self.0).kmeans_trainset_fraction = kmeans_trainset_fraction;
}
self
}
/// The bit length of the vector element after quantization.
pub fn set_pq_bits(self, pq_bits: u32) -> IndexParams {
unsafe {
(*self.0).pq_bits = pq_bits;
}
self
}
/// The dimensionality of a the vector after product quantization.
/// When zero, an optimal value is selected using a heuristic. Note
/// pq_dim * pq_bits must be a multiple of 8. Hint: a smaller 'pq_dim'
/// results in a smaller index size and better search performance, but
/// lower recall. If 'pq_bits' is 8, 'pq_dim' can be set to any number,
/// but multiple of 8 are desirable for good performance. If 'pq_bits'
/// is not 8, 'pq_dim' should be a multiple of 8. For good performance,
/// it is desirable that 'pq_dim' is a multiple of 32. Ideally,
/// 'pq_dim' should be also a divisor of the dataset dim.
pub fn set_pq_dim(self, pq_dim: u32) -> IndexParams {
unsafe {
(*self.0).pq_dim = pq_dim;
}
self
}
pub fn set_codebook_kind(self, codebook_kind: cuvsIvfPqCodebookGen) -> IndexParams {
unsafe {
(*self.0).codebook_kind = codebook_kind;
}
self
}
/// Memory layout of the IVF-PQ list data.
/// - FLAT: Codes are stored contiguously, one vector's codes after another.
/// - INTERLEAVED: Codes are interleaved for optimized search performance.
/// This is the default and recommended for search workloads.
pub fn set_codes_layout(self, codes_layout: cuvsIvfPqListLayout) -> IndexParams {
unsafe {
(*self.0).codes_layout = codes_layout;
}
self
}
/// Apply a random rotation matrix on the input data and queries even
/// if `dim % pq_dim == 0`. Note: if `dim` is not multiple of `pq_dim`,
/// a random rotation is always applied to the input data and queries
/// to transform the working space from `dim` to `rot_dim`, which may
/// be slightly larger than the original space and and is a multiple
/// of `pq_dim` (`rot_dim % pq_dim == 0`). However, this transform is
/// not necessary when `dim` is multiple of `pq_dim` (`dim == rot_dim`,
/// hence no need in adding "extra" data columns / features). By
/// default, if `dim == rot_dim`, the rotation transform is
/// initialized with the identity matrix. When
/// `force_random_rotation == True`, a random orthogonal transform
pub fn set_force_random_rotation(self, force_random_rotation: bool) -> IndexParams {
unsafe {
(*self.0).force_random_rotation = force_random_rotation;
}
self
}
/// The max number of data points to use per PQ code during PQ codebook training. Using more data
/// points per PQ code may increase the quality of PQ codebook but may also increase the build
/// time. The parameter is applied to both PQ codebook generation methods, i.e., PER_SUBSPACE and
/// PER_CLUSTER. In both cases, we will use `pq_book_size * max_train_points_per_pq_code` training
/// points to train each codebook.
pub fn set_max_train_points_per_pq_code(self, max_pq_points: u32) -> IndexParams {
unsafe {
(*self.0).max_train_points_per_pq_code = max_pq_points;
}
self
}
/// After training the coarse and fine quantizers, we will populate
/// the index with the dataset if add_data_on_build == true, otherwise
/// the index is left empty, and the extend method can be used
/// to add new vectors to the index.
pub fn set_add_data_on_build(self, add_data_on_build: bool) -> IndexParams {
unsafe {
(*self.0).add_data_on_build = add_data_on_build;
}
self
}
}
impl fmt::Debug for IndexParams {
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
// custom debug trait here, default value will show the pointer address
// for the inner params object which isn't that useful.
write!(f, "IndexParams({:?})", unsafe { *self.0 })
}
}
impl Drop for IndexParams {
fn drop(&mut self) {
if let Err(e) = check_cuvs(unsafe { ffi::cuvsIvfPqIndexParamsDestroy(self.0) }) {
write!(
stderr(),
"failed to call cuvsIvfPqIndexParamsDestroy {:?}",
e
)
.expect("failed to write to stderr");
}
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_index_params() {
let params = IndexParams::new()
.unwrap()
.set_n_lists(128)
.set_add_data_on_build(false);
unsafe {
assert_eq!((*params.0).n_lists, 128);
assert_eq!((*params.0).add_data_on_build, false);
}
}
}