hannoy 0.1.3

HNSW Approximate Nearest Neighbors in Rust, based on LMDB and optimized for memory usage
Documentation 
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use std::arch::x86_64::*;
use std::ptr::read_unaligned;

use crate::unaligned_vector::UnalignedVector;

#[target_feature(enable = "avx")]
#[target_feature(enable = "fma")]
unsafe fn hsum256_ps_avx(x: __m256) -> f32 {
    let x128: __m128 = _mm_add_ps(_mm256_extractf128_ps(x, 1), _mm256_castps256_ps128(x));
    let x64: __m128 = _mm_add_ps(x128, _mm_movehl_ps(x128, x128));
    let x32: __m128 = _mm_add_ss(x64, _mm_shuffle_ps(x64, x64, 0x55));
    _mm_cvtss_f32(x32)
}

#[target_feature(enable = "avx")]
#[target_feature(enable = "fma")]
pub(crate) unsafe fn euclid_similarity_avx(
    v1: &UnalignedVector<f32>,
    v2: &UnalignedVector<f32>,
) -> f32 {
    // It is safe to load unaligned floats from a pointer.
    // <https://www.intel.com/content/www/us/en/docs/intrinsics-guide/index.html#text=_mm256_loadu_ps&ig_expand=4134>

    let n = v1.len();
    let m = n - (n % 32);
    let mut ptr1 = v1.as_ptr() as *const f32;
    let mut ptr2 = v2.as_ptr() as *const f32;
    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 sub256_1: __m256 =
            _mm256_sub_ps(_mm256_loadu_ps(ptr1.add(0)), _mm256_loadu_ps(ptr2.add(0)));
        sum256_1 = _mm256_fmadd_ps(sub256_1, sub256_1, sum256_1);

        let sub256_2: __m256 =
            _mm256_sub_ps(_mm256_loadu_ps(ptr1.add(8)), _mm256_loadu_ps(ptr2.add(8)));
        sum256_2 = _mm256_fmadd_ps(sub256_2, sub256_2, sum256_2);

        let sub256_3: __m256 =
            _mm256_sub_ps(_mm256_loadu_ps(ptr1.add(16)), _mm256_loadu_ps(ptr2.add(16)));
        sum256_3 = _mm256_fmadd_ps(sub256_3, sub256_3, sum256_3);

        let sub256_4: __m256 =
            _mm256_sub_ps(_mm256_loadu_ps(ptr1.add(24)), _mm256_loadu_ps(ptr2.add(24)));
        sum256_4 = _mm256_fmadd_ps(sub256_4, sub256_4, 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 {
        let a = read_unaligned(ptr1.add(i));
        let b = read_unaligned(ptr2.add(i));
        result += (a - b).powi(2);
    }
    result
}

#[target_feature(enable = "avx")]
#[target_feature(enable = "fma")]
pub(crate) unsafe fn dot_similarity_avx(
    v1: &UnalignedVector<f32>,
    v2: &UnalignedVector<f32>,
) -> f32 {
    // It is safe to load unaligned floats from a pointer.
    // <https://www.intel.com/content/www/us/en/docs/intrinsics-guide/index.html#text=_mm256_loadu_ps&ig_expand=4134>

    let n = v1.len();
    let m = n - (n % 32);
    let mut ptr1 = v1.as_ptr() as *const f32;
    let mut ptr2 = v2.as_ptr() as *const f32;
    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 {
        let a = read_unaligned(ptr1.add(i));
        let b = read_unaligned(ptr2.add(i));
        result += a * b;
    }
    result
}

#[cfg(test)]
mod tests {
    #[test]
    fn test_spaces_avx() {
        use super::*;
        use crate::spaces::simple::*;

        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 v1 = UnalignedVector::from_slice(&v1[..]);
            let v2 = UnalignedVector::from_slice(&v2[..]);

            let euclid_simd = unsafe { euclid_similarity_avx(&v1, &v2) };
            let euclid = euclidean_distance_non_optimized(&v1, &v2);
            assert_eq!(euclid_simd, euclid);

            let dot_simd = unsafe { dot_similarity_avx(&v1, &v2) };
            let dot = dot_product_non_optimized(&v1, &v2);
            assert_eq!(dot_simd, dot);

            // let cosine_simd = unsafe { cosine_preprocess_avx(v1.clone()) };
            // let cosine = cosine_preprocess(v1);
            // assert_eq!(cosine_simd, cosine);
        } else {
            println!("avx test skipped");
        }
    }
}