Add Cosine distance metric

Makefile

@@ -8,6 +8,7 @@ test-python: instant-distance-py/test/instant_distance.so
 
 bench-python: instant-distance-py/test/instant_distance.so
 	PYTHONPATH=instant-distance-py/test/ python3 -m timeit -n 10 -s 'import random, instant_distance; points = [[random.random() for _ in range(300)] for _ in range(1024)]; config = instant_distance.Config()' 'instant_distance.Hnsw.build(points, config)'
+	PYTHONPATH=instant-distance-py/test/ python3 -m timeit -n 10 -s 'import random, instant_distance; points = [[random.random() for _ in range(300)] for _ in range(1024)]; config = instant_distance.Config(); config.distance_metric = instant_distance.DistanceMetric.Cosine' 'instant_distance.Hnsw.build(points, config)'
 
 clean:
 	cargo clean

distance-metrics/benches/all.rs

@@ -1,10 +1,18 @@
 use bencher::{benchmark_group, benchmark_main, Bencher};
 
-use distance_metrics::{EuclidMetric, Metric};
+use distance_metrics::{
+    Metric, {CosineMetric, EuclidMetric},
+};
 use rand::{rngs::StdRng, Rng, SeedableRng};
 
 benchmark_main!(benches);
-benchmark_group!(benches, legacy, non_simd, metric::<EuclidMetric>,);
+benchmark_group!(
+    benches,
+    legacy,
+    non_simd,
+    metric::<EuclidMetric>,
+    metric::<CosineMetric>
+);
 
 fn legacy(bench: &mut Bencher) {
     let mut rng = StdRng::seed_from_u64(SEED);
@@ -24,8 +32,10 @@ fn non_simd(bench: &mut Bencher) {
 
 fn metric<M: Metric>(bench: &mut Bencher) {
     let mut rng = StdRng::seed_from_u64(SEED);
-    let point_a = [rng.gen(); 300];
-    let point_b = [rng.gen(); 300];
+    let mut point_a = [rng.gen(); 300];
+    let mut point_b = [rng.gen(); 300];
+    M::preprocess(&mut point_a);
+    M::preprocess(&mut point_b);
 
     bench.iter(|| M::distance(&point_a, &point_b))
 }

distance-metrics/src/lib.rs

@@ -11,6 +11,9 @@ pub mod simd_neon;
 pub trait Metric {
     /// Greater the value - more distant the vectors
     fn distance(v1: &[f32], v2: &[f32]) -> f32;
+
+    /// Necessary vector transformations performed before adding it to the collection (like normalization)
+    fn preprocess(vector: &mut [f32]);
 }
 
 #[cfg(target_arch = "x86_64")]
@@ -54,6 +57,72 @@ impl Metric for EuclidMetric {
 
         euclid_distance(v1, v2)
     }
+
+    fn preprocess(_vector: &mut [f32]) {
+        // no-op
+    }
+}
+
+#[derive(Clone, Copy)]
+pub struct CosineMetric {}
+
+impl Metric for CosineMetric {
+    fn distance(v1: &[f32], v2: &[f32]) -> f32 {
+        #[cfg(target_arch = "x86_64")]
+        {
+            if is_x86_feature_detected!("avx")
+                && is_x86_feature_detected!("fma")
+                && v1.len() >= MIN_DIM_SIZE_AVX
+            {
+                return 1.0 - unsafe { simd_avx::dot_similarity_avx(v1, v2) };
+            }
+        }
+
+        #[cfg(any(target_arch = "x86", target_arch = "x86_64"))]
+        {
+            if is_x86_feature_detected!("sse") && v1.len() >= MIN_DIM_SIZE_SIMD {
+                return 1.0 - unsafe { simd_sse::dot_similarity_sse(v1, v2) };
+            }
+        }
+
+        #[cfg(all(target_arch = "aarch64", target_feature = "neon"))]
+        {
+            if std::arch::is_aarch64_feature_detected!("neon") && v1.len() >= MIN_DIM_SIZE_SIMD {
+                return 1.0 - unsafe { simd_neon::dot_similarity_neon(v1, v2) };
+            }
+        }
+
+        1.0 - dot_similarity(v1, v2)
+    }
+
+    fn preprocess(vector: &mut [f32]) {
+        #[cfg(target_arch = "x86_64")]
+        {
+            if is_x86_feature_detected!("avx")
+                && is_x86_feature_detected!("fma")
+                && vector.len() >= MIN_DIM_SIZE_AVX
+            {
+                return unsafe { simd_avx::cosine_preprocess_avx(vector) };
+            }
+        }
+
+        #[cfg(any(target_arch = "x86", target_arch = "x86_64"))]
+        {
+            if is_x86_feature_detected!("sse") && vector.len() >= MIN_DIM_SIZE_SIMD {
+                return unsafe { simd_sse::cosine_preprocess_sse(vector) };
+            }
+        }
+
+        #[cfg(all(target_arch = "aarch64", target_feature = "neon"))]
+        {
+            if std::arch::is_aarch64_feature_detected!("neon") && vector.len() >= MIN_DIM_SIZE_SIMD
+            {
+                return unsafe { simd_neon::cosine_preprocess_neon(vector) };
+            }
+        }
+
+        cosine_preprocess(vector);
+    }
 }
 
 pub fn euclid_distance(v1: &[f32], v2: &[f32]) -> f32 {
@@ -66,6 +135,21 @@ pub fn euclid_distance(v1: &[f32], v2: &[f32]) -> f32 {
     s.abs().sqrt()
 }
 
+pub fn cosine_preprocess(vector: &mut [f32]) {
+    let mut length: f32 = vector.iter().map(|x| x * x).sum();
+    if length < f32::EPSILON {
+        return;
+    }
+    length = length.sqrt();
+    for x in vector.iter_mut() {
+        *x /= length;
+    }
+}
+
+pub fn dot_similarity(v1: &[f32], v2: &[f32]) -> f32 {
+    v1.iter().zip(v2).map(|(a, b)| a * b).sum()
+}
+
 pub fn legacy_distance(lhs: &FloatArray, rhs: &FloatArray) -> f32 {
     #[cfg(target_arch = "x86_64")]
     {

distance-metrics/src/simd_avx.rs

@@ -52,3 +52,135 @@ pub(crate) unsafe fn euclid_distance_avx(v1: &[f32], v2: &[f32]) -> f32 {
     }
     result.abs().sqrt()
 }
+
+#[target_feature(enable = "avx")]
+#[target_feature(enable = "fma")]
+pub(crate) unsafe fn cosine_preprocess_avx(vector: &mut [f32]) {
+    let n = vector.len();
+    let m = n - (n % 32);
+    let mut ptr: *const f32 = vector.as_ptr();
+    let mut sum256_1: __m256 = _mm256_setzero_ps();
+    let mut sum256_2: __m256 = _mm256_setzero_ps();
+    let mut sum256_3: __m256 = _mm256_setzero_ps();
+    let mut sum256_4: __m256 = _mm256_setzero_ps();
+    let mut i: usize = 0;
+    while i < m {
+        let m256_1 = _mm256_loadu_ps(ptr);
+        sum256_1 = _mm256_fmadd_ps(m256_1, m256_1, sum256_1);
+
+        let m256_2 = _mm256_loadu_ps(ptr.add(8));
+        sum256_2 = _mm256_fmadd_ps(m256_2, m256_2, sum256_2);
+
+        let m256_3 = _mm256_loadu_ps(ptr.add(16));
+        sum256_3 = _mm256_fmadd_ps(m256_3, m256_3, sum256_3);
+
+        let m256_4 = _mm256_loadu_ps(ptr.add(24));
+        sum256_4 = _mm256_fmadd_ps(m256_4, m256_4, sum256_4);
+
+        ptr = ptr.add(32);
+        i += 32;
+    }
+
+    let mut length = hsum256_ps_avx(sum256_1)
+        + hsum256_ps_avx(sum256_2)
+        + hsum256_ps_avx(sum256_3)
+        + hsum256_ps_avx(sum256_4);
+    for i in 0..n - m {
+        length += (*ptr.add(i)).powi(2);
+    }
+    if length < f32::EPSILON {
+        return;
+    }
+    length = length.sqrt();
+    for x in vector.iter_mut() {
+        *x /= length;
+    }
+}
+
+#[target_feature(enable = "avx")]
+#[target_feature(enable = "fma")]
+pub(crate) unsafe fn dot_similarity_avx(v1: &[f32], v2: &[f32]) -> f32 {
+    let n = v1.len();
+    let m = n - (n % 32);
+    let mut ptr1: *const f32 = v1.as_ptr();
+    let mut ptr2: *const f32 = v2.as_ptr();
+    let mut sum256_1: __m256 = _mm256_setzero_ps();
+    let mut sum256_2: __m256 = _mm256_setzero_ps();
+    let mut sum256_3: __m256 = _mm256_setzero_ps();
+    let mut sum256_4: __m256 = _mm256_setzero_ps();
+    let mut i: usize = 0;
+    while i < m {
+        sum256_1 = _mm256_fmadd_ps(_mm256_loadu_ps(ptr1), _mm256_loadu_ps(ptr2), sum256_1);
+        sum256_2 = _mm256_fmadd_ps(
+            _mm256_loadu_ps(ptr1.add(8)),
+            _mm256_loadu_ps(ptr2.add(8)),
+            sum256_2,
+        );
+        sum256_3 = _mm256_fmadd_ps(
+            _mm256_loadu_ps(ptr1.add(16)),
+            _mm256_loadu_ps(ptr2.add(16)),
+            sum256_3,
+        );
+        sum256_4 = _mm256_fmadd_ps(
+            _mm256_loadu_ps(ptr1.add(24)),
+            _mm256_loadu_ps(ptr2.add(24)),
+            sum256_4,
+        );
+
+        ptr1 = ptr1.add(32);
+        ptr2 = ptr2.add(32);
+        i += 32;
+    }
+
+    let mut result = hsum256_ps_avx(sum256_1)
+        + hsum256_ps_avx(sum256_2)
+        + hsum256_ps_avx(sum256_3)
+        + hsum256_ps_avx(sum256_4);
+
+    for i in 0..n - m {
+        result += (*ptr1.add(i)) * (*ptr2.add(i));
+    }
+    result
+}
+
+#[cfg(test)]
+mod tests {
+    #[test]
+    fn test_spaces_avx() {
+        use super::*;
+        use crate::*;
+
+        if is_x86_feature_detected!("avx") && is_x86_feature_detected!("fma") {
+            let v1: Vec<f32> = vec![
+                10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,
+                10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,
+                10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,
+                10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,
+                26., 27., 28., 29., 30., 31.,
+            ];
+            let v2: Vec<f32> = vec![
+                40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55.,
+                10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,
+                10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,
+                10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,
+                56., 57., 58., 59., 60., 61.,
+            ];
+
+            let euclid_simd = unsafe { euclid_distance_avx(&v1, &v2) };
+            let euclid = euclid_distance(&v1, &v2);
+            assert_eq!(euclid_simd, euclid);
+
+            let dot_simd = unsafe { dot_similarity_avx(&v1, &v2) };
+            let dot = dot_similarity(&v1, &v2);
+            assert_eq!(dot_simd, dot);
+
+            let mut v1 = v1;
+            let mut v1_copy = v1.clone();
+            unsafe { cosine_preprocess_avx(&mut v1) };
+            cosine_preprocess(&mut v1_copy);
+            assert_eq!(v1, v1_copy);
+        } else {
+            println!("avx test skipped");
+        }
+    }
+}

distance-metrics/src/simd_neon.rs

@@ -36,3 +36,108 @@ pub(crate) unsafe fn euclid_distance_neon(v1: &[f32], v2: &[f32]) -> f32 {
     }
     result.abs().sqrt()
 }
+
+#[cfg(target_feature = "neon")]
+pub(crate) unsafe fn cosine_preprocess_neon(vector: &mut [f32]) {
+    let n = vector.len();
+    let m = n - (n % 16);
+    let mut ptr: *const f32 = vector.as_ptr();
+    let mut sum1 = vdupq_n_f32(0.);
+    let mut sum2 = vdupq_n_f32(0.);
+    let mut sum3 = vdupq_n_f32(0.);
+    let mut sum4 = vdupq_n_f32(0.);
+
+    let mut i: usize = 0;
+    while i < m {
+        let d1 = vld1q_f32(ptr);
+        sum1 = vfmaq_f32(sum1, d1, d1);
+
+        let d2 = vld1q_f32(ptr.add(4));
+        sum2 = vfmaq_f32(sum2, d2, d2);
+
+        let d3 = vld1q_f32(ptr.add(8));
+        sum3 = vfmaq_f32(sum3, d3, d3);
+
+        let d4 = vld1q_f32(ptr.add(12));
+        sum4 = vfmaq_f32(sum4, d4, d4);
+
+        ptr = ptr.add(16);
+        i += 16;
+    }
+    let mut length = vaddvq_f32(sum1) + vaddvq_f32(sum2) + vaddvq_f32(sum3) + vaddvq_f32(sum4);
+    for v in vector.iter().take(n).skip(m) {
+        length += v.powi(2);
+    }
+    if length < f32::EPSILON {
+        return;
+    }
+    let length = length.sqrt();
+    for x in vector.iter_mut() {
+        *x /= length;
+    }
+}
+
+#[cfg(target_feature = "neon")]
+pub(crate) unsafe fn dot_similarity_neon(v1: &[f32], v2: &[f32]) -> f32 {
+    let n = v1.len();
+    let m = n - (n % 16);
+    let mut ptr1: *const f32 = v1.as_ptr();
+    let mut ptr2: *const f32 = v2.as_ptr();
+    let mut sum1 = vdupq_n_f32(0.);
+    let mut sum2 = vdupq_n_f32(0.);
+    let mut sum3 = vdupq_n_f32(0.);
+    let mut sum4 = vdupq_n_f32(0.);
+
+    let mut i: usize = 0;
+    while i < m {
+        sum1 = vfmaq_f32(sum1, vld1q_f32(ptr1), vld1q_f32(ptr2));
+        sum2 = vfmaq_f32(sum2, vld1q_f32(ptr1.add(4)), vld1q_f32(ptr2.add(4)));
+        sum3 = vfmaq_f32(sum3, vld1q_f32(ptr1.add(8)), vld1q_f32(ptr2.add(8)));
+        sum4 = vfmaq_f32(sum4, vld1q_f32(ptr1.add(12)), vld1q_f32(ptr2.add(12)));
+        ptr1 = ptr1.add(16);
+        ptr2 = ptr2.add(16);
+        i += 16;
+    }
+    let mut result = vaddvq_f32(sum1) + vaddvq_f32(sum2) + vaddvq_f32(sum3) + vaddvq_f32(sum4);
+    for i in 0..n - m {
+        result += (*ptr1.add(i)) * (*ptr2.add(i));
+    }
+    result
+}
+
+#[cfg(test)]
+mod tests {
+    #[cfg(target_feature = "neon")]
+    #[test]
+    fn test_spaces_neon() {
+        use super::*;
+        use crate::*;
+
+        if std::arch::is_aarch64_feature_detected!("neon") {
+            let v1: Vec<f32> = vec![
+                10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,
+                26., 27., 28., 29., 30., 31.,
+            ];
+            let v2: Vec<f32> = vec![
+                40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55.,
+                56., 57., 58., 59., 60., 61.,
+            ];
+
+            let euclid_simd = unsafe { euclid_distance_neon(&v1, &v2) };
+            let euclid = euclid_distance(&v1, &v2);
+            assert_eq!(euclid_simd, euclid);
+
+            let dot_simd = unsafe { dot_similarity_neon(&v1, &v2) };
+            let dot = dot_similarity(&v1, &v2);
+            assert_eq!(dot_simd, dot);
+
+            let mut v1 = v1;
+            let mut v1_copy = v1.clone();
+            unsafe { cosine_preprocess_neon(&mut v1) };
+            cosine_preprocess(&mut v1_copy);
+            assert_eq!(v1, v1_copy);
+        } else {
+            println!("neon test skipped");
+        }
+    }
+}

distance-metrics/src/simd_sse.rs

@@ -48,3 +48,132 @@ pub(crate) unsafe fn euclid_distance_sse(v1: &[f32], v2: &[f32]) -> f32 {
     }
     result.abs().sqrt()
 }
+
+#[target_feature(enable = "sse")]
+pub(crate) unsafe fn cosine_preprocess_sse(vector: &mut [f32]) {
+    let n = vector.len();
+    let m = n - (n % 16);
+    let mut ptr: *const f32 = vector.as_ptr();
+    let mut sum128_1: __m128 = _mm_setzero_ps();
+    let mut sum128_2: __m128 = _mm_setzero_ps();
+    let mut sum128_3: __m128 = _mm_setzero_ps();
+    let mut sum128_4: __m128 = _mm_setzero_ps();
+
+    let mut i: usize = 0;
+    while i < m {
+        let m128_1 = _mm_loadu_ps(ptr);
+        sum128_1 = _mm_add_ps(_mm_mul_ps(m128_1, m128_1), sum128_1);
+
+        let m128_2 = _mm_loadu_ps(ptr.add(4));
+        sum128_2 = _mm_add_ps(_mm_mul_ps(m128_2, m128_2), sum128_2);
+
+        let m128_3 = _mm_loadu_ps(ptr.add(8));
+        sum128_3 = _mm_add_ps(_mm_mul_ps(m128_3, m128_3), sum128_3);
+
+        let m128_4 = _mm_loadu_ps(ptr.add(12));
+        sum128_4 = _mm_add_ps(_mm_mul_ps(m128_4, m128_4), sum128_4);
+
+        ptr = ptr.add(16);
+        i += 16;
+    }
+
+    let mut length = hsum128_ps_sse(sum128_1)
+        + hsum128_ps_sse(sum128_2)
+        + hsum128_ps_sse(sum128_3)
+        + hsum128_ps_sse(sum128_4);
+    for i in 0..n - m {
+        length += (*ptr.add(i)).powi(2);
+    }
+    if length < f32::EPSILON {
+        return;
+    }
+    length = length.sqrt();
+    for x in vector.iter_mut() {
+        *x /= length;
+    }
+}
+
+#[target_feature(enable = "sse")]
+pub(crate) unsafe fn dot_similarity_sse(v1: &[f32], v2: &[f32]) -> f32 {
+    let n = v1.len();
+    let m = n - (n % 16);
+    let mut ptr1: *const f32 = v1.as_ptr();
+    let mut ptr2: *const f32 = v2.as_ptr();
+    let mut sum128_1: __m128 = _mm_setzero_ps();
+    let mut sum128_2: __m128 = _mm_setzero_ps();
+    let mut sum128_3: __m128 = _mm_setzero_ps();
+    let mut sum128_4: __m128 = _mm_setzero_ps();
+
+    let mut i: usize = 0;
+    while i < m {
+        sum128_1 = _mm_add_ps(_mm_mul_ps(_mm_loadu_ps(ptr1), _mm_loadu_ps(ptr2)), sum128_1);
+
+        sum128_2 = _mm_add_ps(
+            _mm_mul_ps(_mm_loadu_ps(ptr1.add(4)), _mm_loadu_ps(ptr2.add(4))),
+            sum128_2,
+        );
+
+        sum128_3 = _mm_add_ps(
+            _mm_mul_ps(_mm_loadu_ps(ptr1.add(8)), _mm_loadu_ps(ptr2.add(8))),
+            sum128_3,
+        );
+
+        sum128_4 = _mm_add_ps(
+            _mm_mul_ps(_mm_loadu_ps(ptr1.add(12)), _mm_loadu_ps(ptr2.add(12))),
+            sum128_4,
+        );
+
+        ptr1 = ptr1.add(16);
+        ptr2 = ptr2.add(16);
+        i += 16;
+    }
+
+    let mut result = hsum128_ps_sse(sum128_1)
+        + hsum128_ps_sse(sum128_2)
+        + hsum128_ps_sse(sum128_3)
+        + hsum128_ps_sse(sum128_4);
+    for i in 0..n - m {
+        result += (*ptr1.add(i)) * (*ptr2.add(i));
+    }
+    result
+}
+
+#[cfg(test)]
+mod tests {
+    #[test]
+    fn test_spaces_sse() {
+        use super::*;
+        use crate::*;
+
+        if is_x86_feature_detected!("sse") {
+            let v1: Vec<f32> = vec![
+                10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,
+                10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,
+                10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,
+                26., 27., 28., 29., 30., 31.,
+            ];
+            let v2: Vec<f32> = vec![
+                40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55.,
+                10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,
+                10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,
+                56., 57., 58., 59., 60., 61.,
+            ];
+
+            let euclid_simd = unsafe { euclid_distance_sse(&v1, &v2) };
+            let euclid = euclid_distance(&v1, &v2);
+            assert_eq!(euclid_simd, euclid);
+
+            let dot_simd = unsafe { dot_similarity_sse(&v1, &v2) };
+            let dot = dot_similarity(&v1, &v2);
+            assert_eq!(dot_simd, dot);
+
+            let mut v1 = v1;
+            let mut v1_copy = v1.clone();
+            unsafe { cosine_preprocess_sse(&mut v1) };
+            cosine_preprocess(&mut v1_copy);
+            assert_eq!(v1, v1_copy);
+        } else {
+            println!("sse test skipped");
+        }
+    }
+}

instant-distance-py/src/lib.rs

@@ -7,8 +7,8 @@ use std::io::{BufReader, BufWriter};
 use std::iter::FromIterator;
 use std::marker::PhantomData;
 
-use distance_metrics::EuclidMetric;
 use distance_metrics::Metric;
+use distance_metrics::{CosineMetric, EuclidMetric};
 use instant_distance::Point;
 use pyo3::conversion::IntoPy;
 use pyo3::exceptions::PyValueError;
@@ -65,7 +65,7 @@ struct HnswMap {
 #[derive(Deserialize, Serialize)]
 enum HnswMapWithMetric {
     Euclid(instant_distance::HnswMap<FloatArray<EuclidMetric>, MapValue>),
-    Cosine(instant_distance::HnswMap<FloatArray<EuclidMetric>, MapValue>),
+    Cosine(instant_distance::HnswMap<FloatArray<CosineMetric>, MapValue>),
 }
 
 #[pymethods]
@@ -136,7 +136,7 @@ struct Hnsw {
 #[derive(Deserialize, Serialize)]
 enum HnswWithMetric {
     Euclid(instant_distance::Hnsw<FloatArray<EuclidMetric>>),
-    Cosine(instant_distance::Hnsw<FloatArray<EuclidMetric>>),
+    Cosine(instant_distance::Hnsw<FloatArray<CosineMetric>>),
 }
 
 #[pymethods]
@@ -426,7 +426,8 @@ impl<M: Metric> FloatArray<M> {
 }
 
 impl<M: Metric> From<Vec<f32>> for FloatArray<M> {
-    fn from(array: Vec<f32>) -> Self {
+    fn from(mut array: Vec<f32>) -> Self {
+        M::preprocess(&mut array);
         Self {
             array,
             phantom: PhantomData,