dtourolleandClaude Opus 5
0407fb8d2d
They were written after the last fmt run and CI gates on --check. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
111 lines
3.8 KiB
Rust
111 lines
3.8 KiB
Rust
//! How fast this machine can scan a library for face pairs.
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//!
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//! cargo run --release -p dr-face --example scan_bench [-- FACES…]
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//!
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//! Synthetic embeddings, because the scan's cost is `n²/2` dot products and
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//! does not care what the vectors mean — which is what makes this runnable on
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//! a phone with no library on it, over `adb shell`, beside
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//! `tools/face-tests-on-device.sh`.
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//!
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//! # What it is for
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//!
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//! docs/faces.md §9 has the desktop numbers and the question they leave open:
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//! a GPU GEMM is worth roughly 1.5× of a regroup on a twenty-core desktop,
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//! because the scan is under a third of the pass there. On a tablet the CPU is
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//! several times slower and the GPU is not, so the same optimisation is worth
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//! something different — and nobody had measured which.
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//!
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//! No weights, no catalog, no display: it needs nothing but the binary.
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use dr_face::neighbours::{above_threshold, Faces};
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use dr_face::{Calibration, EMBEDDING_DIM, RIVAL_FLOOR};
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/// Sizes to time, unless the command line names others.
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const DEFAULT_SIZES: [usize; 4] = [2_000, 4_000, 8_000, 18_000];
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fn main() {
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let sizes: Vec<usize> = {
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let given: Vec<usize> = std::env::args()
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.skip(1)
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.filter_map(|a| a.parse().ok())
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.collect();
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if given.is_empty() {
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DEFAULT_SIZES.to_vec()
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} else {
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given
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}
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};
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// The reference curve, so the cosine floor is the one a real library with
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// no fit of its own would scan at.
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let cal = Calibration::default();
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println!(
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"{:>8} {:>9} {:>10} {:>9}",
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"faces", "scan", "pairs", "GFLOP/s"
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);
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for n in sizes {
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let (embeddings, crop_px, images) = population(n);
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let faces = Faces {
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embeddings: &embeddings,
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dim: EMBEDDING_DIM,
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crop_px: &crop_px,
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images: &images,
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};
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let start = std::time::Instant::now();
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let pairs = above_threshold(&faces, &cal, RIVAL_FLOOR);
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let secs = start.elapsed().as_secs_f64();
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let flop = n as f64 * n as f64 / 2.0 * EMBEDDING_DIM as f64 * 2.0;
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println!(
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"{n:>8} {secs:>8.2}s {:>10} {:>9.1}",
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pairs.len(),
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flop / secs / 1e9
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);
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}
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}
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/// `n` L2-normalised embeddings in a handful of loose clusters.
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///
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/// Clustered rather than uniform so the scan finds a plausible number of pairs
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/// to keep — a population where nothing survives the threshold would time the
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/// rejection path alone, which is not the path that matters. Hashed from an
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/// index rather than drawn from an RNG, so a number is reproducible from the
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/// command that produced it.
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fn population(n: usize) -> (Vec<f32>, Vec<f32>, Vec<u64>) {
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let identities = (n / 12).max(1);
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let mut embeddings = Vec::with_capacity(n * EMBEDDING_DIM);
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for i in 0..n {
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let mut v = unit(i % identities);
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let noise = unit(i + 1_000_000);
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for (x, e) in v.iter_mut().zip(&noise) {
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*x = 0.75 * *x + 0.25 * e;
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}
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embeddings.extend(normalise(v));
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}
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// Every face in its own photograph: the co-occurrence rule skips pairs
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// rather than scoring them, and skipped pairs are not what is being timed.
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((embeddings), vec![150.0; n], (0..n as u64).collect())
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}
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fn unit(seed: usize) -> Vec<f32> {
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let mut s = (seed as u64).wrapping_mul(0x9E37_79B9_7F4A_7C15) | 1;
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let mut v = Vec::with_capacity(EMBEDDING_DIM);
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for _ in 0..EMBEDDING_DIM {
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s ^= s << 13;
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s ^= s >> 7;
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s ^= s << 17;
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v.push(((s >> 11) as f64 / (1u64 << 53) as f64) as f32 - 0.5);
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}
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normalise(v)
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}
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fn normalise(mut v: Vec<f32>) -> Vec<f32> {
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let len = v.iter().map(|x| x * x).sum::<f32>().sqrt();
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for x in &mut v {
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*x /= len;
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}
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v
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}
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