mirror of
https://github.com/tursodatabase/limbo.git
synced 2025-12-23 08:21:09 +00:00
use simsimd for dense operations
This commit is contained in:
Nikita Sivukhin
parent
f764f3061d
commit
948bd557cd
5 changed files with 196 additions and 67 deletions
21
Cargo.lock
generated
21
Cargo.lock
generated
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@ -523,10 +523,11 @@ dependencies = [
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[[package]]
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name = "cc"
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version = "1.2.17"
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version = "1.2.41"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "1fcb57c740ae1daf453ae85f16e37396f672b039e00d9d866e07ddb24e328e3a"
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checksum = "ac9fe6cdbb24b6ade63616c0a0688e45bb56732262c158df3c0c4bea4ca47cb7"
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dependencies = [
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"find-msvc-tools",
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"jobserver",
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"libc",
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"shlex",
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@ -1504,6 +1505,12 @@ dependencies = [
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"windows-sys 0.59.0",
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]
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[[package]]
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name = "find-msvc-tools"
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version = "0.1.4"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "52051878f80a721bb68ebfbc930e07b65ba72f2da88968ea5c06fd6ca3d3a127"
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[[package]]
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name = "findshlibs"
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version = "0.10.2"
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@ -4143,6 +4150,15 @@ dependencies = [
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"similar",
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]
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[[package]]
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name = "simsimd"
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version = "6.5.3"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "0e3f209c5a8155b8458b1a0d3a6fc9fa09d201e6086fdaae18e9e283b9274f8f"
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dependencies = [
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"cc",
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]
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[[package]]
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name = "slab"
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version = "0.4.9"
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@ -4925,6 +4941,7 @@ dependencies = [
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"rustix 1.0.7",
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"ryu",
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"serde",
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"simsimd",
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"sorted-vec",
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"strum",
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"strum_macros",
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@ -1,6 +1,19 @@
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import { expect, test } from 'vitest'
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import { connect, Database } from './promise-default.js'
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test('vector-test', async () => {
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const db = await connect(":memory:");
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const v1 = new Array(1024).fill(0).map((_, i) => i);
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const v2 = new Array(1024).fill(0).map((_, i) => 1024 - i);
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const result = await db.prepare(`SELECT
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vector_distance_cos(vector32('${JSON.stringify(v1)}'), vector32('${JSON.stringify(v2)}')) as cosf32,
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vector_distance_cos(vector64('${JSON.stringify(v1)}'), vector64('${JSON.stringify(v2)}')) as cosf64,
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vector_distance_l2(vector32('${JSON.stringify(v1)}'), vector32('${JSON.stringify(v2)}')) as l2f32,
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vector_distance_l2(vector64('${JSON.stringify(v1)}'), vector64('${JSON.stringify(v2)}')) as l2f64
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`).all();
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console.info(result);
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})
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test('explain', async () => {
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const db = await connect(":memory:");
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const stmt = db.prepare("EXPLAIN SELECT 1");
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@ -84,6 +84,7 @@ aegis = "0.9.0"
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twox-hash = "2.1.1"
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intrusive-collections = "0.9.7"
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roaring = "0.11.2"
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simsimd = "6.5.3"
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[build-dependencies]
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chrono = { workspace = true, default-features = false }
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@ -2,6 +2,7 @@ use crate::{
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vector::vector_types::{Vector, VectorSparse, VectorType},
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LimboError, Result,
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};
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use simsimd::SpatialSimilarity;
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pub fn vector_distance_cos(v1: &Vector, v2: &Vector) -> Result<f64> {
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if v1.dims != v2.dims {
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@ -15,11 +16,23 @@ pub fn vector_distance_cos(v1: &Vector, v2: &Vector) -> Result<f64> {
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));
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}
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match v1.vector_type {
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VectorType::Float32Dense => Ok(vector_f32_distance_cos(
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#[cfg(not(target_family = "wasm"))]
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VectorType::Float32Dense => Ok(vector_f32_distance_cos_simsimd(
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v1.as_f32_slice(),
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v2.as_f32_slice(),
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)),
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VectorType::Float64Dense => Ok(vector_f64_distance_cos(
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#[cfg(target_family = "wasm")]
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VectorType::Float32Dense => Ok(vector_f32_distance_cos_rust(
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v1.as_f32_slice(),
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v2.as_f32_slice(),
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)),
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#[cfg(not(target_family = "wasm"))]
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VectorType::Float64Dense => Ok(vector_f64_distance_cos_simsimd(
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v1.as_f64_slice(),
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v2.as_f64_slice(),
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)),
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#[cfg(target_family = "wasm")]
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VectorType::Float64Dense => Ok(vector_f64_distance_cos_rust(
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v1.as_f64_slice(),
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v2.as_f64_slice(),
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)),
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@ -30,44 +43,44 @@ pub fn vector_distance_cos(v1: &Vector, v2: &Vector) -> Result<f64> {
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}
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}
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fn vector_f32_distance_cos(v1: &[f32], v2: &[f32]) -> f64 {
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let (mut dot, mut norm1, mut norm2) = (0.0, 0.0, 0.0);
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let dims = v1.len();
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for i in 0..dims {
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let e1 = v1[i];
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let e2 = v2[i];
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dot += e1 * e2;
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norm1 += e1 * e1;
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norm2 += e2 * e2;
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}
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// Check for zero norms to avoid division by zero
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if norm1 == 0.0 || norm2 == 0.0 {
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return f64::NAN;
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}
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1.0 - (dot / (norm1 * norm2).sqrt()) as f64
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#[allow(dead_code)]
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fn vector_f32_distance_cos_simsimd(v1: &[f32], v2: &[f32]) -> f64 {
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f32::cosine(v1, v2).unwrap_or(f64::NAN)
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}
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fn vector_f64_distance_cos(v1: &[f64], v2: &[f64]) -> f64 {
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// SimSIMD do not support WASM for now, so we have alternative implementation: https://github.com/ashvardanian/SimSIMD/issues/189
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#[allow(dead_code)]
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fn vector_f32_distance_cos_rust(v1: &[f32], v2: &[f32]) -> f64 {
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let (mut dot, mut norm1, mut norm2) = (0.0, 0.0, 0.0);
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let dims = v1.len();
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for i in 0..dims {
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let e1 = v1[i];
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let e2 = v2[i];
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dot += e1 * e2;
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norm1 += e1 * e1;
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norm2 += e2 * e2;
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for (a, b) in v1.iter().zip(v2.iter()) {
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dot += a * b;
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norm1 += a * a;
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norm2 += b * b;
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}
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// Check for zero norms
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if norm1 == 0.0 || norm2 == 0.0 {
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return f64::NAN;
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return 0.0;
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}
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(1.0 - dot / (norm1 * norm2).sqrt()) as f64
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}
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1.0 - (dot / (norm1 * norm2).sqrt())
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#[allow(dead_code)]
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fn vector_f64_distance_cos_simsimd(v1: &[f64], v2: &[f64]) -> f64 {
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f64::cosine(v1, v2).unwrap_or(f64::NAN)
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}
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// SimSIMD do not support WASM for now, so we have alternative implementation: https://github.com/ashvardanian/SimSIMD/issues/189
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#[allow(dead_code)]
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fn vector_f64_distance_cos_rust(v1: &[f64], v2: &[f64]) -> f64 {
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let (mut dot, mut norm1, mut norm2) = (0.0, 0.0, 0.0);
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for (a, b) in v1.iter().zip(v2.iter()) {
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dot += a * b;
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norm1 += a * a;
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norm2 += b * b;
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}
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if norm1 == 0.0 || norm2 == 0.0 {
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return 0.0;
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}
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1.0 - dot / (norm1 * norm2).sqrt()
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}
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fn vector_f32_sparse_distance_cos(v1: VectorSparse<f32>, v2: VectorSparse<f32>) -> f64 {
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@ -120,20 +133,26 @@ mod tests {
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#[test]
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fn test_vector_distance_cos_f32() {
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assert!(vector_f32_distance_cos(&[], &[]).is_nan());
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assert!(vector_f32_distance_cos(&[1.0, 2.0], &[0.0, 0.0]).is_nan());
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assert_eq!(vector_f32_distance_cos(&[1.0, 2.0], &[1.0, 2.0]), 0.0);
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assert_eq!(vector_f32_distance_cos(&[1.0, 2.0], &[-1.0, -2.0]), 2.0);
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assert_eq!(vector_f32_distance_cos(&[1.0, 2.0], &[-2.0, 1.0]), 1.0);
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assert_eq!(vector_f32_distance_cos_simsimd(&[], &[]), 0.0);
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assert_eq!(
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vector_f32_distance_cos_simsimd(&[1.0, 2.0], &[0.0, 0.0]),
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1.0
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);
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assert!(vector_f32_distance_cos_simsimd(&[1.0, 2.0], &[1.0, 2.0]).abs() < 1e-9);
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assert!((vector_f32_distance_cos_simsimd(&[1.0, 2.0], &[-1.0, -2.0]) - 2.0).abs() < 1e-9);
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assert!((vector_f32_distance_cos_simsimd(&[1.0, 2.0], &[-2.0, 1.0]) - 1.0).abs() < 1e-9);
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}
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#[test]
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fn test_vector_distance_cos_f64() {
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assert!(vector_f64_distance_cos(&[], &[]).is_nan());
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assert!(vector_f64_distance_cos(&[1.0, 2.0], &[0.0, 0.0]).is_nan());
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assert_eq!(vector_f64_distance_cos(&[1.0, 2.0], &[1.0, 2.0]), 0.0);
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assert_eq!(vector_f64_distance_cos(&[1.0, 2.0], &[-1.0, -2.0]), 2.0);
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assert_eq!(vector_f64_distance_cos(&[1.0, 2.0], &[-2.0, 1.0]), 1.0);
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assert_eq!(vector_f64_distance_cos_simsimd(&[], &[]), 0.0);
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assert_eq!(
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vector_f64_distance_cos_simsimd(&[1.0, 2.0], &[0.0, 0.0]),
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1.0
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);
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assert!(vector_f64_distance_cos_simsimd(&[1.0, 2.0], &[1.0, 2.0]).abs() < 1e-9);
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assert!((vector_f64_distance_cos_simsimd(&[1.0, 2.0], &[-1.0, -2.0]) - 2.0).abs() < 1e-9);
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assert!((vector_f64_distance_cos_simsimd(&[1.0, 2.0], &[-2.0, 1.0]) - 1.0).abs() < 1e-9);
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}
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#[test]
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@ -148,7 +167,7 @@ mod tests {
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idx: &[1, 2],
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values: &[1.0, 3.0]
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},
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) - vector_f32_distance_cos(&[1.0, 2.0, 0.0], &[0.0, 1.0, 3.0]))
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) - vector_f32_distance_cos_simsimd(&[1.0, 2.0, 0.0], &[0.0, 1.0, 3.0]))
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.abs()
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< 1e-7
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);
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@ -169,4 +188,30 @@ mod tests {
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(d1.is_nan() && d2.is_nan()) || (d1 - d2).abs() < 1e-6
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}
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#[quickcheck]
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fn prop_vector_distance_cos_rust_vs_simsimd_f32(
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v1: ArbitraryVector<100>,
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v2: ArbitraryVector<100>,
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) -> bool {
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let v1 = vector_convert(v1.into(), VectorType::Float32Dense).unwrap();
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let v2 = vector_convert(v2.into(), VectorType::Float32Dense).unwrap();
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let d1 = vector_f32_distance_cos_rust(v1.as_f32_slice(), v2.as_f32_slice());
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let d2 = vector_f32_distance_cos_simsimd(v1.as_f32_slice(), v2.as_f32_slice());
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println!("d1 vs d2: {} vs {}", d1, d2);
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(d1.is_nan() && d2.is_nan()) || (d1 - d2).abs() < 1e-4
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}
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#[quickcheck]
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fn prop_vector_distance_cos_rust_vs_simsimd_f64(
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v1: ArbitraryVector<100>,
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v2: ArbitraryVector<100>,
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) -> bool {
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let v1 = vector_convert(v1.into(), VectorType::Float64Dense).unwrap();
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let v2 = vector_convert(v2.into(), VectorType::Float64Dense).unwrap();
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let d1 = vector_f64_distance_cos_rust(v1.as_f64_slice(), v2.as_f64_slice());
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let d2 = vector_f64_distance_cos_simsimd(v1.as_f64_slice(), v2.as_f64_slice());
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println!("d1 vs d2: {} vs {}", d1, d2);
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(d1.is_nan() && d2.is_nan()) || (d1 - d2).abs() < 1e-6
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}
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}
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@ -2,6 +2,7 @@ use crate::{
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vector::vector_types::{Vector, VectorSparse, VectorType},
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LimboError, Result,
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};
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use simsimd::SpatialSimilarity;
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pub fn vector_distance_l2(v1: &Vector, v2: &Vector) -> Result<f64> {
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if v1.dims != v2.dims {
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@ -15,12 +16,26 @@ pub fn vector_distance_l2(v1: &Vector, v2: &Vector) -> Result<f64> {
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));
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}
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match v1.vector_type {
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VectorType::Float32Dense => {
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Ok(vector_f32_distance_l2(v1.as_f32_slice(), v2.as_f32_slice()))
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}
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VectorType::Float64Dense => {
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Ok(vector_f64_distance_l2(v1.as_f64_slice(), v2.as_f64_slice()))
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}
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#[cfg(not(target_family = "wasm"))]
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VectorType::Float32Dense => Ok(vector_f32_distance_l2_simsimd(
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v1.as_f32_slice(),
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v2.as_f32_slice(),
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)),
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#[cfg(target_family = "wasm")]
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VectorType::Float32Dense => Ok(vector_f32_distance_l2_rust(
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v1.as_f32_slice(),
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v2.as_f32_slice(),
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)),
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#[cfg(not(target_family = "wasm"))]
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VectorType::Float64Dense => Ok(vector_f64_distance_l2_simsimd(
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v1.as_f64_slice(),
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v2.as_f64_slice(),
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)),
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#[cfg(target_family = "wasm")]
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VectorType::Float64Dense => Ok(vector_f64_distance_l2_rust(
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v1.as_f64_slice(),
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v2.as_f64_slice(),
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)),
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VectorType::Float32Sparse => Ok(vector_f32_sparse_distance_l2(
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v1.as_f32_sparse(),
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v2.as_f32_sparse(),
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|
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@ -28,7 +43,14 @@ pub fn vector_distance_l2(v1: &Vector, v2: &Vector) -> Result<f64> {
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}
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}
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fn vector_f32_distance_l2(v1: &[f32], v2: &[f32]) -> f64 {
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#[allow(dead_code)]
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fn vector_f32_distance_l2_simsimd(v1: &[f32], v2: &[f32]) -> f64 {
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f32::euclidean(v1, v2).unwrap_or(f64::NAN)
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}
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// SimSIMD do not support WASM for now, so we have alternative implementation: https://github.com/ashvardanian/SimSIMD/issues/189
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#[allow(dead_code)]
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fn vector_f32_distance_l2_rust(v1: &[f32], v2: &[f32]) -> f64 {
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let sum = v1
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.iter()
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.zip(v2.iter())
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|
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@ -37,7 +59,14 @@ fn vector_f32_distance_l2(v1: &[f32], v2: &[f32]) -> f64 {
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sum.sqrt()
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}
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fn vector_f64_distance_l2(v1: &[f64], v2: &[f64]) -> f64 {
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#[allow(dead_code)]
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fn vector_f64_distance_l2_simsimd(v1: &[f64], v2: &[f64]) -> f64 {
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f64::euclidean(v1, v2).unwrap_or(f64::NAN)
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}
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|
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// SimSIMD do not support WASM for now, so we have alternative implementation: https://github.com/ashvardanian/SimSIMD/issues/189
|
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#[allow(dead_code)]
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fn vector_f64_distance_l2_rust(v1: &[f64], v2: &[f64]) -> f64 {
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let sum = v1
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.iter()
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.zip(v2.iter())
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|
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@ -102,7 +131,7 @@ mod tests {
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];
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let results = vectors
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.iter()
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.map(|v| vector_f32_distance_l2(&query, v))
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.map(|v| vector_f32_distance_l2_rust(&query, v))
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.collect::<Vec<f64>>();
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assert_eq!(results, expected);
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}
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|
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@ -111,41 +140,41 @@ mod tests {
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fn test_vector_distance_l2_odd_len() {
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let v = (0..5).map(|x| x as f32).collect::<Vec<f32>>();
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let query = (2..7).map(|x| x as f32).collect::<Vec<f32>>();
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assert_eq!(vector_f32_distance_l2(&v, &query), 20.0_f64.sqrt());
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assert_eq!(vector_f32_distance_l2_rust(&v, &query), 20.0_f64.sqrt());
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}
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#[test]
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fn test_vector_distance_l2_f32() {
|
||||
assert_eq!(vector_f32_distance_l2(&[], &[]), 0.0);
|
||||
assert_eq!(vector_f32_distance_l2_rust(&[], &[]), 0.0);
|
||||
assert_eq!(
|
||||
vector_f32_distance_l2(&[1.0, 2.0], &[0.0, 0.0]),
|
||||
vector_f32_distance_l2_rust(&[1.0, 2.0], &[0.0, 0.0]),
|
||||
(1f64 + 2f64 * 2f64).sqrt()
|
||||
);
|
||||
assert_eq!(vector_f32_distance_l2(&[1.0, 2.0], &[1.0, 2.0]), 0.0);
|
||||
assert_eq!(vector_f32_distance_l2_rust(&[1.0, 2.0], &[1.0, 2.0]), 0.0);
|
||||
assert_eq!(
|
||||
vector_f32_distance_l2(&[1.0, 2.0], &[-1.0, -2.0]),
|
||||
vector_f32_distance_l2_rust(&[1.0, 2.0], &[-1.0, -2.0]),
|
||||
(2f64 * 2f64 + 4f64 * 4f64).sqrt()
|
||||
);
|
||||
assert_eq!(
|
||||
vector_f32_distance_l2(&[1.0, 2.0], &[-2.0, 1.0]),
|
||||
vector_f32_distance_l2_rust(&[1.0, 2.0], &[-2.0, 1.0]),
|
||||
(3f64 * 3f64 + 1f64 * 1f64).sqrt()
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_vector_distance_l2_f64() {
|
||||
assert_eq!(vector_f64_distance_l2(&[], &[]), 0.0);
|
||||
assert_eq!(vector_f64_distance_l2_rust(&[], &[]), 0.0);
|
||||
assert_eq!(
|
||||
vector_f64_distance_l2(&[1.0, 2.0], &[0.0, 0.0]),
|
||||
vector_f64_distance_l2_rust(&[1.0, 2.0], &[0.0, 0.0]),
|
||||
(1f64 + 2f64 * 2f64).sqrt()
|
||||
);
|
||||
assert_eq!(vector_f64_distance_l2(&[1.0, 2.0], &[1.0, 2.0]), 0.0);
|
||||
assert_eq!(vector_f64_distance_l2_rust(&[1.0, 2.0], &[1.0, 2.0]), 0.0);
|
||||
assert_eq!(
|
||||
vector_f64_distance_l2(&[1.0, 2.0], &[-1.0, -2.0]),
|
||||
vector_f64_distance_l2_rust(&[1.0, 2.0], &[-1.0, -2.0]),
|
||||
(2f64 * 2f64 + 4f64 * 4f64).sqrt()
|
||||
);
|
||||
assert_eq!(
|
||||
vector_f64_distance_l2(&[1.0, 2.0], &[-2.0, 1.0]),
|
||||
vector_f64_distance_l2_rust(&[1.0, 2.0], &[-2.0, 1.0]),
|
||||
(3f64 * 3f64 + 1f64 * 1f64).sqrt()
|
||||
);
|
||||
}
|
||||
|
|
@ -162,7 +191,7 @@ mod tests {
|
|||
idx: &[1, 2],
|
||||
values: &[1.0, 3.0]
|
||||
},
|
||||
) - vector_f32_distance_l2(&[1.0, 2.0, 0.0], &[0.0, 1.0, 3.0]))
|
||||
) - vector_f32_distance_l2_rust(&[1.0, 2.0, 0.0], &[0.0, 1.0, 3.0]))
|
||||
.abs()
|
||||
< 1e-7
|
||||
);
|
||||
|
|
@ -183,4 +212,28 @@ mod tests {
|
|||
|
||||
(d1.is_nan() && d2.is_nan()) || (d1 - d2).abs() < 1e-6
|
||||
}
|
||||
|
||||
#[quickcheck]
|
||||
fn prop_vector_distance_l2_rust_vs_simsimd_f32(
|
||||
v1: ArbitraryVector<100>,
|
||||
v2: ArbitraryVector<100>,
|
||||
) -> bool {
|
||||
let v1 = vector_convert(v1.into(), VectorType::Float32Dense).unwrap();
|
||||
let v2 = vector_convert(v2.into(), VectorType::Float32Dense).unwrap();
|
||||
let d1 = vector_f32_distance_l2_rust(v1.as_f32_slice(), v2.as_f32_slice());
|
||||
let d2 = vector_f32_distance_l2_simsimd(v1.as_f32_slice(), v2.as_f32_slice());
|
||||
(d1.is_nan() && d2.is_nan()) || (d1 - d2).abs() < 1e-4
|
||||
}
|
||||
|
||||
#[quickcheck]
|
||||
fn prop_vector_distance_l2_rust_vs_simsimd_f64(
|
||||
v1: ArbitraryVector<100>,
|
||||
v2: ArbitraryVector<100>,
|
||||
) -> bool {
|
||||
let v1 = vector_convert(v1.into(), VectorType::Float64Dense).unwrap();
|
||||
let v2 = vector_convert(v2.into(), VectorType::Float64Dense).unwrap();
|
||||
let d1 = vector_f64_distance_l2_rust(v1.as_f64_slice(), v2.as_f64_slice());
|
||||
let d2 = vector_f64_distance_l2_simsimd(v1.as_f64_slice(), v2.as_f64_slice());
|
||||
(d1.is_nan() && d2.is_nan()) || (d1 - d2).abs() < 1e-6
|
||||
}
|
||||
}
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue