DarkRoom/core/dr-face/examples/scan_bench.rs

//! How fast this machine can scan a library for face pairs.
//!
//!   cargo run --release -p dr-face --example scan_bench [-- FACES…]
//!
//! Synthetic embeddings, because the scan's cost is `n²/2` dot products and
//! does not care what the vectors mean — which is what makes this runnable on
//! a phone with no library on it, over `adb shell`, beside
//! `tools/face-tests-on-device.sh`.
//!
//! # What it is for
//!
//! docs/faces.md §9 has the desktop numbers and the question they leave open:
//! a GPU GEMM is worth roughly 1.5× of a regroup on a twenty-core desktop,
//! because the scan is under a third of the pass there. On a tablet the CPU is
//! several times slower and the GPU is not, so the same optimisation is worth
//! something different — and nobody had measured which.
//!
//! No weights, no catalog, no display: it needs nothing but the binary.

use dr_face::neighbours::{above_threshold, Faces};
use dr_face::{Calibration, EMBEDDING_DIM, RIVAL_FLOOR};

/// Sizes to time, unless the command line names others.
const DEFAULT_SIZES: [usize; 4] = [2_000, 4_000, 8_000, 18_000];

fn main() {
    let sizes: Vec<usize> = {
        let given: Vec<usize> = std::env::args().skip(1).filter_map(|a| a.parse().ok()).collect();
        if given.is_empty() {
            DEFAULT_SIZES.to_vec()
        } else {
            given
        }
    };

    // The reference curve, so the cosine floor is the one a real library with
    // no fit of its own would scan at.
    let cal = Calibration::default();

    println!("{:>8}  {:>9}  {:>10}  {:>9}", "faces", "scan", "pairs", "GFLOP/s");
    for n in sizes {
        let (embeddings, crop_px, images) = population(n);
        let faces = Faces {
            embeddings: &embeddings,
            dim: EMBEDDING_DIM,
            crop_px: &crop_px,
            images: &images,
        };

        let start = std::time::Instant::now();
        let pairs = above_threshold(&faces, &cal, RIVAL_FLOOR);
        let secs = start.elapsed().as_secs_f64();

        let flop = n as f64 * n as f64 / 2.0 * EMBEDDING_DIM as f64 * 2.0;
        println!(
            "{n:>8}  {secs:>8.2}s  {:>10}  {:>9.1}",
            pairs.len(),
            flop / secs / 1e9
        );
    }
}

/// `n` L2-normalised embeddings in a handful of loose clusters.
///
/// Clustered rather than uniform so the scan finds a plausible number of pairs
/// to keep — a population where nothing survives the threshold would time the
/// rejection path alone, which is not the path that matters. Hashed from an
/// index rather than drawn from an RNG, so a number is reproducible from the
/// command that produced it.
fn population(n: usize) -> (Vec<f32>, Vec<f32>, Vec<u64>) {
    let identities = (n / 12).max(1);
    let mut embeddings = Vec::with_capacity(n * EMBEDDING_DIM);
    for i in 0..n {
        let mut v = unit(i % identities);
        let noise = unit(i + 1_000_000);
        for (x, e) in v.iter_mut().zip(&noise) {
            *x = 0.75 * *x + 0.25 * e;
        }
        embeddings.extend(normalise(v));
    }
    // Every face in its own photograph: the co-occurrence rule skips pairs
    // rather than scoring them, and skipped pairs are not what is being timed.
    ((embeddings), vec![150.0; n], (0..n as u64).collect())
}

fn unit(seed: usize) -> Vec<f32> {
    let mut s = (seed as u64).wrapping_mul(0x9E37_79B9_7F4A_7C15) | 1;
    let mut v = Vec::with_capacity(EMBEDDING_DIM);
    for _ in 0..EMBEDDING_DIM {
        s ^= s << 13;
        s ^= s >> 7;
        s ^= s << 17;
        v.push(((s >> 11) as f64 / (1u64 << 53) as f64) as f32 - 0.5);
    }
    normalise(v)
}

fn normalise(mut v: Vec<f32>) -> Vec<f32> {
    let len = v.iter().map(|x| x * x).sum::<f32>().sqrt();
    for x in &mut v {
        *x /= len;
    }
    v
}