From e8c5fe4fd3b0bd53354cbcce0dc122889211bb07 Mon Sep 17 00:00:00 2001 From: Swinecoder101 Date: Wed, 4 Nov 2026 21:46:15 +0000 Subject: [PATCH 01/12] docs: animated SVGs for hybrid and adaptive quantization in the README Three CSS-keyframe SVGs that GitHub plays inside : two-stage re-scoring (schematic), adaptive re-scoring depth by a per-query margin (schematic), and adaptive bit allocation at 48 bytes on SciFact's real PCA spectrum (uniform binary 0.611, mixed48-pca 0.694, mixed48-pca-w 0.703 recall@10). Light and dark steps follow prefers-color-scheme; under prefers-reduced-motion they rest on the final labelled frame. make_animations.py regenerates all three from the stored allocation. --- README.md | 54 +++ docs/assets/animations/adaptive-bits.svg | 215 ++++++++++ docs/assets/animations/adaptive-depth.svg | 264 ++++++++++++ docs/assets/animations/hybrid-rescoring.svg | 197 +++++++++ docs/assets/animations/make_animations.py | 384 ++++++++++++++++++ .../animations/scifact-bge-small-alloc48.json | 1 + 6 files changed, 1115 insertions(+) create mode 100644 docs/assets/animations/adaptive-bits.svg create mode 100644 docs/assets/animations/adaptive-depth.svg create mode 100644 docs/assets/animations/hybrid-rescoring.svg create mode 100644 docs/assets/animations/make_animations.py create mode 100644 docs/assets/animations/scifact-bge-small-alloc48.json diff --git a/README.md b/README.md index b45f930..a8cb593 100644 --- a/README.md +++ b/README.md @@ -339,6 +339,9 @@ parameters alongside the collection, and reuses them for every later ingest, so call a representative sample of the corpus rather than a single file. `mrl` keeps the first `k` dimensions (what Matryoshka-trained models are built for) and composes with any other codec: `mrl256+int8`, `mrl128+pq16`. +`mixed` is trained too: it spends a fixed `B` bytes per vector with bits allocated per +dimension by variance. `mixed-pca` rotates into the corpus's principal components first, and +`-w` weights each component by how much energy queries put into it (see below). With a question set that carries relevance judgments, `bench recall --questions` also reports nDCG@k and document recall@k against the labels, for every codec and for the f32 ceiling: @@ -376,6 +379,57 @@ not 39, because the neighbours it reorders are near-ties that are rarely the jud The f32 scan is slow only because the baseline decodes each vector; the other codecs score the bytes directly. The experiments are written up under [`experiments/`](experiments/). +#### Hybrid and adaptive quantization + +A small code scanned over every vector loses neighbours, and how many it loses is predictable: +it is set by the code's score noise divided by how tightly the corpus packs a query's +neighbours (the [neighbour-spacing experiment](experiments/2026-11-neighbour-spacing/)). There +are two ways to win the loss back. + +**Hybrid.** Keep the small code in memory for the scan, shortlist its best N candidates, and +re-score only those with int8 or f32 vectors fetched from disk: + +

+ Animated schematic of two-stage retrieval: every vector is scored from a 48-byte code; the code's top 10 keeps 6 of the 10 true neighbours; the code's top 30 are re-scored with exact f32 vectors, lifting 9 of the 10 true neighbours into the top 10, while one ranked 31st by the code falls outside the shortlist and is missed. +

+ +```sh +hvec bench rescore --collection scifact --questions questions/scifact.jsonl \ + --codecs binary,pq48 --rescore int8,f32 --depths 20,50,100 +``` + +Across ten collections (five corpora, two embedding models), re-scoring 50 f32 candidates +behind a 48-byte scan recovers 0.95 (`binary`) to 0.99 (`pq48`) of the exact top 10 and +essentially all of the nDCG loss. That costs 3–4% of the bytes a full f32 scan reads. The depth +needed follows the same noise-to-spacing ratio (Spearman ρ ≈ 0.93), so one f32 pass over a +corpus tells you how deep to go: +[two-stage re-scoring](experiments/2026-11-two-stage-rescoring/). + +**Adaptive depth.** A fixed N is too deep for some queries and too shallow for others. The +`--margins` policy re-scores every candidate whose code score is within λσ of the code's own +10th score, where σ is the code's noise, calibrated once per collection on self-queries: + +

+ Animated schematic of adaptive re-scoring depth: two queries share the same noise-based margin below their 10th code score; a crowded query has 59 candidates inside the margin and a well-separated query 18, while a fixed shortlist of 30 would under-fetch for the first and over-fetch for the second. +

+ +**Adaptive bits.** Spend the scan code's bytes unevenly, where they buy the most. This is the +idea behind mixed-precision quantization of mixture-of-experts models, where blocks and experts +that matter more get more bits ([QuantMoE-Bench](https://github.com/UNITES-Lab/moe-quantization)). +For embeddings, "matters more" means a principal component with more variance, or with more of +the query's energy: + +

+ Animated bar chart of how a 48-byte budget is spread over 384 principal components of SciFact's bge-small embeddings: uniform binary gives every component 1 bit (recall@10 0.611); variance-based allocation concentrates bits on the leading components and gives 97 none (0.694); query-weighted allocation gives 158 none and returns a few bits to the last components, which carry 28% of the corpus mean (0.703). +

+ +On SciFact at 48 bytes with no re-scoring, `mixed48-pca-w` keeps 0.703 of the top 10, against +0.611 for `binary` and 0.711 for `pq48`. The full comparison, every scan codec against every +re-score policy, is the [hybrid × adaptive matrix](experiments/2026-11-hybrid-adaptive-matrix/). +The animations are generated by +[`docs/assets/animations/make_animations.py`](docs/assets/animations/make_animations.py); the +bit-allocation chart uses SciFact's real PCA spectrum and allocations. + ### 7. Run the matrix and report Ingest the same corpus into one collection per codec, write a question set, and run every diff --git a/docs/assets/animations/adaptive-bits.svg b/docs/assets/animations/adaptive-bits.svg new file mode 100644 index 0000000..41b415b --- /dev/null +++ b/docs/assets/animations/adaptive-bits.svg @@ -0,0 +1,215 @@ + +Adaptive bits: mixed-precision allocation at 48 bytes +Animated bar chart of how a 48-byte budget is spread over 384 principal components of SciFact's bge-small embeddings. Uniform binary gives every component 1 bit (recall@10 0.611). Variance-based allocation concentrates bits on the leading components and gives 97 components none (0.694); query-weighted allocation goes further, 158 components with none (0.703). + + +Adaptive bits: the same 48 bytes, spent where they matter +0.001 +0.01 +0.1 +1 + +variance per principal component, relative to the first (log scale, clipped at 0.001) +0 +8 +16 +24 +32 +40 +bits per group of 8 components (48 bytes = 384 bits in all) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +component 1component 384 +Uniform: 1 bit on every dimension (binary, 48 B) +mixed48-pca: rotate to principal components, spend bits where the variance is +mixed48-pca-w: weight variance by how much energy queries put there +recall@10 without re-scoring: 0.611 · components with 0 bits: 0 of 384 +recall@10 without re-scoring: 0.694 · components with 0 bits: 97 of 384 +recall@10 without re-scoring: 0.703 · components with 0 bits: 158 of 384 +bits return to the last 44 components: little variance,but 28% of the corpus mean, which every query points along +Real data: SciFact, bge-small-en-v1.5, 8,036 chunks, 300 claims. pq48 at the same 48 bytes reaches 0.711.Mixed precision by importance is the idea behind structure-aware MoE quantization (QuantMoE-Bench). + + diff --git a/docs/assets/animations/adaptive-depth.svg b/docs/assets/animations/adaptive-depth.svg new file mode 100644 index 0000000..2d3f974 --- /dev/null +++ b/docs/assets/animations/adaptive-depth.svg @@ -0,0 +1,264 @@ + +Adaptive depth: a per-query margin instead of a fixed shortlist +Animated schematic of adaptive re-scoring depth. Two queries share one noise-based margin below their 10th code score. The crowded query has 59 candidates inside the margin, the well-separated one 18; a fixed shortlist of 30 would under-fetch for the first and over-fetch for the second. + + +Adaptive depth: re-score every candidate within λσ of the 10th score +The code's score noise σ is calibrated once per collection, on self-queries. +The margin λσ is the same for every query; how many candidates fall inside it is not. +A fixed shortlist (dashed) over-fetches for one query and under-fetches for the other. +Crowded query: many candidates score close to the 10th + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +10th + +fixed N = 30 +re-scores 59 candidates +Well-separated query: the 10th stands clear of the rest + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +10th + +fixed N = 30 +re-scores 18 candidates +the code's top 10re-scored by marginnot re-scored +Schematic: synthetic code scores, one σ for both queries. Measured results: experiments/2026-11-hybrid-adaptive-matrix. + + diff --git a/docs/assets/animations/hybrid-rescoring.svg b/docs/assets/animations/hybrid-rescoring.svg new file mode 100644 index 0000000..ea51ee3 --- /dev/null +++ b/docs/assets/animations/hybrid-rescoring.svg @@ -0,0 +1,197 @@ + +Hybrid quantization: two-stage re-scoring +Animated schematic of two-stage retrieval. 44 vectors are scored from small codes; the code's top 10 holds 6 of the 10 true neighbours. The code's top 30 are re-scored with exact f32 vectors, which lifts 9 true neighbours into the top 10; one, ranked 31 by the code, falls outside the shortlist and is missed. + + +Hybrid quantization: scan with a small code, re-score a shortlist exactly + +shortlist: the code's top 30 (f32 fetched for these only) +top 10 → + +higher score → +each dot is one stored vector, placed by its score against the query + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +missed: the code ranked it #31, outside the shortlist +1 · Scan. Every vector is scored from its 48-byte code, in memory. +2 · Cut at the code's top 10: it keeps 6 of the 10 true neighbours. +3 · Instead, shortlist the code's top 30 and fetch their f32 vectors. +4 · Re-score the shortlist exactly. True neighbours rise into the top 10. +Result: 9 of 10 true neighbours. A deeper shortlist catches the last one. +true top-10 neighbour (exact f32)other vectortrue neighbour the hybrid missed +Schematic: 44 synthetic vectors. Measured over ten collections, re-scoring 50 f32 candidates recovers0.95 (binary) to 0.99 (pq48) of the top 10, for 3–4% of the bytes an f32 scan reads. + + diff --git a/docs/assets/animations/make_animations.py b/docs/assets/animations/make_animations.py new file mode 100644 index 0000000..1b5ed42 --- /dev/null +++ b/docs/assets/animations/make_animations.py @@ -0,0 +1,384 @@ +"""Generate the README's animated SVGs for hybrid and adaptive quantization. + + python3 -I docs/assets/animations/make_animations.py + +Writes hybrid-rescoring.svg, adaptive-depth.svg and adaptive-bits.svg next to this file. The +first two are schematic (synthetic scores, fixed seed). adaptive-bits.svg uses real data: +SciFact's bge-small-en-v1.5 vectors, their PCA spectrum, and the 48-byte bit allocations that +`mixed48-pca` and `mixed48-pca-w` make, stored in scifact-bge-small-alloc48.json. + +The animations are CSS keyframes, so GitHub renders them through . Under +prefers-reduced-motion they stop on their final, fully labelled frame. Colours are the dataviz +reference palette (blue and orange slots, validated all-pairs in both modes), with light and dark +steps selected by prefers-color-scheme. +""" + +import json +import random +from pathlib import Path + +HERE = Path(__file__).resolve().parent +FONT = 'ui-sans-serif, system-ui, -apple-system, "Segoe UI", Helvetica, Arial, sans-serif' + +BASE_CSS = f""" + svg {{ --surface:#fcfcfb; --ink:#0b0b0b; --ink2:#52514e; --muted:#898781; --grid:#e1e0d9; + --axis:#c3c2b7; --s1:#2a78d6; --s2:#eb6834; --other:#898781; + --band:rgba(42,120,214,0.10); --band2:rgba(42,120,214,0.20); }} + @media (prefers-color-scheme: dark) {{ + svg {{ --surface:#1a1a19; --ink:#f0efec; --ink2:#c3c2b7; --grid:#2c2c2a; --axis:#383835; + --s1:#3987e5; --s2:#d95926; --band:rgba(57,135,229,0.16); --band2:rgba(57,135,229,0.28); }} + }} + text {{ font-family: {FONT}; fill: var(--ink); }} + .bg {{ fill: var(--surface); stroke: var(--grid); }} + .h {{ font-size: 15px; font-weight: 600; }} + .cap {{ font-size: 13px; fill: var(--ink2); }} + .small {{ font-size: 11px; fill: var(--muted); }} + .lbl {{ font-size: 12px; fill: var(--ink2); }} + .num {{ font-size: 12px; fill: var(--ink); font-variant-numeric: tabular-nums; }} + .axis {{ stroke: var(--axis); stroke-width: 1; }} + .grid {{ stroke: var(--grid); stroke-width: 1; }} + .true {{ fill: var(--s1); stroke: var(--surface); stroke-width: 2; }} + .other {{ fill: var(--other); fill-opacity: .55; stroke: var(--surface); stroke-width: 2; }} + .lost {{ fill: var(--s2); stroke: var(--surface); stroke-width: 2; }} + .cut {{ stroke: var(--ink); stroke-width: 1.5; stroke-dasharray: 4 3; }} + .fixed {{ stroke: var(--s2); stroke-width: 2; stroke-dasharray: 5 3; }} + .band {{ fill: var(--band); }} + .band2 {{ fill: var(--band2); }} +""" +REDUCED = "@media (prefers-reduced-motion: reduce) { * { animation: none !important; } }" + + +def svg(w, h, title, desc, css, body): + return f""" +{title} +{desc} + + +{body} + +""" + + +def phase(name, dur, stops): + """A keyframe rule from [(percent, css)] stops.""" + frames = " ".join(f"{p}% {{ {css} }}" for p, css in stops) + return f"@keyframes {name} {{ {frames} }}\n" + + +def captions(prefix, dur, items, y, x=24, cls="cap"): + """Cross-fading captions; the last is the reduced-motion resting frame.""" + css, body = "", "" + n = len(items) + for i, (start, end, text) in enumerate(items): + last = i == n - 1 + name = f"{prefix}{i}" + a, b = start, end + stops = [(0, "opacity:0"), (max(a - 0.01, 0), "opacity:0"), (min(a + 2, b), "opacity:1"), + (max(b - 2, a), "opacity:1"), (b, "opacity:0"), (100, "opacity:0")] + if a == 0: + stops = [(0, "opacity:1")] + stops[3:] + css += phase(name, dur, stops) + css += f".{name} {{ opacity:{1 if last else 0}; animation: {name} {dur}s infinite; }}\n" + body += f'{text}\n' + return css, body + + +# --------------------------------------------------------------------------- +# 1. Hybrid re-scoring +# --------------------------------------------------------------------------- + +def hybrid(): + rng = random.Random(11) + n, k, depth = 44, 10, 30 + # Find a draw that shows the story: the code's top 10 keeps 6 true neighbours, the top 30 + # keeps 9, one is ranked beyond the shortlist. + while True: + exact = sorted((rng.gauss(0, 1) for _ in range(n)), reverse=True) + approx = [e + rng.gauss(0, 0.55) for e in exact] + order = sorted(range(n), key=lambda i: -approx[i]) + rank = {i: r for r, i in enumerate(order)} + true = set(range(k)) + in10 = sum(1 for i in true if rank[i] < k) + in30 = sum(1 for i in true if rank[i] < depth) + if in10 == 6 and in30 == 9: + break + lost = next(i for i in true if rank[i] >= depth) + short = set(order[:depth]) + rescored_order = sorted(short, key=lambda i: -exact[i]) + final10 = set(rescored_order[:k]) + assert len(final10 & true) == 9 + + W, H = 820, 370 + x0, x1 = 50, 790 + lo = min(min(exact), min(approx)) - 0.1 + hi = max(max(exact), max(approx)) + 0.1 + X = lambda s: x0 + (s - lo) / (hi - lo) * (x1 - x0) + lanes = [150, 166, 182, 198, 214, 230] + dur = 16 + css = "" + body = 'Hybrid quantization: scan with a small code, re-score a shortlist exactly\n' + + # The code's 10th and the shortlist edge, both on approx scores; the final 10th on exact. + a_sorted = sorted(approx, reverse=True) + cut10 = (a_sorted[k - 1] + a_sorted[k]) / 2 + cut30 = (a_sorted[depth - 1] + a_sorted[depth]) / 2 + e_short = sorted((exact[i] for i in short), reverse=True) + final_cut = (e_short[k - 1] + e_short[k]) / 2 + + # Shortlist band (phase 2 on) and top-10 zone. + css += phase("sl", dur, [(0, "opacity:0"), (28, "opacity:0"), (32, "opacity:1"), (94, "opacity:1"), (100, "opacity:0")]) + css += ".sl { opacity:1; animation: sl %ds infinite; }\n" % dur + body += f'\n' + body += f'shortlist: the code\'s top {depth} (f32 fetched for these only)\n' + + # Top-10 cut: appears at the code's 10th, then moves to the exact 10th after re-scoring. + shift = X(final_cut) - X(cut10) + css += phase("cut", dur, [(0, "opacity:0;transform:translateX(0)"), (12, "opacity:0;transform:translateX(0)"), + (16, "opacity:1;transform:translateX(0)"), (50, "opacity:1;transform:translateX(0)"), + (62, f"opacity:1;transform:translateX({shift:.1f}px)"), + (94, f"opacity:1;transform:translateX({shift:.1f}px)"), + (100, f"opacity:0;transform:translateX({shift:.1f}px)")]) + css += f".cutg {{ transform: translateX({shift:.1f}px); animation: cut {dur}s infinite; }}\n" + body += (f'' + f'top 10 →\n') + + # Axis. + body += f'\n' + body += f'higher score →\n' + body += f'each dot is one stored vector, placed by its score against the query\n' + + # Dots. Shortlisted ones slide from the code's score to the exact score. + for i in range(n): + y = lanes[i % len(lanes)] + cls = "true" if i in true else "other" + dx = X(exact[i]) - X(approx[i]) if i in short else 0.0 + name = f"d{i}" + stops = [(0, "opacity:0;transform:translateX(0)"), (4, "opacity:1;transform:translateX(0)"), + (50, "opacity:1;transform:translateX(0)"), (62, f"opacity:1;transform:translateX({dx:.1f}px)"), + (94, f"opacity:1;transform:translateX({dx:.1f}px)"), (100, f"opacity:0;transform:translateX({dx:.1f}px)")] + if i not in short and i != lost: + stops = [(0, "opacity:0"), (4, "opacity:1"), (50, "opacity:1"), (60, "opacity:.35"), + (94, "opacity:.35"), (100, "opacity:0")] + rest = "opacity:.35" + else: + rest = f"transform:translateX({dx:.1f}px)" + css += phase(name, dur, stops) + css += f".{name} {{ {rest}; animation: {name} {dur}s infinite; }}\n" + if i == lost: + css += phase("lostc", dur, [(0, "fill:var(--s1)"), (64, "fill:var(--s1)"), (68, "fill:var(--s2)"), + (100, "fill:var(--s2)")]) + body += f'\n' + lost_xy = (X(approx[i]), y) + else: + body += f'\n' + + # Label for the missed neighbour, shown in the last phase. + css += phase("ml", dur, [(0, "opacity:0"), (66, "opacity:0"), (70, "opacity:1"), (94, "opacity:1"), (100, "opacity:0")]) + css += f".ml {{ opacity:1; animation: ml {dur}s infinite; }}\n" + lx, ly = lost_xy + body += (f'' + f'missed: the code ranked it #{rank[lost] + 1}, outside the shortlist\n') + + # Phase counters. + c, b = captions("hc", dur, [ + (0, 14, "1 · Scan. Every vector is scored from its 48-byte code, in memory."), + (14, 30, f"2 · Cut at the code's top 10: it keeps {in10} of the 10 true neighbours."), + (30, 50, f"3 · Instead, shortlist the code's top {depth} and fetch their f32 vectors."), + (50, 66, "4 · Re-score the shortlist exactly. True neighbours rise into the top 10."), + (66, 100, f"Result: {len(final10 & true)} of 10 true neighbours. A deeper shortlist catches the last one."), + ], y=64) + css += c + body += b + + # Legend (two series plus the missed state; text in ink, colour on the mark). + lg = 312 + body += (f'true top-10 neighbour (exact f32)' + f'other vector' + f'true neighbour the hybrid missed\n') + body += (f'Schematic: {n} synthetic vectors. Measured over ten collections, re-scoring 50 f32 candidates recovers' + f'0.95 (binary) to 0.99 (pq48) of the top 10, for 3–4% of the bytes an f32 scan reads.\n') + + desc = (f"Animated schematic of two-stage retrieval. {n} vectors are scored from small codes; the code's top 10 holds " + f"{in10} of the 10 true neighbours. The code's top {depth} are re-scored with exact f32 vectors, which lifts " + f"{len(final10 & true)} true neighbours into the top 10; one, ranked {rank[lost] + 1} by the code, falls outside " + "the shortlist and is missed.") + return svg(W, H, "Hybrid quantization: two-stage re-scoring", desc, css, body) + + +# --------------------------------------------------------------------------- +# 2. Adaptive depth +# --------------------------------------------------------------------------- + +def adaptive_depth(): + rng = random.Random(10) + k, fixed, margin = 10, 30, 0.045 + # Codec scores for two queries: one with a crowded top, one well separated. + crowded = sorted((0.80 - abs(rng.gauss(0, 0.04)) for _ in range(70)), reverse=True) + separated = sorted((0.80 - abs(rng.gauss(0, 0.18)) for _ in range(70)), reverse=True) + + W, H = 820, 446 + x0, x1 = 50, 790 + lo, hi = 0.33, 0.81 + X = lambda s: x0 + (s - lo) / (hi - lo) * (x1 - x0) + dur = 12 + css, body = "", "" + body += 'Adaptive depth: re-score every candidate within λσ of the 10th score\n' + c, b = captions("ac", dur, [ + (0, 30, "The code's score noise σ is calibrated once per collection, on self-queries."), + (30, 62, "The margin λσ is the same for every query; how many candidates fall inside it is not."), + (62, 100, "A fixed shortlist (dashed) over-fetches for one query and under-fetches for the other."), + ], y=60) + css += c + body += b + + css += phase("grow", dur, [(0, "transform:scaleX(0)"), (30, "transform:scaleX(0)"), (52, "transform:scaleX(1)"), + (94, "transform:scaleX(1)"), (100, "transform:scaleX(0)")]) + css += phase("show", dur, [(0, "opacity:0"), (52, "opacity:0"), (56, "opacity:1"), (94, "opacity:1"), (100, "opacity:0")]) + css += phase("fix", dur, [(0, "opacity:0"), (62, "opacity:0"), (66, "opacity:1"), (94, "opacity:1"), (100, "opacity:0")]) + css += f".grow {{ transform-box: fill-box; transform-origin: right; animation: grow {dur}s infinite; }}\n" + css += f".show {{ opacity:1; animation: show {dur}s infinite; }}\n" + css += f".fix {{ opacity:1; animation: fix {dur}s infinite; }}\n" + + def panel(scores, top, name): + out = "" + kth = scores[k - 1] + inside = sum(1 for s in scores if s >= kth - margin) + base = top + 112 + out += f'{name}\n' + out += f'\n' + out += (f'\n') + # Dots in lanes; candidates inside the margin turn blue once the band has grown. + for i, s in enumerate(scores): + y = top + 24 + (i % 6) * 15 + if s >= kth - margin and i >= k: + n2 = f"in{name[:3]}{i}" + css_dot = phase(n2, dur, [(0, "fill:var(--other);fill-opacity:.55"), (52, "fill:var(--other);fill-opacity:.55"), + (56, "fill:var(--s1);fill-opacity:1"), (94, "fill:var(--s1);fill-opacity:1"), + (100, "fill:var(--other);fill-opacity:.55")]) + nonlocal_css.append(css_dot) + out += f'\n' + elif i < k: + out += f'\n' + else: + out += f'\n' + out += f'\n' + out += f'10th\n' + fx = X(scores[fixed - 1]) + out += f'\n' + out += f'fixed N = {fixed}\n' + out += f're-scores {inside} candidates\n' + return out, inside + + nonlocal_css = [] + p1, n1 = panel(crowded, 96, "Crowded query: many candidates score close to the 10th") + p2, n2 = panel(separated, 256, "Well-separated query: the 10th stands clear of the rest") + css += "".join(nonlocal_css) + body += p1 + p2 + lg = 412 + body += (f'the code\'s top 10' + f're-scored by margin' + f'not re-scored\n') + body += (f'Schematic: synthetic code scores, one σ for both queries. ' + 'Measured results: experiments/2026-11-hybrid-adaptive-matrix.\n') + desc = (f"Animated schematic of adaptive re-scoring depth. Two queries share one noise-based margin below their 10th " + f"code score. The crowded query has {n1} candidates inside the margin, the well-separated one {n2}; a fixed " + f"shortlist of {fixed} would under-fetch for the first and over-fetch for the second.") + return svg(W, H, "Adaptive depth: a per-query margin instead of a fixed shortlist", desc, css, body) + + +# --------------------------------------------------------------------------- +# 3. Adaptive bits (real data) +# --------------------------------------------------------------------------- + +def adaptive_bits(): + data = json.loads((HERE / "scifact-bge-small-alloc48.json").read_text()) + var, plain, wt = data["var"], data["plain"], data["weighted"] + d, group = len(var), 8 + groups = d // group + states = [ + ("uniform", [1] * d, "Uniform: 1 bit on every dimension (binary, 48 B)", "0.611"), + ("plain", plain, "mixed48-pca: rotate to principal components, spend bits where the variance is", "0.694"), + ("weighted", wt, "mixed48-pca-w: weight variance by how much energy queries put there", "0.703"), + ] + sums = {name: [sum(bits[g * group:(g + 1) * group]) for g in range(groups)] for name, bits, _, _ in states} + zero = {name: sum(1 for b in bits if b == 0) for name, bits, _, _ in states} + + W, H = 820, 486 + x0, x1 = 64, 790 + bw = (x1 - x0) / groups + top, base = 180, 370 + ymax = 40 + Y = lambda v: base - v / ymax * (base - top) + dur = 15 + css, body = "", "" + body += 'Adaptive bits: the same 48 bytes, spent where they matter\n' + + # Variance spectrum, its own small chart (one axis), log scale. + import math + vt, vb = 70, 138 + # Clip at 0.001: one near-dead component would otherwise stretch the axis to 1e-5. + lmin = -3 + lv = [max(math.log10(max(v, 1e-12)), lmin) for v in var] + VY = lambda l: vb - (l - lmin) / (0 - lmin) * (vb - vt) + VX = lambda i: x0 + (i + 0.5) / d * (x1 - x0) + pts = " ".join(f"{VX(i):.1f},{VY(lv[i]):.1f}" for i in range(d)) + for t in range(lmin, 1): + body += f'' + body += f'{10 ** t:g}\n' + body += f'\n' + body += f'variance per principal component, relative to the first (log scale, clipped at 0.001)\n' + + # Bars: total bits per group of 8 components, morphing between states. + for g in range(0, ymax + 1, 8): + body += f'' + body += f'{g}\n' + body += f'bits per group of 8 components (48 bytes = 384 bits in all)\n' + seq = ["uniform", "plain", "weighted"] + # 0–28% uniform, 33–61% plain, 66–94% weighted, back to uniform. + for gi in range(groups): + s = [sums[n][gi] / ymax for n in seq] + name = f"b{gi}" + stops = [(0, f"transform:scaleY({s[0]:.4f})"), (28, f"transform:scaleY({s[0]:.4f})"), + (33, f"transform:scaleY({s[1]:.4f})"), (61, f"transform:scaleY({s[1]:.4f})"), + (66, f"transform:scaleY({s[2]:.4f})"), (94, f"transform:scaleY({s[2]:.4f})"), + (100, f"transform:scaleY({s[0]:.4f})")] + css += phase(name, dur, stops) + css += f".{name} {{ transform-box: fill-box; transform-origin: bottom; transform: scaleY({s[2]:.4f}); animation: {name} {dur}s infinite; }}\n" + body += f'\n' + body += f'\n' + body += f'component 1' + body += f'component 384\n' + + items = [] + spans = [(0, 30), (31, 63), (64, 100)] + for (name, _, label, rec), (a, b) in zip(states, spans): + items.append((a, b, f"{label}")) + c1, b1 = captions("bc", dur, items, y=410, cls="cap") + stats = [] + for (name, _, _, rec), (a, b) in zip(states, spans): + stats.append((a, b, f"recall@10 without re-scoring: {rec} · components with 0 bits: {zero[name]} of {d}")) + c2, b2 = captions("bs", dur, stats, y=432, cls="num") + css += c1 + c2 + body += b1 + b2 + # The query-weighted state puts a few bits back on the last components: low variance, but + # they carry the corpus's shared mean direction, which every query projects onto. + css += phase("tail", dur, [(0, "opacity:0"), (66, "opacity:0"), (70, "opacity:1"), (94, "opacity:1"), (100, "opacity:0")]) + css += f".tail {{ opacity:1; animation: tail {dur}s infinite; }}\n" + body += (f'bits return to the last 44 components: little variance,' + f'but 28% of the corpus mean, which every query points along\n') + body += ('Real data: SciFact, bge-small-en-v1.5, 8,036 chunks, 300 claims. pq48 at the same 48 bytes reaches 0.711.' + 'Mixed precision by importance is the idea behind structure-aware MoE quantization (QuantMoE-Bench).\n') + desc = ("Animated bar chart of how a 48-byte budget is spread over 384 principal components of SciFact's bge-small " + f"embeddings. Uniform binary gives every component 1 bit (recall@10 0.611). Variance-based allocation " + f"concentrates bits on the leading components and gives {zero['plain']} components none (0.694); " + f"query-weighted allocation goes further, {zero['weighted']} components with none (0.703).") + return svg(W, H, "Adaptive bits: mixed-precision allocation at 48 bytes", desc, css, body) + + +if __name__ == "__main__": + for name, fn in [("hybrid-rescoring.svg", hybrid), ("adaptive-depth.svg", adaptive_depth), ("adaptive-bits.svg", adaptive_bits)]: + (HERE / name).write_text(fn()) + print("wrote", HERE / name) diff --git a/docs/assets/animations/scifact-bge-small-alloc48.json b/docs/assets/animations/scifact-bge-small-alloc48.json new file mode 100644 index 0000000..1e89078 --- /dev/null +++ b/docs/assets/animations/scifact-bge-small-alloc48.json @@ -0,0 +1 @@ +{"var": [1.0, 0.39134126439623, 0.3496060513273873, 0.31595562269325783, 0.27839887791768925, 0.2560764744407206, 0.2106557348265734, 0.20141261230956678, 0.17205700091762918, 0.1694689781684781, 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0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 1, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 2, 2, 0, 0, 0, 0, 0, 0]} \ No newline at end of file From 65cb61dd4266da5f9568081990a9b718e1fac499 Mon Sep 17 00:00:00 2001 From: Swinecoder101 Date: Wed, 4 Nov 2026 21:47:11 +0000 Subject: [PATCH 02/12] docs(roadmap): phase 8, spacing, hybrid and adaptive quantization Records the neighbour-spacing and two-stage re-scoring results, the margin policy, the mixed codec, the query-preparation hook and the README animations as done; the hybrid x adaptive matrix as running; and the follow-ups: automatic re-score depth at ingest, per-vector adaptive bits, per-cluster PCA, and the spacing caveats. Phase 7 notes that its query hook now exists. Next drops the finished re-scoring item and adds graded relevance for NFCorpus. --- ROADMAP.md | 65 +++++++++++++++++++++++++++++++++++++++++++++++------- 1 file changed, 57 insertions(+), 8 deletions(-) diff --git a/ROADMAP.md b/ROADMAP.md index 35749fc..0a23916 100644 --- a/ROADMAP.md +++ b/ROADMAP.md @@ -107,7 +107,9 @@ rotation is a `d × d` matrix fitted on the corpus. `rot+pq48`, `mrl256+rot+binary`. It is a trained codec in the existing sense: `fit` learns the rotation on the first ingest, `params` stores the matrix (or the seed) with the collection, and `score` rotates the f32 query once per search before the asymmetric scan, so the stored - side still scores on the bytes. + side still scores on the bytes. The hook for that now exists (`Codec::prepare_query` and + `score_prepared`, added in phase 8), and `mixed-pca` already carries a PCA fit that + `rot:itq` can start from. - [ ] Two fits behind the same name. `rot` with a seed is a random orthogonal rotation (RaBitQ-style, a Haar-random matrix from the QR of a Gaussian), which needs no corpus pass and gives a baseline for how much the learned one buys. `rot:itq` is Iterative Quantization: PCA @@ -127,6 +129,55 @@ rotation is a `d × d` matrix fitted on the corpus. Exit: Experiment 4 published, and a stated answer to whether rotation makes binary usable on this embedding model. +## Phase 8 · Spacing, hybrid and adaptive quantization (in progress) + +Every codec number before this phase came from one corpus, so none of them said what a codec +would cost elsewhere. This phase finds the quantity that sets the cost, then uses it twice: to +decide how deep a hybrid re-score must go, and to decide where an adaptive code spends its bits. + +- [x] Neighbour spacing in `bench recall`: `neighbour_band` (exact score gap from rank k to 2k), + `boundary_gap` (k to k + 1) and `rank_noise` (the codec's score-error spread over the exact + top 2k, per-query bias removed). +- [x] **Neighbour-spacing experiment** (`experiments/2026-11-neighbour-spacing`): SciFact, + NFCorpus, ArguAna, FiQA and CoSQA (code, via CoIR) × bge-small and MiniLM × nine codecs. + Noise ÷ band ranks recall@10 loss with ρ = 0.98 over 90 points (noise alone 0.64) and predicts + a held-out corpus within 0.024. Content type matters only through spacing, and the embedding + model moves spacing as much as the corpus does. Published with a site page and a post. +- [x] `hvec bench rescore`: scan with one codec, re-score candidates with another (int8 or f32), + and report recall, worst-decile recall, nDCG, depth and bytes read per query. It also reports + the exact depth an f32 re-score needs for 90/95/99/100% recall. +- [x] **Two-stage re-scoring experiment** (`experiments/2026-11-two-stage-rescoring`): the + depth needed tracks the same ratio (ρ ≈ 0.93; depth ≈ 4.8·k·r^1.29 for 95% recall). At 48 + bytes, 50 f32 candidates recover 0.95 (`binary`) to 0.99 (`pq48`) of the top 10 and nearly all + of the nDCG loss, for 3–4% of an f32 scan's bytes. An int8 second stage stops at int8's own + 0.990. +- [x] Adaptive depth: a `--margins` policy that re-scores every candidate within λσ of the + scan's own 10th score, with σ calibrated on self-queries only. +- [x] Adaptive bits: `mixed[-pca][-w]`, a fixed byte budget with bits allocated per + dimension by greedy reverse water-filling and a Gaussian-optimal quantizer step. `-pca` + rotates into principal components first; `-w` weights each component by query energy, which + targets score error rather than reconstruction error. On SciFact at 48 bytes with no + re-score: `binary` 0.611, `mixed48` 0.653, `mixed48-pca` 0.694, `mixed48-pca-w` 0.703, `pq48` + 0.711. +- [x] `Codec::prepare_query` / `score_prepared`, so transformed-space codecs rotate the query + once per search; used by the store's search, `bench recall` and `bench rescore`. +- [x] Animated README figures for hybrid re-scoring, adaptive depth and adaptive bits + (`docs/assets/animations/`, regenerated by `make_animations.py`). +- [ ] **Hybrid × adaptive matrix** (`experiments/2026-11-hybrid-adaptive-matrix`, running): 11 + scan codecs (uniform `binary`/`pq` against adaptive `mixed` at 24, 48 and 96 bytes) × none, + fixed-depth and margin policies × int8/f32 second stages, over the ten collections. To + publish: the matrix, adaptive against fixed depth at equal mean depth, and a table of + benefits and drawbacks for each combination. +- [ ] Choose the re-score depth automatically at ingest: measure the band on a sample, take the + codec's noise, and store the depth the two-stage curve predicts for a target recall. +- [ ] Per-vector adaptive bits, the analogue of MoE PTQ's outlier scorer: more bits for vectors + in crowded neighbourhoods. Needs a variable-size encoding or two tiers. +- [ ] Per-cluster PCA (Sub-MoE's grouped subspaces), if `mixed-pca` gets within reach of + `pq` at equal bytes. +- [ ] Spacing follow-ups the write-up flagged: a deduplicated code corpus (CoSQA is 70% + duplicates), FiQA subsampled to 8,000 chunks to separate corpus size from spacing, a third + embedding model, and a tail statistic for the aggressive codecs the curve misses. + ## Ideas from MoE compression Weight compression for Mixture-of-Experts models is working on a problem shaped like this one: @@ -160,13 +211,11 @@ Three lines of work, and what each would mean as an hvec codec or experiment: - Run Experiments 1 and 3 and the judge re-scoring as soon as two chat models are reachable; the scripts need only `ANTHROPIC_API_KEY` (or an Ollama server) and a built `hvec`. -- Codec follow-ups the write-ups flagged: a per-vector scale for `binary-centred`, and a - rescoring stage (fetch 5-10× candidates by codec, re-rank with int8 or f32). -- A second corpus with graded, denser judgments than SciFact's. -- Done 2026-11-04: neighbour spacing across five corpora (`experiments/2026-11-neighbour-spacing`). - Codec noise ÷ neighbour band predicts recall@10 loss (ρ = 0.98). Follow-ups: rescoring depth - as a function of that ratio, a deduplicated code corpus, and FiQA subsampled to separate - corpus size from spacing. +- Finish phase 8: publish the hybrid × adaptive matrix. +- Codec follow-ups the write-ups flagged: a per-vector scale for `binary-centred`. The + re-scoring stage is done (phase 8). +- Graded relevance: NFCorpus ships graded judgments, but hvec's nDCG treats relevance as + binary. Use the grades, then re-score the five-corpus results. ## Always From cd1864a903dbb3716771b8eaf43543c3c19c44dc Mon Sep 17 00:00:00 2001 From: Swinecoder101 Date: Wed, 4 Nov 2026 21:54:31 +0000 Subject: [PATCH 03/12] feat(bench): per-query traces from bench rescore bench rescore --trace writes one JSON line per scan codec with, for every query, the scan's rank of each exact top-k neighbour, the exact band and the band read from the scan's own scores, and the margin policy's depth on a 0.25-sigma grid. With an exact second stage that is enough to replay any depth policy offline; a test checks the replay against the computed fixed and margin cells. evaluate now returns an Evaluation struct carrying the traces. --- crates/hvec-bench/src/rescore.rs | 124 ++++++++++++++++++++++++++++-- crates/hvec/src/cli.rs | 5 ++ crates/hvec/src/commands/bench.rs | 34 +++++++- 3 files changed, 157 insertions(+), 6 deletions(-) diff --git a/crates/hvec-bench/src/rescore.rs b/crates/hvec-bench/src/rescore.rs index eb1a594..728349b 100644 --- a/crates/hvec-bench/src/rescore.rs +++ b/crates/hvec-bench/src/rescore.rs @@ -77,6 +77,37 @@ pub struct Plan<'a> { pub noise: f64, /// Cap on any policy's per-query depth. pub max_depth: usize, + /// When non-empty, record a [`QueryTrace`] per query, with the margin + /// policy's depth at each of these margins. + pub trace_margins: &'a [f64], +} + +/// Everything about one query that a routing policy could use or be judged +/// by, so policies can be replayed offline without re-running the scan. +/// +/// With an exact second stage, the query's recall at depth `N` is the number +/// of `ranks` at most `N`, divided by k: any fixed, adaptive or oracle depth +/// can be scored from this alone. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct QueryTrace { + /// First-stage rank (1-based) of each exact top-k neighbour, in exact order. + pub ranks: Vec, + /// Exact score gap from rank k to rank 2k: the true neighbour band. + pub band: f64, + /// The same gap measured on the first stage's own scores: the band a + /// router can see at query time without any exact scores. + pub band_hat: f64, + /// The margin policy's depth at each of the plan's `trace_margins`. + pub margin_depths: Vec, +} + +/// The result of [`evaluate`]. +#[derive(Debug, Clone)] +pub struct Evaluation { + pub profile: FirstStageProfile, + pub reports: Vec, + /// One per query when the plan asked for traces, otherwise empty. + pub traces: Vec, } /// One (first stage, second stage, policy) cell. @@ -145,7 +176,7 @@ pub fn evaluate( k: usize, plan: &Plan<'_>, sources: Option<&[String]>, -) -> Result<(FirstStageProfile, Vec), CodecError> { +) -> Result { assert_eq!(ids.len(), vectors.len(), "ids and vectors must align"); if let Some(s) = sources { assert_eq!(s.len(), ids.len(), "ids and sources must align"); @@ -197,6 +228,7 @@ pub fn evaluate( let mut bands: Vec = Vec::with_capacity(queries.len()); let mut noise_sum = 0f64; let mut first_recall_sum = 0f64; + let mut traces: Vec = Vec::new(); for q in queries { // Scores indexed like `ids`; the excluded id scores worst everywhere. @@ -215,9 +247,16 @@ pub fn evaluate( let truth: Vec = exact_order.iter().take(k).map(|&i| ids[i]).collect(); // Spacing and noise, exactly as `recall::evaluate` measures them. + let mut band = 0.0; + let mut band_hat = 0.0; if exact_order.len() >= 2 * k { let s = |r: usize| f64::from(exact[exact_order[r - 1]].1); - bands.push((s(k) - s(2 * k)).abs()); + band = (s(k) - s(2 * k)).abs(); + bands.push(band); + } + if n_live >= 2 * k { + let h = |r: usize| f64::from(approx[approx_order[r - 1]].1); + band_hat = (h(k) - h(2 * k)).abs(); } noise_sum += centred_noise(&exact, &approx, &exact_order[..exact_order.len().min(2 * k)]); @@ -229,6 +268,7 @@ pub fn evaluate( let ranks: Vec = exact_order.iter().take(k).map(|&i| rank_of[i]).collect(); first_recall_sum += ranks.iter().filter(|&&r| r <= k).count() as f64 / k as f64; full_depths.push(ranks.iter().copied().max().unwrap_or(0) as f64); + let trace_ranks = (!plan.trace_margins.is_empty()).then(|| ranks.clone()); pooled_ranks.extend(ranks); // The adaptive policy's depth: candidates within the margin of the @@ -243,6 +283,14 @@ pub fn evaluate( }; n.max(k).min(cap.min(n_live)) }; + if let Some(ranks) = trace_ranks { + traces.push(QueryTrace { + ranks, + band, + band_hat, + margin_depths: plan.trace_margins.iter().map(|&m| within(m)).collect(), + }); + } let is_labelled = sources.is_some() && !q.relevant.is_empty(); if is_labelled { @@ -357,7 +405,11 @@ pub fn evaluate( } }) .collect(); - Ok((profile, reports)) + Ok(Evaluation { + profile, + reports, + traces, + }) } #[cfg(test)] @@ -407,8 +459,9 @@ mod tests { margins, noise, max_depth, + trace_margins: &[], }; - evaluate( + let e = evaluate( &ids, &vecs, &queries, @@ -418,7 +471,9 @@ mod tests { &plan, None, ) - .unwrap() + .unwrap(); + assert!(e.traces.is_empty()); + (e.profile, e.reports) } fn fixed(reports: &[RescoreReport]) -> Vec<&RescoreReport> { @@ -508,4 +563,63 @@ mod tests { let (_, all) = run(&[&i8c], &[1000], &[], 0.0, 1000); assert_eq!(fixed(&all)[0].depth, 320); } + + #[test] + fn traces_replay_the_fixed_and_margin_cells() { + let (ids, vecs) = clustered(); + let queries = self_queries(&ids, &vecs, 30); + let f32c = F32Codec::new(64); + let noise = profile_noise(); + let margins = [1.0, 2.0]; + let plan = Plan { + seconds: &[&f32c], + depths: &[10, 20, 40], + margins: &margins, + noise, + max_depth: 200, + trace_margins: &margins, + }; + let e = evaluate( + &ids, + &vecs, + &queries, + &BinaryCodec::new(64), + Metric::Cosine, + 10, + &plan, + None, + ) + .unwrap(); + assert_eq!(e.traces.len(), 30); + let replay = |depth_of: &dyn Fn(&QueryTrace) -> usize| -> f64 { + e.traces + .iter() + .map(|t| t.ranks.iter().filter(|&&r| r <= depth_of(t)).count() as f64 / 10.0) + .sum::() + / 30.0 + }; + for r in &e.reports { + let replayed = match r.policy { + Policy::None => replay(&|_| 10), + Policy::Fixed { depth } => replay(&|_| depth), + Policy::Margin { sigmas } => { + let m = margins.iter().position(|&x| x == sigmas).unwrap(); + let mean: f64 = e.traces.iter().map(|t| t.margin_depths[m] as f64).sum::() / 30.0; + assert!((mean - r.mean_depth).abs() < 1e-9); + replay(&|t| t.margin_depths[m]) + } + }; + assert!( + (replayed - r.recall_at_k).abs() < 1e-9, + "{:?}: {replayed} vs {}", + r.policy, + r.recall_at_k + ); + } + assert!( + e.traces + .iter() + .all(|t| t.band > 0.0 && t.band_hat > 0.0 && t.ranks.len() == 10) + ); + } } diff --git a/crates/hvec/src/cli.rs b/crates/hvec/src/cli.rs index 60b001c..a00b4a4 100644 --- a/crates/hvec/src/cli.rs +++ b/crates/hvec/src/cli.rs @@ -338,6 +338,11 @@ pub struct RescoreArgs { /// Self-queries used to calibrate the first stage's noise for the adaptive policy. #[arg(long, default_value_t = 200)] pub calibration_sample: usize, + /// Write per-query traces (first-stage ranks of the true neighbours, exact and + /// first-stage bands, margin depths on a 0.25σ grid) as JSON Lines, one line per first + /// stage, so routing policies can be replayed offline. + #[arg(long)] + pub trace: Option, /// Neighbours per query. #[arg(short, long, default_value_t = 10)] pub k: usize, diff --git a/crates/hvec/src/commands/bench.rs b/crates/hvec/src/commands/bench.rs index 99361e5..31c307b 100644 --- a/crates/hvec/src/commands/bench.rs +++ b/crates/hvec/src/commands/bench.rs @@ -546,6 +546,16 @@ async fn rescore_cmd(config: &Option, args: RescoreArgs) -> Result<()> // queries' exact scores never inform their own depth. let calibration = recall::self_queries(&ids, &vectors, args.calibration_sample); + // Margin grid for per-query traces: fine enough to trace the adaptive + // policy's whole recall-against-depth curve offline. + let trace_margins: Vec = (1..=24).map(|i| f64::from(i) * 0.25).collect(); + let mut trace_out = match &args.trace { + Some(path) => Some(std::io::BufWriter::new( + std::fs::File::create(path).with_context(|| format!("creating {}", path.display()))?, + )), + None => None, + }; + let mut profiles = Vec::new(); let mut cells = Vec::new(); for name in &args.codecs { @@ -566,8 +576,13 @@ async fn rescore_cmd(config: &Option, args: RescoreArgs) -> Result<()> margins: &args.margins, noise, max_depth: args.max_depth, + trace_margins: if args.trace.is_some() { &trace_margins } else { &[] }, }; - let (profile, reports) = rescore::evaluate( + let rescore::Evaluation { + profile, + reports, + traces, + } = rescore::evaluate( &ids, &vectors, &queries, @@ -600,6 +615,23 @@ async fn rescore_cmd(config: &Option, args: RescoreArgs) -> Result<()> store.insert_run(&row)?; } } + if let Some(out) = trace_out.as_mut() { + use std::io::Write as _; + let line = serde_json::json!({ + "collection": collection.name, + "first": profile.first, + "first_bytes": profile.first_bytes, + "vectors": vectors.len(), + "k": args.k, + "calibrated_noise": noise, + "trace_margins": trace_margins, + "ranks": traces.iter().map(|t| &t.ranks).collect::>(), + "band": traces.iter().map(|t| t.band).collect::>(), + "band_hat": traces.iter().map(|t| t.band_hat).collect::>(), + "margin_depths": traces.iter().map(|t| &t.margin_depths).collect::>(), + }); + writeln!(out, "{line}")?; + } profiles.push(profile); cells.extend(reports); } From c501a2d4889a5bc5932ebb4c8a3628b047903ea8 Mon Sep 17 00:00:00 2001 From: Swinecoder101 Date: Wed, 4 Nov 2026 21:54:31 +0000 Subject: [PATCH 04/12] experiment: routing oracle scripts (run in progress) Replays fixed depth, the margin policy, a ratio router (depth from noise over the scan's own band) and a hindsight oracle from per-query traces, compared at equal mean depth: headroom, share captured, worst-decile recall. Also tests per-collection routing, predicting each collection's cheapest scan codec and depth for a recall target with the curve fitted on the other corpora. Results follow when the run completes. --- experiments/2026-11-routing-oracle/analyse.py | 233 ++++++++++++++++++ experiments/2026-11-routing-oracle/run.sh | 35 +++ 2 files changed, 268 insertions(+) create mode 100644 experiments/2026-11-routing-oracle/analyse.py create mode 100755 experiments/2026-11-routing-oracle/run.sh diff --git a/experiments/2026-11-routing-oracle/analyse.py b/experiments/2026-11-routing-oracle/analyse.py new file mode 100644 index 0000000..5422ac2 --- /dev/null +++ b/experiments/2026-11-routing-oracle/analyse.py @@ -0,0 +1,233 @@ +"""Replay depth-routing policies from `bench rescore --trace` output. + + python3 -I analyse.py + +With an exact (f32) second stage, a query's recall@k at depth N is the share of its true top-k +neighbours that the scan ranks within N, so every policy below is scored exactly from the traces: + + fixed the same depth N for every query + margin every candidate within λσ of the scan's own k-th score (σ calibrated on self-queries) + ratio depth = a·k·(σ / band_hat)^β, band_hat read from the scan's own scores (free at query time) + oracle the best per-query depths for a mean-depth budget, in hindsight (Lagrangian, exact) + +Each policy traces a curve of mean depth against mean recall@k. They are compared at equal mean +depth. Headroom = oracle − fixed; captured = (policy − fixed) / headroom. The second part asks the +per-collection question: predict each collection's cheapest (scan codec, fixed depth) for a recall +target from noise ÷ band, with the curve fitted on the other corpora, and check it. +""" + +import json +import math +import sys +from collections import defaultdict +from pathlib import Path + +CAP = 1000 +BUDGETS = [15, 20, 30, 50, 100] +TARGETS = [0.90, 0.95, 0.99] + + +def mean(xs): + xs = list(xs) + return sum(xs) / len(xs) if xs else float("nan") + + +def pct(xs, p): + s = sorted(xs) + return s[min(len(s) - 1, max(0, math.ceil(p * len(s)) - 1))] + + +def spearman(a, b): + def rk(x): + o = sorted(range(len(x)), key=lambda i: x[i]) + r = [0.0] * len(x) + i = 0 + while i < len(o): + j = i + while j + 1 < len(o) and x[o[j + 1]] == x[o[i]]: + j += 1 + for t in range(i, j + 1): + r[o[t]] = (i + j) / 2 + i = j + 1 + return r + ra, rb = rk(a), rk(b) + ma, mb = mean(ra), mean(rb) + cov = sum((x - ma) * (y - mb) for x, y in zip(ra, rb)) + return cov / math.sqrt(sum((x - ma) ** 2 for x in ra) * sum((y - mb) ** 2 for y in rb)) + + +class Trace: + def __init__(self, line): + d = json.loads(line) + self.collection, self.first, self.k = d["collection"], d["first"], d["k"] + self.first_bytes, self.vectors, self.noise = d["first_bytes"], d["vectors"], d["calibrated_noise"] + self.margins = d["trace_margins"] + self.ranks, self.band, self.band_hat = d["ranks"], d["band"], d["band_hat"] + self.margin_depths = d["margin_depths"] + self.n = len(self.ranks) + + def recall(self, q, depth): + return sum(1 for r in self.ranks[q] if r <= depth) / self.k + + def evaluate(self, depths): + rec = [self.recall(q, d) for q, d in enumerate(depths)] + return dict(depth=mean(depths), recall=mean(rec), p10=pct(rec, 0.10), p90_depth=pct(depths, 0.90)) + + # Policy curves: lists of evaluate() results, ordered by mean depth. + def fixed_curve(self): + return [self.evaluate([d] * self.n) for d in sorted({self.k, 12, 15, 20, 25, 30, 40, 50, 70, 100, 150, 200, 300, 500, CAP})] + + def margin_curve(self): + return [self.evaluate([md[i] for md in self.margin_depths]) for i in range(len(self.margins))] + + def ratio_curve(self, beta): + out = [] + for a in [0.25 * 1.25 ** i for i in range(30)]: + depths = [] + for bh in self.band_hat: + r = self.noise / bh if bh > 0 else 50.0 + depths.append(int(min(CAP, max(self.k, round(a * self.k * r ** beta))))) + out.append(self.evaluate(depths)) + return sorted(out, key=lambda e: e["depth"]) + + def oracle_curve(self): + # Per query, the candidate depths are k and each true neighbour's rank (≤ CAP); recall is a + # step function of depth, so the optimum for a budget is on these points. For each + # multiplier λ every query maximises recall − λ·depth independently. + options = [] + for q in range(self.n): + pts = sorted({self.k} | {r for r in self.ranks[q] if self.k < r <= CAP}) + options.append([(d, self.recall(q, d)) for d in pts]) + out = [] + for lam in [0.0] + [10 ** (e / 8) for e in range(-48, 9)]: + depths = [] + for opts in options: + best = max(opts, key=lambda o: (o[1] - lam * o[0], -o[0])) + depths.append(best[0]) + out.append(self.evaluate(depths)) + return sorted(out, key=lambda e: e["depth"]) + + +def at_budget(curve, budget): + """Linear interpolation of recall and p10 along a curve at a mean depth.""" + for a, b in zip(curve, curve[1:]): + if a["depth"] <= budget <= b["depth"]: + t = 0.0 if b["depth"] == a["depth"] else (budget - a["depth"]) / (b["depth"] - a["depth"]) + return {key: a[key] + t * (b[key] - a[key]) for key in ("recall", "p10", "p90_depth")} + return None + + +def main(traces_dir: Path, k: int) -> None: + traces = [Trace(l) for p in sorted(traces_dir.glob("*.jsonl")) for l in p.read_text().splitlines() if l.strip()] + print(f"{len(traces)} (collection, scan codec) traces, {sum(t.n for t in traces)} query traces in all\n") + + # Does the scan's own band track the exact one, query by query? + print("1. Can a router see the band? Spearman, per trace, over queries") + rows = defaultdict(list) + for t in traces: + full = [max(r) for r in t.ranks] + rows[t.first].append((spearman(t.band_hat, t.band), spearman([1 / b if b > 0 else 1e9 for b in t.band_hat], full))) + for first, v in sorted(rows.items()): + print(f" {first:<15} band_hat vs exact band {mean(x[0] for x in v):.3f} 1/band_hat vs depth for full recall {mean(x[1] for x in v):.3f}") + print(" (how much per-query depth needed varies:", + f"median p90/p10 ratio of full-recall depth {mean(pct([max(r) for r in t.ranks], .9) / max(1, pct([max(r) for r in t.ranks], .1)) for t in traces):.1f}×)") + + print("\n2. Per-query routing at equal mean depth (means over traces). Δ against fixed depth") + print(f" {'budget':>6} {'fixed':>7} {'margin':>8} {'ratio β=1':>10} {'ratio β=1.3':>11} {'oracle':>8} " + f"{'headroom':>8} {'margin cap.':>11} {'ratio cap.':>10} {'p10 fixed→margin':>17}") + table = defaultdict(lambda: defaultdict(list)) + for t in traces: + curves = dict(fixed=t.fixed_curve(), margin=t.margin_curve(), r1=t.ratio_curve(1.0), + r13=t.ratio_curve(1.29), oracle=t.oracle_curve()) + for b in BUDGETS: + vals = {name: at_budget(c, b) for name, c in curves.items()} + if any(v is None for v in vals.values()): + continue + for name, v in vals.items(): + table[b][name].append(v) + table[b]["first"].append(t.first) + for b in BUDGETS: + row = table[b] + if not row["fixed"]: + continue + r = {name: mean(v["recall"] for v in row[name]) for name in ("fixed", "margin", "r1", "r13", "oracle")} + head = r["oracle"] - r["fixed"] + cap = lambda name: (r[name] - r["fixed"]) / head if head > 1e-9 else float("nan") + best_ratio = max(("r1", "r13"), key=lambda n: r[n]) + p10f, p10m = mean(v["p10"] for v in row["fixed"]), mean(v["p10"] for v in row["margin"]) + print(f" {b:>6} {r['fixed']:>7.4f} {r['margin'] - r['fixed']:>+8.4f} {r['r1'] - r['fixed']:>+10.4f} " + f"{r['r13'] - r['fixed']:>+11.4f} {r['oracle'] - r['fixed']:>+8.4f} {head:>8.4f} {cap('margin'):>11.0%} " + f"{cap(best_ratio):>10.0%} {p10f:>8.3f}→{p10m:.3f} (n={len(row['fixed'])})") + + print("\n by scan codec, at a mean depth of 30: fixed recall, then Δ for margin, best ratio, oracle") + b = 30 + by = defaultdict(lambda: defaultdict(list)) + for i, first in enumerate(table[b]["first"]): + for name in ("fixed", "margin", "r1", "r13", "oracle"): + by[first][name].append(table[b][name][i]["recall"]) + for first in sorted(by): + r = {n: mean(v) for n, v in by[first].items()} + print(f" {first:<15} fixed {r['fixed']:.4f} margin {r['margin'] - r['fixed']:+.4f} " + f"ratio {max(r['r1'], r['r13']) - r['fixed']:+.4f} oracle {r['oracle'] - r['fixed']:+.4f}") + print(" p90 depth at a mean of 30: fixed 30, margin " + f"{mean(v['p90_depth'] for v in table[b]['margin']):.0f}, oracle {mean(v['p90_depth'] for v in table[b]['oracle']):.0f}") + + # Per-collection routing: choose the scan codec and a fixed depth for a target recall. + print("\n3. Per-collection routing: the cheapest (scan codec, fixed depth) reaching a recall target") + print(" cost = bytes read per query = vectors × scan bytes + depth × 1536; RAM = scan bytes per vector") + by_col = defaultdict(list) + for t in traces: + by_col[t.collection].append(t) + if len({c.split("-")[0] for c in by_col}) < 2: + print(" (needs at least two corpora: the depth curve is fitted on the others)") + return + + def needed(t, target): + # Smallest fixed depth whose mean recall reaches the target (exact, from the ranks). + ranks = sorted(r for rs in t.ranks for r in rs) + need = math.ceil(target * len(ranks)) + return max(t.k, ranks[need - 1]) if need > 0 else t.k + + for target in TARGETS: + pts = [] + for col, ts in by_col.items(): + for t in ts: + ratio = t.noise / sorted(t.band)[len(t.band) // 2] + pts.append((col, t, ratio, needed(t, target))) + corpora = sorted({c.split("-")[0] for c in by_col}) + hits, regret, ram_match = 0, [], 0 + for col in sorted(by_col): + corpus = col.split("-")[0] + train = [p for p in pts if p[0].split("-")[0] != corpus] + xs = [math.log(p[2]) for p in train] + ys = [math.log(p[3] / k) for p in train] + mx, my = mean(xs), mean(ys) + beta = sum((x - mx) * (y - my) for x, y in zip(xs, ys)) / sum((x - mx) ** 2 for x in xs) + alpha = my - beta * mx + mine = [p for p in pts if p[0] == col] + cost = lambda t, d: t.vectors * t.first_bytes + d * 1536 + best = min(mine, key=lambda p: (cost(p[1], p[3]), p[1].first_bytes)) + pred = [] + for p in mine: + d = int(min(CAP, max(k, round(k * math.exp(alpha + beta * math.log(p[2])))))) + pred.append((p, d)) + chosen, d = min(pred, key=lambda x: (cost(x[0][1], x[1]), x[0][1].first_bytes)) + got = chosen[1].evaluate([d] * chosen[1].n)["recall"] + ok = got >= target - 0.005 + hits += ok + regret.append(cost(chosen[1], d) / cost(best[1], best[3])) + ram_match += chosen[1].first_bytes == best[1].first_bytes + print(f" target {target:.0%}: predicted choice reaches the target (within 0.005) in {hits}/{len(by_col)} collections; " + f"cost vs the best choice ×{mean(regret):.2f} on average; same scan size as the best in {ram_match}/{len(by_col)}") + print("\n best choice per collection at 95%:") + for col in sorted(by_col): + opts = [(t.vectors * t.first_bytes + needed(t, 0.95) * 1536, t.first, needed(t, 0.95), t.first_bytes) for t in by_col[col]] + opts.sort() + c, first, d, b = opts[0] + cheapest_ram = min(opts, key=lambda o: (o[3], o[0])) + print(f" {col:<20} {first:<15} depth {d:>4} read {c / (by_col[col][0].vectors * 1536):.4f} of f32 " + f"(smallest scan: {cheapest_ram[1]} at depth {cheapest_ram[2]})") + + +if __name__ == "__main__": + main(Path(sys.argv[1]), int(sys.argv[2])) diff --git a/experiments/2026-11-routing-oracle/run.sh b/experiments/2026-11-routing-oracle/run.sh new file mode 100755 index 0000000..a3bb4cb --- /dev/null +++ b/experiments/2026-11-routing-oracle/run.sh @@ -0,0 +1,35 @@ +#!/usr/bin/env bash +# Routing: how much is there to gain by choosing the compression per query or per collection, +# and how much of it a free router captures. Records per-query traces (where the scan ranks each +# true neighbour, the band the scan itself sees, the margin policy's depth on a 0.25σ grid) for +# six scan codecs on the ten collections, then replays fixed, margin, ratio-router and oracle +# policies offline. Retrieval only. Reuses ../2026-11-neighbour-spacing's collections (and +# builds them if missing). Needs: a built `hvec` (cargo build --release). +set -euo pipefail +HERE="$(cd "$(dirname "$0")" && pwd)" +ROOT="$HERE/../.." +HVEC="${HVEC:-$ROOT/target/release/hvec}" +SPACING="$HERE/../2026-11-neighbour-spacing" +SPACING_WORK="${SPACING_WORK:-$SPACING/work}" +K="${K:-10}" +CORPORA="${CORPORA:-scifact nfcorpus arguana cosqa fiqa}" +MODELS="${MODELS:-bge-small minilm}" +FIRST="${FIRST:-binary,binary-centred,pq48,pq24,mixed48-pca-w,mixed24-pca-w}" +mkdir -p "$HERE/results/traces" + +export HVEC_CONFIG="$SPACING_WORK/config.toml" +if [ ! -f "$HVEC_CONFIG" ]; then + WORK="$SPACING_WORK" CORPORA="$CORPORA" MODELS="$MODELS" "$SPACING/run.sh" +fi + +for c in $CORPORA; do + for m in $MODELS; do + col="$c-$m" + # The cells themselves are not needed: the traces replay any depth policy exactly. + "$HVEC" bench rescore --collection "$col" -k "$K" --codecs "$FIRST" --rescore f32 \ + --depths "$K" --margins 1 --max-depth 1000 --questions "$SPACING_WORK/$c.jsonl" \ + --trace "$HERE/results/traces/$col.jsonl" --no-record --json > /dev/null + echo "== $col done" + done +done +python3 -I "$HERE/analyse.py" "$HERE/results/traces" "$K" | tee "$HERE/results/summary.txt" From 017c60ea835f689ece6308633638582a71fa6e97 Mon Sep 17 00:00:00 2001 From: Swinecoder101 Date: Wed, 4 Nov 2026 22:15:15 +0000 Subject: [PATCH 05/12] feat(core): borrowed codes, in-place f32 scoring, and binary-hamming Codec::decode, score and score_prepared take &[u8] instead of &Encoded, so a codec can score codes in place in a contiguous buffer or a database page. Encoded stays as encode's owned output and derefs to [u8], so existing callers compile unchanged. F32Codec::score reads the stored floats in place in one fused pass, with no Vec per vector; distance::cosine is one pass instead of three. Both are bit-identical to the previous results (tested). binary-hamming stores the same sign bits as binary but binarises the query once in prepare_query and scores by XOR and popcount over 64-bit words, estimating cosine as cos(pi * hamming / d). It is the symmetric form a scan-speed claim can be made for; its error model is documented as the one codec whose score is not the metric on its decoded vector. --- crates/hvec-core/src/codecs/hamming.rs | 191 ++++++++++++++++++++++++ crates/hvec-core/src/codecs/mixed.rs | 14 +- crates/hvec-core/src/codecs/mod.rs | 96 ++++++++---- crates/hvec-core/src/codecs/pq.rs | 12 +- crates/hvec-core/src/codecs/truncate.rs | 6 +- crates/hvec-core/src/distance.rs | 14 +- crates/hvec-core/src/lib.rs | 29 +++- 7 files changed, 308 insertions(+), 54 deletions(-) create mode 100644 crates/hvec-core/src/codecs/hamming.rs diff --git a/crates/hvec-core/src/codecs/hamming.rs b/crates/hvec-core/src/codecs/hamming.rs new file mode 100644 index 0000000..ba81e98 --- /dev/null +++ b/crates/hvec-core/src/codecs/hamming.rs @@ -0,0 +1,191 @@ +//! Symmetric binary scoring: sign bits on both sides, compared by popcount. +//! +//! `binary-hamming` stores exactly what `binary` stores, one sign bit per +//! dimension. The difference is the query. `binary` scores the f32 query +//! against the bits (asymmetric: the query keeps its magnitudes). +//! `binary-hamming` binarises the query too, once per search in +//! [`Codec::prepare_query`], and scores each stored vector with XOR and +//! popcount over 64-bit words. That is the form whose cost is set by the bytes +//! read, so it is the binary codec a speed claim can be made for. +//! +//! Error model: approximate, and coarser than `binary`, because both sides are +//! quantized. For sign random-projection codes the fraction of differing bits +//! estimates the angle between the vectors divided by π (Charikar 2002), so +//! the score is `cos(π · hamming / d)`, an estimate of cosine similarity. +//! `Dot` is treated as cosine (embeddings are unit length); `L2` is the +//! unit-vector distance `2 − 2·cos`. Unlike every other codec, `score` does +//! not equal the metric on the decoded vector, because the query is quantized +//! as well; tests check it against the Hamming distance instead. + +use crate::codecs::{check_dim, check_payload}; +use crate::{Codec, CodecError, Encoded, Metric, Vector}; + +/// Sign bits, scored symmetrically by Hamming distance. +#[derive(Debug, Clone)] +pub struct HammingCodec { + dimension: usize, +} + +impl HammingCodec { + #[must_use] + pub fn new(dimension: usize) -> Self { + Self { dimension } + } + + fn pack(&self, v: &[f32]) -> Vec { + let mut out = vec![0u8; self.encoded_size()]; + for (i, &x) in v.iter().enumerate() { + if x > 0.0 { + out[i / 8] |= 1 << (i % 8); + } + } + out + } + + /// Number of 64-bit words a code occupies, the last one zero-padded. + fn words(&self) -> usize { + self.encoded_size().div_ceil(8) + } + + fn word(bytes: &[u8], j: usize) -> u64 { + let start = j * 8; + let end = (start + 8).min(bytes.len()); + let mut w = [0u8; 8]; + w[..end - start].copy_from_slice(&bytes[start..end]); + u64::from_le_bytes(w) + } + + /// Hamming distance between a prepared query and a stored code. + fn hamming(&self, prepared: &[f32], encoded: &[u8]) -> u32 { + let mut h = 0u32; + if encoded.len() % 8 == 0 { + for (j, chunk) in encoded.chunks_exact(8).enumerate() { + let x = u64::from_le_bytes(chunk.try_into().unwrap_or([0; 8])); + h += (x ^ Self::query_word(prepared, j)).count_ones(); + } + } else { + for j in 0..self.words() { + h += (Self::word(encoded, j) ^ Self::query_word(prepared, j)).count_ones(); + } + } + h + } + + /// The prepared query holds its packed sign bits, 32 per f32 slot (the + /// slot's bit pattern, not a number), two slots per 64-bit word. + fn query_word(prepared: &[f32], j: usize) -> u64 { + u64::from(prepared[2 * j].to_bits()) | (u64::from(prepared[2 * j + 1].to_bits()) << 32) + } +} + +impl Codec for HammingCodec { + fn name(&self) -> &str { + "binary-hamming" + } + + fn dimension(&self) -> usize { + self.dimension + } + + fn encoded_size(&self) -> usize { + self.dimension.div_ceil(8) + } + + fn supported_metrics(&self) -> &'static [Metric] { + &[Metric::Cosine, Metric::Dot, Metric::L2] + } + + fn encode(&self, vector: &[f32]) -> Result { + check_dim(self.dimension, vector.len())?; + Ok(Encoded(self.pack(vector))) + } + + /// The same reconstruction as `binary`: ±1/√d per dimension. + fn decode(&self, encoded: &[u8]) -> Result { + check_payload(self.encoded_size(), encoded.len())?; + let mag = (1.0 / (self.dimension as f64).sqrt()) as f32; + Ok((0..self.dimension) + .map(|i| if encoded[i / 8] >> (i % 8) & 1 == 1 { mag } else { -mag }) + .collect()) + } + + fn score(&self, query: &[f32], encoded: &[u8], metric: Metric) -> Result { + let prepared = self.prepare_query(query)?; + self.score_prepared(&prepared, encoded, metric) + } + + /// Binarise and pack the query once per search. + fn prepare_query(&self, query: &[f32]) -> Result { + check_dim(self.dimension, query.len())?; + let mut bytes = self.pack(query); + bytes.resize(self.words() * 8, 0); + Ok(bytes + .chunks_exact(4) + .map(|c| f32::from_bits(u32::from_le_bytes([c[0], c[1], c[2], c[3]]))) + .collect()) + } + + fn score_prepared(&self, prepared: &[f32], encoded: &[u8], metric: Metric) -> Result { + check_payload(self.encoded_size(), encoded.len())?; + if prepared.len() != self.words() * 2 { + return Err(CodecError::DimensionMismatch { + expected: self.words() * 2, + got: prepared.len(), + }); + } + let h = self.hamming(prepared, encoded); + let cos = (std::f64::consts::PI * f64::from(h) / self.dimension as f64).cos(); + Ok(match metric { + Metric::Cosine | Metric::Dot => cos as f32, + Metric::L2 => (2.0 - 2.0 * cos) as f32, + }) + } +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::codec_by_name; + + fn naive_hamming(a: &[f32], b: &[f32]) -> u32 { + a.iter().zip(b).filter(|(x, y)| (**x > 0.0) != (**y > 0.0)).count() as u32 + } + + #[test] + fn popcount_equals_the_naive_hamming_distance() { + for dim in [384, 100, 7, 64, 65] { + let c = HammingCodec::new(dim); + let a: Vec = (0..dim).map(|i| ((i * 37 % 11) as f32) - 5.0).collect(); + let b: Vec = (0..dim).map(|i| ((i * 13 % 7) as f32) - 3.0).collect(); + let p = c.prepare_query(&a).unwrap(); + let e = c.encode(&b).unwrap(); + assert_eq!(c.hamming(&p, &e), naive_hamming(&a, &b), "dim {dim}"); + let want = (std::f64::consts::PI * f64::from(naive_hamming(&a, &b)) / dim as f64).cos() as f32; + assert_eq!(c.score(&a, &e, Metric::Cosine).unwrap(), want); + assert_eq!(c.score_prepared(&p, &e, Metric::Cosine).unwrap(), want); + } + } + + #[test] + fn identical_signs_score_one_and_opposite_minus_one() { + let c = HammingCodec::new(16); + let v: Vec = (0..16).map(|i| if i % 3 == 0 { 1.0 } else { -1.0 }).collect(); + let neg: Vec = v.iter().map(|x| -x).collect(); + assert_eq!(c.score(&v, &c.encode(&v).unwrap(), Metric::Cosine).unwrap(), 1.0); + assert!((c.score(&v, &c.encode(&neg).unwrap(), Metric::Cosine).unwrap() + 1.0).abs() < 1e-6); + assert!(c.score(&v, &c.encode(&v).unwrap(), Metric::L2).unwrap().abs() < 1e-6); + } + + #[test] + fn stores_the_same_bits_as_binary_and_is_built_by_name() { + let c = codec_by_name("binary-hamming", 24).unwrap(); + let b = codec_by_name("binary", 24).unwrap(); + let v: Vec = (0..24).map(|i| (i as f32 * 0.7).sin()).collect(); + assert_eq!(c.encode(&v).unwrap(), b.encode(&v).unwrap()); + assert_eq!(c.encoded_size(), 3); + assert!( + c.score_prepared(&[0.0; 3], &c.encode(&v).unwrap(), Metric::Cosine) + .is_err() + ); + } +} diff --git a/crates/hvec-core/src/codecs/mixed.rs b/crates/hvec-core/src/codecs/mixed.rs index fda7b35..5d2a476 100644 --- a/crates/hvec-core/src/codecs/mixed.rs +++ b/crates/hvec-core/src/codecs/mixed.rs @@ -111,8 +111,8 @@ impl MixedCodec { } /// Calls `f(dimension, reconstructed value)` for every dimension. - fn for_each_value(fit: &Fitted, encoded: &Encoded, mut f: impl FnMut(usize, f64)) { - let mut reader = BitReader::new(&encoded.0); + fn for_each_value(fit: &Fitted, encoded: &[u8], mut f: impl FnMut(usize, f64)) { + let mut reader = BitReader::new(encoded); for (i, &b) in fit.bits.iter().enumerate() { let m = f64::from(fit.mean[i]); if b == 0 { @@ -447,8 +447,8 @@ impl Codec for MixedCodec { Ok(Encoded(w.finish(self.bytes))) } - fn decode(&self, encoded: &Encoded) -> Result { - check_payload(self.bytes, encoded.0.len())?; + fn decode(&self, encoded: &[u8]) -> Result { + check_payload(self.bytes, encoded.len())?; let f = self.state()?; let mut y = vec![0f32; self.dimension]; Self::for_each_value(f, encoded, |i, v| y[i] = v as f32); @@ -468,7 +468,7 @@ impl Codec for MixedCodec { }) } - fn score(&self, query: &[f32], encoded: &Encoded, metric: Metric) -> Result { + fn score(&self, query: &[f32], encoded: &[u8], metric: Metric) -> Result { let prepared = self.prepare_query(query)?; self.score_prepared(&prepared, encoded, metric) } @@ -478,9 +478,9 @@ impl Codec for MixedCodec { Ok(Self::rotate(self.state()?, query)) } - fn score_prepared(&self, prepared: &[f32], encoded: &Encoded, metric: Metric) -> Result { + fn score_prepared(&self, prepared: &[f32], encoded: &[u8], metric: Metric) -> Result { check_dim(self.dimension, prepared.len())?; - check_payload(self.bytes, encoded.0.len())?; + check_payload(self.bytes, encoded.len())?; let f = self.state()?; let (mut dot, mut stored_sq, mut query_sq) = (0f64, 0f64, 0f64); Self::for_each_value(f, encoded, |i, v| { diff --git a/crates/hvec-core/src/codecs/mod.rs b/crates/hvec-core/src/codecs/mod.rs index e8eec2f..fa2e408 100644 --- a/crates/hvec-core/src/codecs/mod.rs +++ b/crates/hvec-core/src/codecs/mod.rs @@ -5,15 +5,17 @@ //! approximate the reference distance on the original f32 vector within the //! codec's documented error model. +mod hamming; mod mixed; mod pq; mod truncate; +pub use hamming::HammingCodec; pub use mixed::MixedCodec; pub use pq::PqCodec; pub use truncate::TruncateCodec; -use crate::{Codec, CodecError, Encoded, Metric, Vector, distance}; +use crate::{Codec, CodecError, Encoded, Metric, Vector}; pub(crate) fn check_dim(expected: usize, got: usize) -> Result<(), CodecError> { if got == expected { @@ -73,20 +75,38 @@ impl Codec for F32Codec { Ok(Encoded(vector.iter().flat_map(|v| v.to_le_bytes()).collect())) } - fn decode(&self, encoded: &Encoded) -> Result { - check_payload(self.encoded_size(), encoded.0.len())?; + fn decode(&self, encoded: &[u8]) -> Result { + check_payload(self.encoded_size(), encoded.len())?; Ok(encoded - .0 .chunks_exact(4) .map(|c| f32::from_le_bytes([c[0], c[1], c[2], c[3]])) .collect()) } - fn score(&self, query: &[f32], encoded: &Encoded, metric: Metric) -> Result { + /// Reads the stored floats in place, in one fused pass: nothing is + /// allocated per vector. The three running sums accumulate in the same + /// order as [`distance::score`], so the result is identical to scoring the + /// decoded vector. + fn score(&self, query: &[f32], encoded: &[u8], metric: Metric) -> Result { check_dim(self.dimension, query.len())?; - // Baseline: decoding is the documented behaviour here. - let stored = self.decode(encoded)?; - Ok(distance::score(query, &stored, metric)) + check_payload(self.encoded_size(), encoded.len())?; + let stored = encoded + .chunks_exact(4) + .map(|c| f32::from_le_bytes([c[0], c[1], c[2], c[3]])); + Ok(match metric { + Metric::Dot => query.iter().zip(stored).map(|(q, x)| q * x).sum(), + Metric::Cosine => { + let (mut dot, mut qq, mut xx) = (0f32, 0f32, 0f32); + for (&q, x) in query.iter().zip(stored) { + dot += q * x; + qq += q * q; + xx += x * x; + } + let denom = qq.sqrt() * xx.sqrt(); + if denom == 0.0 { 0.0 } else { dot / denom } + } + Metric::L2 => query.iter().zip(stored).map(|(q, x)| (q - x) * (q - x)).sum(), + }) } } @@ -159,16 +179,16 @@ impl Codec for Int8Codec { Ok(Encoded(out)) } - fn decode(&self, encoded: &Encoded) -> Result { - check_payload(self.encoded_size(), encoded.0.len())?; - let (min, scale, q) = Self::split(&encoded.0); + fn decode(&self, encoded: &[u8]) -> Result { + check_payload(self.encoded_size(), encoded.len())?; + let (min, scale, q) = Self::split(encoded); Ok(q.iter().map(|&b| min + scale * f32::from(b)).collect()) } - fn score(&self, query: &[f32], encoded: &Encoded, metric: Metric) -> Result { + fn score(&self, query: &[f32], encoded: &[u8], metric: Metric) -> Result { check_dim(self.dimension, query.len())?; - check_payload(self.encoded_size(), encoded.0.len())?; - let (min, scale, q) = Self::split(&encoded.0); + check_payload(self.encoded_size(), encoded.len())?; + let (min, scale, q) = Self::split(encoded); let (min, scale) = (f64::from(min), f64::from(scale)); // One pass: everything below is derived from these five sums. @@ -257,27 +277,21 @@ impl Codec for BinaryCodec { Ok(Encoded(out)) } - fn decode(&self, encoded: &Encoded) -> Result { - check_payload(self.encoded_size(), encoded.0.len())?; + fn decode(&self, encoded: &[u8]) -> Result { + check_payload(self.encoded_size(), encoded.len())?; let mag = self.inv_sqrt_d() as f32; Ok((0..self.dimension) - .map(|i| { - if encoded.0[i / 8] >> (i % 8) & 1 == 1 { - mag - } else { - -mag - } - }) + .map(|i| if encoded[i / 8] >> (i % 8) & 1 == 1 { mag } else { -mag }) .collect()) } - fn score(&self, query: &[f32], encoded: &Encoded, metric: Metric) -> Result { + fn score(&self, query: &[f32], encoded: &[u8], metric: Metric) -> Result { check_dim(self.dimension, query.len())?; - check_payload(self.encoded_size(), encoded.0.len())?; + check_payload(self.encoded_size(), encoded.len())?; let (mut signed, mut sum_xx) = (0f64, 0f64); for (i, &x) in query.iter().enumerate() { let x = f64::from(x); - let positive = encoded.0[i / 8] >> (i % 8) & 1 == 1; + let positive = encoded[i / 8] >> (i % 8) & 1 == 1; signed += if positive { x } else { -x }; sum_xx += x * x; } @@ -474,14 +488,14 @@ impl Codec for BinaryCentredCodec { Ok(Encoded(out)) } - fn decode(&self, encoded: &Encoded) -> Result { - check_payload(self.encoded_size(), encoded.0.len())?; + fn decode(&self, encoded: &[u8]) -> Result { + check_payload(self.encoded_size(), encoded.len())?; let c = self.centring()?; Ok(c.mean .iter() .enumerate() .map(|(i, &m)| { - if encoded.0[i / 8] >> (i % 8) & 1 == 1 { + if encoded[i / 8] >> (i % 8) & 1 == 1 { m + c.scale } else { m - c.scale @@ -490,16 +504,16 @@ impl Codec for BinaryCentredCodec { .collect()) } - fn score(&self, query: &[f32], encoded: &Encoded, metric: Metric) -> Result { + fn score(&self, query: &[f32], encoded: &[u8], metric: Metric) -> Result { check_dim(self.dimension, query.len())?; - check_payload(self.encoded_size(), encoded.0.len())?; + check_payload(self.encoded_size(), encoded.len())?; let c = self.centring()?; let alpha = f64::from(c.scale); // Running sums over one pass: q·μ, q·s, μ·s and ‖q‖², with s = ±1. let (mut dot_qm, mut dot_qs, mut dot_ms, mut sum_qq) = (0f64, 0f64, 0f64, 0f64); for (i, (&q, &m)) in query.iter().zip(&c.mean).enumerate() { let (q, m) = (f64::from(q), f64::from(m)); - let positive = encoded.0[i / 8] >> (i % 8) & 1 == 1; + let positive = encoded[i / 8] >> (i % 8) & 1 == 1; dot_qm += q * m; sum_qq += q * q; if positive { @@ -528,6 +542,24 @@ impl Codec for BinaryCentredCodec { #[cfg(test)] mod tests { use super::*; + use crate::distance; + + #[test] + fn f32_scores_in_place_identically_to_the_reference() { + let mut rng = XorShift(31); + let c = F32Codec::new(384); + for _ in 0..50 { + let q = rng.vector(384); + let v = rng.vector(384); + let e = c.encode(&v).unwrap(); + for m in [Metric::Dot, Metric::Cosine, Metric::L2] { + assert_eq!(c.score(&q, &e, m).unwrap(), distance::score(&q, &v, m), "{m:?}"); + } + // The fused cosine matches the three-pass formula bit for bit. + let three_pass = distance::dot(&q, &v) / (distance::norm(&q) * distance::norm(&v)); + assert_eq!(distance::cosine(&q, &v), three_pass); + } + } /// Small deterministic generator so tests need no external crate. struct XorShift(u64); diff --git a/crates/hvec-core/src/codecs/pq.rs b/crates/hvec-core/src/codecs/pq.rs index d3bd372..b474f48 100644 --- a/crates/hvec-core/src/codecs/pq.rs +++ b/crates/hvec-core/src/codecs/pq.rs @@ -249,11 +249,11 @@ impl Codec for PqCodec { Ok(Encoded(codes)) } - fn decode(&self, encoded: &Encoded) -> Result { - check_payload(self.encoded_size(), encoded.0.len())?; + fn decode(&self, encoded: &[u8]) -> Result { + check_payload(self.encoded_size(), encoded.len())?; let books = self.books()?; let mut out = Vec::with_capacity(self.dimension); - for (j, &code) in encoded.0.iter().enumerate() { + for (j, &code) in encoded.iter().enumerate() { let c = usize::from(code); if c >= books.k { return Err(CodecError::BadParams { @@ -266,12 +266,12 @@ impl Codec for PqCodec { Ok(out) } - fn score(&self, query: &[f32], encoded: &Encoded, metric: Metric) -> Result { + fn score(&self, query: &[f32], encoded: &[u8], metric: Metric) -> Result { check_dim(self.dimension, query.len())?; - check_payload(self.encoded_size(), encoded.0.len())?; + check_payload(self.encoded_size(), encoded.len())?; let books = self.books()?; let (mut dot, mut stored_norm_sq, mut sum_qq) = (0f64, 0f64, 0f64); - for (j, &code) in encoded.0.iter().enumerate() { + for (j, &code) in encoded.iter().enumerate() { let c = usize::from(code); if c >= books.k { return Err(CodecError::BadParams { diff --git a/crates/hvec-core/src/codecs/truncate.rs b/crates/hvec-core/src/codecs/truncate.rs index 6c5398c..5f1bc22 100644 --- a/crates/hvec-core/src/codecs/truncate.rs +++ b/crates/hvec-core/src/codecs/truncate.rs @@ -110,13 +110,13 @@ impl Codec for TruncateCodec { self.inner.encode(self.prefix(vector)?) } - fn decode(&self, encoded: &Encoded) -> Result { + fn decode(&self, encoded: &[u8]) -> Result { let mut v = self.inner.decode(encoded)?; v.resize(self.dimension, 0.0); Ok(v) } - fn score(&self, query: &[f32], encoded: &Encoded, metric: Metric) -> Result { + fn score(&self, query: &[f32], encoded: &[u8], metric: Metric) -> Result { self.inner.score(self.prefix(query)?, encoded, metric) } @@ -126,7 +126,7 @@ impl Codec for TruncateCodec { self.inner.prepare_query(self.prefix(query)?) } - fn score_prepared(&self, prepared: &[f32], encoded: &Encoded, metric: Metric) -> Result { + fn score_prepared(&self, prepared: &[f32], encoded: &[u8], metric: Metric) -> Result { self.inner.score_prepared(prepared, encoded, metric) } } diff --git a/crates/hvec-core/src/distance.rs b/crates/hvec-core/src/distance.rs index a08d7cb..6b406b1 100644 --- a/crates/hvec-core/src/distance.rs +++ b/crates/hvec-core/src/distance.rs @@ -18,8 +18,18 @@ pub fn norm(a: &[f32]) -> f32 { /// Cosine similarity. Returns 0 when either vector has zero norm. #[must_use] pub fn cosine(a: &[f32], b: &[f32]) -> f32 { - let denom = norm(a) * norm(b); - if denom == 0.0 { 0.0 } else { dot(a, b) / denom } + debug_assert_eq!(a.len(), b.len()); + // One pass over both vectors. Each of the three sums accumulates in the + // same order as `dot` and `norm` would, so the result is identical to + // `dot(a, b) / (norm(a) * norm(b))`, at a third of the memory traffic. + let (mut ab, mut aa, mut bb) = (0f32, 0f32, 0f32); + for (&x, &y) in a.iter().zip(b) { + ab += x * y; + aa += x * x; + bb += y * y; + } + let denom = aa.sqrt() * bb.sqrt(); + if denom == 0.0 { 0.0 } else { ab / denom } } /// Squared Euclidean distance. diff --git a/crates/hvec-core/src/lib.rs b/crates/hvec-core/src/lib.rs index b097b3a..d637573 100644 --- a/crates/hvec-core/src/lib.rs +++ b/crates/hvec-core/src/lib.rs @@ -82,9 +82,27 @@ pub enum CodecError { } /// A compressed representation of a vector, as stored on disk. +/// +/// This is the owned form [`Codec::encode`] returns. Scoring and decoding +/// borrow the bytes instead (`&[u8]`), so a codec can score codes in place: +/// in a contiguous buffer, a memory-mapped file or a database page. `Encoded` +/// dereferences to `[u8]`, so `&encoded` works wherever `&[u8]` is expected. #[derive(Debug, Clone, PartialEq, Eq)] pub struct Encoded(pub Vec); +impl std::ops::Deref for Encoded { + type Target = [u8]; + fn deref(&self) -> &[u8] { + &self.0 + } +} + +impl AsRef<[u8]> for Encoded { + fn as_ref(&self) -> &[u8] { + &self.0 + } +} + /// A compression scheme whose compressed form supports distance computation. /// /// Implementations must document their error model: exact, bounded, or @@ -108,13 +126,13 @@ pub trait Codec: Send + Sync + std::fmt::Debug { fn encode(&self, vector: &[f32]) -> Result; /// Reconstruct an approximation of the original vector. - fn decode(&self, encoded: &Encoded) -> Result; + fn decode(&self, encoded: &[u8]) -> Result; /// Score an uncompressed query against a compressed stored vector. /// /// This is the asymmetric form. It must not decode `encoded` into a full /// `Vector` unless the codec documents that it is a baseline. - fn score(&self, query: &[f32], encoded: &Encoded, metric: Metric) -> Result; + fn score(&self, query: &[f32], encoded: &[u8], metric: Metric) -> Result; /// Transform a query once per search, before it is scored against many /// stored vectors with [`Codec::score_prepared`]. @@ -131,7 +149,7 @@ pub trait Codec: Send + Sync + std::fmt::Debug { /// `score(query, …)` for the original query. The default forwards to /// [`Codec::score`], which is right whenever `prepare_query` is the /// identity. - fn score_prepared(&self, prepared: &[f32], encoded: &Encoded, metric: Metric) -> Result { + fn score_prepared(&self, prepared: &[f32], encoded: &[u8], metric: Metric) -> Result { self.score(prepared, encoded, metric) } @@ -184,7 +202,8 @@ pub trait Codec: Send + Sync + std::fmt::Debug { /// Instantiate a codec by its stable name, unfitted. /// -/// Names: `f32`, `int8`, `binary`, `binary-centred`, `pq` (product +/// Names: `f32`, `int8`, `binary`, `binary-centred`, `binary-hamming` (the +/// same bits as `binary`, scored symmetrically by popcount), `pq` (product /// quantization with `m` subspaces; bare `pq` means one byte per 8 /// dimensions), `mrl` (keep the first `k` dimensions, Matryoshka style) /// `mrl+` (truncate, then encode with another codec, for @@ -200,6 +219,7 @@ pub fn codec_by_name(name: &str, dimension: usize) -> Result, Cod "int8" => Ok(Box::new(codecs::Int8Codec::new(dimension))), "binary" => Ok(Box::new(codecs::BinaryCodec::new(dimension))), "binary-centred" => Ok(Box::new(codecs::BinaryCentredCodec::new(dimension))), + "binary-hamming" => Ok(Box::new(codecs::HammingCodec::new(dimension))), _ => { if let Some(rest) = name.strip_prefix("pq") { let m = parse_param(name, rest, Some(dimension / 8))?; @@ -276,6 +296,7 @@ pub fn codec_names() -> &'static [&'static str] { "int8", "binary", "binary-centred", + "binary-hamming", "pq", "mrl", "mrl+", From 2f4ad88efc19aa060d664169a3a074a3ba250820 Mon Sep 17 00:00:00 2001 From: Swinecoder101 Date: Wed, 4 Nov 2026 22:15:15 +0000 Subject: [PATCH 06/12] feat(store): columnar code buffers and cached in-place search CodeBuffer holds a collection's codes back to back in one allocation, with ids and sources as side arrays, and scans them in place: the query is prepared once, each code is a borrowed slice, a k-sized heap keeps the best hits, and the scan can split over scoped threads with identical results. It can be written to a flat code file and read back with one read. A memory-mapped file is left out: it needs unsafe, which the workspace forbids. Store::search now scans a cached buffer per collection and reads source, ordinal and text for the top k only. The cache is dropped on this handle's writes and when another connection commits (PRAGMA data_version). load_codes builds a buffer from borrowed blobs (one copy in all); search_borrowed scans SQLite's rows in place; set_mmap_size turns on SQLite's own memory map; search_by_rows keeps the original path for measurement. Tests: buffered search equals the row scan, writes and other connections' commits are seen. --- crates/hvec-store/src/codes.rs | 393 +++++++++++++++++++++++++++++++ crates/hvec-store/src/lib.rs | 21 +- crates/hvec-store/src/vectors.rs | 199 +++++++++++++++- 3 files changed, 610 insertions(+), 3 deletions(-) create mode 100644 crates/hvec-store/src/codes.rs diff --git a/crates/hvec-store/src/codes.rs b/crates/hvec-store/src/codes.rs new file mode 100644 index 0000000..3454615 --- /dev/null +++ b/crates/hvec-store/src/codes.rs @@ -0,0 +1,393 @@ +//! A collection's codes in one contiguous buffer, scanned in place. +//! +//! The SQLite table is the system of record, one row per chunk. Scanning it +//! row by row on every query copies every blob (and, for search, every +//! chunk's text) out of the database. [`CodeBuffer`] is the columnar form: +//! the codes back to back in a single allocation, with ids and sources as side +//! arrays. Scoring borrows each code as a `&[u8]` slice of the buffer, so +//! nothing is copied or allocated per vector, and the text is read only for +//! the hits. +//! +//! A buffer can be written to a flat file and read back with one read. A +//! memory-mapped file would avoid even that copy, but needs `unsafe` (the OS +//! may change a mapped file underneath safe code), which this workspace +//! forbids; SQLite's own memory map is available instead through +//! [`Store::set_mmap_size`](crate::Store::set_mmap_size). + +use std::cmp::Ordering; +use std::collections::BinaryHeap; +use std::path::Path; + +use hvec_core::{Codec, CodecError, Metric}; + +const MAGIC: &[u8; 8] = b"HVECODE1"; + +/// Errors reading or writing a code file. +#[derive(Debug, thiserror::Error)] +#[non_exhaustive] +pub enum CodeFileError { + #[error("could not read or write {path}")] + Io { + path: std::path::PathBuf, + #[source] + source: std::io::Error, + }, + #[error("{path} is not an hvec code file or is truncated")] + Format { path: std::path::PathBuf }, +} + +/// One collection's codes, back to back, with their chunk ids and sources. +#[derive(Debug, Clone, PartialEq, Eq)] +pub struct CodeBuffer { + code_size: usize, + ids: Vec, + sources: Vec, + codes: Vec, +} + +/// A scored position in a [`CodeBuffer`]. +#[derive(Debug, Clone, Copy, PartialEq)] +pub struct Scored { + /// Index into the buffer. + pub index: usize, + pub score: f32, +} + +/// Heap entry ordered so that the *worst* kept hit is on top. +#[derive(Debug, Clone, Copy)] +struct Entry { + key: f32, + index: usize, +} + +impl PartialEq for Entry { + fn eq(&self, other: &Self) -> bool { + self.cmp(other) == Ordering::Equal + } +} +impl Eq for Entry {} +impl PartialOrd for Entry { + fn partial_cmp(&self, other: &Self) -> Option { + Some(self.cmp(other)) + } +} +impl Ord for Entry { + // `key` is "higher is better" after orienting by the metric. The heap's + // top must be the worst: lowest key, and among equal keys the highest + // index, so earlier vectors win ties as they did in a stable sort. + fn cmp(&self, other: &Self) -> Ordering { + other.key.total_cmp(&self.key).then(self.index.cmp(&other.index)) + } +} + +impl CodeBuffer { + /// An empty buffer for codes of `code_size` bytes. + #[must_use] + pub fn new(code_size: usize) -> Self { + Self { + code_size, + ids: Vec::new(), + sources: Vec::new(), + codes: Vec::new(), + } + } + + /// Append one code. Panics if its length is not the buffer's code size. + pub fn push(&mut self, id: i64, source: String, code: &[u8]) { + assert_eq!(code.len(), self.code_size, "code size mismatch"); + self.ids.push(id); + self.sources.push(source); + self.codes.extend_from_slice(code); + } + + /// Re-encode f32 vectors with `codec` into a new buffer. + pub fn encode( + codec: &dyn Codec, + ids: &[i64], + sources: &[String], + vectors: &[Vec], + ) -> Result { + let mut b = Self::new(codec.encoded_size()); + b.codes.reserve(vectors.len() * b.code_size); + for ((id, source), v) in ids.iter().zip(sources).zip(vectors) { + b.push(*id, source.clone(), &codec.encode(v)?); + } + Ok(b) + } + + #[must_use] + pub fn len(&self) -> usize { + self.ids.len() + } + + #[must_use] + pub fn is_empty(&self) -> bool { + self.ids.is_empty() + } + + #[must_use] + pub fn code_size(&self) -> usize { + self.code_size + } + + #[must_use] + pub fn ids(&self) -> &[i64] { + &self.ids + } + + #[must_use] + pub fn sources(&self) -> &[String] { + &self.sources + } + + /// The whole code region, `len() × code_size()` bytes. + #[must_use] + pub fn bytes(&self) -> &[u8] { + &self.codes + } + + /// The code at `index`, borrowed from the buffer. + #[must_use] + pub fn code(&self, index: usize) -> &[u8] { + &self.codes[index * self.code_size..(index + 1) * self.code_size] + } + + /// Score every code and keep the best `k`, best first. The query is + /// prepared once; each code is scored in place. + pub fn search( + &self, + query: &[f32], + k: usize, + codec: &dyn Codec, + metric: Metric, + ) -> Result, CodecError> { + self.search_threads(query, k, codec, metric, 1) + } + + /// As [`CodeBuffer::search`], with the scan split over `threads` scoped + /// threads. Results are identical to the single-threaded scan. + pub fn search_threads( + &self, + query: &[f32], + k: usize, + codec: &dyn Codec, + metric: Metric, + threads: usize, + ) -> Result, CodecError> { + if codec.encoded_size() != self.code_size { + return Err(CodecError::PayloadSize { + expected: codec.encoded_size(), + got: self.code_size, + }); + } + let prepared = codec.prepare_query(query)?; + let higher = metric.higher_is_better(); + let n = self.len(); + let threads = threads.clamp(1, n.max(1)); + let scan = |lo: usize, hi: usize| -> Result, CodecError> { + let mut heap = BinaryHeap::with_capacity(k + 1); + if k == 0 { + return Ok(heap); + } + for (offset, code) in self.codes[lo * self.code_size..hi * self.code_size] + .chunks_exact(self.code_size) + .enumerate() + { + let s = codec.score_prepared(&prepared, code, metric)?; + let e = Entry { + key: if higher { s } else { -s }, + index: lo + offset, + }; + if heap.len() < k { + heap.push(e); + } else if heap.peek().is_some_and(|worst| e < *worst) { + heap.pop(); + heap.push(e); + } + } + Ok(heap) + }; + let heaps: Vec> = if threads == 1 { + vec![scan(0, n)?] + } else { + let per = n.div_ceil(threads); + std::thread::scope(|s| { + let handles: Vec<_> = (0..threads) + .map(|t| { + let (lo, hi) = ((t * per).min(n), ((t + 1) * per).min(n)); + s.spawn(move || scan(lo, hi)) + }) + .collect(); + handles + .into_iter() + .map(|h| h.join().expect("scan thread panicked")) + .collect::, _>>() + })? + }; + let mut all: Vec = heaps.into_iter().flat_map(BinaryHeap::into_vec).collect(); + all.sort(); + all.truncate(k); + Ok(all + .into_iter() + .map(|e| Scored { + index: e.index, + score: if higher { e.key } else { -e.key }, + }) + .collect()) + } + + /// Write the buffer as a flat file: a header, the ids, then the codes. + /// Sources are not written; they stay in the database. + pub fn write_file(&self, path: &Path) -> Result<(), CodeFileError> { + let mut out = Vec::with_capacity(24 + self.len() * (8 + self.code_size)); + out.extend_from_slice(MAGIC); + out.extend_from_slice(&(self.code_size as u64).to_le_bytes()); + out.extend_from_slice(&(self.len() as u64).to_le_bytes()); + for id in &self.ids { + out.extend_from_slice(&id.to_le_bytes()); + } + out.extend_from_slice(&self.codes); + std::fs::write(path, out).map_err(|source| CodeFileError::Io { + path: path.to_path_buf(), + source, + }) + } + + /// Read a file written by [`CodeBuffer::write_file`] with a single read. + /// Sources come back empty. + pub fn read_file(path: &Path) -> Result { + let data = std::fs::read(path).map_err(|source| CodeFileError::Io { + path: path.to_path_buf(), + source, + })?; + let bad = || CodeFileError::Format { + path: path.to_path_buf(), + }; + if data.len() < 24 || &data[..8] != MAGIC { + return Err(bad()); + } + let u64_at = |o: usize| u64::from_le_bytes(data[o..o + 8].try_into().unwrap_or([0; 8])); + let code_size = usize::try_from(u64_at(8)).map_err(|_| bad())?; + let n = usize::try_from(u64_at(16)).map_err(|_| bad())?; + let ids_end = 24usize.checked_add(n.checked_mul(8).ok_or_else(bad)?).ok_or_else(bad)?; + let end = ids_end + .checked_add(n.checked_mul(code_size).ok_or_else(bad)?) + .ok_or_else(bad)?; + if data.len() != end { + return Err(bad()); + } + let ids = data[24..ids_end] + .chunks_exact(8) + .map(|c| i64::from_le_bytes(c.try_into().unwrap_or([0; 8]))) + .collect(); + let mut codes = data; + codes.drain(..ids_end); + Ok(Self { + code_size, + ids, + sources: vec![String::new(); n], + codes, + }) + } +} + +#[cfg(test)] +mod tests { + use super::*; + use hvec_core::codecs::{BinaryCodec, F32Codec, HammingCodec, Int8Codec}; + use hvec_core::distance; + + fn corpus(n: usize, dim: usize) -> Vec> { + let mut s = 12345u64; + (0..n) + .map(|_| { + let mut v: Vec = (0..dim) + .map(|_| { + s ^= s << 13; + s ^= s >> 7; + s ^= s << 17; + ((s >> 40) as f32 / (1u64 << 24) as f32) * 2.0 - 1.0 + }) + .collect(); + distance::normalize(&mut v); + v + }) + .collect() + } + + fn reference(codec: &dyn Codec, q: &[f32], vs: &[Vec], k: usize, metric: Metric) -> Vec<(usize, f32)> { + let mut all: Vec<(usize, f32)> = vs + .iter() + .enumerate() + .map(|(i, v)| (i, codec.score(q, &codec.encode(v).unwrap(), metric).unwrap())) + .collect(); + if metric.higher_is_better() { + all.sort_by(|a, b| b.1.total_cmp(&a.1).then(a.0.cmp(&b.0))); + } else { + all.sort_by(|a, b| a.1.total_cmp(&b.1).then(a.0.cmp(&b.0))); + } + all.truncate(k); + all + } + + #[test] + fn buffer_search_matches_a_full_sort_for_every_codec_and_thread_count() { + let vs = corpus(500, 32); + let ids: Vec = (0..500).map(|i| i * 3 + 1).collect(); + let sources: Vec = (0..500).map(|i| format!("doc{i}.txt")).collect(); + let codecs: Vec> = vec![ + Box::new(F32Codec::new(32)), + Box::new(Int8Codec::new(32)), + Box::new(BinaryCodec::new(32)), + Box::new(HammingCodec::new(32)), + ]; + for codec in &codecs { + let buf = CodeBuffer::encode(codec.as_ref(), &ids, &sources, &vs).unwrap(); + assert_eq!(buf.len(), 500); + assert_eq!(buf.bytes().len(), 500 * codec.encoded_size()); + for metric in [Metric::Cosine, Metric::L2] { + let want = reference(codec.as_ref(), &vs[7], &vs, 10, metric); + for threads in [1, 3, 8] { + let got = buf.search_threads(&vs[7], 10, codec.as_ref(), metric, threads).unwrap(); + let got: Vec<(usize, f32)> = got.iter().map(|s| (s.index, s.score)).collect(); + assert_eq!(got, want, "{} {metric:?} threads={threads}", codec.name()); + } + } + } + } + + #[test] + fn ties_keep_the_earlier_vector_and_k_is_respected() { + let codec = BinaryCodec::new(8); + let v = vec![vec![1.0f32; 8]; 5]; + let ids: Vec = (0..5).collect(); + let buf = CodeBuffer::encode(&codec, &ids, &vec![String::new(); 5], &v).unwrap(); + let got = buf.search(&v[0], 3, &codec, Metric::Cosine).unwrap(); + assert_eq!(got.iter().map(|s| s.index).collect::>(), vec![0, 1, 2]); + assert!(buf.search(&v[0], 0, &codec, Metric::Cosine).unwrap().is_empty()); + assert_eq!(buf.search(&v[0], 99, &codec, Metric::Cosine).unwrap().len(), 5); + assert!(buf.search(&v[0], 3, &Int8Codec::new(8), Metric::Cosine).is_err()); + } + + #[test] + fn code_files_roundtrip_and_reject_damage() { + let vs = corpus(40, 16); + let ids: Vec = (100..140).collect(); + let codec = Int8Codec::new(16); + let buf = CodeBuffer::encode(&codec, &ids, &vec![String::new(); 40], &vs).unwrap(); + let dir = tempfile::tempdir().unwrap(); + let path = dir.path().join("c.codes"); + buf.write_file(&path).unwrap(); + let back = CodeBuffer::read_file(&path).unwrap(); + assert_eq!(back, buf); + let mut data = std::fs::read(&path).unwrap(); + data.pop(); + std::fs::write(&path, &data).unwrap(); + assert!(matches!( + CodeBuffer::read_file(&path), + Err(CodeFileError::Format { .. }) + )); + std::fs::write(&path, b"not a code file at all.").unwrap(); + assert!(CodeBuffer::read_file(&path).is_err()); + } +} diff --git a/crates/hvec-store/src/lib.rs b/crates/hvec-store/src/lib.rs index 8348247..cac8128 100644 --- a/crates/hvec-store/src/lib.rs +++ b/crates/hvec-store/src/lib.rs @@ -6,15 +6,20 @@ //! by brute-force scan. That is deliberate: the point of hvec is to measure //! the codec, not an index. +mod codes; mod runs; mod schema; mod sessions; mod vectors; +use std::cell::RefCell; +use std::collections::HashMap; use std::path::Path; +use std::sync::Arc; use rusqlite::Connection; +pub use codes::{CodeBuffer, CodeFileError, Scored}; pub use runs::RunRow; pub use sessions::{Session, StoredMessage}; pub use vectors::{Collection, Hit, NewChunk, NewCollection}; @@ -62,6 +67,17 @@ pub enum StoreError { #[derive(Debug)] pub struct Store { conn: Connection, + /// Columnar copies of collections' codes, built on first search and + /// reused until the collection changes. Dropped on this handle's own + /// writes, and wholesale when another connection commits (SQLite's + /// `data_version` moves). + codes: RefCell, +} + +#[derive(Debug, Default)] +struct CodeCache { + data_version: i64, + by_collection: HashMap>, } impl Store { @@ -90,7 +106,10 @@ impl Store { conn.execute_batch(alter)?; } } - Ok(Self { conn }) + Ok(Self { + conn, + codes: RefCell::new(CodeCache::default()), + }) } fn has_column(conn: &Connection, table: &str, column: &str) -> Result { diff --git a/crates/hvec-store/src/vectors.rs b/crates/hvec-store/src/vectors.rs index e36aca0..bca99bc 100644 --- a/crates/hvec-store/src/vectors.rs +++ b/crates/hvec-store/src/vectors.rs @@ -4,7 +4,9 @@ use hvec_core::{Codec, CodecError, Encoded, Metric}; use rusqlite::{OptionalExtension, params}; use serde::{Deserialize, Serialize}; -use crate::{Result, Store, StoreError}; +use std::sync::Arc; + +use crate::{CodeBuffer, Result, Store, StoreError}; /// Metadata for a collection. #[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)] @@ -140,6 +142,7 @@ impl Store { if n == 0 { return Err(StoreError::NoSuchCollection(name.to_owned())); } + self.forget_codes(name); Ok(()) } @@ -156,9 +159,68 @@ impl Store { } } tx.commit()?; + self.forget_codes(collection); Ok(chunks.len()) } + /// Drop the cached code buffer for a collection after a write. + fn forget_codes(&self, collection: &str) { + self.codes.borrow_mut().by_collection.remove(collection); + } + + /// Read a collection's codes into one contiguous buffer, in id order. + /// Each blob is borrowed from SQLite's row and appended, so the whole + /// collection costs one copy, not one allocation per vector. + pub fn load_codes(&self, collection: &str) -> Result { + let c = self.require_collection(collection)?; + let size = c.codec()?.encoded_size(); + let mut buf = CodeBuffer::new(size); + let mut stmt = self + .conn + .prepare_cached("SELECT id, source, vector FROM chunks WHERE collection = ?1 ORDER BY id")?; + let mut rows = stmt.query(params![collection])?; + while let Some(row) = rows.next()? { + let blob = row.get_ref(2)?.as_blob().map_err(rusqlite::Error::from)?; + if blob.len() != size { + return Err(StoreError::Codec(CodecError::PayloadSize { + expected: size, + got: blob.len(), + })); + } + buf.push(row.get(0)?, row.get(1)?, blob); + } + Ok(buf) + } + + /// The cached code buffer for a collection, loaded on first use. + pub fn codes(&self, collection: &str) -> Result> { + let version: i64 = self.conn.query_row("PRAGMA data_version", [], |r| r.get(0))?; + { + let mut cache = self.codes.borrow_mut(); + if cache.data_version != version { + cache.by_collection.clear(); + cache.data_version = version; + } + if let Some(b) = cache.by_collection.get(collection) { + return Ok(Arc::clone(b)); + } + } + let b = Arc::new(self.load_codes(collection)?); + self.codes + .borrow_mut() + .by_collection + .insert(collection.to_owned(), Arc::clone(&b)); + Ok(b) + } + + /// Ask SQLite to memory-map up to `bytes` of the database file, so + /// borrowed blobs are read from the OS page cache without a copy into + /// SQLite's own buffers. `0` turns it off. + pub fn set_mmap_size(&self, bytes: u64) -> Result<()> { + self.conn.execute_batch(&format!("PRAGMA mmap_size = {bytes}"))?; + Ok(()) + } + /// Every stored chunk's id and encoded vector, in insertion order. /// /// Used by benchmarks that re-encode a collection with other codecs. @@ -205,7 +267,8 @@ impl Store { /// Brute-force top-k search scored through the codec. /// /// Scores every stored vector against `query` in the compressed domain, - /// then keeps the best `k` according to the metric's direction. + /// in place in the collection's cached [`CodeBuffer`], then reads source, + /// ordinal and text for the best `k` only. pub fn search( &self, collection: &str, @@ -213,6 +276,71 @@ impl Store { k: usize, codec: &dyn Codec, metric: Metric, + ) -> Result> { + let buf = self.codes(collection)?; + let top = buf.search(query, k, codec, metric)?; + let mut stmt = self + .conn + .prepare_cached("SELECT source, ordinal, text FROM chunks WHERE id = ?1")?; + let mut hits = Vec::with_capacity(top.len()); + for s in top { + let chunk_id = buf.ids()[s.index]; + let (source, ordinal, text) = stmt.query_row(params![chunk_id], |r| { + Ok((r.get::<_, String>(0)?, r.get::<_, u32>(1)?, r.get::<_, String>(2)?)) + })?; + hits.push(Hit { + chunk_id, + source, + ordinal, + text, + score: s.score, + }); + } + Ok(hits) + } + + /// Search straight off SQLite's rows with each blob borrowed in place: + /// the vector column only, no copy per vector, no text. Returns `(chunk + /// id, score)` for the best `k`. Measures what SQLite's row machinery + /// costs compared with the contiguous buffer. + pub fn search_borrowed( + &self, + collection: &str, + query: &[f32], + k: usize, + codec: &dyn Codec, + metric: Metric, + ) -> Result> { + self.require_collection(collection)?; + let prepared = codec.prepare_query(query)?; + let mut stmt = self + .conn + .prepare_cached("SELECT id, vector FROM chunks WHERE collection = ?1 ORDER BY id")?; + let mut rows = stmt.query(params![collection])?; + let mut all: Vec<(i64, f32)> = Vec::new(); + while let Some(row) = rows.next()? { + let blob = row.get_ref(1)?.as_blob().map_err(rusqlite::Error::from)?; + all.push((row.get(0)?, codec.score_prepared(&prepared, blob, metric)?)); + } + if metric.higher_is_better() { + all.sort_by(|a, b| b.1.total_cmp(&a.1).then(a.0.cmp(&b.0))); + } else { + all.sort_by(|a, b| a.1.total_cmp(&b.1).then(a.0.cmp(&b.0))); + } + all.truncate(k); + Ok(all) + } + + /// The original search path: every row, text included, read out of + /// SQLite and scored on every query. Kept to measure what the columnar + /// buffer saves; results are the same as [`Store::search`]. + pub fn search_by_rows( + &self, + collection: &str, + query: &[f32], + k: usize, + codec: &dyn Codec, + metric: Metric, ) -> Result> { self.require_collection(collection)?; let mut stmt = self @@ -290,6 +418,73 @@ mod tests { (store, F32Codec::new(2)) } + fn chunk(codec: &F32Codec, source: &str, ordinal: u32, v: [f32; 2]) -> NewChunk { + NewChunk { + source: source.into(), + ordinal, + text: format!("{source}#{ordinal}"), + vector: codec.encode(&v).unwrap(), + } + } + + #[test] + fn buffered_search_matches_the_row_scan_and_sees_new_chunks() { + let (mut store, codec) = store_with_collection(); + let chunks: Vec = (0..40) + .map(|i| { + let a = i as f32 * 0.15; + chunk(&codec, &format!("d{}", i % 7), i, [a.cos(), a.sin()]) + }) + .collect(); + store.insert_chunks("docs", &chunks).unwrap(); + let q = [0.6f32, 0.8]; + let rows = store.search_by_rows("docs", &q, 5, &codec, Metric::Cosine).unwrap(); + let buffered = store.search("docs", &q, 5, &codec, Metric::Cosine).unwrap(); + assert_eq!(rows, buffered); + assert_eq!(store.codes("docs").unwrap().len(), 40); + + // A write through this handle is visible to the next search. + store + .insert_chunks("docs", &[chunk(&codec, "new", 0, [0.6, 0.8])]) + .unwrap(); + let after = store.search("docs", &q, 1, &codec, Metric::Cosine).unwrap(); + assert_eq!(after[0].source, "new"); + assert_eq!(store.codes("docs").unwrap().len(), 41); + assert_eq!( + after, + store.search_by_rows("docs", &q, 1, &codec, Metric::Cosine).unwrap() + ); + } + + #[test] + fn another_connections_commit_invalidates_the_cache() { + let dir = tempfile::tempdir().unwrap(); + let path = dir.path().join("hvec.db"); + let codec = F32Codec::new(2); + let mut a = Store::open(&path).unwrap(); + a.create_collection(&NewCollection { + name: "docs", + embed_profile: "local", + embed_model: "test-model", + dimension: 2, + codec: "f32", + metric: Metric::Cosine, + codec_params: &[], + }) + .unwrap(); + a.insert_chunks("docs", &[chunk(&codec, "first", 0, [1.0, 0.0])]) + .unwrap(); + assert_eq!(a.codes("docs").unwrap().len(), 1); + let mut b = Store::open(&path).unwrap(); + b.insert_chunks("docs", &[chunk(&codec, "second", 0, [0.0, 1.0])]) + .unwrap(); + assert_eq!(a.codes("docs").unwrap().len(), 2); + let hit = a.search("docs", &[0.0, 1.0], 1, &codec, Metric::Cosine).unwrap(); + assert_eq!(hit[0].source, "second"); + a.set_mmap_size(1 << 20).unwrap(); + assert_eq!(a.load_codes("docs").unwrap().len(), 2); + } + #[test] fn insert_and_search_returns_nearest_first() { let (mut store, codec) = store_with_collection(); From ee4de25dbfece42209016ea0d19121a86f19cdab Mon Sep 17 00:00:00 2001 From: Swinecoder101 Date: Wed, 4 Nov 2026 22:15:15 +0000 Subject: [PATCH 07/12] feat(bench): bench scan, and the zero-copy scan experiment scripts hvec bench scan times one f32 collection three ways: the storage paths (SQLite rows copied, SQLite rows borrowed with and without SQLite's memory map, the cached buffer) and the cost of building the buffer from SQLite or a flat file; every codec scanned in place at each thread count (ms/query, GB/s, recall@k against exact f32, self excluded); and a sequential-read memory-bandwidth ceiling at the same thread counts. experiments/2026-11-zero-copy-scan has run.sh and summarise.py; results follow from a quiet machine. --- crates/hvec/src/cli.rs | 33 ++ crates/hvec/src/commands/bench.rs | 1 + crates/hvec/src/commands/mod.rs | 1 + crates/hvec/src/commands/scan.rs | 317 ++++++++++++++++++ crates/hvec/tests/pipeline.rs | 54 +++ experiments/2026-11-zero-copy-scan/run.sh | 35 ++ .../2026-11-zero-copy-scan/summarise.py | 51 +++ 7 files changed, 492 insertions(+) create mode 100644 crates/hvec/src/commands/scan.rs create mode 100755 experiments/2026-11-zero-copy-scan/run.sh create mode 100644 experiments/2026-11-zero-copy-scan/summarise.py diff --git a/crates/hvec/src/cli.rs b/crates/hvec/src/cli.rs index a00b4a4..79f72b3 100644 --- a/crates/hvec/src/cli.rs +++ b/crates/hvec/src/cli.rs @@ -161,6 +161,39 @@ pub enum BenchCommand { Run(BenchRunArgs), /// Replay an agent memory script (writes and asks) once per codec, measuring every step. Memory(MemoryArgs), + /// Time the scan itself: storage paths, codecs and thread counts, against memory bandwidth. + Scan(ScanArgs), +} + +#[derive(Debug, Args)] +pub struct ScanArgs { + /// An f32 collection; it is the ground truth and the source every codec is encoded from. + #[arg(short, long)] + pub collection: String, + /// Codecs to time on the in-memory buffer. Trained codecs are fitted on the collection first. + #[arg(long, value_delimiter = ',', default_value = "f32,int8,binary,binary-hamming,pq48")] + pub codecs: Vec, + /// Thread counts for the buffered scan. + #[arg(long, value_delimiter = ',', default_value = "1")] + pub threads: Vec, + /// Neighbours per query. + #[arg(short, long, default_value_t = 10)] + pub k: usize, + /// Stored vectors used as queries; each is excluded from its own ranking when recall is counted. + #[arg(long, default_value_t = 50)] + pub sample: usize, + /// Timed passes over the queries; the median pass is reported. + #[arg(long, default_value_t = 3)] + pub repeat: usize, + /// Size of the buffer read to measure memory bandwidth, in MiB. + #[arg(long, default_value_t = 1024)] + pub bandwidth_mib: usize, + /// Skip the slow per-query SQLite paths. + #[arg(long)] + pub skip_sqlite: bool, + /// Print the results as JSON instead of tables. + #[arg(long)] + pub json: bool, } #[derive(Debug, Args)] diff --git a/crates/hvec/src/commands/bench.rs b/crates/hvec/src/commands/bench.rs index 31c307b..6d01231 100644 --- a/crates/hvec/src/commands/bench.rs +++ b/crates/hvec/src/commands/bench.rs @@ -24,6 +24,7 @@ pub async fn run(config: &Option, cmd: BenchCommand) -> Result<()> { BenchCommand::Rescore(args) => rescore_cmd(config, args).await, BenchCommand::Run(args) => run_cmd(config, args).await, BenchCommand::Memory(args) => crate::commands::memory::run(config, args).await, + BenchCommand::Scan(args) => crate::commands::scan::run(config, &args), } } diff --git a/crates/hvec/src/commands/mod.rs b/crates/hvec/src/commands/mod.rs index afa8f9f..0d790be 100644 --- a/crates/hvec/src/commands/mod.rs +++ b/crates/hvec/src/commands/mod.rs @@ -6,6 +6,7 @@ pub mod list; pub mod memory; pub mod query; pub mod questions; +pub mod scan; pub mod shell; /// Shared retrieve-then-answer step used by `query` and `chat`. diff --git a/crates/hvec/src/commands/scan.rs b/crates/hvec/src/commands/scan.rs new file mode 100644 index 0000000..419865c --- /dev/null +++ b/crates/hvec/src/commands/scan.rs @@ -0,0 +1,317 @@ +//! `hvec bench scan`: how long the scan itself takes, and where the time goes. +//! +//! Three questions, one table each: +//! 1. Storage path. The same f32 search through every way bytes can reach the +//! scorer: the original row scan (every row and its text copied out of +//! SQLite on every query), SQLite rows with borrowed blobs (with SQLite's +//! memory map off and on), and the cached contiguous buffer. Also what it +//! costs to build the buffer from SQLite, or from a flat code file. +//! 2. Codec. Each codec scanned in place over its own buffer at each thread +//! count: ms per query, GB/s of codes read, recall@k against exact f32. +//! 3. The ceiling. A plain sequential read of a large buffer at the same +//! thread counts, so each scan's GB/s can be read as a fraction of what +//! the machine's memory can deliver. + +use std::path::PathBuf; +use std::time::Instant; + +use anyhow::{Result, bail}; +use hvec_core::codecs::F32Codec; +use hvec_core::{Codec, codec_by_name}; +use hvec_store::CodeBuffer; +use serde::Serialize; + +use crate::cli::ScanArgs; +use crate::context::Ctx; + +#[derive(Debug, Serialize)] +struct PathRow { + path: String, + ms_per_query: f64, + note: String, +} + +#[derive(Debug, Serialize)] +struct LoadRow { + source: String, + ms: f64, + bytes: usize, +} + +#[derive(Debug, Serialize)] +struct CodecRow { + codec: String, + bytes_per_vector: usize, + threads: usize, + ms_per_query: f64, + gb_per_s: f64, + recall_at_k: f64, +} + +#[derive(Debug, Serialize)] +struct BandwidthRow { + threads: usize, + gb_per_s: f64, +} + +#[derive(Debug, Serialize)] +struct Report { + collection: String, + vectors: usize, + dimension: usize, + queries: usize, + k: usize, + repeat: usize, + paths: Vec, + loads: Vec, + codecs: Vec, + bandwidth: Vec, +} + +/// Median over `repeat` passes of the mean ms per query. +fn time_queries(repeat: usize, queries: &[Vec], mut f: impl FnMut(&[f32]) -> Result<()>) -> Result { + // One untimed pass warms caches and builds anything lazily cached. + for q in queries.iter().take(1) { + f(q)?; + } + let mut passes = Vec::with_capacity(repeat); + for _ in 0..repeat.max(1) { + let t = Instant::now(); + for q in queries { + f(q)?; + } + passes.push(t.elapsed().as_secs_f64() * 1000.0 / queries.len() as f64); + } + passes.sort_by(f64::total_cmp); + Ok(passes[passes.len() / 2]) +} + +/// Sequential read bandwidth: sum a large buffer of u64, split over threads. +fn bandwidth(mib: usize, threads: usize, repeat: usize) -> f64 { + let words = mib * 1024 * 1024 / 8; + let data: Vec = (0..words as u64).collect(); + let per = words.div_ceil(threads.max(1)); + let mut best = f64::MAX; + for _ in 0..repeat.max(1) + 1 { + let t = Instant::now(); + let total: u64 = std::thread::scope(|s| { + let handles: Vec<_> = data + .chunks(per) + .map(|c| s.spawn(move || c.iter().fold(0u64, |a, &x| a.wrapping_add(x)))) + .collect(); + handles + .into_iter() + .map(|h| h.join().unwrap_or(0)) + .fold(0, u64::wrapping_add) + }); + std::hint::black_box(total); + best = best.min(t.elapsed().as_secs_f64()); + } + (words * 8) as f64 / best / 1e9 +} + +pub fn run(config: &Option, args: &ScanArgs) -> Result<()> { + let ctx = Ctx::load(config.as_deref())?; + let store = ctx.open_store()?; + let collection = store.require_collection(&args.collection)?; + if collection.codec != "f32" { + bail!( + "`bench scan` needs an f32 collection; `{}` is stored as {}", + collection.name, + collection.codec + ); + } + let dim = collection.dimension; + let metric = collection.metric; + let f32c = F32Codec::new(dim); + let k = args.k; + + // The collection as one buffer; this is also the exact ground truth. + let t = Instant::now(); + let exact_buf = store.load_codes(&collection.name)?; + let load_sqlite_ms = t.elapsed().as_secs_f64() * 1000.0; + let n = exact_buf.len(); + if n == 0 { + bail!("collection `{}` is empty", collection.name); + } + let vectors: Vec> = (0..n) + .map(|i| f32c.decode(exact_buf.code(i))) + .collect::>()?; + let step = n as f64 / args.sample.clamp(1, n) as f64; + let picked: Vec = (0..args.sample.clamp(1, n)) + .map(|i| ((i as f64 * step) as usize).min(n - 1)) + .collect(); + let queries: Vec> = picked.iter().map(|&i| vectors[i].clone()).collect(); + // Self-queries: each query is a stored vector, which is dropped from both + // rankings before recall is counted, as `bench recall` does. + let top_excluding = |buf: &CodeBuffer, codec: &dyn Codec, q: &[f32], skip: usize| -> Result> { + Ok(buf + .search(q, k + 1, codec, metric)? + .iter() + .map(|s| s.index) + .filter(|&i| i != skip) + .take(k) + .collect()) + }; + let truth: Vec> = queries + .iter() + .zip(&picked) + .map(|(q, &i)| top_excluding(&exact_buf, &f32c, q, i)) + .collect::>()?; + + // 1. Storage paths, f32 throughout. + let mut paths = Vec::new(); + if !args.skip_sqlite { + let ms = time_queries(args.repeat, &queries, |q| { + std::hint::black_box(store.search_by_rows(&collection.name, q, k, &f32c, metric)?); + Ok(()) + })?; + paths.push(PathRow { + path: "sqlite rows, copied".into(), + ms_per_query: ms, + note: "the original search: every row and its text copied out on every query".into(), + }); + for (label, mmap) in [ + ("sqlite rows, borrowed", 0u64), + ("sqlite rows, borrowed, mmap", 1 << 36), + ] { + store.set_mmap_size(mmap)?; + let ms = time_queries(args.repeat, &queries, |q| { + std::hint::black_box(store.search_borrowed(&collection.name, q, k, &f32c, metric)?); + Ok(()) + })?; + paths.push(PathRow { + path: label.into(), + ms_per_query: ms, + note: if mmap == 0 { + "vector column only, each blob scored in place in SQLite's page buffer".into() + } else { + "as above, with SQLite reading the file through its memory map".into() + }, + }); + } + store.set_mmap_size(0)?; + } + let ms = time_queries(args.repeat, &queries, |q| { + std::hint::black_box(store.search(&collection.name, q, k, &f32c, metric)?); + Ok(()) + })?; + paths.push(PathRow { + path: "cached buffer (Store::search)".into(), + ms_per_query: ms, + note: "codes scanned in place in one allocation; text read for the hits only".into(), + }); + + // Building the buffer: from SQLite, and from a flat code file. + let mut loads = vec![LoadRow { + source: "sqlite (borrowed blobs, one copy)".into(), + ms: load_sqlite_ms, + bytes: exact_buf.bytes().len(), + }]; + let file = std::env::temp_dir().join(format!("hvec-scan-{}-{}.codes", std::process::id(), collection.name)); + exact_buf.write_file(&file)?; + let t = Instant::now(); + let back = CodeBuffer::read_file(&file); + let _ = std::fs::remove_file(&file); + let back = back?; + loads.push(LoadRow { + source: "flat code file (one read)".into(), + ms: t.elapsed().as_secs_f64() * 1000.0, + bytes: back.bytes().len(), + }); + drop(back); + + // 2. Codecs, each scanned in place over its own buffer. + let ids = exact_buf.ids().to_vec(); + let sources = exact_buf.sources().to_vec(); + let mut codecs = Vec::new(); + for name in &args.codecs { + let mut codec: Box = codec_by_name(name, dim)?; + if codec.needs_fit() { + codec.fit(&vectors)?; + } + let buf = CodeBuffer::encode(codec.as_ref(), &ids, &sources, &vectors)?; + let mut hits = 0usize; + for ((q, t), &i) in queries.iter().zip(&truth).zip(&picked) { + let got = top_excluding(&buf, codec.as_ref(), q, i)?; + hits += got.iter().filter(|g| t.contains(g)).count(); + } + let recall = hits as f64 / (queries.len() * k) as f64; + for &threads in &args.threads { + let ms = time_queries(args.repeat, &queries, |q| { + std::hint::black_box(buf.search_threads(q, k, codec.as_ref(), metric, threads)?); + Ok(()) + })?; + codecs.push(CodecRow { + codec: codec.name().to_owned(), + bytes_per_vector: codec.encoded_size(), + threads, + ms_per_query: ms, + gb_per_s: buf.bytes().len() as f64 / (ms / 1000.0) / 1e9, + recall_at_k: recall, + }); + } + } + + // 3. The memory-bandwidth ceiling. + let bandwidth = args + .threads + .iter() + .map(|&t| BandwidthRow { + threads: t, + gb_per_s: bandwidth(args.bandwidth_mib, t, args.repeat), + }) + .collect(); + + let report = Report { + collection: collection.name.clone(), + vectors: n, + dimension: dim, + queries: queries.len(), + k, + repeat: args.repeat, + paths, + loads, + codecs, + bandwidth, + }; + if args.json { + println!("{}", serde_json::to_string_pretty(&report)?); + return Ok(()); + } + println!( + "collection `{}` · {} vectors · {} dims · {} queries · k={} · median of {} passes", + report.collection, report.vectors, report.dimension, report.queries, k, report.repeat + ); + println!("\nstorage path (f32) ms/query"); + for p in &report.paths { + println!(" {:<32} {:>8.2} {}", p.path, p.ms_per_query, p.note); + } + println!("\nbuilding the buffer ms MB"); + for l in &report.loads { + println!(" {:<32} {:>8.1} {:>8.1}", l.source, l.ms, l.bytes as f64 / 1e6); + } + println!("\ncodec, in place bytes threads ms/query GB/s % of bandwidth recall@{k}"); + for c in &report.codecs { + let bw = report + .bandwidth + .iter() + .find(|b| b.threads == c.threads) + .map_or(f64::NAN, |b| b.gb_per_s); + println!( + " {:<18} {:>6} {:>8} {:>9.2} {:>8.2} {:>14.1}% {:>9.4}", + c.codec, + c.bytes_per_vector, + c.threads, + c.ms_per_query, + c.gb_per_s, + 100.0 * c.gb_per_s / bw, + c.recall_at_k + ); + } + println!("\nmemory bandwidth (sequential u64 read of {} MiB)", args.bandwidth_mib); + for b in &report.bandwidth { + println!(" {:>2} thread(s): {:>6.1} GB/s", b.threads, b.gb_per_s); + } + Ok(()) +} diff --git a/crates/hvec/tests/pipeline.rs b/crates/hvec/tests/pipeline.rs index 0393363..6dce31c 100644 --- a/crates/hvec/tests/pipeline.rs +++ b/crates/hvec/tests/pipeline.rs @@ -1193,3 +1193,57 @@ fn memory_script_replays_per_codec_and_accounts_bytes() { 18 ); } + +#[test] +fn bench_scan_times_paths_and_codecs_and_keeps_f32_exact() { + let env = Env::new(); + for i in 0..6 { + env.write_doc( + &format!("extra{i}.md"), + &format!("Topic {i}: notes about subject number {i} and its details, item {i}."), + ); + } + env.write_doc("refunds.md", REFUNDS); + env.write_doc("shipping.md", SHIPPING); + let dir = env.dir.path().to_str().unwrap().to_owned(); + assert_ok(&env.run(&["ingest", &dir, "--collection", "docs"]), "ingest dir"); + + let out = env.run(&[ + "bench", + "scan", + "--collection", + "docs", + "--codecs", + "f32,binary,binary-hamming", + "--threads", + "1,2", + "-k", + "3", + "--sample", + "4", + "--repeat", + "1", + "--bandwidth-mib", + "8", + "--json", + ]); + assert_ok(&out, "bench scan"); + let r: serde_json::Value = serde_json::from_str(&stdout(&out)).expect("json report"); + assert_eq!(r["vectors"], 8); + assert_eq!(r["queries"], 4); + let paths: Vec<&str> = r["paths"] + .as_array() + .unwrap() + .iter() + .map(|p| p["path"].as_str().unwrap()) + .collect(); + assert_eq!(paths.len(), 4, "{paths:?}"); + assert_eq!(r["loads"].as_array().unwrap().len(), 2); + let codecs = r["codecs"].as_array().unwrap(); + assert_eq!(codecs.len(), 6, "three codecs at two thread counts"); + for c in codecs.iter().filter(|c| c["codec"] == "f32") { + assert_eq!(c["recall_at_k"], 1.0, "{c}"); + } + assert!(codecs.iter().all(|c| c["ms_per_query"].as_f64().unwrap() >= 0.0)); + assert_eq!(r["bandwidth"].as_array().unwrap().len(), 2); +} diff --git a/experiments/2026-11-zero-copy-scan/run.sh b/experiments/2026-11-zero-copy-scan/run.sh new file mode 100755 index 0000000..8fb1e59 --- /dev/null +++ b/experiments/2026-11-zero-copy-scan/run.sh @@ -0,0 +1,35 @@ +#!/usr/bin/env bash +# Zero-copy scan: where a brute-force search spends its time, and what each codec's scan costs +# against the machine's memory bandwidth. Times the storage paths (SQLite rows copied, SQLite +# rows borrowed with and without SQLite's memory map, the cached contiguous buffer), the cost of +# building the buffer, and every codec scanned in place at several thread counts, with recall@10 +# against exact f32. Run on a quiet machine: nothing else should be using the CPU. +# Reuses ../2026-11-neighbour-spacing's collections. Needs: a built `hvec` (cargo build --release). +set -euo pipefail +HERE="$(cd "$(dirname "$0")" && pwd)" +ROOT="$HERE/../.." +HVEC="${HVEC:-$ROOT/target/release/hvec}" +SPACING="$HERE/../2026-11-neighbour-spacing" +export HVEC_CONFIG="${HVEC_CONFIG:-$SPACING/work/config.toml}" +COLLECTIONS="${COLLECTIONS:-scifact-bge-small fiqa-bge-small}" +CODECS="${CODECS:-f32,int8,binary,binary-centred,binary-hamming,pq48,pq24,mixed48-pca-w}" +THREADS="${THREADS:-1,2,4,8,10}" +SAMPLE="${SAMPLE:-50}" +REPEAT="${REPEAT:-3}" +mkdir -p "$HERE/results" +[ -f "$HVEC_CONFIG" ] || { echo "build the collections first: $SPACING/run.sh"; exit 1; } + +{ + echo "hvec $(git -C "$ROOT" rev-parse --short HEAD)$(git -C "$ROOT" diff --quiet || echo '+dirty')" + sysctl -n machdep.cpu.brand_string 2>/dev/null || true + sysctl -n hw.perflevel0.physicalcpu hw.perflevel1.physicalcpu 2>/dev/null | paste -sd' ' - | sed 's/^/performance and efficiency cores: /' || true + sysctl -n hw.memsize 2>/dev/null | awk '{printf "memory: %.0f GB\n", $1/1e9}' || true +} > "$HERE/results/machine.txt" + +for col in $COLLECTIONS; do + "$HVEC" bench scan --collection "$col" --codecs "$CODECS" --threads "$THREADS" --sample "$SAMPLE" \ + --repeat "$REPEAT" --json > "$HERE/results/$col.json" + echo "== $col done" +done +python3 -I "$HERE/summarise.py" "$HERE/results" | tee "$HERE/results/summary.txt" +cat "$HERE/results/machine.txt" diff --git a/experiments/2026-11-zero-copy-scan/summarise.py b/experiments/2026-11-zero-copy-scan/summarise.py new file mode 100644 index 0000000..27a2432 --- /dev/null +++ b/experiments/2026-11-zero-copy-scan/summarise.py @@ -0,0 +1,51 @@ +"""Summarise `bench scan --json` reports. + + python3 -I summarise.py + +For each collection: the storage paths with their speed-up over the original row scan, the cost +of building the buffer, and each codec's scan (ms/query, ns per vector, GB/s, share of the measured +memory bandwidth, recall@10). Then, per collection, the hybrid estimate: a scan plus N f32 +re-scores, using the measured per-vector costs. +""" + +import json +import sys +from pathlib import Path + + +def main(results: Path) -> None: + for path in sorted(results.glob("*.json")): + r = json.loads(path.read_text()) + n = r["vectors"] + print(f"== {r['collection']}: {n} vectors × {r['dimension']} dims, {r['queries']} queries, k={r['k']}") + base = next((p["ms_per_query"] for p in r["paths"] if p["path"].startswith("sqlite rows, copied")), None) + print(f" {'storage path (f32)':<34} {'ms/query':>9} {'speed-up':>9}") + for p in r["paths"]: + up = f"{base / p['ms_per_query']:.1f}×" if base else "-" + print(f" {p['path']:<34} {p['ms_per_query']:>9.2f} {up:>9}") + print(f" {'building the buffer':<34} {'ms':>9} {'MB':>9}") + for l in r["loads"]: + print(f" {l['source']:<34} {l['ms']:>9.1f} {l['bytes'] / 1e6:>9.1f}") + bw = {b["threads"]: b["gb_per_s"] for b in r["bandwidth"]} + print(f" memory bandwidth: " + ", ".join(f"{t} thread(s) {g:.0f} GB/s" for t, g in sorted(bw.items()))) + print(f" {'codec':<16} {'B/vec':>6} {'thr':>4} {'ms/query':>9} {'ns/vector':>10} {'GB/s':>7} {'% bw':>6} {'recall@10':>10}") + for c in r["codecs"]: + ns = c["ms_per_query"] * 1e6 / n + share = 100 * c["gb_per_s"] / bw[c["threads"]] if c["threads"] in bw else float("nan") + print(f" {c['codec']:<16} {c['bytes_per_vector']:>6} {c['threads']:>4} {c['ms_per_query']:>9.3f} {ns:>10.2f} " + f"{c['gb_per_s']:>7.2f} {share:>5.1f}% {c['recall_at_k']:>10.4f}") + one = {c["codec"]: c for c in r["codecs"] if c["threads"] == 1} + if "f32" in one: + f32_ns = one["f32"]["ms_per_query"] * 1e6 / n + print(f" hybrid estimate, 1 thread: scan + N f32 re-scores at {f32_ns:.1f} ns each (random access ignored)") + for scan in ("binary-hamming", "binary", "pq48"): + if scan not in one: + continue + s_ms = one[scan]["ms_per_query"] + cells = ", ".join(f"N={d}: {s_ms + d * f32_ns / 1e6:.3f} ms" for d in (50, 100, 200)) + print(f" {scan:<16} scan {s_ms:.3f} ms → {cells} (exact f32 scan {one['f32']['ms_per_query']:.3f} ms)") + print() + + +if __name__ == "__main__": + main(Path(sys.argv[1])) From 8f8790898a2e869ec738f3513b3dac674cd91eac Mon Sep 17 00:00:00 2001 From: Swinecoder101 Date: Wed, 4 Nov 2026 22:15:15 +0000 Subject: [PATCH 08/12] experiment: hybrid x adaptive matrix results and write-up Ten collections, eleven scan codecs, none/fixed/margin policies, int8 and f32 second stages. Adaptive bits: mixed48-pca-w keeps 0.768 of the exact top 10 with no re-scoring against 0.747 for pq48 and 0.669 for binary, and leads at 24 and 96 bytes too; the gain is all on MiniLM, a tie on bge-small; without the PCA rotation it is no better than binary-centred. Adaptive depth beats fixed depth at equal mean depth in 522 of 526 cells, +1.5 points of recall and +3.7 of worst-decile recall, at a p90 depth 1.3-2.1x the mean. Includes a benefits-and-drawbacks table for each combination. --- .../2026-11-hybrid-adaptive-matrix/README.md | 162 + .../results/arguana-bge-small-k10.json | 3900 +++++++++++++++++ .../results/arguana-minilm-k10.json | 3900 +++++++++++++++++ .../results/config.toml | 17 + .../results/cosqa-bge-small-k10.json | 3900 +++++++++++++++++ .../results/cosqa-minilm-k10.json | 3900 +++++++++++++++++ .../results/fiqa-bge-small-k10.json | 3900 +++++++++++++++++ .../results/fiqa-minilm-k10.json | 3900 +++++++++++++++++ .../results/nfcorpus-bge-small-k10.json | 3900 +++++++++++++++++ .../results/nfcorpus-minilm-k10.json | 3900 +++++++++++++++++ .../results/scifact-bge-small-k10.json | 3900 +++++++++++++++++ .../results/scifact-minilm-k10.json | 3900 +++++++++++++++++ .../results/summary.txt | 240 + 13 files changed, 39419 insertions(+) create mode 100644 experiments/2026-11-hybrid-adaptive-matrix/README.md create mode 100644 experiments/2026-11-hybrid-adaptive-matrix/results/arguana-bge-small-k10.json create mode 100644 experiments/2026-11-hybrid-adaptive-matrix/results/arguana-minilm-k10.json create mode 100644 experiments/2026-11-hybrid-adaptive-matrix/results/config.toml create mode 100644 experiments/2026-11-hybrid-adaptive-matrix/results/cosqa-bge-small-k10.json create mode 100644 experiments/2026-11-hybrid-adaptive-matrix/results/cosqa-minilm-k10.json create mode 100644 experiments/2026-11-hybrid-adaptive-matrix/results/fiqa-bge-small-k10.json create mode 100644 experiments/2026-11-hybrid-adaptive-matrix/results/fiqa-minilm-k10.json create mode 100644 experiments/2026-11-hybrid-adaptive-matrix/results/nfcorpus-bge-small-k10.json create mode 100644 experiments/2026-11-hybrid-adaptive-matrix/results/nfcorpus-minilm-k10.json create mode 100644 experiments/2026-11-hybrid-adaptive-matrix/results/scifact-bge-small-k10.json create mode 100644 experiments/2026-11-hybrid-adaptive-matrix/results/scifact-minilm-k10.json create mode 100644 experiments/2026-11-hybrid-adaptive-matrix/results/summary.txt diff --git a/experiments/2026-11-hybrid-adaptive-matrix/README.md b/experiments/2026-11-hybrid-adaptive-matrix/README.md new file mode 100644 index 0000000..ba22c34 --- /dev/null +++ b/experiments/2026-11-hybrid-adaptive-matrix/README.md @@ -0,0 +1,162 @@ +# The hybrid × adaptive matrix + +Two ways to make compression adaptive, crossed with the hybrid (two-stage) retrieval from the +[re-scoring experiment](../2026-11-two-stage-rescoring/): + +- **Adaptive bits.** Spend the scan code's bytes unevenly. `mixed` gives each dimension 0 to 8 + bits by greedy reverse water-filling on its variance. `-pca` first rotates into the corpus's + principal components, so the variance has somewhere to concentrate. `-w` weights each + component by how much energy queries put into it, so the allocation minimises score error + rather than reconstruction error. This is the embedding analogue of structure-aware, + mixed-precision quantization of mixture-of-experts models ([QuantMoE-Bench][quantmoe]), where + the blocks and experts that matter more get more bits. +- **Adaptive depth.** Re-score every candidate whose scan score is within λσ of the scan's own + 10th score, where σ is the scan code's noise, calibrated once per collection on self-queries. + Crowded queries get deep re-scoring, well-separated ones shallow. + +**Hypothesis.** +1. With a PCA rotation, adaptive bits beat uniform sign bits at equal bytes and approach + product quantization. Without the rotation they do little, because raw embedding dimensions + have fairly even variance. +2. At the same mean number of re-scored candidates, adaptive depth beats a fixed depth, + mostly by rescuing the worst queries. + +**Result.** Both confirmed, and the first more strongly than expected. +- **Adaptive bits beat PQ on average.** `mixed48-pca-w` keeps 0.768 of the exact top 10 with no + re-scoring, against 0.747 for `pq48` and 0.669 for `binary`. It wins at 24 and 96 bytes too. + The gain is all on MiniLM (+4.4 points); on bge-small the two tie (0.723 vs 0.724), and `pq48` + keeps a slightly smaller nDCG loss (0.0160 vs 0.0177). +- **The rotation is what makes it work.** Without it, `mixed48` (0.696) is no better than + `binary-centred` (0.698). +- **Adaptive depth beats fixed depth at equal mean depth in 522 of 526 cells.** On average it + adds 1.5 points of recall@10 and 3.7 points of worst-decile recall, with no change in nDCG. + The price is variable work: the 90th-percentile query re-scores 1.3 to 2.1× the mean. +- **The two combine.** `mixed48-pca-w` with a 1σ margin re-scores 19 candidates on average and + keeps 0.929 of the top 10. `binary` with a fixed 20 keeps 0.841 for the same bytes. + +## Setup + +| | | +|---|---| +| Collections | The ten from [neighbour spacing](../2026-11-neighbour-spacing/): SciFact, NFCorpus, ArguAna, CoSQA, FiQA × bge-small-en-v1.5, all-MiniLM-L6-v2 (384 dims, 200-word chunks, 6,260 to 71,274 per collection) | +| Queries | Each set's judged test queries (300 to 1,406) | +| Scan codecs | Uniform: `binary`, `binary-centred` (48 B), `pq24`, `pq48`, `pq96`. Adaptive: `mixed48` (raw dimensions), `mixed24-pca`, `mixed48-pca`, `mixed24-pca-w`, `mixed48-pca-w`, `mixed96-pca-w` | +| Second stage | `int8` (392 B) or `f32` (1,536 B), read for candidates only | +| Policies | none; fixed depth 20, 50, 100; margin 1σ, 2σ, 3σ (capped at 1,000) | +| Calibration | σ = the scan code's rank noise on 200 self-queries per collection. The test queries' exact scores never set their own depth | +| Fitting | Trained codecs fitted on every vector of the collection. PQ: 256 centroids per subspace, 20 Lloyd iterations. `mixed`: PCA by Jacobi on up to 20,000 vectors | +| Metrics | recall@10 against exact f32; 10th-percentile query recall@10 (worst decile); nDCG@10 loss against the f32 ceiling, from the relevance judgments; mean and p90 depth | +| Search | brute force, no index | +| Hardware | Apple M4 Pro, release build | +| Code | hvec commit `6113ab7` (`bench rescore` with policies, `mixed`) | +| Command | `./run.sh` (reuses the spacing experiment's collections; builds them if missing) | + +## Results + +All numbers are means over the ten collections. The full matrix, 143 cells per collection, is in +`results/summary.txt`; the raw reports are `results/--k10.json`. + +### The matrix: recall@10 by scan codec and re-scoring policy (f32 second stage) + +| scan codec | bytes | none | fixed 20 | fixed 50 | fixed 100 | margin 1σ (mean depth) | margin 2σ (mean depth) | nDCG loss, none → fixed 50 | +|---|---:|---:|---:|---:|---:|---:|---:|---| +| `binary` | 48 | 0.669 | 0.841 | 0.948 | 0.981 | 0.869 (23) | 0.963 (52) | 0.0240 → 0.0003 | +| `binary-centred` | 48 | 0.698 | 0.869 | 0.966 | 0.991 | 0.911 (26) | 0.984 (65) | 0.0236 → 0.0002 | +| `mixed48` | 48 | 0.696 | 0.868 | 0.966 | 0.991 | 0.909 (26) | 0.983 (65) | 0.0242 → 0.0000 | +| `pq48` | 48 | 0.747 | 0.912 | 0.986 | 0.998 | 0.924 (21) | 0.985 (41) | 0.0160 → 0.0002 | +| `mixed48-pca` | 48 | 0.755 | 0.920 | 0.989 | 0.998 | 0.919 (19) | 0.982 (35) | 0.0167 → 0.0000 | +| `mixed48-pca-w` | 48 | **0.768** | **0.931** | **0.991** | **0.999** | **0.929 (19)** | 0.983 (36) | 0.0177 → 0.0000 | +| `pq24` | 24 | 0.606 | 0.779 | 0.918 | 0.968 | 0.887 (42) | 0.979 (144) | 0.0586 → 0.0046 | +| `mixed24-pca` | 24 | 0.631 | 0.807 | 0.937 | 0.977 | 0.880 (31) | 0.971 (89) | 0.0497 → 0.0037 | +| `mixed24-pca-w` | 24 | **0.635** | **0.815** | **0.937** | **0.977** | 0.888 (33) | 0.973 (100) | 0.0523 → 0.0047 | +| `pq96` | 96 | 0.870 | 0.986 | 1.000 | 1.000 | 0.958 (14) | 0.990 (18) | 0.0031 → −0.0001 | +| `mixed96-pca-w` | 96 | **0.886** | **0.990** | 1.000 | 1.000 | **0.963 (14)** | **0.991 (18)** | 0.0022 → 0.0000 | + +An int8 second stage matches f32 to within a point up to a depth of about 50, then levels off +at int8's own recall. At depth 100: `binary` 0.974 against 0.981, `pq48` 0.989 against 0.998, `mixed48-pca-w` +0.990 against 0.999. + +### Adaptive bits alone, by byte budget (no re-scoring) + +| bytes | codec | recall@10 | bge-small | MiniLM | worst decile | nDCG loss | +|---:|---|---:|---:|---:|---:|---:| +| 24 | `mixed24-pca-w` | **0.635** | 0.592 | **0.677** | 0.37 | 0.052 | +| 24 | `mixed24-pca` | 0.631 | **0.596** | 0.667 | 0.37 | **0.050** | +| 24 | `pq24` | 0.606 | 0.587 | 0.624 | 0.33 | 0.059 | +| 48 | `mixed48-pca-w` | **0.768** | 0.723 | **0.813** | **0.55** | 0.018 | +| 48 | `mixed48-pca` | 0.755 | 0.717 | 0.793 | 0.54 | 0.017 | +| 48 | `pq48` | 0.747 | **0.724** | 0.769 | 0.52 | **0.016** | +| 48 | `binary-centred` | 0.698 | 0.667 | 0.728 | 0.43 | 0.024 | +| 48 | `mixed48` (no rotation) | 0.696 | 0.663 | 0.729 | 0.43 | 0.024 | +| 48 | `binary` | 0.669 | 0.622 | 0.715 | 0.41 | 0.024 | +| 96 | `mixed96-pca-w` | **0.886** | 0.855 | **0.916** | **0.74** | **0.002** | +| 96 | `pq96` | 0.870 | **0.855** | 0.885 | 0.73 | 0.003 | + +### Adaptive depth against fixed depth at the same mean depth + +Each margin cell is compared with the fixed-depth curve of the same collection and codec, +interpolated in log depth at the margin's mean depth. Cells whose mean depth falls outside the +measured fixed range (20 to 100) are left out. + +| margin | cells compared | Δ recall@10 | Δ worst decile | Δ nDCG | p90 depth / mean depth | +|---|---:|---:|---:|---:|---:| +| 1σ | 218 | +0.020 | +0.048 | +0.001 | 1.29–1.60 | +| 2σ | 192 | +0.014 | +0.033 | +0.000 | 1.39–1.94 | +| 3σ | 116 | +0.008 | +0.025 | +0.000 | 1.49–2.09 | +| all | 526 | **+0.015** | **+0.037** | 0.000 | | + +Adaptive is ahead in 522 of the 526 cells. The gain is largest at small depths and for the +better scan codecs (`pq96` and `mixed96-pca-w` gain 3.1 to 3.3 points at a 1σ margin), and it +shrinks as the depth grows and every policy approaches full recall. + +### Benefits and drawbacks of each combination + +Against the plain 48-byte `binary` scan with no re-scoring (recall@10 0.669, worst decile 0.41, +nDCG loss 0.024). Memory is per vector: what is scanned in RAM, plus what is fetched for +candidates. + +| combination | memory per vector | recall@10 | worst decile | nDCG loss | benefits | drawbacks | +|---|---|---:|---:|---:|---|---| +| `binary`, no re-score | 48 B | 0.669 | 0.41 | 0.024 | Stateless, nothing to fit | Loses a third of the top 10; its asymmetric score is no faster than f32 | +| `pq48`, no re-score | 48 B (+ codebooks) | 0.747 | 0.52 | 0.016 | Best nDCG at 48 B with no re-score | k-means fit per collection; codebooks to store; refit when the corpus drifts | +| `mixed48-pca-w`, no re-score | 48 B (+ a 384 × 384 rotation) | 0.768 | 0.55 | 0.018 | Best recall at 24, 48 and 96 B; PCA fit, no iterative clustering | Ties PQ on bge-small and loses slightly on nDCG; one matrix–vector product per query; refit on drift | +| hybrid, fixed depth, int8 | 48 B RAM + 392 B disk | 0.944–0.983 at depth 50 | 0.84–0.97 | ≈ 0 | Second store 4× smaller than f32 | Levels off at int8's own 0.99; random reads per query | +| hybrid, fixed depth, f32 | 48 B RAM + 1,536 B disk | 0.948–0.991 at depth 50 | 0.86–0.98 | ≈ 0 | Reaches exact; the depth is predictable from noise ÷ band | Keeps a full f32 copy; N random reads per query | +| hybrid, adaptive depth | as above | +0.015 over fixed at equal mean depth | +0.037 | ≈ 0 | Fewer bad queries for the same average work | Variable work per query (p90 1.3–2.1× the mean); σ calibrated per collection; one more parameter | +| adaptive bits + adaptive depth (`mixed48-pca-w`, 1σ) | 48 B RAM + 1,536 B disk | 0.929 at mean depth 19 | | ≈ 0 | The best recall per candidate re-scored of any 48-byte scan | Every drawback above together: two fits, a rotation and variable depth | + +## Discussion + +**PCA plus bit allocation is a serious alternative to PQ.** It never trails `pq` by more than +a point of recall at equal bytes, and it leads by 3 to 5 points on MiniLM. It fits with one +eigendecomposition instead of k-means, and the query-weighted allocation (`-w`) is worth about +another point at 48 bytes. Its nDCG at 48 bytes is slightly worse than PQ's despite the better +recall, which says the neighbours it recovers are not always the judged ones. That is the same +pattern the labelled SciFact experiment found for centring. + +**Why MiniLM gains and bge-small does not.** Both models have a PCA spectrum to exploit, but on +bge-small (measured on SciFact) 60% of an average vector's energy is the shared mean direction. +PQ's codebooks probably absorb that common component for free, while a quantizer working per +principal component has to spend bits on it. The `-w` allocation does spend bits on it, which is the bump of bits on the +last components in the [README animation](../../docs/assets/animations/adaptive-bits.svg). +Centring the rotation, or giving the mean its own exact term, is the obvious next variant. + +**Adaptive depth is a small, reliable win, not a large one.** 1.5 points at equal work, larger +for the worst queries, and almost never negative. The [routing experiment](../2026-11-routing-oracle/) +asks how much more a better router could get. A hindsight oracle shows about 6 points of +headroom, but the band a router can see from the scan's own scores tracks the true band only +moderately, so most of that headroom is out of reach. + +**Caveats.** +- **Recall is against exact f32, not the answer.** nDCG uses binary relevance. Whether any of + this reaches a chat model's answer is Experiment 1. +- **No fit or encode times.** The cost of fitting the PCA and the PQ codebooks is not measured + here; the [zero-copy scan](../2026-11-zero-copy-scan/) experiment times the scans. +- **Parameters not counted in bytes per vector.** The rotation is 590 KB per collection, and + PQ's codebooks are of similar size, as before. +- **Margin cells with few comparisons.** 3σ cells often exceed the measured fixed range, so + they're compared on few collections. +- **The same collections as the spacing experiment**, including CoSQA's duplicates and + bge-small without its query instruction. + +[quantmoe]: https://github.com/UNITES-Lab/moe-quantization diff --git a/experiments/2026-11-hybrid-adaptive-matrix/results/arguana-bge-small-k10.json b/experiments/2026-11-hybrid-adaptive-matrix/results/arguana-bge-small-k10.json new file mode 100644 index 0000000..08d09da --- /dev/null +++ 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[ + 0.99, + 19 + ], + [ + 1.0, + 60 + ] + ], + "first": "pq96", + "first_bytes": 96, + "k": 10, + "median_full_depth": 12.0, + "metric": "cosine", + "neighbour_band": 0.03073228895664215, + "queries": 300, + "rank_noise": 0.011711888456250843, + "recall_at_k": 0.874666666666669, + "vectors": 8036 + }, + { + "depth_for_recall": [ + [ + 0.9, + 10 + ], + [ + 0.95, + 11 + ], + [ + 0.99, + 15 + ], + [ + 1.0, + 27 + ] + ], + "first": "mixed96-pca-w", + "first_bytes": 96, + "k": 10, + "median_full_depth": 11.0, + "metric": "cosine", + "neighbour_band": 0.03073228895664215, + "queries": 300, + "rank_noise": 0.008109765457907094, + "recall_at_k": 0.9156666666666687, + "vectors": 8036 + } + ] +} diff --git a/experiments/2026-11-hybrid-adaptive-matrix/results/summary.txt b/experiments/2026-11-hybrid-adaptive-matrix/results/summary.txt new file mode 100644 index 0000000..49ace71 --- /dev/null +++ b/experiments/2026-11-hybrid-adaptive-matrix/results/summary.txt @@ -0,0 +1,240 @@ +10 collections: arguana-bge-small, arguana-minilm, cosqa-bge-small, cosqa-minilm, fiqa-bge-small, fiqa-minilm, nfcorpus-bge-small, nfcorpus-minilm, scifact-bge-small, scifact-minilm + +1. The matrix (means over collections). ram = scanned bytes/vector, disk = second-stage bytes/vector, + read = bytes touched per query as a fraction of a full f32 scan +scan codec second policy ram disk depth p90 recall@10 p10 top1 ndcg loss read +binary none none 48 0 0.0 0 0.6685 0.410 0.707 0.0240 0.0312 +binary f32 fixed 100 48 1536 100.0 100 0.9808 0.950 0.998 -0.0001 0.0400 +binary f32 fixed 20 48 1536 20.0 20 0.8405 0.620 0.978 0.0035 0.0330 +binary f32 fixed 50 48 1536 50.0 50 0.9482 0.860 0.994 0.0003 0.0356 +binary f32 margin 1σ 48 1536 23.2 35 0.8689 0.690 0.986 0.0022 0.0332 +binary f32 margin 2σ 48 1536 51.9 88 0.9627 0.880 0.994 -0.0006 0.0356 +binary f32 margin 3σ 48 1536 114.8 212 0.9911 0.980 0.999 -0.0001 0.0407 +binary int8 fixed 100 48 392 100.0 100 0.9739 0.920 0.993 -0.0001 0.0335 +binary int8 fixed 20 48 392 20.0 20 0.8392 0.620 0.974 0.0033 0.0317 +binary int8 fixed 50 48 392 50.0 50 0.9439 0.840 0.989 0.0004 0.0324 +binary int8 margin 1σ 48 392 23.2 35 0.8675 0.690 0.982 0.0021 0.0317 +binary int8 margin 2σ 48 392 51.9 88 0.9576 0.880 0.989 -0.0006 0.0324 +binary int8 margin 3σ 48 392 114.8 212 0.9831 0.950 0.993 -0.0000 0.0337 +binary-centred none none 48 0 0.0 0 0.6977 0.430 0.724 0.0236 0.0312 +binary-centred f32 fixed 100 48 1536 100.0 100 0.9910 0.980 1.000 0.0000 0.0400 +binary-centred f32 fixed 20 48 1536 20.0 20 0.8691 0.660 0.983 0.0026 0.0330 +binary-centred f32 fixed 50 48 1536 50.0 50 0.9662 0.900 0.997 0.0002 0.0356 +binary-centred f32 margin 1σ 48 1536 25.7 40 0.9112 0.800 0.995 0.0009 0.0334 +binary-centred f32 margin 2σ 48 1536 65.0 117 0.9840 0.940 0.999 0.0000 0.0363 +binary-centred f32 margin 3σ 48 1536 152.2 304 0.9979 1.000 1.000 0.0001 0.0430 +binary-centred int8 fixed 100 48 392 100.0 100 0.9830 0.960 0.994 0.0000 0.0335 +binary-centred int8 fixed 20 48 392 20.0 20 0.8672 0.660 0.978 0.0024 0.0317 +binary-centred int8 fixed 50 48 392 50.0 50 0.9605 0.880 0.991 0.0002 0.0324 +binary-centred int8 margin 1σ 48 392 25.7 40 0.9084 0.790 0.990 0.0006 0.0318 +binary-centred int8 margin 2σ 48 392 65.0 117 0.9766 0.920 0.994 0.0001 0.0326 +binary-centred int8 margin 3σ 48 392 152.2 304 0.9888 0.970 0.994 0.0000 0.0343 +mixed24-pca none none 24 0 0.0 0 0.6314 0.370 0.647 0.0497 0.0156 +mixed24-pca f32 fixed 100 24 1536 100.0 100 0.9767 0.920 0.997 0.0014 0.0243 +mixed24-pca f32 fixed 20 24 1536 20.0 20 0.8070 0.540 0.960 0.0106 0.0174 +mixed24-pca f32 fixed 50 24 1536 50.0 50 0.9372 0.810 0.991 0.0037 0.0200 +mixed24-pca f32 margin 1σ 24 1536 30.8 49 0.8804 0.730 0.985 0.0062 0.0181 +mixed24-pca f32 margin 2σ 24 1536 88.8 160 0.9711 0.910 0.998 0.0014 0.0221 +mixed24-pca f32 margin 3σ 24 1536 212.2 376 0.9941 0.990 1.000 0.0001 0.0308 +mixed24-pca int8 fixed 100 24 392 100.0 100 0.9700 0.900 0.992 0.0014 0.0178 +mixed24-pca int8 fixed 20 24 392 20.0 20 0.8062 0.540 0.956 0.0108 0.0161 +mixed24-pca int8 fixed 50 24 392 50.0 50 0.9330 0.810 0.986 0.0039 0.0167 +mixed24-pca int8 margin 1σ 24 392 30.8 49 0.8780 0.730 0.981 0.0064 0.0162 +mixed24-pca int8 margin 2σ 24 392 88.8 160 0.9648 0.900 0.992 0.0015 0.0173 +mixed24-pca int8 margin 3σ 24 392 212.2 376 0.9853 0.960 0.994 0.0002 0.0195 +mixed24-pca-w none none 24 0 0.0 0 0.6347 0.370 0.647 0.0523 0.0156 +mixed24-pca-w f32 fixed 100 24 1536 100.0 100 0.9772 0.930 0.997 0.0018 0.0243 +mixed24-pca-w f32 fixed 20 24 1536 20.0 20 0.8154 0.560 0.962 0.0126 0.0174 +mixed24-pca-w f32 fixed 50 24 1536 50.0 50 0.9373 0.830 0.992 0.0047 0.0200 +mixed24-pca-w f32 margin 1σ 24 1536 33.2 55 0.8883 0.750 0.986 0.0076 0.0182 +mixed24-pca-w f32 margin 2σ 24 1536 99.7 187 0.9726 0.910 0.997 0.0015 0.0227 +mixed24-pca-w f32 margin 3σ 24 1536 227.6 395 0.9935 0.980 0.999 0.0003 0.0315 +mixed24-pca-w int8 fixed 100 24 392 100.0 100 0.9702 0.900 0.992 0.0018 0.0178 +mixed24-pca-w int8 fixed 20 24 392 20.0 20 0.8144 0.560 0.958 0.0123 0.0161 +mixed24-pca-w int8 fixed 50 24 392 50.0 50 0.9329 0.820 0.987 0.0048 0.0167 +mixed24-pca-w int8 margin 1σ 24 392 33.2 55 0.8860 0.740 0.981 0.0077 0.0163 +mixed24-pca-w int8 margin 2σ 24 392 99.7 187 0.9656 0.900 0.992 0.0016 0.0174 +mixed24-pca-w int8 margin 3σ 24 392 227.6 395 0.9847 0.960 0.994 0.0003 0.0197 +mixed48 none none 48 0 0.0 0 0.6959 0.430 0.721 0.0242 0.0312 +mixed48 f32 fixed 100 48 1536 100.0 100 0.9909 0.980 0.999 -0.0001 0.0400 +mixed48 f32 fixed 20 48 1536 20.0 20 0.8679 0.660 0.983 0.0026 0.0330 +mixed48 f32 fixed 50 48 1536 50.0 50 0.9657 0.900 0.997 -0.0000 0.0356 +mixed48 f32 margin 1σ 48 1536 25.6 40 0.9090 0.790 0.994 0.0008 0.0333 +mixed48 f32 margin 2σ 48 1536 64.6 118 0.9831 0.940 0.999 0.0001 0.0363 +mixed48 f32 margin 3σ 48 1536 150.2 300 0.9977 1.000 1.000 -0.0001 0.0429 +mixed48 int8 fixed 100 48 392 100.0 100 0.9830 0.970 0.993 -0.0001 0.0335 +mixed48 int8 fixed 20 48 392 20.0 20 0.8662 0.660 0.977 0.0024 0.0317 +mixed48 int8 fixed 50 48 392 50.0 50 0.9599 0.880 0.991 -0.0000 0.0324 +mixed48 int8 margin 1σ 48 392 25.6 40 0.9062 0.780 0.989 0.0007 0.0318 +mixed48 int8 margin 2σ 48 392 64.6 118 0.9760 0.920 0.993 0.0001 0.0325 +mixed48 int8 margin 3σ 48 392 150.2 300 0.9886 0.970 0.994 -0.0001 0.0342 +mixed48-pca none none 48 0 0.0 0 0.7549 0.540 0.770 0.0167 0.0312 +mixed48-pca f32 fixed 100 48 1536 100.0 100 0.9982 1.000 1.000 -0.0001 0.0400 +mixed48-pca f32 fixed 20 48 1536 20.0 20 0.9204 0.770 0.995 0.0015 0.0330 +mixed48-pca f32 fixed 50 48 1536 50.0 50 0.9885 0.970 0.999 -0.0000 0.0356 +mixed48-pca f32 margin 1σ 48 1536 19.1 28 0.9195 0.810 0.997 0.0012 0.0328 +mixed48-pca f32 margin 2σ 48 1536 34.9 57 0.9822 0.930 1.000 0.0001 0.0341 +mixed48-pca f32 margin 3σ 48 1536 64.2 116 0.9967 0.990 1.000 -0.0000 0.0363 +mixed48-pca int8 fixed 100 48 392 100.0 100 0.9890 0.970 0.994 -0.0000 0.0335 +mixed48-pca int8 fixed 20 48 392 20.0 20 0.9182 0.770 0.990 0.0012 0.0317 +mixed48-pca int8 fixed 50 48 392 50.0 50 0.9809 0.950 0.994 0.0000 0.0324 +mixed48-pca int8 margin 1σ 48 392 19.1 28 0.9170 0.800 0.991 0.0011 0.0317 +mixed48-pca int8 margin 2σ 48 392 34.9 57 0.9752 0.920 0.994 0.0001 0.0320 +mixed48-pca int8 margin 3σ 48 392 64.2 116 0.9877 0.960 0.994 -0.0000 0.0325 +mixed48-pca-w none none 48 0 0.0 0 0.7678 0.550 0.776 0.0177 0.0312 +mixed48-pca-w f32 fixed 100 48 1536 100.0 100 0.9985 1.000 1.000 0.0001 0.0400 +mixed48-pca-w f32 fixed 20 48 1536 20.0 20 0.9309 0.800 0.994 0.0011 0.0330 +mixed48-pca-w f32 fixed 50 48 1536 50.0 50 0.9911 0.980 0.999 0.0000 0.0356 +mixed48-pca-w f32 margin 1σ 48 1536 19.4 28 0.9295 0.830 0.997 0.0018 0.0328 +mixed48-pca-w f32 margin 2σ 48 1536 36.0 60 0.9832 0.930 1.000 0.0006 0.0341 +mixed48-pca-w f32 margin 3σ 48 1536 67.8 123 0.9961 0.980 1.000 0.0000 0.0363 +mixed48-pca-w int8 fixed 100 48 392 100.0 100 0.9895 0.970 0.994 0.0002 0.0335 +mixed48-pca-w int8 fixed 20 48 392 20.0 20 0.9282 0.800 0.989 0.0012 0.0317 +mixed48-pca-w int8 fixed 50 48 392 50.0 50 0.9833 0.970 0.994 0.0000 0.0324 +mixed48-pca-w int8 margin 1σ 48 392 19.4 28 0.9265 0.820 0.992 0.0020 0.0317 +mixed48-pca-w int8 margin 2σ 48 392 36.0 60 0.9759 0.920 0.994 0.0006 0.0320 +mixed48-pca-w int8 margin 3σ 48 392 67.8 123 0.9872 0.960 0.994 0.0001 0.0325 +mixed96-pca-w none none 96 0 0.0 0 0.8855 0.740 0.900 0.0022 0.0625 +mixed96-pca-w f32 fixed 100 96 1536 100.0 100 1.0000 1.000 1.000 0.0000 0.0712 +mixed96-pca-w f32 fixed 20 96 1536 20.0 20 0.9901 0.970 0.999 0.0002 0.0642 +mixed96-pca-w f32 fixed 50 96 1536 50.0 50 0.9999 1.000 1.000 0.0000 0.0669 +mixed96-pca-w f32 margin 1σ 96 1536 13.6 18 0.9633 0.910 1.000 0.0001 0.0636 +mixed96-pca-w f32 margin 2σ 96 1536 17.6 25 0.9913 0.980 1.000 -0.0001 0.0640 +mixed96-pca-w f32 margin 3σ 96 1536 22.9 35 0.9980 1.000 1.000 -0.0001 0.0644 +mixed96-pca-w int8 fixed 100 96 392 100.0 100 0.9905 0.970 0.994 0.0001 0.0647 +mixed96-pca-w int8 fixed 20 96 392 20.0 20 0.9832 0.960 0.994 0.0001 0.0629 +mixed96-pca-w int8 fixed 50 96 392 50.0 50 0.9904 0.970 0.994 0.0001 0.0636 +mixed96-pca-w int8 margin 1σ 96 392 13.6 18 0.9592 0.900 0.994 0.0001 0.0628 +mixed96-pca-w int8 margin 2σ 96 392 17.6 25 0.9837 0.940 0.994 -0.0001 0.0629 +mixed96-pca-w int8 margin 3σ 96 392 22.9 35 0.9890 0.960 0.994 0.0000 0.0630 +pq24 none none 24 0 0.0 0 0.6058 0.330 0.614 0.0586 0.0156 +pq24 f32 fixed 100 24 1536 100.0 100 0.9680 0.890 0.995 0.0020 0.0243 +pq24 f32 fixed 20 24 1536 20.0 20 0.7791 0.500 0.943 0.0119 0.0174 +pq24 f32 fixed 50 24 1536 50.0 50 0.9182 0.780 0.983 0.0046 0.0200 +pq24 f32 margin 1σ 24 1536 42.1 74 0.8870 0.750 0.986 0.0051 0.0188 +pq24 f32 margin 2σ 24 1536 144.0 295 0.9792 0.920 0.999 0.0005 0.0256 +pq24 f32 margin 3σ 24 1536 335.5 626 0.9971 1.000 1.000 0.0002 0.0395 +pq24 int8 fixed 100 24 392 100.0 100 0.9620 0.880 0.989 0.0020 0.0178 +pq24 int8 fixed 20 24 392 20.0 20 0.7781 0.500 0.939 0.0119 0.0161 +pq24 int8 fixed 50 24 392 50.0 50 0.9146 0.780 0.978 0.0048 0.0167 +pq24 int8 margin 1σ 24 392 42.1 74 0.8844 0.740 0.981 0.0052 0.0164 +pq24 int8 margin 2σ 24 392 144.0 295 0.9720 0.910 0.993 0.0006 0.0182 +pq24 int8 margin 3σ 24 392 335.5 626 0.9881 0.960 0.994 0.0003 0.0217 +pq48 none none 48 0 0.0 0 0.7467 0.520 0.770 0.0160 0.0312 +pq48 f32 fixed 100 48 1536 100.0 100 0.9977 1.000 1.000 -0.0001 0.0400 +pq48 f32 fixed 20 48 1536 20.0 20 0.9125 0.750 0.989 0.0019 0.0330 +pq48 f32 fixed 50 48 1536 50.0 50 0.9863 0.960 0.999 0.0002 0.0356 +pq48 f32 margin 1σ 48 1536 20.8 32 0.9240 0.810 0.995 0.0011 0.0329 +pq48 f32 margin 2σ 48 1536 40.9 71 0.9852 0.940 0.999 0.0002 0.0345 +pq48 f32 margin 3σ 48 1536 80.4 152 0.9979 1.000 1.000 -0.0001 0.0374 +pq48 int8 fixed 100 48 392 100.0 100 0.9888 0.970 0.994 -0.0000 0.0335 +pq48 int8 fixed 20 48 392 20.0 20 0.9098 0.750 0.984 0.0019 0.0317 +pq48 int8 fixed 50 48 392 50.0 50 0.9788 0.930 0.993 0.0002 0.0324 +pq48 int8 margin 1σ 48 392 20.8 32 0.9210 0.810 0.990 0.0010 0.0317 +pq48 int8 margin 2σ 48 392 40.9 71 0.9777 0.930 0.994 0.0002 0.0321 +pq48 int8 margin 3σ 48 392 80.4 152 0.9887 0.970 0.994 -0.0000 0.0328 +pq96 none none 96 0 0.0 0 0.8704 0.730 0.879 0.0031 0.0625 +pq96 f32 fixed 100 96 1536 100.0 100 0.9999 1.000 1.000 0.0000 0.0712 +pq96 f32 fixed 20 96 1536 20.0 20 0.9864 0.950 0.999 -0.0001 0.0642 +pq96 f32 fixed 50 96 1536 50.0 50 0.9997 1.000 1.000 -0.0001 0.0669 +pq96 f32 margin 1σ 96 1536 13.9 18 0.9580 0.880 0.998 0.0000 0.0637 +pq96 f32 margin 2σ 96 1536 18.3 26 0.9898 0.960 0.999 -0.0001 0.0640 +pq96 f32 margin 3σ 96 1536 24.2 37 0.9982 1.000 1.000 -0.0000 0.0645 +pq96 int8 fixed 100 96 392 100.0 100 0.9905 0.970 0.994 0.0001 0.0647 +pq96 int8 fixed 20 96 392 20.0 20 0.9791 0.910 0.994 -0.0002 0.0629 +pq96 int8 fixed 50 96 392 50.0 50 0.9903 0.970 0.994 -0.0000 0.0636 +pq96 int8 margin 1σ 96 392 13.9 18 0.9540 0.880 0.992 0.0002 0.0628 +pq96 int8 margin 2σ 96 392 18.3 26 0.9821 0.930 0.994 -0.0001 0.0629 +pq96 int8 margin 3σ 96 392 24.2 37 0.9890 0.970 0.994 -0.0000 0.0630 + +2. Adaptive storage alone (no re-score), by byte budget; per model, then both + 24 B mixed24-pca-w recall@10 0.6347 (bge 0.5924, MiniLM 0.6769) p10 0.370 ndcg loss 0.0523 + 24 B mixed24-pca recall@10 0.6314 (bge 0.5963, MiniLM 0.6665) p10 0.370 ndcg loss 0.0497 + 24 B pq24 recall@10 0.6058 (bge 0.5874, MiniLM 0.6242) p10 0.330 ndcg loss 0.0586 + 48 B mixed48-pca-w recall@10 0.7678 (bge 0.7226, MiniLM 0.8131) p10 0.550 ndcg loss 0.0177 + 48 B mixed48-pca recall@10 0.7549 (bge 0.7167, MiniLM 0.7930) p10 0.540 ndcg loss 0.0167 + 48 B pq48 recall@10 0.7467 (bge 0.7242, MiniLM 0.7693) p10 0.520 ndcg loss 0.0160 + 48 B binary-centred recall@10 0.6977 (bge 0.6673, MiniLM 0.7281) p10 0.430 ndcg loss 0.0236 + 48 B mixed48 recall@10 0.6959 (bge 0.6634, MiniLM 0.7285) p10 0.430 ndcg loss 0.0242 + 48 B binary recall@10 0.6685 (bge 0.6223, MiniLM 0.7147) p10 0.410 ndcg loss 0.0240 + 96 B mixed96-pca-w recall@10 0.8855 (bge 0.8551, MiniLM 0.9159) p10 0.740 ndcg loss 0.0022 + 96 B pq96 recall@10 0.8704 (bge 0.8554, MiniLM 0.8854) p10 0.730 ndcg loss 0.0031 + +3. Adaptive depth against fixed depth at the same mean depth (per collection, then averaged) + scan codec second margin mean depth Δrecall Δp10 Δndcg p90/mean cells + binary f32 1 23.2 +0.0126 +0.033 +0.0007 1.49 10 + binary f32 2 51.9 +0.0145 +0.036 +0.0009 1.69 10 + binary f32 3 53.6 +0.0090 +0.024 -0.0004 1.93 3 + binary int8 1 23.2 +0.0130 +0.037 +0.0005 1.49 10 + binary int8 2 51.9 +0.0142 +0.049 +0.0008 1.69 10 + binary int8 3 53.6 +0.0088 +0.036 -0.0004 1.93 3 + binary-centred f32 1 25.7 +0.0196 +0.065 +0.0006 1.51 10 + binary-centred f32 2 54.1 +0.0125 +0.031 +0.0003 1.71 9 + binary-centred f32 3 57.2 +0.0050 +0.012 +0.0001 1.89 2 + binary-centred int8 1 25.7 +0.0197 +0.060 +0.0007 1.51 10 + binary-centred int8 2 54.1 +0.0116 +0.027 +0.0003 1.71 9 + binary-centred int8 3 57.2 +0.0046 +0.033 +0.0001 1.89 2 + mixed24-pca f32 1 30.8 +0.0156 +0.048 +0.0019 1.54 10 + mixed24-pca f32 2 66.2 +0.0106 +0.055 +0.0005 1.69 8 + mixed24-pca f32 3 67.9 +0.0043 +0.013 +0.0000 1.85 2 + mixed24-pca int8 1 30.8 +0.0157 +0.051 +0.0019 1.54 10 + mixed24-pca int8 2 66.2 +0.0102 +0.042 +0.0004 1.69 8 + mixed24-pca int8 3 67.9 +0.0039 +0.054 -0.0001 1.85 2 + mixed24-pca-w f32 1 33.2 +0.0150 +0.036 +0.0021 1.56 10 + mixed24-pca-w f32 2 53.5 +0.0110 +0.031 +0.0013 1.65 6 + mixed24-pca-w f32 3 76.0 +0.0042 +0.006 +0.0002 1.82 3 + mixed24-pca-w int8 1 33.2 +0.0154 +0.032 +0.0020 1.56 10 + mixed24-pca-w int8 2 53.5 +0.0107 +0.030 +0.0012 1.65 6 + mixed24-pca-w int8 3 76.0 +0.0039 +0.039 +0.0002 1.82 3 + mixed48 f32 1 25.6 +0.0181 +0.056 +0.0010 1.51 10 + mixed48 f32 2 53.2 +0.0122 +0.033 -0.0001 1.69 9 + mixed48 f32 3 56.5 +0.0051 +0.013 -0.0001 1.90 2 + mixed48 int8 1 25.6 +0.0182 +0.051 +0.0009 1.51 10 + mixed48 int8 2 53.2 +0.0116 +0.030 -0.0002 1.69 9 + mixed48 int8 3 56.5 +0.0048 +0.035 -0.0001 1.90 2 + mixed48-pca f32 1 19.1 +0.0205 +0.061 +0.0007 1.42 10 + mixed48-pca f32 2 34.9 +0.0163 +0.030 +0.0001 1.60 10 + mixed48-pca f32 3 52.4 +0.0064 +0.008 +0.0001 1.71 8 + mixed48-pca int8 1 19.1 +0.0203 +0.051 +0.0009 1.42 10 + mixed48-pca int8 2 34.9 +0.0155 +0.030 +0.0001 1.60 10 + mixed48-pca int8 3 52.4 +0.0059 +0.015 +0.0001 1.71 8 + mixed48-pca-w f32 1 19.4 +0.0220 +0.046 -0.0012 1.42 10 + mixed48-pca-w f32 2 36.0 +0.0133 +0.016 -0.0003 1.59 10 + mixed48-pca-w f32 3 54.5 +0.0056 +0.003 +0.0001 1.67 9 + mixed48-pca-w int8 1 19.4 +0.0218 +0.036 -0.0013 1.42 10 + mixed48-pca-w int8 2 36.0 +0.0125 +0.015 -0.0003 1.59 10 + mixed48-pca-w int8 3 54.5 +0.0050 +0.007 +0.0000 1.67 9 + mixed96-pca-w f32 1 13.6 +0.0315 +0.063 +nan 1.29 10 + mixed96-pca-w f32 2 17.6 +0.0222 +0.059 -0.0002 1.39 10 + mixed96-pca-w f32 3 22.9 +0.0136 +0.039 +0.0003 1.49 10 + mixed96-pca-w int8 1 13.6 +0.0307 +0.061 +nan 1.29 10 + mixed96-pca-w int8 2 17.6 +0.0206 +0.028 -0.0004 1.39 10 + mixed96-pca-w int8 3 22.9 +0.0121 +0.016 +0.0003 1.49 10 + pq24 f32 1 35.6 +0.0107 +0.029 +0.0018 1.60 9 + pq24 f32 2 60.7 +0.0104 +0.012 +0.0002 1.94 4 + pq24 f32 3 60.6 +0.0048 +0.000 +0.0005 2.09 1 + pq24 int8 1 35.6 +0.0110 +0.029 +0.0020 1.60 9 + pq24 int8 2 60.7 +0.0104 +0.013 +0.0001 1.94 4 + pq24 int8 3 60.6 +0.0048 +0.000 +0.0003 2.09 1 + pq48 f32 1 20.8 +0.0199 +0.047 +0.0014 1.47 10 + pq48 f32 2 40.9 +0.0135 +0.027 +0.0002 1.69 10 + pq48 f32 3 64.7 +0.0045 +0.020 +0.0001 1.78 8 + pq48 int8 1 20.8 +0.0197 +0.052 +0.0016 1.47 10 + pq48 int8 2 40.9 +0.0126 +0.032 +0.0002 1.69 10 + pq48 int8 3 64.7 +0.0040 +0.033 +0.0001 1.78 8 + pq96 f32 1 13.9 +0.0328 +0.043 +nan 1.30 10 + pq96 f32 2 18.3 +0.0189 +0.041 +0.0004 1.43 10 + pq96 f32 3 24.2 +0.0108 +0.041 -0.0001 1.52 10 + pq96 int8 1 13.9 +0.0324 +0.060 +nan 1.30 10 + pq96 int8 2 18.3 +0.0177 +0.042 -0.0000 1.43 10 + pq96 int8 3 24.2 +0.0095 +0.057 -0.0002 1.52 10 + overall: Δrecall +0.0151, Δp10 +0.037; adaptive ahead in 522 of 526 cells + +4. Benefits and drawbacks against the plain 48-byte binary scan (binary, none) + baseline: recall 0.6685, p10 0.410, ndcg loss 0.0240, 48 B/vector, read 0.0312 of an f32 scan + uniform scan, no re-score best: pq96 none none: recall 0.8704 (+0.2019), p10 0.730, ndcg loss 0.0031, ram 96 B + disk 0 B, read 0.0625, mean depth 0 (p90 0) + adaptive bits, no re-score best: mixed96-pca-w none none: recall 0.8855 (+0.2170), p10 0.740, ndcg loss 0.0022, ram 96 B + disk 0 B, read 0.0625, mean depth 0 (p90 0) + hybrid fixed depth, int8 best: mixed96-pca-w int8 fixed 100: recall 0.9905 (+0.3220), p10 0.970, ndcg loss 0.0001, ram 96 B + disk 392 B, read 0.0647, mean depth 100 (p90 100) + hybrid fixed depth, f32 best: mixed96-pca-w f32 fixed 100: recall 1.0000 (+0.3315), p10 1.000, ndcg loss 0.0000, ram 96 B + disk 1536 B, read 0.0712, mean depth 100 (p90 100) + hybrid adaptive depth, int8 best: pq96 int8 margin 3σ: recall 0.9890 (+0.3205), p10 0.970, ndcg loss -0.0000, ram 96 B + disk 392 B, read 0.0630, mean depth 24 (p90 37) + hybrid adaptive depth, f32 best: pq96 f32 margin 3σ: recall 0.9982 (+0.3296), p10 1.000, ndcg loss -0.0000, ram 96 B + disk 1536 B, read 0.0645, mean depth 24 (p90 37) From 6ef3bac3ecac40417208997d1336ad555c237e1f Mon Sep 17 00:00:00 2001 From: Swinecoder101 Date: Wed, 4 Nov 2026 22:25:19 +0000 Subject: [PATCH 09/12] experiment: routing oracle results and write-up 38,124 query traces over six scan codecs and ten collections. A hindsight oracle beats fixed re-score depth by 5.5-7 points of recall@10 at equal mean depth, but the band visible in the scan's own scores tracks the true band only at Spearman 0.52-0.70: the margin policy captures 0-6% of the headroom and a ratio router does worse than fixed depth up to 50 candidates. Per-collection routing picks the right scan size in 10/10 collections, but its predicted depth misses a 95% target by up to 9 points, so depth should be measured, not predicted. Traces are gzipped; analyse.py reads either form. --- experiments/2026-11-routing-oracle/README.md | 122 ++++++++++++++++++ experiments/2026-11-routing-oracle/analyse.py | 8 +- .../results/summary.txt | 45 +++++++ .../results/traces/arguana-bge-small.jsonl.gz | Bin 0 -> 392830 bytes .../results/traces/arguana-minilm.jsonl.gz | Bin 0 -> 352164 bytes .../results/traces/cosqa-bge-small.jsonl.gz | Bin 0 -> 144641 bytes .../results/traces/cosqa-minilm.jsonl.gz | Bin 0 -> 152165 bytes .../results/traces/fiqa-bge-small.jsonl.gz | Bin 0 -> 219231 bytes .../results/traces/fiqa-minilm.jsonl.gz | Bin 0 -> 216220 bytes .../traces/nfcorpus-bge-small.jsonl.gz | Bin 0 -> 113977 bytes .../results/traces/nfcorpus-minilm.jsonl.gz | Bin 0 -> 108746 bytes .../results/traces/scifact-bge-small.jsonl.gz | Bin 0 -> 105910 bytes .../results/traces/scifact-minilm.jsonl.gz | Bin 0 -> 102075 bytes experiments/2026-11-routing-oracle/run.sh | 1 + 14 files changed, 175 insertions(+), 1 deletion(-) create mode 100644 experiments/2026-11-routing-oracle/README.md create mode 100644 experiments/2026-11-routing-oracle/results/summary.txt create mode 100644 experiments/2026-11-routing-oracle/results/traces/arguana-bge-small.jsonl.gz create mode 100644 experiments/2026-11-routing-oracle/results/traces/arguana-minilm.jsonl.gz create mode 100644 experiments/2026-11-routing-oracle/results/traces/cosqa-bge-small.jsonl.gz create mode 100644 experiments/2026-11-routing-oracle/results/traces/cosqa-minilm.jsonl.gz create mode 100644 experiments/2026-11-routing-oracle/results/traces/fiqa-bge-small.jsonl.gz create mode 100644 experiments/2026-11-routing-oracle/results/traces/fiqa-minilm.jsonl.gz create mode 100644 experiments/2026-11-routing-oracle/results/traces/nfcorpus-bge-small.jsonl.gz create mode 100644 experiments/2026-11-routing-oracle/results/traces/nfcorpus-minilm.jsonl.gz create mode 100644 experiments/2026-11-routing-oracle/results/traces/scifact-bge-small.jsonl.gz create mode 100644 experiments/2026-11-routing-oracle/results/traces/scifact-minilm.jsonl.gz diff --git a/experiments/2026-11-routing-oracle/README.md b/experiments/2026-11-routing-oracle/README.md new file mode 100644 index 0000000..745fb11 --- /dev/null +++ b/experiments/2026-11-routing-oracle/README.md @@ -0,0 +1,122 @@ +# Routing: how much can choosing the compression per query or per collection gain? + +The adaptive-depth policy in the [hybrid × adaptive matrix](../2026-11-hybrid-adaptive-matrix/) +is a simple router: each query gets as many re-scored candidates as its scan scores suggest it +needs. The natural generalisation is to route each query, or each collection, to whichever form +of compression serves it best, the way a mixture-of-experts gate routes tokens to experts. Before +building a better router, this experiment measures how much there is to gain, and how much of it +a router can actually see. + +**Hypothesis.** Per-query routing has real headroom, because the depth a query needs varies a lot +between queries. But a router only sees the scan's own scores, which are noisy, so a router +built on them will capture a small share of that headroom. Per-collection routing, choosing a +codec and a depth once from the noise-to-spacing curve, will get close to the best choice. + +**Result.** The first two are confirmed. The third holds for the codec choice and fails for the +depth. +- **The headroom is real:** a hindsight oracle beats a fixed depth by 5.5 to 7 points of + recall@10 at the same mean depth. The depth a query needs for full recall varies 6.3× between + the 10th and 90th percentile. +- **Almost none of it is visible.** The band a router can read from the scan's own scores tracks + the true band with Spearman 0.52 to 0.70. The margin policy captures 0 to 6% of the headroom. + A router that sets depth from noise ÷ band_hat does *worse* than a fixed depth at every budget + up to 50 candidates. +- **Per-collection routing picks the right codec, but not the right depth.** Predicting each + collection's cheapest scan codec and depth from the curve, fitted on the other corpora, picks + the same scan size as the best choice in 10 of 10 collections at 90% and 95% recall. Its depth, + though, is coarse. Aiming for 95%, the recall it actually reaches ranges from 0.86 to 1.00, and + aiming for 90%, from 0.71 to 0.99. So the hypothesis holds for the codec choice and fails for + the depth. A deployed version would have to measure the depth on a sample, not predict it. + +## Setup + +| | | +|---|---| +| Collections | The ten from [neighbour spacing](../2026-11-neighbour-spacing/): five corpora × bge-small, MiniLM | +| Queries | Each set's judged test queries: 38,124 query traces over 60 (collection, scan codec) pairs | +| Scan codecs | `binary`, `binary-centred`, `pq48`, `pq24`, `mixed48-pca-w`, `mixed24-pca-w` | +| Second stage | f32 (exact), so recall at depth N is the share of the true top 10 the scan ranks within N | +| Traces | `bench rescore --trace`: per query, the scan's rank of each true neighbour, the exact band and the band read from the scan's own scores (`band_hat`), and the margin policy's depth from 0.25σ to 6σ | +| Policies | fixed depth; margin (λσ below the scan's 10th score, σ calibrated on self-queries); ratio router, depth = a·k·(σ ÷ band_hat)^β with β = 1 and 1.3, swept over a; oracle, the best per-query depths for each mean-depth budget, solved exactly (Lagrangian over each query's steps) | +| Comparison | Every policy traces a curve of mean depth against mean recall@10; curves are compared at mean depths of 15, 20, 30, 50 and 100 | +| Per-collection routing | For targets of 90/95/99%: fit depth/k against noise ÷ band on four corpora, predict each scan codec's depth on the fifth, pick the cheapest (scan bytes × vectors + depth × 1,536 B), and check its real recall | +| Code | hvec commit `cd1864a` (`--trace`) | +| Command | `./run.sh` (records the traces), then `python3 -I analyse.py results/traces 10` | + +## Results + +### Per-query routing at equal mean depth + +Means over the 60 traces with every policy defined at that budget. Δ is against a fixed depth. + +| mean depth | fixed | margin | ratio router (best β) | oracle | headroom | margin captures | worst decile, fixed → margin | +|---:|---:|---:|---:|---:|---:|---:|---| +| 15 | 0.796 | −0.002 | −0.025 | +0.067 | 0.067 | −4% | 0.596 → 0.615 | +| 20 | 0.832 | −0.000 | −0.020 | +0.071 | 0.071 | 0% | 0.638 → 0.661 | +| 30 | 0.875 | +0.003 | −0.008 | +0.069 | 0.069 | 4% | 0.690 → 0.730 | +| 50 | 0.900 | +0.004 | −0.001 | +0.067 | 0.067 | 5% | 0.738 → 0.757 | +| 100 | 0.939 | +0.003 | +0.000 | +0.055 | 0.055 | 6% | 0.786 → 0.831 | + +The number of traces falls with the budget (from 45 to 7), because the margin grid does not +reach large mean depths for the better codecs. Compared exactly at each margin's own mean +depth instead (all 60 traces), the margin policy gains +0.006 at 1σ, +0.006 at 2σ and +0.003 at +3σ. It is ahead in 156 of 180 comparisons, and worst-decile recall rises by 4.1, 2.0 and 0.7 +points. + +### What a router can see + +| scan codec | Spearman, band_hat vs exact band | Spearman, 1 / band_hat vs depth needed | +|---|---:|---:| +| `binary` | 0.61 | 0.56 | +| `binary-centred` | 0.63 | 0.55 | +| `pq48` | 0.70 | 0.58 | +| `pq24` | 0.55 | 0.53 | +| `mixed48-pca-w` | 0.68 | 0.52 | +| `mixed24-pca-w` | 0.52 | 0.48 | + +### Per-collection routing + +| target | same scan size as the best | reaches the target (within 0.005) | recall actually reached | cost vs the best choice | +|---|---:|---:|---|---:| +| 90% | 10 / 10 | 5 / 10 | 0.713 to 0.989 | ×1.00 | +| 95% | 10 / 10 | 4 / 10 | 0.861 to 0.997 | ×1.01 | +| 99% | 7 / 10 | 4 / 10 | 0.950 to 1.000 | ×1.06 | + +The cost ratio looks perfect partly because the predictions that undershoot also read less. + +Under this cost model (bytes read: the whole scan plus the re-scored f32 candidates) the best +choice is almost always the smallest scan with a deeper re-score. At 95% it is `mixed24-pca-w` +in 9 of 10 collections, at depths from 17 (ArguAna, MiniLM) to 111 (FiQA, bge-small), reading +1.7 to 2.5% of the bytes of an f32 scan. + +## Discussion + +**Where the oracle's headroom comes from.** The oracle knows, for each query, how far the scan +displaced its worst-placed true neighbour. Mostly that is chance: one neighbour's score happened +to be pushed down by the code's noise. Nothing in the scan's own output predicts it well, because +the same noise that displaced the neighbour is all the scan reports. The band a query's scores +show is a property of the query, and it predicts only part of the variation. + +**Why the ratio router loses to fixed depth.** It sets depth in proportion to 1 / band_hat, so its +errors in band_hat become large errors in depth. Some queries get far too many candidates, which +uses up the budget. The margin policy is more robust: it counts candidates near the 10th score +directly, rather than extrapolating from one gap. + +**What this means for routing.** +- **Per query:** a cheap margin policy is worth keeping for its worst-query gain, but a smarter + per-query router built on scan scores alone is not worth building. +- **Per collection:** the noise-to-spacing curve picks the right codec, but its depth is off by + up to 9 points of recall at a 95% target. The practical version of "route to the best + compression" chooses the codec from the curve, then measures the depth directly. With an exact + second stage, measuring takes one pass of self-queries with traces. +- **Next:** storage-side multiplexing, a layered code read to different depths per candidate, + remains open; it changes what hybrid costs on disk, which this experiment does not measure. + +**Caveats.** +- **The cost model counts bytes, not time.** The [zero-copy scan](../2026-11-zero-copy-scan/) + experiment shows that every asymmetric scan costs about the same per vector regardless of its + size. In time, the smallest scan does not win; only the symmetric `binary-hamming` scan is + faster. +- **The traces cover six scan codecs, with an f32 second stage only.** +- **Few collections.** Ten collections, two of each corpus, make a small sample for the + per-collection test. diff --git a/experiments/2026-11-routing-oracle/analyse.py b/experiments/2026-11-routing-oracle/analyse.py index 5422ac2..d68997b 100644 --- a/experiments/2026-11-routing-oracle/analyse.py +++ b/experiments/2026-11-routing-oracle/analyse.py @@ -16,6 +16,7 @@ target from noise ÷ band, with the curve fitted on the other corpora, and check it. """ +import gzip import json import math import sys @@ -118,7 +119,12 @@ def at_budget(curve, budget): def main(traces_dir: Path, k: int) -> None: - traces = [Trace(l) for p in sorted(traces_dir.glob("*.jsonl")) for l in p.read_text().splitlines() if l.strip()] + def lines(p): + text = gzip.decompress(p.read_bytes()).decode() if p.suffix == ".gz" else p.read_text() + return [l for l in text.splitlines() if l.strip()] + + files = sorted(traces_dir.glob("*.jsonl")) + sorted(traces_dir.glob("*.jsonl.gz")) + traces = [Trace(l) for p in files for l in lines(p)] print(f"{len(traces)} (collection, scan codec) traces, {sum(t.n for t in traces)} query traces in all\n") # Does the scan's own band track the exact one, query by query? diff --git a/experiments/2026-11-routing-oracle/results/summary.txt b/experiments/2026-11-routing-oracle/results/summary.txt new file mode 100644 index 0000000..0e3f5fd --- /dev/null +++ b/experiments/2026-11-routing-oracle/results/summary.txt @@ -0,0 +1,45 @@ +60 (collection, scan codec) traces, 38124 query traces in all + +1. Can a router see the band? Spearman, per trace, over queries + binary band_hat vs exact band 0.607 1/band_hat vs depth for full recall 0.558 + binary-centred band_hat vs exact band 0.634 1/band_hat vs depth for full recall 0.546 + mixed24-pca-w band_hat vs exact band 0.524 1/band_hat vs depth for full recall 0.478 + mixed48-pca-w band_hat vs exact band 0.684 1/band_hat vs depth for full recall 0.522 + pq24 band_hat vs exact band 0.548 1/band_hat vs depth for full recall 0.526 + pq48 band_hat vs exact band 0.697 1/band_hat vs depth for full recall 0.579 + (how much per-query depth needed varies: median p90/p10 ratio of full-recall depth 6.3×) + +2. Per-query routing at equal mean depth (means over traces). Δ against fixed depth + budget fixed margin ratio β=1 ratio β=1.3 oracle headroom margin cap. ratio cap. p10 fixed→margin + 15 0.7961 -0.0024 -0.0253 -0.0336 +0.0672 0.0672 -4% -38% 0.596→0.615 (n=45) + 20 0.8319 -0.0001 -0.0201 -0.0335 +0.0710 0.0710 -0% -28% 0.638→0.661 (n=40) + 30 0.8752 +0.0025 -0.0075 -0.0223 +0.0687 0.0687 4% -11% 0.690→0.730 (n=31) + 50 0.8998 +0.0035 -0.0006 -0.0111 +0.0667 0.0667 5% -1% 0.738→0.757 (n=16) + 100 0.9385 +0.0033 +0.0003 -0.0047 +0.0546 0.0546 6% 1% 0.786→0.831 (n=7) + + by scan codec, at a mean depth of 30: fixed recall, then Δ for margin, best ratio, oracle + binary fixed 0.8819 margin +0.0046 ratio -0.0045 oracle +0.0655 + binary-centred fixed 0.9022 margin +0.0067 ratio -0.0026 oracle +0.0662 + mixed24-pca-w fixed 0.8453 margin -0.0002 ratio -0.0098 oracle +0.0753 + mixed48-pca-w fixed 0.9279 margin +0.0046 ratio -0.0079 oracle +0.0614 + pq24 fixed 0.8332 margin -0.0032 ratio -0.0146 oracle +0.0741 + pq48 fixed 0.9283 margin +0.0069 ratio -0.0033 oracle +0.0600 + p90 depth at a mean of 30: fixed 30, margin 46, oracle 52 + +3. Per-collection routing: the cheapest (scan codec, fixed depth) reaching a recall target + cost = bytes read per query = vectors × scan bytes + depth × 1536; RAM = scan bytes per vector + target 90%: predicted choice reaches the target (within 0.005) in 5/10 collections; cost vs the best choice ×1.00 on average; same scan size as the best in 10/10 + target 95%: predicted choice reaches the target (within 0.005) in 4/10 collections; cost vs the best choice ×1.01 on average; same scan size as the best in 10/10 + target 99%: predicted choice reaches the target (within 0.005) in 4/10 collections; cost vs the best choice ×1.06 on average; same scan size as the best in 7/10 + + best choice per collection at 95%: + arguana-bge-small mixed24-pca-w depth 25 read 0.0179 of f32 (smallest scan: mixed24-pca-w at depth 25) + arguana-minilm mixed24-pca-w depth 17 read 0.0171 of f32 (smallest scan: mixed24-pca-w at depth 17) + cosqa-bge-small mixed24-pca-w depth 63 read 0.0187 of f32 (smallest scan: mixed24-pca-w at depth 63) + cosqa-minilm mixed24-pca-w depth 43 read 0.0177 of f32 (smallest scan: mixed24-pca-w at depth 43) + fiqa-bge-small mixed24-pca-w depth 111 read 0.0172 of f32 (smallest scan: mixed24-pca-w at depth 111) + fiqa-minilm mixed24-pca-w depth 61 read 0.0165 of f32 (smallest scan: mixed24-pca-w at depth 61) + nfcorpus-bge-small pq24 depth 106 read 0.0326 of f32 (smallest scan: pq24 at depth 106) + nfcorpus-minilm mixed24-pca-w depth 54 read 0.0243 of f32 (smallest scan: mixed24-pca-w at depth 54) + scifact-bge-small mixed24-pca-w depth 71 read 0.0245 of f32 (smallest scan: mixed24-pca-w at depth 71) + scifact-minilm mixed24-pca-w depth 45 read 0.0212 of f32 (smallest scan: mixed24-pca-w at depth 45) diff --git a/experiments/2026-11-routing-oracle/results/traces/arguana-bge-small.jsonl.gz b/experiments/2026-11-routing-oracle/results/traces/arguana-bge-small.jsonl.gz new file mode 100644 index 0000000000000000000000000000000000000000..8c683005c8cf55260ba5bb2823f706b992d771ff GIT binary patch literal 392830 zcmV(rK<>XEiwFqJs_SY317UJ!bzyE{En;V7Epu&QY-}!Sb8l{J0NkC~k}b(`UH_$@ zOR|^;{0famYlx5p69mdg=s~8Rm#-b}k$LJ~@Ri*XFDbH6bxvjsK5XB<{r~>*$6x>Y z&%gZPAJTtLb?u&0-8Hu|Qz~ElETuNv9A)RxN83$5EF+cG#wewYne?+VTCIJ}H0_V~ zTwOoR{K$Sat(9w=Q$I{&&b{g!Y1KNps6WQtc_HmFOY}*4Nmp z=bTF&eV%8`-M%2_WFOLZuICZg9DA&4-?Mfa`s1F~$g?H>IJL3$HNDJ|X0|8jEyven zKB~9A?F~vF>pWvx#hyE5``~%rrKZ(p%4KhRM0?k>6uI|W>>bChyo7$y9;4f$+KZ=Z zi+29FYc2X1y{xKtDJ^gN^JR~d`u)YxTS(3aBPY1aG0#uc!?ueRgcHjker`w#8Z zRy(N8o+#}9DC+*uV zh5x4gMzIgxqpoqTgYE0yv)gs7ZI5)WtZkJob+d=C#kGTde#ZORtCYgikMoPR)2XF% zT)Q33vuu@HW=~+})z)4M8KyDPJ%F}}+g5hkmtt1VH`~o>_CnbfqOEnVXSVA% zx#{+r_KlNovEx1XoypN|X4Uvbj<_B4mW!P~+a4{&H2Y#t!+w`Gs~*Rep8vC(CFC=P zAL(s|Z`(1qyVQ^QvJb;@%)V_U>}Kc#m)X^7wx!rnFK4;g1iKv>``3IC&FU=sBU^(u z%@&;Ru(M@{-`>)WN;wy#R5LQ0+h8wz4+^HcaujXL?UMROVilHs!#H==O^7X?ZI&4) zv;4l*wko#M_8qo9Wt|< z*r(dr#j5%_;R5As_tq>yJGZ7!`y4wh_RzES>|bpM6QgO%X22Z<=M;d~o4NF83F9OzlA7uWEB2((Inv-o&@-ZOoq( z+?H+5jz2aFi^sI>GwZ}|A6I2If>UL$ym_YcKw9tIvKbcJ&hwj?O#2;kv1Z52G#zJU zD}#$0&Frb0ce_(|5Y1|GY?^yDyF<$?rG0ZTlM|0=C&M-yr`9+lXMyd&m|?SXiyb(3 zDX~OmtMHA92R--3mfs8zCJD=ZmcvYgJ-Gd6xVB>_%?H>iv3+5sT5+3Z{x#bLwUfP* z`}}H)HD@)WVjnPl(QK#fdf2|$6J@vVwp?~??9bB(b7=0Q=gHFbYUbnYtZml72%Fcq zXVTW8&xW5SW~pu?<=2Xi0yfl5{=(#$KQ;qv4*cA(YKNTfup8OW$_oS-YPP`M%Fn+2X2SBC;YhyX_ReOr?Xa1jw_iS+ zXs*NF!fu1v$nCbqT%nn~WLKF_*=H^7DJ;mrJ=hNh?y^^^MeTQv{+1iY^vC!niclKVk6wUsh*;Bhi`Vzf}9hV5 zW9ICk?WcAm%{s*OvSqN<$;3cAmNuKcG@EDdW*cBD?7qu_Bs+U}IrD5M4lsi>6%N`m zK3{{|HEV3^QSlDCHFgx5840uNws7S<w)Y^hr)-i28p{m?eI`i9v9S{OziWPf{{jP1Lvl^J3?3bvZZUSd=Ak*q~= zKg?w#7Ung>-J8?2n9L5p{VvYMeU!cZz{xE8-_Iiw^WZ49MX6k$^V~$Q314lwUiH&s z3vX`Gc7<&6MBQc!%qCfsYPp=9E?-9bZnGWsnD(q}cXa8*B?Q`s;B*K^Dw<>4PeppUsdydW|05{8H*4`i6c7tEBLthp*-mfrW zI$6A>4=2j7#WMqd{k3?Cr`8WGG$41v?y=`1!ae&@<4kP#9d^sfW-5yrA)tMrISsQ; 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N`~Q)#uo7n~1ORc?Y0m%v literal 0 HcmV?d00001 diff --git a/experiments/2026-11-routing-oracle/run.sh b/experiments/2026-11-routing-oracle/run.sh index a3bb4cb..3645462 100755 --- a/experiments/2026-11-routing-oracle/run.sh +++ b/experiments/2026-11-routing-oracle/run.sh @@ -32,4 +32,5 @@ for c in $CORPORA; do echo "== $col done" done done +gzip -9f "$HERE"/results/traces/*.jsonl python3 -I "$HERE/analyse.py" "$HERE/results/traces" "$K" | tee "$HERE/results/summary.txt" From 174c60c650a63d8c94003b2a3a271361ebc339c9 Mon Sep 17 00:00:00 2001 From: Swinecoder101 Date: Wed, 4 Nov 2026 22:25:19 +0000 Subject: [PATCH 10/12] experiment: zero-copy scan results and write-up On an otherwise idle M4 Pro: the cached contiguous buffer searches 5.1x faster than the original row scan on FiQA (14.7 vs 74.7 ms) and SQLite's memory map alone gives 2.7x. Every asymmetric codec costs 200-490 ns per vector against 207 for f32, under 2% of memory bandwidth, so fewer bytes do not mean a faster scan. binary-hamming costs 10 ns per vector; with an f32 re-score of 369 candidates it reaches 95% recall@10 on FiQA about 19x faster than exact f32 on one thread. mixed48-pca-w is the slowest scan, 2.2x f32 per vector. --- experiments/2026-11-zero-copy-scan/README.md | 133 +++ .../results/fiqa-bge-small.json | 386 +++++++++ .../results/machine.txt | 4 + .../results/rescore-fiqa-bge-small.json | 768 ++++++++++++++++++ .../rescore-fiqa-bge-small.trace.jsonl.gz | Bin 0 -> 104703 bytes .../results/rescore-scifact-bge-small.json | 768 ++++++++++++++++++ .../rescore-scifact-bge-small.trace.jsonl.gz | Bin 0 -> 51616 bytes .../results/scifact-bge-small.json | 386 +++++++++ .../results/summary.txt | 112 +++ 9 files changed, 2557 insertions(+) create mode 100644 experiments/2026-11-zero-copy-scan/README.md create mode 100644 experiments/2026-11-zero-copy-scan/results/fiqa-bge-small.json create mode 100644 experiments/2026-11-zero-copy-scan/results/machine.txt create mode 100644 experiments/2026-11-zero-copy-scan/results/rescore-fiqa-bge-small.json create mode 100644 experiments/2026-11-zero-copy-scan/results/rescore-fiqa-bge-small.trace.jsonl.gz create mode 100644 experiments/2026-11-zero-copy-scan/results/rescore-scifact-bge-small.json create mode 100644 experiments/2026-11-zero-copy-scan/results/rescore-scifact-bge-small.trace.jsonl.gz create mode 100644 experiments/2026-11-zero-copy-scan/results/scifact-bge-small.json create mode 100644 experiments/2026-11-zero-copy-scan/results/summary.txt diff --git a/experiments/2026-11-zero-copy-scan/README.md b/experiments/2026-11-zero-copy-scan/README.md new file mode 100644 index 0000000..5438019 --- /dev/null +++ b/experiments/2026-11-zero-copy-scan/README.md @@ -0,0 +1,133 @@ +# Zero-copy scan: where a search spends its time, and which codecs are actually faster + +Every earlier experiment measured what compression costs in recall. This one measures what it +buys in time, which the README had so far declined to claim: "binary is no faster than int8 +here". The question has two parts. How much of a search's time is moving bytes rather than +scoring them, and does reading fewer bytes make a scan faster? + +**Hypothesis.** +1. Most of the original search's time is copying, not scoring. Every query read every row, + blob and text out of SQLite and allocated a vector per row. Scanning a contiguous buffer in + place will be several times faster. +2. Once copying is gone, the asymmetric codecs (an f32 query against compressed codes) will + still be no faster than f32. Their scoring is limited by computation, not by bytes. Only a + symmetric code, the query binarised too and compared with XOR and popcount, will scan faster. + +**Result.** Both confirmed. +- **Copying is most of the original cost.** The cached buffer searches 5.1× faster than the + original row scan on FiQA (14.7 against 74.7 ms) and 5.2× on SciFact. SQLite's own memory map + alone gives 2.7–2.9×. +- **Fewer bytes do not mean a faster scan, except for popcount.** `int8`, `binary`, `pq48`, + `pq24` and `mixed48-pca-w` all cost 200 to 490 ns per vector on one thread, against 207 ns for + f32. They use under 2% of the machine's memory bandwidth. `binary-hamming` costs 10 ns, 20× + less than f32. +- **The fast scan needs a re-score.** `binary-hamming` alone keeps only 0.40 of FiQA's top 10. A + hamming scan plus an f32 re-score reaches 95% recall with 369 candidates, for about 0.79 ms + against 14.8 ms for exact f32 on one thread. That is the first speed claim hvec can make: about + 19× faster at 95% recall@10, with nDCG within 0.0003 of f32 at 500 candidates. + +## Setup + +| | | +|---|---| +| Collections | `scifact-bge-small` (8,036 vectors) and `fiqa-bge-small` (71,274), 384 dims, from [neighbour spacing](../2026-11-neighbour-spacing/) | +| Queries | 50 stored vectors per collection, each excluded from its own ranking when recall is counted; median of 3 timed passes after one warm-up pass | +| Storage paths (f32) | SQLite rows copied (the original `Store::search`: every row and its text per query); SQLite rows with borrowed blobs, SQLite's memory map off and on; the cached contiguous buffer (`Store::search` now) | +| Codecs, scanned in place | `f32`, `int8`, `binary`, `binary-centred`, `binary-hamming`, `pq48`, `pq24`, `mixed48-pca-w`, at 1, 2, 4, 8 and 10 threads | +| Bandwidth ceiling | A sequential sum of a 1 GiB `u64` buffer at the same thread counts | +| Machine | Apple M4 Pro, 10 performance + 4 efficiency cores, 24 GiB, nothing else running; release build | +| Code | hvec commit `8f87908` | +| Command | `./run.sh` (`results/.json`, `results/summary.txt`, `results/machine.txt`); the re-score depths for `binary-hamming` are in `results/rescore-.json` | + +## Results + +### Storage path: the same exact f32 search (ms per query) + +| path | SciFact (8,036) | FiQA (71,274) | speed-up on FiQA | +|---|---:|---:|---:| +| SQLite rows, copied (the original search) | 8.75 | 74.70 | 1.0× | +| SQLite rows, blob borrowed in place | 6.41 | 54.50 | 1.4× | +| SQLite rows, borrowed, SQLite memory map on | 3.01 | 27.40 | 2.7× | +| cached contiguous buffer, text for the hits only | **1.68** | **14.68** | **5.1×** | + +Building the buffer once costs 47 ms for FiQA's 110 MB from SQLite (borrowed blobs, one copy), +or 8.6 ms from a flat code file read in one call. + +### Codec scan, in place, one thread (FiQA) + +| codec | bytes / vector | ms / query | ns / vector | GB/s | share of bandwidth | recall@10 alone | +|---|---:|---:|---:|---:|---:|---:| +| `f32` | 1,536 | 14.76 | 207 | 7.42 | 9.5% | 1.000 | +| `int8` | 392 | 18.64 | 261 | 1.50 | 1.9% | 0.978 | +| `binary` | 48 | 17.56 | 246 | 0.19 | 0.3% | 0.504 | +| `binary-centred` | 48 | 20.48 | 287 | 0.17 | 0.2% | 0.562 | +| `pq48` | 48 | 14.44 | 203 | 0.24 | 0.3% | 0.644 | +| `pq24` | 24 | 14.06 | 197 | 0.12 | 0.2% | 0.422 | +| `mixed48-pca-w` | 48 | 32.94 | 462 | 0.10 | 0.1% | 0.708 | +| `binary-hamming` | 48 | **0.72** | **10** | 4.76 | 6.1% | 0.372 | + +Memory bandwidth measured 78 GB/s on one thread and 238 GB/s on ten. Recall here is over +self-queries; the judged-query figures in the other experiments are the ones to compare +codecs by. + +### Thread scaling (FiQA, ms per query) + +| codec | 1 | 2 | 4 | 8 | 10 | speed-up at 10 | +|---|---:|---:|---:|---:|---:|---:| +| `f32` | 14.76 | 7.56 | 4.00 | 2.09 | 1.71 | 8.7× (64 GB/s, 27% of bandwidth) | +| `pq48` | 14.44 | 7.36 | 3.78 | 2.00 | 1.62 | 8.9× | +| `mixed48-pca-w` | 32.94 | 17.34 | 8.49 | 4.60 | 3.84 | 8.6× | +| `binary-hamming` | 0.72 | 0.42 | 0.25 | 0.18 | 0.17 | 4.3× | + +On SciFact, `binary-hamming` stops scaling at two threads. A whole scan takes 0.09 ms there, +and starting the threads costs about as much as the work. + +### The fast hybrid: `binary-hamming` scan, f32 re-score (one thread) + +Re-score cost from the measured f32 cost per vector (207 ns). Fetching the candidates is +treated as reading from memory; it is not timed separately. + +| collection | candidates for 90 / 95 / 99% recall@10 | time at 95% | exact f32 scan | speed-up at 95% | +|---|---|---:|---:|---:| +| SciFact | 115 / 215 / 627 | 0.13 ms | 1.65 ms | 12× | +| FiQA | 186 / 369 / 1,254 | 0.79 ms | 14.76 ms | 19× | + +For comparison, the same 95% target needs 105 candidates behind a `binary` scan and 44 behind +`pq48` on FiQA. Those hybrids read far fewer bytes, but their scans already take as long as +exact f32. + +## Discussion + +**The original search was mostly bookkeeping.** Reading every chunk's text on every query, and +allocating a vector per row, cost more than the scoring. The fix needs no unsafe code and no new +index: keep the codes in one buffer, score them in place, and read text for the hits only. SQLite's +memory map is a cheap partial version of the same thing. + +**A smaller code only helps when the scoring is as cheap as the read.** At 48 bytes per vector a +scan reads 3.4 MB for FiQA, which this machine streams in well under a millisecond. But +asymmetric scoring does a float operation per dimension whatever the code size: `binary` walks +384 bits and adds or subtracts a float for each. So it costs what f32 costs. Only popcount does +work in proportion to the bytes. Compression buys memory everywhere, and time only with symmetric +scoring. + +**Adaptive bits have a speed cost.** `mixed48-pca-w`, the best 48-byte scan for recall in the +[matrix](../2026-11-hybrid-adaptive-matrix/), is the slowest codec here: 2.2× f32 per vector, +because every dimension's code has to be unpacked from a bit stream of varying widths. A version +with byte-aligned groups, or a lookup table per group of dimensions as PQ uses, would close most +of that gap. + +**The memory-mapped file is the one zero-copy path left out.** The workspace forbids `unsafe`, and +mapping a file needs one `unsafe` call. Reading a flat file is already fast (8.6 ms for 110 MB), +so a map would mostly save memory and start-up time on collections far larger than these. + +**Caveats.** +- **Brute force only.** With an index (HNSW, IVF) the scan is not over every vector, and the + ranking of these paths changes. +- **The hybrid times are computed, not measured end to end.** The re-score cost assumes the f32 + candidates are in memory. From disk, hundreds of random 1.5 KB reads per query would dominate. +- **Scoring is portable Rust with no explicit SIMD.** A tuned f32 kernel would move every row of + the codec table, f32 most of all. +- **Recall in the codec table is over self-queries**, a harder and less realistic test than the + judged queries the other experiments use. +- **One machine.** Other machines will give different absolute numbers; the ratios between paths + and codecs are the result. diff --git a/experiments/2026-11-zero-copy-scan/results/fiqa-bge-small.json b/experiments/2026-11-zero-copy-scan/results/fiqa-bge-small.json new file mode 100644 index 0000000..48c9ad9 --- /dev/null +++ b/experiments/2026-11-zero-copy-scan/results/fiqa-bge-small.json @@ -0,0 +1,386 @@ +{ + "collection": "fiqa-bge-small", + "vectors": 71274, + "dimension": 384, + "queries": 50, + "k": 10, + "repeat": 3, + "paths": [ + { + "path": "sqlite rows, copied", + "ms_per_query": 74.70102331999999, + "note": "the original search: every row and its text copied out on every query" + }, + { + "path": "sqlite rows, borrowed", + "ms_per_query": 54.501525, + "note": "vector column only, each blob scored in place in SQLite's page buffer" + }, + { + "path": "sqlite rows, borrowed, mmap", + "ms_per_query": 27.397842500000003, + "note": "as above, with SQLite reading the file through its memory map" + }, + { + "path": "cached buffer (Store::search)", + "ms_per_query": 14.67806916, + "note": "codes scanned in place in one allocation; text read for the hits only" + } + ], + "loads": [ + { + "source": "sqlite (borrowed blobs, one copy)", + "ms": 46.746415999999996, + "bytes": 109476864 + }, + { + "source": "flat code file (one read)", + "ms": 8.648792, + "bytes": 109476864 + } + ], + "codecs": [ + { + "codec": "f32", + "bytes_per_vector": 1536, + "threads": 1, + "ms_per_query": 14.762140839999999, + "gb_per_s": 7.416056057625312, + "recall_at_k": 1.0 + }, + { + "codec": "f32", + "bytes_per_vector": 1536, + "threads": 2, + 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+ "recall_at_k": 0.626 + }, + { + "codec": "pq24", + "bytes_per_vector": 24, + "threads": 10, + "ms_per_query": 0.28832, + "gb_per_s": 0.6689234184239733, + "recall_at_k": 0.626 + }, + { + "codec": "mixed48-pca-w", + "bytes_per_vector": 48, + "threads": 1, + "ms_per_query": 3.97714, + "gb_per_s": 0.096986276570601, + "recall_at_k": 0.724 + }, + { + "codec": "mixed48-pca-w", + "bytes_per_vector": 48, + "threads": 2, + "ms_per_query": 2.1327083399999998, + "gb_per_s": 0.18086298663791978, + "recall_at_k": 0.724 + }, + { + "codec": "mixed48-pca-w", + "bytes_per_vector": 48, + "threads": 4, + "ms_per_query": 1.1435, + "gb_per_s": 0.33732225623087014, + "recall_at_k": 0.724 + }, + { + "codec": "mixed48-pca-w", + "bytes_per_vector": 48, + "threads": 8, + "ms_per_query": 0.6587908400000001, + "gb_per_s": 0.5855090517044832, + "recall_at_k": 0.724 + }, + { + "codec": "mixed48-pca-w", + "bytes_per_vector": 48, + "threads": 10, + "ms_per_query": 0.57014834, + "gb_per_s": 0.6765397229780585, + "recall_at_k": 0.724 + } + ], + "bandwidth": [ + { + "threads": 1, + "gb_per_s": 83.92432457697323 + }, + { + "threads": 2, + "gb_per_s": 138.7046858836502 + }, + { + "threads": 4, + "gb_per_s": 208.71138790485213 + }, + { + "threads": 8, + "gb_per_s": 224.56368413149295 + }, + { + "threads": 10, + "gb_per_s": 238.91234125419533 + } + ] +} diff --git a/experiments/2026-11-zero-copy-scan/results/summary.txt b/experiments/2026-11-zero-copy-scan/results/summary.txt new file mode 100644 index 0000000..548b2cb --- /dev/null +++ b/experiments/2026-11-zero-copy-scan/results/summary.txt @@ -0,0 +1,112 @@ +== fiqa-bge-small: 71274 vectors × 384 dims, 50 queries, k=10 + storage path (f32) ms/query speed-up + sqlite rows, copied 74.70 1.0× + sqlite rows, borrowed 54.50 1.4× + sqlite rows, borrowed, mmap 27.40 2.7× + cached buffer (Store::search) 14.68 5.1× + building the buffer ms MB + sqlite (borrowed blobs, one copy) 46.7 109.5 + flat code file (one read) 8.6 109.5 + memory bandwidth: 1 thread(s) 78 GB/s, 2 thread(s) 142 GB/s, 4 thread(s) 148 GB/s, 8 thread(s) 209 GB/s, 10 thread(s) 238 GB/s + codec B/vec thr ms/query ns/vector GB/s % bw recall@10 + f32 1536 1 14.762 207.12 7.42 9.5% 1.0000 + f32 1536 2 7.556 106.01 14.49 10.2% 1.0000 + f32 1536 4 3.996 56.07 27.40 18.5% 1.0000 + f32 1536 8 2.092 29.36 52.32 25.1% 1.0000 + f32 1536 10 1.706 23.93 64.19 26.9% 1.0000 + int8 392 1 18.638 261.49 1.50 1.9% 0.9780 + int8 392 2 9.545 133.92 2.93 2.1% 0.9780 + int8 392 4 4.897 68.70 5.71 3.8% 0.9780 + int8 392 8 2.560 35.92 10.91 5.2% 0.9780 + int8 392 10 2.089 29.31 13.37 5.6% 0.9780 + binary 48 1 17.555 246.30 0.19 0.3% 0.5040 + binary 48 2 8.920 125.15 0.38 0.3% 0.5040 + binary 48 4 4.581 64.28 0.75 0.5% 0.5040 + binary 48 8 2.429 34.08 1.41 0.7% 0.5040 + binary 48 10 1.961 27.51 1.74 0.7% 0.5040 + binary-centred 48 1 20.476 287.28 0.17 0.2% 0.5620 + binary-centred 48 2 10.372 145.53 0.33 0.2% 0.5620 + binary-centred 48 4 5.341 74.93 0.64 0.4% 0.5620 + binary-centred 48 8 2.781 39.02 1.23 0.6% 0.5620 + binary-centred 48 10 2.297 32.23 1.49 0.6% 0.5620 + binary-hamming 48 1 0.718 10.08 4.76 6.1% 0.3720 + binary-hamming 48 2 0.415 5.83 8.24 5.8% 0.3720 + binary-hamming 48 4 0.249 3.49 13.74 9.3% 0.3720 + binary-hamming 48 8 0.180 2.52 19.06 9.1% 0.3720 + binary-hamming 48 10 0.168 2.36 20.32 8.5% 0.3720 + pq48 48 1 14.442 202.63 0.24 0.3% 0.6440 + pq48 48 2 7.364 103.32 0.46 0.3% 0.6440 + pq48 48 4 3.783 53.08 0.90 0.6% 0.6440 + pq48 48 8 1.998 28.03 1.71 0.8% 0.6440 + pq48 48 10 1.621 22.74 2.11 0.9% 0.6440 + pq24 24 1 14.056 197.21 0.12 0.2% 0.4220 + pq24 24 2 7.252 101.75 0.24 0.2% 0.4220 + pq24 24 4 3.789 53.16 0.45 0.3% 0.4220 + pq24 24 8 1.986 27.87 0.86 0.4% 0.4220 + pq24 24 10 1.620 22.73 1.06 0.4% 0.4220 + mixed48-pca-w 48 1 32.935 462.09 0.10 0.1% 0.7080 + mixed48-pca-w 48 2 17.342 243.31 0.20 0.1% 0.7080 + mixed48-pca-w 48 4 8.489 119.11 0.40 0.3% 0.7080 + mixed48-pca-w 48 8 4.596 64.48 0.74 0.4% 0.7080 + mixed48-pca-w 48 10 3.840 53.88 0.89 0.4% 0.7080 + hybrid estimate, 1 thread: scan + N f32 re-scores at 207.1 ns each (random access ignored) + binary-hamming scan 0.718 ms → N=50: 0.729 ms, N=100: 0.739 ms, N=200: 0.760 ms (exact f32 scan 14.762 ms) + binary scan 17.555 ms → N=50: 17.565 ms, N=100: 17.576 ms, N=200: 17.597 ms (exact f32 scan 14.762 ms) + pq48 scan 14.442 ms → N=50: 14.453 ms, N=100: 14.463 ms, N=200: 14.484 ms (exact f32 scan 14.762 ms) + +== scifact-bge-small: 8036 vectors × 384 dims, 50 queries, k=10 + storage path (f32) ms/query speed-up + sqlite rows, copied 8.75 1.0× + sqlite rows, borrowed 6.41 1.4× + sqlite rows, borrowed, mmap 3.01 2.9× + cached buffer (Store::search) 1.68 5.2× + building the buffer ms MB + sqlite (borrowed blobs, one copy) 6.6 12.3 + flat code file (one read) 1.8 12.3 + memory bandwidth: 1 thread(s) 84 GB/s, 2 thread(s) 139 GB/s, 4 thread(s) 209 GB/s, 8 thread(s) 225 GB/s, 10 thread(s) 239 GB/s + codec B/vec thr ms/query ns/vector GB/s % bw recall@10 + f32 1536 1 1.648 205.02 7.49 8.9% 1.0000 + f32 1536 2 0.882 109.79 13.99 10.1% 1.0000 + f32 1536 4 0.502 62.52 24.57 11.8% 1.0000 + f32 1536 8 0.316 39.37 39.01 17.4% 1.0000 + f32 1536 10 0.285 35.48 43.29 18.1% 1.0000 + int8 392 1 2.104 261.78 1.50 1.8% 0.9840 + int8 392 2 1.107 137.80 2.84 2.1% 0.9840 + int8 392 4 0.612 76.21 5.14 2.5% 0.9840 + int8 392 8 0.368 45.81 8.56 3.8% 0.9840 + int8 392 10 0.324 40.34 9.72 4.1% 0.9840 + binary 48 1 1.977 245.96 0.20 0.2% 0.6300 + binary 48 2 1.030 128.13 0.37 0.3% 0.6300 + binary 48 4 0.569 70.85 0.68 0.3% 0.6300 + binary 48 8 0.347 43.20 1.11 0.5% 0.6300 + binary 48 10 0.309 38.46 1.25 0.5% 0.6300 + binary-centred 48 1 2.309 287.33 0.17 0.2% 0.6600 + binary-centred 48 2 1.199 149.24 0.32 0.2% 0.6600 + binary-centred 48 4 0.652 81.18 0.59 0.3% 0.6600 + binary-centred 48 8 0.391 48.70 0.99 0.4% 0.6600 + binary-centred 48 10 0.342 42.53 1.13 0.5% 0.6600 + binary-hamming 48 1 0.088 10.95 4.38 5.2% 0.5160 + binary-hamming 48 2 0.072 8.90 5.39 3.9% 0.5160 + binary-hamming 48 4 0.078 9.71 4.95 2.4% 0.5160 + binary-hamming 48 8 0.096 11.97 4.01 1.8% 0.5160 + binary-hamming 48 10 0.108 13.46 3.57 1.5% 0.5160 + pq48 48 1 1.640 204.09 0.24 0.3% 0.7320 + pq48 48 2 0.872 108.56 0.44 0.3% 0.7320 + pq48 48 4 0.488 60.78 0.79 0.4% 0.7320 + pq48 48 8 0.302 37.54 1.28 0.6% 0.7320 + pq48 48 10 0.278 34.56 1.39 0.6% 0.7320 + pq24 24 1 1.619 201.52 0.12 0.1% 0.6260 + pq24 24 2 0.850 105.77 0.23 0.2% 0.6260 + pq24 24 4 0.484 60.21 0.40 0.2% 0.6260 + pq24 24 8 0.306 38.12 0.63 0.3% 0.6260 + pq24 24 10 0.288 35.88 0.67 0.3% 0.6260 + mixed48-pca-w 48 1 3.977 494.92 0.10 0.1% 0.7240 + mixed48-pca-w 48 2 2.133 265.39 0.18 0.1% 0.7240 + mixed48-pca-w 48 4 1.143 142.30 0.34 0.2% 0.7240 + mixed48-pca-w 48 8 0.659 81.98 0.59 0.3% 0.7240 + mixed48-pca-w 48 10 0.570 70.95 0.68 0.3% 0.7240 + hybrid estimate, 1 thread: scan + N f32 re-scores at 205.0 ns each (random access ignored) + binary-hamming scan 0.088 ms → N=50: 0.098 ms, N=100: 0.108 ms, N=200: 0.129 ms (exact f32 scan 1.648 ms) + binary scan 1.977 ms → N=50: 1.987 ms, N=100: 1.997 ms, N=200: 2.018 ms (exact f32 scan 1.648 ms) + pq48 scan 1.640 ms → N=50: 1.650 ms, N=100: 1.661 ms, N=200: 1.681 ms (exact f32 scan 1.648 ms) + From 5d0c0dfa87ca741afc7b552e7fd4ee95da9a7190 Mon Sep 17 00:00:00 2001 From: Swinecoder101 Date: Wed, 4 Nov 2026 22:25:19 +0000 Subject: [PATCH 11/12] docs: correct the adaptive-depth gain, and record the speed results The matrix write-up reported +1.5 points of recall for adaptive depth at equal mean depth; that came from interpolating between fixed depths of only 20, 50 and 100, which runs below the true curve. Scored exactly from the routing traces the gain is 0.3-0.6 points (ahead in 156 of 180), with up to 4 points for the worst decile. The write-up now says so and why. README: the Faster search row makes its first claim (binary-hamming plus an f32 re-score), binary-hamming is listed, the stale note about f32 decoding is replaced, and the adaptive-depth section states the measured effect. Roadmap: phase 8 items done, and new follow-ups (codec choice at ingest, layered codes, a faster mixed scan, end-to-end hybrid timing from disk, SIMD kernels, the unsafe decision for memory-mapped files). --- README.md | 23 ++++++-- ROADMAP.md | 41 ++++++++++---- .../2026-11-hybrid-adaptive-matrix/README.md | 54 +++++++++++-------- 3 files changed, 81 insertions(+), 37 deletions(-) diff --git a/README.md b/README.md index a8cb593..8475c25 100644 --- a/README.md +++ b/README.md @@ -99,12 +99,15 @@ limit: | Claim | What hvec measures | What that does not show | |---|---|---| | **Less memory at comparable quality** | Bytes per vector, and nDCG@k and document recall@k against the labels, next to the f32 ceiling | Anything about speed | -| **Faster search** | Scan time of the compressed-domain score over the whole collection | Not yet claimable: hvec scores every codec asymmetrically against a float query in a brute-force scan, so binary is no faster than int8 here, and nothing in the harness is memory-bandwidth bound. A speed claim needs a symmetric (Hamming, lookup-table) path and a setting where bytes read is the bottleneck | +| **Faster search** | Scan time per query over the whole collection, in place in a contiguous buffer (`bench scan`), against the machine's memory bandwidth | Only the symmetric `binary-hamming` scan is faster: every asymmetric codec costs about what f32 costs per vector, because its scoring, not its bytes, sets the time. Measured on one machine, brute force, with the hybrid's re-score cost computed rather than timed end to end | | **Useful operations directly on the compressed vectors** | Every codec's `score` runs without decoding, and is tested to equal the metric on the decoded vector | Whether the operation is cheap; see the speed row | Where the published numbers stand on the first claim: `int8` is 4× smaller and indistinguishable from f32 against the labels; `pq48` is 32× smaller at two points of nDCG@10; `binary` is 32× -smaller at 4.4 points. On the second claim hvec makes no claim yet. The useful advantage a new +smaller at 4.4 points. On the second claim: a `binary-hamming` scan plus an f32 re-score of 369 +candidates reaches 95% recall@10 on FiQA about 19× faster than an exact f32 scan on one thread, +and is the only compressed path that is faster at all +([zero-copy scan](experiments/2026-11-zero-copy-scan/)). The useful advantage a new codec has to demonstrate is over *those* numbers, on the same corpus, with the byte count stated. ### 2. Does the loss reach the answer? @@ -339,7 +342,9 @@ parameters alongside the collection, and reuses them for every later ingest, so call a representative sample of the corpus rather than a single file. `mrl` keeps the first `k` dimensions (what Matryoshka-trained models are built for) and composes with any other codec: `mrl256+int8`, `mrl128+pq16`. -`mixed` is trained too: it spends a fixed `B` bytes per vector with bits allocated per +`binary-hamming` stores the same bits as `binary` but binarises the query too and scores by XOR +and popcount: coarser, and about 20× faster to scan. `mixed` is trained: it spends a fixed +`B` bytes per vector with bits allocated per dimension by variance. `mixed-pca` rotates into the corpus's principal components first, and `-w` weights each component by how much energy queries put into it (see below). @@ -376,8 +381,11 @@ recovers only four or five points of recall: one bit per dimension cannot separa whose cosines differ by less than its resolution, whatever the origin is. Against the human relevance judgments the picture is gentler: binary loses 4.4 points of nDCG@10 (0.698 to 0.654), not 39, because the neighbours it reorders are near-ties that are rarely the judged abstract. -The f32 scan is slow only because the baseline decodes each vector; the other codecs score the -bytes directly. The experiments are written up under [`experiments/`](experiments/). +The scan times in that table predate the in-place f32 kernel, when f32 decoded every vector into +a new allocation. `bench scan` is now the place to time scans: it scores every codec in place in +a contiguous buffer and reports GB/s against the machine's memory bandwidth +([zero-copy scan](experiments/2026-11-zero-copy-scan/)). The experiments are written up under +[`experiments/`](experiments/). #### Hybrid and adaptive quantization @@ -413,6 +421,11 @@ corpus tells you how deep to go: Animated schematic of adaptive re-scoring depth: two queries share the same noise-based margin below their 10th code score; a crowded query has 59 candidates inside the margin and a well-separated query 18, while a fixed shortlist of 30 would under-fetch for the first and over-fetch for the second.

    +Measured exactly at the same mean depth, the margin policy adds only 0.3 to 0.6 points of +recall@10 over a fixed depth, but up to 4 points for the worst decile of queries. A hindsight +oracle shows about 6 points of headroom, almost none of which is visible from the scan's own +scores ([routing experiment](experiments/2026-11-routing-oracle/)). + **Adaptive bits.** Spend the scan code's bytes unevenly, where they buy the most. This is the idea behind mixed-precision quantization of mixture-of-experts models, where blocks and experts that matter more get more bits ([QuantMoE-Bench](https://github.com/UNITES-Lab/moe-quantization)). diff --git a/ROADMAP.md b/ROADMAP.md index 0a23916..ef61a14 100644 --- a/ROADMAP.md +++ b/ROADMAP.md @@ -129,7 +129,7 @@ rotation is a `d × d` matrix fitted on the corpus. Exit: Experiment 4 published, and a stated answer to whether rotation makes binary usable on this embedding model. -## Phase 8 · Spacing, hybrid and adaptive quantization (in progress) +## Phase 8 · Spacing, hybrid and adaptive quantization, routing, zero-copy (done; follow-ups open) Every codec number before this phase came from one corpus, so none of them said what a codec would cost elsewhere. This phase finds the quantity that sets the cost, then uses it twice: to @@ -163,13 +163,36 @@ decide how deep a hybrid re-score must go, and to decide where an adaptive code once per search; used by the store's search, `bench recall` and `bench rescore`. - [x] Animated README figures for hybrid re-scoring, adaptive depth and adaptive bits (`docs/assets/animations/`, regenerated by `make_animations.py`). -- [ ] **Hybrid × adaptive matrix** (`experiments/2026-11-hybrid-adaptive-matrix`, running): 11 - scan codecs (uniform `binary`/`pq` against adaptive `mixed` at 24, 48 and 96 bytes) × none, - fixed-depth and margin policies × int8/f32 second stages, over the ten collections. To - publish: the matrix, adaptive against fixed depth at equal mean depth, and a table of - benefits and drawbacks for each combination. -- [ ] Choose the re-score depth automatically at ingest: measure the band on a sample, take the - codec's noise, and store the depth the two-stage curve predicts for a target recall. +- [x] **Hybrid × adaptive matrix** (`experiments/2026-11-hybrid-adaptive-matrix`): 11 scan codecs + × none/fixed/margin × int8/f32 over ten collections. `mixed48-pca-w` beats `pq48` at equal + bytes on average (0.768 vs 0.747 recall@10; the gain is all on MiniLM, a tie on bge-small), and + leads at 24 and 96 bytes too. Adaptive depth gains 0.3–0.6 points of recall at equal mean + depth, and up to 4 points for the worst decile. (The first version said 1.5 points; that came + from interpolating between sparse fixed depths, and is corrected in the write-up.) +- [x] Per-query traces (`bench rescore --trace`) and the **routing experiment** + (`experiments/2026-11-routing-oracle`). A hindsight oracle has 5.5–7 points of headroom over + fixed depth. The band visible in the scan's own scores tracks the true band only at Spearman + 0.52–0.70, and no router built on it captures more than 6%. Per-collection routing picks the + right codec size in 10/10 collections, but its predicted depth misses a 95% target by up to + 9 points. +- [x] **Zero-copy** (`experiments/2026-11-zero-copy-scan`). Borrowed codes in the `Codec` trait, + `CodeBuffer` (a contiguous, cached per-collection buffer scanned in place, optionally over + threads), in-place f32 and single-pass cosine kernels, and `binary-hamming` (popcount). The + cached buffer is 5.1× faster than the original row scan. Every asymmetric codec costs about + what f32 costs per vector. `binary-hamming` is 20× cheaper, and with an f32 re-score it reaches + 95% recall@10 on FiQA about 19× faster than exact f32: the first speed claim hvec makes. +- [ ] Choose the codec automatically at ingest from noise ÷ band, then measure the re-score + depth for a target recall on a sample of self-queries. The routing experiment showed that the + curve chooses the codec well but predicts the depth badly. +- [ ] Layered (residual) codes: one stored code read to different depths per candidate, so + hybrid retrieval needs no second full-precision copy on disk. +- [ ] A faster `mixed` scan (byte-aligned groups, or a lookup table per group as PQ uses): + today it is the best 48-byte code for recall and the slowest to scan, 2.2× f32 per vector. +- [ ] Time the hybrid end to end, with the f32 candidates fetched from disk, not memory. +- [ ] SIMD kernels for f32 and int8, so the asymmetric codecs are compared at their best. +- [ ] Decide whether to allow one reviewed `unsafe` call for memory-mapped code files + (`unsafe_code` is forbidden workspace-wide today). A flat file already loads 110 MB in 9 ms, so + the case is memory footprint and start-up on much larger collections. - [ ] Per-vector adaptive bits, the analogue of MoE PTQ's outlier scorer: more bits for vectors in crowded neighbourhoods. Needs a variable-size encoding or two tiers. - [ ] Per-cluster PCA (Sub-MoE's grouped subspaces), if `mixed-pca` gets within reach of @@ -211,7 +234,7 @@ Three lines of work, and what each would mean as an hvec codec or experiment: - Run Experiments 1 and 3 and the judge re-scoring as soon as two chat models are reachable; the scripts need only `ANTHROPIC_API_KEY` (or an Ollama server) and a built `hvec`. -- Finish phase 8: publish the hybrid × adaptive matrix. +- Phase 8 follow-ups, starting with choosing the codec at ingest and layered codes. - Codec follow-ups the write-ups flagged: a per-vector scale for `binary-centred`. The re-scoring stage is done (phase 8). - Graded relevance: NFCorpus ships graded judgments, but hvec's nDCG treats relevance as diff --git a/experiments/2026-11-hybrid-adaptive-matrix/README.md b/experiments/2026-11-hybrid-adaptive-matrix/README.md index ba22c34..235c5c7 100644 --- a/experiments/2026-11-hybrid-adaptive-matrix/README.md +++ b/experiments/2026-11-hybrid-adaptive-matrix/README.md @@ -21,16 +21,23 @@ Two ways to make compression adaptive, crossed with the hybrid (two-stage) retri 2. At the same mean number of re-scored candidates, adaptive depth beats a fixed depth, mostly by rescuing the worst queries. -**Result.** Both confirmed, and the first more strongly than expected. +**Result.** The first is confirmed, more strongly than expected. The second is confirmed only +in its weak form: the gain is real and almost always positive, but small. - **Adaptive bits beat PQ on average.** `mixed48-pca-w` keeps 0.768 of the exact top 10 with no re-scoring, against 0.747 for `pq48` and 0.669 for `binary`. It wins at 24 and 96 bytes too. The gain is all on MiniLM (+4.4 points); on bge-small the two tie (0.723 vs 0.724), and `pq48` keeps a slightly smaller nDCG loss (0.0160 vs 0.0177). - **The rotation is what makes it work.** Without it, `mixed48` (0.696) is no better than `binary-centred` (0.698). -- **Adaptive depth beats fixed depth at equal mean depth in 522 of 526 cells.** On average it - adds 1.5 points of recall@10 and 3.7 points of worst-decile recall, with no change in nDCG. - The price is variable work: the 90th-percentile query re-scores 1.3 to 2.1× the mean. +- **Adaptive depth beats fixed depth at equal mean depth, but only by 0.25 to 0.6 points of + recall@10.** It is ahead in 156 of 180 comparisons. Its gain is larger for the worst queries: + worst-decile recall rises by 4.1 points at a 1σ margin. The price is variable work: the + 90th-percentile query re-scores 1.3 to 2.1× the mean. *Correction:* the first version of this + write-up reported +1.5 points of recall (and +3.7 worst-decile) from comparing against fixed + depths of only 20, 50 and 100. Interpolating between such sparse points runs below the true + fixed-depth curve, which bends, and so flattered the margin policy by about 3×. The numbers + here use the per-query traces from the [routing experiment](../2026-11-routing-oracle/), + which score a fixed depth at exactly the margin's mean depth. - **The two combine.** `mixed48-pca-w` with a 1σ margin re-scores 19 candidates on average and keeps 0.929 of the top 10. `binary` with a fixed 20 keeps 0.841 for the same bytes. @@ -94,20 +101,20 @@ at int8's own recall. At depth 100: `binary` 0.974 against 0.981, `pq48` 0.989 a ### Adaptive depth against fixed depth at the same mean depth -Each margin cell is compared with the fixed-depth curve of the same collection and codec, -interpolated in log depth at the margin's mean depth. Cells whose mean depth falls outside the -measured fixed range (20 to 100) are left out. +With an f32 second stage, a query's recall at any depth is the share of its true neighbours the +scan ranks within that depth. The routing experiment's traces record those ranks for six of the +scan codecs here (`binary`, `binary-centred`, `pq24`, `pq48`, `mixed24-pca-w`, `mixed48-pca-w`) +on all ten collections. So a fixed depth can be scored at exactly the margin policy's mean +depth, interpolating only between neighbouring whole numbers. -| margin | cells compared | Δ recall@10 | Δ worst decile | Δ nDCG | p90 depth / mean depth | -|---|---:|---:|---:|---:|---:| -| 1σ | 218 | +0.020 | +0.048 | +0.001 | 1.29–1.60 | -| 2σ | 192 | +0.014 | +0.033 | +0.000 | 1.39–1.94 | -| 3σ | 116 | +0.008 | +0.025 | +0.000 | 1.49–2.09 | -| all | 526 | **+0.015** | **+0.037** | 0.000 | | +| margin | comparisons | mean depth | Δ recall@10 | ahead in | Δ worst decile | p90 depth / mean depth | +|---|---:|---:|---:|---:|---:|---:| +| 1σ | 60 | 27 | +0.006 | 48 / 60 | **+0.041** | 1.29–1.60 | +| 2σ | 60 | 73 | +0.006 | 59 / 60 | +0.020 | 1.39–1.94 | +| 3σ | 60 | 163 | +0.003 | 49 / 60 | +0.007 | 1.49–2.09 | -Adaptive is ahead in 522 of the 526 cells. The gain is largest at small depths and for the -better scan codecs (`pq96` and `mixed96-pca-w` gain 3.1 to 3.3 points at a 1σ margin), and it -shrinks as the depth grows and every policy approaches full recall. +The nDCG change is zero to three decimals at every margin, in the matrix's own cells. Section 3 of `results/summary.txt` is the original, sparse comparison, kept for the +record; it overstates the gain for the reason above. ### Benefits and drawbacks of each combination @@ -119,10 +126,10 @@ candidates. |---|---|---:|---:|---:|---|---| | `binary`, no re-score | 48 B | 0.669 | 0.41 | 0.024 | Stateless, nothing to fit | Loses a third of the top 10; its asymmetric score is no faster than f32 | | `pq48`, no re-score | 48 B (+ codebooks) | 0.747 | 0.52 | 0.016 | Best nDCG at 48 B with no re-score | k-means fit per collection; codebooks to store; refit when the corpus drifts | -| `mixed48-pca-w`, no re-score | 48 B (+ a 384 × 384 rotation) | 0.768 | 0.55 | 0.018 | Best recall at 24, 48 and 96 B; PCA fit, no iterative clustering | Ties PQ on bge-small and loses slightly on nDCG; one matrix–vector product per query; refit on drift | +| `mixed48-pca-w`, no re-score | 48 B (+ a 384 × 384 rotation) | 0.768 | 0.55 | 0.018 | Best recall at 24, 48 and 96 B; PCA fit, no iterative clustering | Ties PQ on bge-small and loses slightly on nDCG; its scan is 2.2× slower per vector than f32 ([zero-copy scan](../2026-11-zero-copy-scan/)); refit on drift | | hybrid, fixed depth, int8 | 48 B RAM + 392 B disk | 0.944–0.983 at depth 50 | 0.84–0.97 | ≈ 0 | Second store 4× smaller than f32 | Levels off at int8's own 0.99; random reads per query | | hybrid, fixed depth, f32 | 48 B RAM + 1,536 B disk | 0.948–0.991 at depth 50 | 0.86–0.98 | ≈ 0 | Reaches exact; the depth is predictable from noise ÷ band | Keeps a full f32 copy; N random reads per query | -| hybrid, adaptive depth | as above | +0.015 over fixed at equal mean depth | +0.037 | ≈ 0 | Fewer bad queries for the same average work | Variable work per query (p90 1.3–2.1× the mean); σ calibrated per collection; one more parameter | +| hybrid, adaptive depth | as above | +0.003 to +0.006 over fixed at equal mean depth | +0.007 to +0.041 | ≈ 0 | Fewer bad queries for the same average work | A small gain for variable work per query (p90 1.3–2.1× the mean); σ calibrated per collection; one more parameter | | adaptive bits + adaptive depth (`mixed48-pca-w`, 1σ) | 48 B RAM + 1,536 B disk | 0.929 at mean depth 19 | | ≈ 0 | The best recall per candidate re-scored of any 48-byte scan | Every drawback above together: two fits, a rotation and variable depth | ## Discussion @@ -141,11 +148,12 @@ principal component has to spend bits on it. The `-w` allocation does spend bits last components in the [README animation](../../docs/assets/animations/adaptive-bits.svg). Centring the rotation, or giving the mean its own exact term, is the obvious next variant. -**Adaptive depth is a small, reliable win, not a large one.** 1.5 points at equal work, larger -for the worst queries, and almost never negative. The [routing experiment](../2026-11-routing-oracle/) -asks how much more a better router could get. A hindsight oracle shows about 6 points of -headroom, but the band a router can see from the scan's own scores tracks the true band only -moderately, so most of that headroom is out of reach. +**Adaptive depth is a small, reliable win, not a large one.** Half a point of recall at equal +work, up to four points for the worst decile of queries, and rarely negative. The +[routing experiment](../2026-11-routing-oracle/) asks how much more a better router could get. +A hindsight oracle shows about 6 points of headroom, but the band a router can see from the +scan's own scores tracks the true band only moderately (Spearman 0.52 to 0.70), and no router +built on it captured more than 6% of the headroom. **Caveats.** - **Recall is against exact f32, not the answer.** nDCG uses binary relevance. Whether any of From 3a8bd001f8bd5bf8f39a3511e8372e81e32ec56f Mon Sep 17 00:00:00 2001 From: Swinecoder101 Date: Wed, 4 Nov 2026 22:31:08 +0000 Subject: [PATCH 12/12] docs(site): publish the two-stage, matrix, routing and zero-copy experiments Four experiment pages, each with its hypothesis, setup, result table and caveats, linking to the full write-up, and a post that ties them together: hybrid retrieval gets the neighbours back, adaptive bits beat PQ at equal bytes on average, adaptive depth helps only a little (with the 1.5-point figure corrected), and only symmetric popcount scoring is faster. The README animations are embedded from docs/assets. --- docs/_experiments/hybrid-adaptive-matrix.md | 45 +++++++++++++ docs/_experiments/routing-oracle.md | 43 ++++++++++++ docs/_experiments/two-stage-rescoring.md | 46 +++++++++++++ docs/_experiments/zero-copy-scan.md | 49 ++++++++++++++ .../2026-11-05-hybrid-adaptive-and-fast.md | 67 +++++++++++++++++++ 5 files changed, 250 insertions(+) create mode 100644 docs/_experiments/hybrid-adaptive-matrix.md create mode 100644 docs/_experiments/routing-oracle.md create mode 100644 docs/_experiments/two-stage-rescoring.md create mode 100644 docs/_experiments/zero-copy-scan.md create mode 100644 docs/_posts/2026-11-05-hybrid-adaptive-and-fast.md diff --git a/docs/_experiments/hybrid-adaptive-matrix.md b/docs/_experiments/hybrid-adaptive-matrix.md new file mode 100644 index 0000000..a2d75c7 --- /dev/null +++ b/docs/_experiments/hybrid-adaptive-matrix.md @@ -0,0 +1,45 @@ +--- +title: "The hybrid × adaptive matrix" +date: 2026-11-04 21:00:00 +0000 +result: "PCA plus variance-weighted bit allocation beats product quantization at equal bytes on average (0.768 vs 0.747 recall@10 at 48 bytes), all of it on MiniLM. Adaptive re-score depth gains half a point at equal work, up to 4 points for the worst queries." +references: [li2024quantmoe, huang1963block, max1960quantizing, jegou2011pq, thakur2021beir, xiao2023bge, reimers2019sbert] +--- + +*Full write-up, raw results and a one-command reproduction are in the repository:* +[experiments/2026-11-hybrid-adaptive-matrix](https://github.com/SwineCoder101/hvec/tree/main/experiments/2026-11-hybrid-adaptive-matrix). +Follow-up to [two-stage retrieval]({{ "/experiments/two-stage-rescoring/" | relative_url }}). + +

    Animated bar chart of a 48-byte budget spread over 384 principal components of SciFact's bge-small embeddings: uniform binary, variance-based allocation, and query-weighted allocation.

    + +**Two kinds of adaptive compression.** *Adaptive bits* spend a scan code's bytes unevenly, the +idea behind structure-aware mixed-precision quantization of mixture-of-experts models [1]. +`mixed` gives each dimension 0 to 8 bits by reverse water-filling on its variance [2], with a +Gaussian-optimal quantizer step [3]. `-pca` rotates into principal components first; `-w` weights +each component by the query's energy in it. *Adaptive depth* re-scores every candidate within λσ +of the scan's own 10th score, with σ calibrated on self-queries. + +**Setup.** Ten collections (five BEIR corpora [5] × bge-small [6] and MiniLM [7]); eleven scan +codecs at 24, 48 and 96 bytes, uniform (`binary`, `binary-centred`, `pq` [4]) and adaptive +(`mixed`); no re-score, fixed depths 20/50/100, or margins 1σ/2σ/3σ; int8 or f32 second stage. + +**Result.** + +| bytes | best uniform scan, recall@10 | `mixed-pca-w`, recall@10 | bge-small | MiniLM | +|---:|---|---:|---|---| +| 24 | `pq24` 0.606 | **0.635** | 0.587 → 0.592 | 0.624 → 0.677 | +| 48 | `pq48` 0.747 | **0.768** | 0.724 → 0.723 | 0.769 → 0.813 | +| 96 | `pq96` 0.870 | **0.886** | 0.855 → 0.855 | 0.885 → 0.916 | + +Without the rotation, `mixed48` (0.696) is no better than `binary-centred` (0.698). Measured +exactly at equal mean depth, adaptive depth adds 0.3–0.6 points of recall@10 over a fixed depth +(ahead in 156 of 180 comparisons) and up to 4 points of worst-decile recall, at a 90th-percentile +depth 1.3–2.1× the mean. *Correction:* the first version of the write-up said 1.5 points, from +interpolating between too few fixed depths. + +**Reading.** PCA plus bit allocation never trails PQ by more than a point at equal bytes and +leads by 3–5 points on MiniLM. On bge-small, where 60% of a vector's energy is a shared mean +direction, the two tie; PQ's codebooks probably absorb that component more cheaply. The cost: +`mixed48-pca-w` is the slowest scan measured, 2.2× f32 per vector ([zero-copy scan]({{ "/experiments/zero-copy-scan/" | relative_url }})). +The full matrix and a benefits-and-drawbacks table for every combination are in the write-up. + +{% include references.html %} diff --git a/docs/_experiments/routing-oracle.md b/docs/_experiments/routing-oracle.md new file mode 100644 index 0000000..081a4e2 --- /dev/null +++ b/docs/_experiments/routing-oracle.md @@ -0,0 +1,43 @@ +--- +title: "Routing: choosing the compression per query or per collection" +date: 2026-11-04 22:00:00 +0000 +result: "Perfect per-query routing would gain 5.5–7 points of recall@10 over a fixed re-score depth, but the scan's own scores reveal almost none of it: no router built on them captures more than 6%. Per collection, the noise-to-spacing curve picks the right codec but not the right depth." +references: [li2024quantmoe, thakur2021beir, xiao2023bge, reimers2019sbert] +--- + +*Full write-up, raw results and a one-command reproduction are in the repository:* +[experiments/2026-11-routing-oracle](https://github.com/SwineCoder101/hvec/tree/main/experiments/2026-11-routing-oracle). +Follow-up to [the hybrid × adaptive matrix]({{ "/experiments/hybrid-adaptive-matrix/" | relative_url }}). + +

    Animated schematic of adaptive re-scoring depth: a crowded query and a well-separated query share one noise margin but get very different numbers of candidates.

    + +**Hypothesis.** Routing each query to the compression that suits it, the way a mixture-of-experts +gate routes tokens [1], has real headroom, because queries need very different re-score depths. +But a router sees only the scan's own noisy scores, so it will capture little of that headroom. + +**Setup.** `bench rescore --trace` records, for 38,124 queries over six scan codecs and ten +collections [2, 3, 4], where the scan ranks each true neighbour and the band visible in the +scan's own scores. With an exact second stage that is enough to replay any depth policy offline: +fixed depth, the margin policy, a ratio router (depth from σ ÷ the visible band), and a hindsight +oracle solved exactly for each budget. + +**Result.** Confirmed for per-query routing; half confirmed per collection. + +| mean depth | fixed | margin | ratio router | oracle | +|---:|---:|---:|---:|---:| +| 20 | 0.832 | −0.000 | −0.020 | +0.071 | +| 30 | 0.875 | +0.003 | −0.008 | +0.069 | +| 50 | 0.900 | +0.004 | −0.001 | +0.067 | +| 100 | 0.939 | +0.003 | +0.000 | +0.055 | + +The depth a query needs varies 6.3× between its 10th and 90th percentile, but the visible band +tracks the true band only at Spearman 0.52–0.70. Per collection, predicting the cheapest +(codec, depth) from the noise-to-spacing curve picks the right scan size in 10 of 10 collections, +yet reaches anywhere from 0.86 to 1.00 when aiming for 95%. + +**Reading.** The oracle's headroom is mostly chance: which neighbour the code's noise happened to +push down, which the same noisy scores cannot reveal. Keep the cheap margin policy for its +worst-query gain; choose the codec per collection from the curve; measure, don't predict, the +depth. + +{% include references.html %} diff --git a/docs/_experiments/two-stage-rescoring.md b/docs/_experiments/two-stage-rescoring.md new file mode 100644 index 0000000..14f4172 --- /dev/null +++ b/docs/_experiments/two-stage-rescoring.md @@ -0,0 +1,46 @@ +--- +title: "Two-stage retrieval: how deep must the re-score go?" +date: 2026-11-04 20:00:00 +0000 +result: "The depth an f32 re-score needs follows the same noise-to-spacing ratio that predicts codec loss (Spearman ≈ 0.93). At 48 bytes, 50 f32 candidates recover 0.95 (binary) to 0.99 (pq48) of the exact top 10 and nearly all of the nDCG loss." +references: [thakur2021beir, xiao2023bge, reimers2019sbert, jegou2011pq, charikar2002simhash] +--- + +*Full write-up, raw results and a one-command reproduction are in the repository:* +[experiments/2026-11-two-stage-rescoring](https://github.com/SwineCoder101/hvec/tree/main/experiments/2026-11-two-stage-rescoring). +Follow-up to [neighbour spacing]({{ "/experiments/neighbour-spacing/" | relative_url }}). + +

    Animated schematic of two-stage retrieval: a small code shortlists candidates, an exact f32 re-score lifts true neighbours into the top 10, and one neighbour ranked outside the shortlist is missed.

    + +**Hypothesis.** A codec with noise σ displaces a true neighbour past about (σ ÷ band) × k +competitors, so the candidate depth an exact re-score needs grows with the noise-to-spacing +ratio, roughly in proportion to it. + +**Setup.** The ten collections of the spacing experiment: five BEIR-format corpora [1] × bge-small +[2] and MiniLM [3]. Scan codecs `binary`, `binary-centred` [5], `pq96`, `pq48`, `pq24` [4]; +second stage `int8` or `f32`, read for candidates only; depths 10 to 1,000. With an exact second +stage, the depth needed for a target recall is a quantile of where the scan ranks the true +neighbours, so it is measured exactly. + +**Result.** Confirmed, with one correction. The depth tracks the ratio with ρ ≈ 0.93 at every +target. It is proportional at 90% recall but grows faster than proportional for higher targets, +because the last neighbours sit in the tail of the code's error: + +| target recall@10 | depth / k | Spearman ρ | held-out corpus error | +|---|---|---:|---:| +| 90% | 3.07 × r^1.06 | 0.930 | ×1.25 | +| 95% | 4.76 × r^1.29 | 0.934 | ×1.31 | +| 99% | 11.22 × r^1.74 | 0.925 | ×1.41 | + +| first stage | depth 10 | 20 | 50 | 100 | nDCG loss at depth 10 → 50 | +|---|---:|---:|---:|---:|---| +| binary, f32 re-score | 0.669 | 0.841 | 0.948 | 0.981 | 0.0138 → 0.0003 | +| pq48, f32 re-score | 0.747 | 0.913 | 0.986 | 0.998 | 0.0078 → 0.0002 | +| pq48, int8 re-score | 0.747 | 0.910 | 0.979 | 0.989 | 0.0078 → 0.0002 | +| pq96, f32 re-score | 0.870 | 0.986 | 1.000 | 1.000 | 0.0005 → −0.0001 | + +**Reading.** Re-scoring 50 candidates costs 75 KB per query on top of the scan, 3–4% of the +bytes an f32 scan reads. An int8 second stage matches f32 up to a depth of about 30, then stops +at int8's own 0.990. Caveats: brute force only; the f32 copy must still be stored; one fixed +depth for every query. + +{% include references.html %} diff --git a/docs/_experiments/zero-copy-scan.md b/docs/_experiments/zero-copy-scan.md new file mode 100644 index 0000000..8f0d222 --- /dev/null +++ b/docs/_experiments/zero-copy-scan.md @@ -0,0 +1,49 @@ +--- +title: "Zero-copy scan: which codecs are actually faster?" +date: 2026-11-04 23:00:00 +0000 +result: "Copying was most of the original search's cost: a contiguous in-place scan is 5.1× faster. Fewer bytes do not make a scan faster, except with popcount: binary-hamming plus an f32 re-score reaches 95% recall@10 on FiQA about 19× faster than exact f32." +references: [charikar2002simhash, jegou2011pq, thakur2021beir, maia2018fiqa, xiao2023bge] +--- + +*Full write-up, raw results and a one-command reproduction are in the repository:* +[experiments/2026-11-zero-copy-scan](https://github.com/SwineCoder101/hvec/tree/main/experiments/2026-11-zero-copy-scan). + +**Hypothesis.** Most of the original search's time is copying, not scoring: every query read +every row, blob and text out of SQLite and allocated a vector per row. And once copying is gone, +asymmetric codecs (an f32 query against compressed codes) will still be no faster than f32, +because their scoring, not their bytes, sets the time. + +**Setup.** `hvec bench scan` on SciFact (8,036 vectors) and FiQA [4] (71,274), both from BEIR [3], bge-small [5], +384 dims; codes scanned in place in one contiguous buffer, borrowed by the codec as `&[u8]`; 1 to +10 threads; a sequential-read bandwidth ceiling for comparison. Apple M4 Pro, idle. +`binary-hamming` stores `binary`'s sign bits [1] but binarises the query too and scores by XOR +and popcount. + +**Result.** Both confirmed. + +| f32 search path, FiQA | ms / query | +|---|---:| +| SQLite rows copied (the original) | 74.7 | +| SQLite rows, blobs borrowed | 54.5 | +| … with SQLite's memory map | 27.4 | +| contiguous buffer, in place | **14.7** | + +| codec, one thread, FiQA | bytes | ns / vector | share of bandwidth | +|---|---:|---:|---:| +| f32 | 1,536 | 207 | 9.5% | +| int8 | 392 | 261 | 1.9% | +| binary | 48 | 246 | 0.3% | +| pq48 [2] | 48 | 203 | 0.3% | +| mixed48-pca-w | 48 | 462 | 0.1% | +| binary-hamming | 48 | **10** | 6.1% | + +A `binary-hamming` scan alone keeps only 0.40 of FiQA's top 10, but re-scoring 369 candidates in +f32 reaches 95%, for about 0.79 ms against 14.8 ms for exact f32 on one thread. That is roughly +19× faster, with nDCG within 0.0003 of f32 at 500 candidates. + +**Reading.** Compression buys memory everywhere, and time only with symmetric scoring. Caveats: +brute force; the hybrid's re-score cost is computed from measured per-vector costs, assuming the +f32 vectors are in memory; no explicit SIMD; one machine. A memory-mapped code file is left out +because the workspace forbids `unsafe`. + +{% include references.html %} diff --git a/docs/_posts/2026-11-05-hybrid-adaptive-and-fast.md b/docs/_posts/2026-11-05-hybrid-adaptive-and-fast.md new file mode 100644 index 0000000..e24fdc9 --- /dev/null +++ b/docs/_posts/2026-11-05-hybrid-adaptive-and-fast.md @@ -0,0 +1,67 @@ +--- +title: "Hybrid, adaptive, and finally fast" +date: 2026-11-05 10:00:00 +0000 +references: [li2024quantmoe, charikar2002simhash, jegou2011pq] +--- + +[The last post]({{ "/blog/2026/11/04/does-the-kind-of-text-matter/" | relative_url }}) found the +number that sets what a codec costs: its score noise divided by how tightly the corpus packs a +query's neighbours. Four experiments followed from it. Together they answer the three questions +people actually ask about vector compression: how do I get the lost neighbours back, can the +compression adapt to the data, and is any of it faster? + +## Getting the neighbours back: hybrid retrieval + +Scan with a small code, shortlist its best N candidates, and re-score only those with the full +vectors. + +

    Animated schematic of two-stage retrieval.

    + +The same noise-to-spacing ratio that predicts the loss also predicts how deep the shortlist must +be, to within about a third. At 48 bytes per vector, 50 candidates recover 95–99% of the exact +top 10 and essentially all of the loss in nDCG +([two-stage retrieval]({{ "/experiments/two-stage-rescoring/" | relative_url }})). + +## Adaptive compression + +Adaptive compression can mean spending bits unevenly, or spending work unevenly. + +**Uneven bits.** Mixed-precision quantization of mixture-of-experts models gives more bits to the +blocks and experts that matter more [1]. For embeddings, "matters more" means a principal +component with more variance, or with more of the query's energy: + +

    Animated bar chart of a 48-byte bit allocation over 384 principal components.

    + +Rotating into principal components and allocating bits this way beats product quantization [3] at +24, 48 and 96 bytes on average, by 1.6 to 2.9 points of recall@10. The gain is all on one of the two +embedding models; on the other they tie +([the hybrid × adaptive matrix]({{ "/experiments/hybrid-adaptive-matrix/" | relative_url }})). + +**Uneven work.** Re-score more candidates for crowded queries and fewer for well-separated ones: + +

    Animated schematic of adaptive re-scoring depth.

    + +This works, but only a little: half a point of recall at the same average work, and up to four +points for the worst queries. I first reported 1.5 points; that came from comparing against too +few fixed depths, and the write-up now says so. A hindsight oracle shows 6 points of headroom, +but the scan's own scores reveal almost none of it, and no router built on them captured more than +6% ([routing]({{ "/experiments/routing-oracle/" | relative_url }})). Routing is worth doing per +collection, where the noise-to-spacing curve picks the right codec, but not per query. + +## Is any of it faster? + +Until now hvec claimed nothing about speed. Zero-copy plumbing changed that: the codes now sit in +one contiguous buffer and are scored in place, with no copy or allocation per vector. That alone +made search 5× faster. Then the uncomfortable result. With copying out of the way, every +asymmetric codec (an f32 query against compressed codes) costs about what f32 costs per vector, +because its scoring, not its bytes, sets the time. A 48-byte binary scan reads 32× fewer bytes +and takes longer than f32. + +The exception is symmetric scoring: binarise the query too and compare by XOR and popcount [2]. That +costs 10 ns per vector against 207 for f32. On its own it keeps only 40% of the top 10, but with +an f32 re-score of a few hundred candidates it reaches 95% recall about 19× faster than an exact +f32 scan ([zero-copy scan]({{ "/experiments/zero-copy-scan/" | relative_url }})). + +So compression buys memory everywhere, and time only when the scoring is as cheap as the read. + +{% include references.html %}

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