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test: add f16/f64 coverage to PQ distance table benchmarks #5745

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Merged
westonpace merged 1 commit into from
Jan 19, 2026
Merged

test: add f16/f64 coverage to PQ distance table benchmarks #5745

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62 changes: 44 additions & 18 deletions rust/lance-index/benches/4bitpq_dist_table.rs
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,13 +5,13 @@

use std::iter::repeat_n;

use arrow_array::types::Float32Type;
use arrow_array::types::{Float16Type, Float32Type, Float64Type};
use arrow_array::{FixedSizeListArray, UInt8Array};
use criterion::{black_box, criterion_group, criterion_main, Criterion};
use lance_arrow::FixedSizeListArrayExt;
use lance_arrow::{ArrowFloatType, FixedSizeListArrayExt, FloatArray};
use lance_index::vector::pq::distance::{build_distance_table_dot, build_distance_table_l2};
use lance_index::vector::pq::ProductQuantizer;
use lance_linalg::distance::DistanceType;
use lance_linalg::distance::{DistanceType, Dot, L2};
use lance_testing::datagen::generate_random_array_with_seed;
use rand::{prelude::StdRng, Rng, SeedableRng};

Expand All @@ -23,70 +23,96 @@ const DIM: usize = 1536;
const TOTAL: usize = 16 * 1000;

fn construct_dist_table(c: &mut Criterion) {
let codebook = generate_random_array_with_seed::<Float32Type>(256 * DIM, [88; 32]);
let query = generate_random_array_with_seed::<Float32Type>(DIM, [32; 32]);
construct_dist_table_for_type::<Float16Type>(c, "f16");
construct_dist_table_for_type::<Float32Type>(c, "f32");
construct_dist_table_for_type::<Float64Type>(c, "f64");
}

fn construct_dist_table_for_type<T: ArrowFloatType>(c: &mut Criterion, type_name: &str)
where
T::Native: L2 + Dot,
T::ArrayType: FloatArray<T>,
{
let codebook = generate_random_array_with_seed::<T>(256 * DIM, [88; 32]);
let query = generate_random_array_with_seed::<T>(DIM, [32; 32]);

c.bench_function(
format!(
"construct_dist_table: {},PQ={}x{},DIM={}",
"construct_dist_table: {},PQ={}x{},DIM={},type={}",
DistanceType::L2,
PQ,
4,
DIM
DIM,
type_name
)
.as_str(),
|b| {
b.iter(|| {
black_box(build_distance_table_l2(
codebook.values(),
codebook.as_slice(),
4,
PQ,
query.values(),
query.as_slice(),
));
})
},
);

c.bench_function(
format!(
"construct_dist_table: {},PQ={}x{},DIM={}",
"construct_dist_table: {},PQ={}x{},DIM={},type={}",
DistanceType::Dot,
PQ,
4,
DIM
DIM,
type_name
)
.as_str(),
|b| {
b.iter(|| {
black_box(build_distance_table_dot(
codebook.values(),
codebook.as_slice(),
4,
PQ,
query.values(),
query.as_slice(),
));
})
},
);
}

fn compute_distances(c: &mut Criterion) {
let codebook = generate_random_array_with_seed::<Float32Type>(256 * DIM, [88; 32]);
let query = generate_random_array_with_seed::<Float32Type>(DIM, [32; 32]);
compute_distances_for_type::<Float16Type>(c, "f16");
compute_distances_for_type::<Float32Type>(c, "f32");
compute_distances_for_type::<Float64Type>(c, "f64");
}

fn compute_distances_for_type<T: ArrowFloatType>(c: &mut Criterion, type_name: &str)
where
T::Native: L2 + Dot,
T::ArrayType: FloatArray<T>,
{
let codebook = generate_random_array_with_seed::<T>(256 * DIM, [88; 32]);
let query = generate_random_array_with_seed::<T>(DIM, [32; 32]);

let mut rnd = StdRng::from_seed([32; 32]);
let code = UInt8Array::from_iter_values(repeat_n(rnd.random::<u8>(), TOTAL * PQ));

for dt in [DistanceType::L2, DistanceType::Cosine, DistanceType::Dot].iter() {
for dt in [DistanceType::L2, DistanceType::Cosine, DistanceType::Dot] {
let pq = ProductQuantizer::new(
PQ,
4,
DIM,
FixedSizeListArray::try_new_from_values(codebook.clone(), DIM as i32).unwrap(),
*dt,
dt,
);

c.bench_function(
format!("{},{},PQ={}x{},DIM={}", TOTAL, dt, PQ, 4, DIM).as_str(),
format!(
"compute_distances: {},{},PQ={}x{},DIM={},type={}",
TOTAL, dt, PQ, 4, DIM, type_name
)
.as_str(),
|b| {
b.iter(|| {
black_box(pq.compute_distances(&query, &code).unwrap());
Expand Down
62 changes: 44 additions & 18 deletions rust/lance-index/benches/pq_dist_table.rs
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,13 +5,13 @@

use std::iter::repeat_n;

use arrow_array::types::Float32Type;
use arrow_array::types::{Float16Type, Float32Type, Float64Type};
use arrow_array::{FixedSizeListArray, UInt8Array};
use criterion::{black_box, criterion_group, criterion_main, Criterion};
use lance_arrow::FixedSizeListArrayExt;
use lance_arrow::{ArrowFloatType, FixedSizeListArrayExt, FloatArray};
use lance_index::vector::pq::distance::*;
use lance_index::vector::pq::ProductQuantizer;
use lance_linalg::distance::DistanceType;
use lance_linalg::distance::{DistanceType, Dot, L2};
use lance_testing::datagen::generate_random_array_with_seed;
use rand::{prelude::StdRng, Rng, SeedableRng};

Expand All @@ -23,68 +23,94 @@ const PQ: usize = DIM / 8;
const TOTAL: usize = 16 * 1000;

fn construct_dist_table(c: &mut Criterion) {
let codebook = generate_random_array_with_seed::<Float32Type>(256 * DIM, [88; 32]);
let query = generate_random_array_with_seed::<Float32Type>(DIM, [32; 32]);
construct_dist_table_for_type::<Float16Type>(c, "f16");
construct_dist_table_for_type::<Float32Type>(c, "f32");
construct_dist_table_for_type::<Float64Type>(c, "f64");
}

fn construct_dist_table_for_type<T: ArrowFloatType>(c: &mut Criterion, type_name: &str)
where
T::Native: L2 + Dot,
T::ArrayType: FloatArray<T>,
{
let codebook = generate_random_array_with_seed::<T>(256 * DIM, [88; 32]);
let query = generate_random_array_with_seed::<T>(DIM, [32; 32]);

c.bench_function(
format!(
"construct_dist_table: {},PQ={},DIM={}",
"construct_dist_table: {},PQ={},DIM={},type={}",
DistanceType::L2,
PQ,
DIM
DIM,
type_name
)
.as_str(),
|b| {
b.iter(|| {
black_box(build_distance_table_l2(
codebook.values(),
codebook.as_slice(),
8,
PQ,
query.values(),
query.as_slice(),
));
})
},
);

c.bench_function(
format!(
"construct_dist_table: {},PQ={},DIM={}",
"construct_dist_table: {},PQ={},DIM={},type={}",
DistanceType::Dot,
PQ,
DIM
DIM,
type_name
)
.as_str(),
|b| {
b.iter(|| {
black_box(build_distance_table_dot(
codebook.values(),
codebook.as_slice(),
8,
PQ,
query.values(),
query.as_slice(),
));
})
},
);
}

fn compute_distances(c: &mut Criterion) {
let codebook = generate_random_array_with_seed::<Float32Type>(256 * DIM, [88; 32]);
let query = generate_random_array_with_seed::<Float32Type>(DIM, [32; 32]);
compute_distances_for_type::<Float16Type>(c, "f16");
compute_distances_for_type::<Float32Type>(c, "f32");
compute_distances_for_type::<Float64Type>(c, "f64");
}

fn compute_distances_for_type<T: ArrowFloatType>(c: &mut Criterion, type_name: &str)
where
T::Native: L2 + Dot,
T::ArrayType: FloatArray<T>,
{
let codebook = generate_random_array_with_seed::<T>(256 * DIM, [88; 32]);
let query = generate_random_array_with_seed::<T>(DIM, [32; 32]);

let mut rnd = StdRng::from_seed([32; 32]);
let code = UInt8Array::from_iter_values(repeat_n(rnd.random::<u8>(), TOTAL * PQ));

for dt in [DistanceType::L2, DistanceType::Cosine, DistanceType::Dot].iter() {
for dt in [DistanceType::L2, DistanceType::Cosine, DistanceType::Dot] {
let pq = ProductQuantizer::new(
PQ,
8,
DIM,
FixedSizeListArray::try_new_from_values(codebook.clone(), DIM as i32).unwrap(),
*dt,
dt,
);

c.bench_function(
format!("compute_distances: {},{},PQ={},DIM={}", TOTAL, dt, PQ, DIM).as_str(),
format!(
"compute_distances: {},{},PQ={},DIM={},type={}",
TOTAL, dt, PQ, DIM, type_name
)
.as_str(),
|b| {
b.iter(|| {
black_box(pq.compute_distances(&query, &code).unwrap());
Expand Down
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