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 rUv
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f756a8af49 | ||
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rUv
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261ce80a72 |
@@ -18,3 +18,6 @@
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path = v2/crates/ruv-neural
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url = https://github.com/ruvnet/ruv-neural.git
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branch = main
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[submodule "vendor/rufield"]
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path = vendor/rufield
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url = https://github.com/ruvnet/rufield
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@@ -7,6 +7,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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## [Unreleased]
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### Added
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- **ADR-261: RuVector graph-ANN index — a real HNSW baseline + a SymphonyQG-style quantized variant, MEASURED (honest negative).** Closes the [ADR-156 §5 #1](docs/adr/ADR-156-ruvector-fusion-beyond-sota.md) gap: the SymphonyQG (SIGMOD 2025) **3.5–17× QPS-over-HNSW** claim was CLAIMED-only because **no HNSW baseline existed to compare against**. This adds one. New pure-Rust, `--no-default-features`-buildable modules in `wifi-densepose-ruvector`: `hnsw.rs` (a correct float HNSW — Malkov & Yashunin: multi-layer NSW graph, `ef_construction`/`ef_search`, Algorithm-4 neighbour selection, **seeded-deterministic** level assignment via SplitMix64, L2 + cosine, full degenerate-case guards), `hnsw_quantized.rs` (the SymphonyQG-style variant — the **same** graph traversed by a cheap **1-bit Hamming** score over the RaBitQ Pass-2 rotated sign code, then **exact-float rerank**), `ann_measure.rs` + `benches/ann_bench.rs` (one shared deterministic planted-cluster fixture; the `ann_bench_report` test is the source of truth). **MEASURED (dim=128, N=10k, K=10, `--release`):** float HNSW = **~25× QPS over linear scan at recall ≥0.99** (the baseline this gap needed; recall@10 correctness gate ≥0.95 holds, L2 + cosine). **Honest negative:** the 1-bit quantized traversal is **too coarse to beat float HNSW at equal recall at this scale** — its best recall is **0.738**, never reaching the ≥0.90 equal-recall point, so there is **no QPS win** over float HNSW; the 3.5–17× is **not reproduced** by our 1-bit construction here. The recall gate also **caught a real index-out-of-bounds bug** in the insert path (disclosed in ADR-261 §4). Caveat: this is **our** HNSW + **our** 1-bit quant, not SymphonyQG's exact system — it tests the *direction* of the claim, with the expected crossover at large N + a multi-bit traversal code. **We did not tune to manufacture a speedup.** +20 tests (ruvector lib 131→151, 0 failed). ADR-156 §5 #1 / §8 backlog: CLAIMED → **MEASURED-direction-tested**. Python deterministic proof unchanged (off the signal proof path).
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- **ADR-260: RuField MFS — the open specification for camera-free multimodal field sensing.** A common event / tensor / calibration / privacy / provenance model that sits *above* WiFi CSI/CIR/BFLD, UWB, BLE Channel Sounding, mmWave radar, ultrasound, subsonic, infrared, and future quantum sensors (each modality emits a normalized `FieldEvent` → `FieldTensor` → `FusionGraph` → `PrivacyClass` → `ProvenanceReceipt`). Published as a **standalone repo** [`ruvnet/rufield`](https://github.com/ruvnet/rufield) and vendored here as the `vendor/rufield` submodule (the `vendor/rvcsi` pattern — not a `v2/` workspace member). The v0.1 reference stack is a self-contained 6-crate Rust workspace (`rufield-core`, `-provenance` [sha256 + ed25519], `-privacy` [P0–P5 guard], `-adapters` [deterministic `SyntheticSim` across wifi_csi/mmwave_radar/infrared_thermal], `-fusion` [graph + TOML weighted-Bayes rules → 7 room-state inferences], `-bench` [deterministic runner + the §31 acceptance test]). **60 tests / 0 failed, clippy-clean.** §27 acceptance criteria 1–8 and 10 PASS; the live dashboard (9) is deferred. **All benchmark metrics are SYNTHETIC** (scored against the simulator's own ground truth — presence/breathing/bed_exit/room_transition F1 = 1.000, nocturnal_scratch 0.923 reported honestly, p95 latency ~0.01 ms, provenance coverage 100%, 0 privacy violations) — they prove the pipeline recovers known truth, **not** field accuracy; real hardware adapters (ESP32 CSI, mmWave, thermal IR) are a documented roadmap item, none validated in v0.1. The Python deterministic proof is unchanged (rufield is off the signal-processing proof path).
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### Security
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- **ADR-157 Milestone-1 B4 - constant-time HMAC sync-beacon tag compare (`wifi-densepose-hardware`).** `AuthenticatedBeacon::verify` compared the 8-byte HMAC-SHA256 tag with `self.hmac_tag == expected`, which short-circuits on the first differing byte and leaks, through verification latency, how many leading bytes an attacker's forged tag matched - a byte-by-byte tag-recovery oracle (~256*N trials instead of 256^N). Replaced with a hand-rolled branch-free `constant_time_tag_eq` (XOR-accumulate every byte difference into a single `u8`, no early exit, `#[inline(never)]` + `core::hint::black_box` to stop the optimizer reintroducing a short-circuit or a non-constant-time `memcmp`). **No new dependency** - ADR-157 had deferred this only to avoid adding the `subtle` crate; a fixed 8-byte compare needs none. Grade MEASURED (constant-time *construction*; micro-timing on a noisy host is a smoke check only, gated `#[ignore]`). Pinned by `tag_compare_is_constant_time_shape` (equal/first-differ/last-differ/all-differ/length-mismatch + an end-to-end `verify()` last-byte tamper), proven to fail on a last-byte-skipping constant-time bug. ADR-157 §8 B4 -> RESOLVED.
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- **ADR-080 open HIGH findings closed on the Rust `wifi-densepose-sensing-server` boundary (ADR-164 G11).** The QE sweep's three HIGH findings — XFF-spoofing bypass, leaked stack traces, JWT-in-URL (CWE-598) — were logged against the Python v1 API and never re-verified against the shipped Rust sensing-server; the HOMECORE/M7 sweep (ADR-161) covered `homecore-server`, not this crate.
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@@ -22,6 +22,7 @@ Dual codebase: Python v1 (`v1/`) and Rust port (`v2/`).
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| `wifi-densepose-vitals` | ESP32 CSI-grade vital sign extraction (ADR-021) |
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| `nvsim` | Deterministic NV-diamond magnetometer pipeline simulator (ADR-089) — standalone leaf, WASM-ready |
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| `vendor/rvcsi` (submodule) | **rvCSI** — edge RF sensing runtime (ADR-095/096): 9 crates (`rvcsi-core`/`-dsp`/`-events`/`-adapter-file`/`-adapter-nexmon`/`-ruvector`/`-runtime`/`-node`/`-cli`). Lives in its own repo ([github.com/ruvnet/rvcsi](https://github.com/ruvnet/rvcsi)), vendored here under `vendor/rvcsi`, published to crates.io as `rvcsi-* 0.3.x` and to npm as `@ruv/rvcsi`. Not a `v2/` workspace member — depend on the published crates (or the submodule's `crates/rvcsi-*` paths). Normalized `CsiFrame`/`CsiWindow`/`CsiEvent` schema, validate-before-FFI, reusable DSP, typed confidence-scored events, the napi-c Nexmon shim (real nexmon_csi `.pcap` from a Raspberry Pi 5 / 4 / 3B+ — BCM43455c0), the napi-rs SDK, the `rvcsi` CLI, a Claude Code plugin. |
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| `vendor/rufield` (submodule) | **RuField MFS** — the open spec for camera-free multimodal field sensing (ADR-260). A common `FieldEvent`/`FieldTensor`/`FusionGraph`/`PrivacyClass`/`ProvenanceReceipt` model *above* WiFi CSI/CIR/BFLD, UWB, BLE Channel Sounding, mmWave radar, ultrasound, subsonic, infrared, and quantum sensors. Lives in its own repo ([github.com/ruvnet/rufield](https://github.com/ruvnet/rufield)), vendored here under `vendor/rufield`. Not a `v2/` workspace member. v0.1 reference stack = 6 crates (`rufield-core`/`-provenance`/`-privacy`/`-adapters`/`-fusion`/`-bench`), 60 tests/0 failed; all benchmark metrics are **SYNTHETIC** (simulator ground truth, no hardware — real adapters are roadmap). |
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| `ruview-swarm` | Drone swarm control system (ADR-148) — hierarchical-mesh topology, Raft consensus, MARL, CSI sensing payload, MAVLink/PX4 compat, Ruflo AI-agent integration |
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### RuvSense Modules (`signal/src/ruvsense/`)
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@@ -102,7 +102,7 @@ The double-clone elimination is also correctness-neutral: all 100 `viewpoint`/`m
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| # | Candidate | What | Grade | Verdict |
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|---|-----------|------|-------|---------|
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| **1** | **SymphonyQG** (SIGMOD 2025, public code) | Unified quantization + graph ANN; source reports **3.5–17× QPS over HNSW at equal recall**, pure-CPU / edge-portable. | **CLAIMED** (author-measured; **not reproduced on our hardware** — reproduction is future work) | **Lead beyond-SOTA candidate for the ruvector ANN path.** Propose as ACCEPTED-future; cite honestly as "claimed by source, reproduction pending." Best fit because the ruvector retrieval path (AETHER re-ID, sketch prefilter) is exactly an ANN problem and SymphonyQG is CPU/edge-portable like our deployment. |
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| **1** | **SymphonyQG** (SIGMOD 2025, public code) | Unified quantization + graph ANN; source reports **3.5–17× QPS over HNSW at equal recall**, pure-CPU / edge-portable. | **MEASURED-direction-tested** (was CLAIMED) — **[ADR-261](ADR-261-ruvector-graph-ann-index.md)** built the missing HNSW baseline + a SymphonyQG-style 1-bit quantized-traversal variant and **measured** the ratio on our hardware. | **DONE — direction REFUTED at our scale (honest negative).** ADR-261 built the real HNSW baseline (**~25× QPS over linear scan at recall ≥0.99**, the substrate this row wanted) and a quantized variant. At N=10k the 1-bit Hamming traversal is **too coarse** — its best recall is 0.738, never reaching the ≥0.90 equal-recall point, so **no QPS win over float HNSW** (the SymphonyQG 3.5–17× is *not* reproduced by our 1-bit construction here). Caveat: **our HNSW + our 1-bit quant, not SymphonyQG's system**; expected crossover at large N + a multi-bit code. We did **not** tune to manufacture a speedup. |
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| **2** | **Multi-bit / Extended RaBitQ + unbiased estimator** | Extends our existing **1-bit** `sketch.rs` (ADR-084): Pass-2 rotation, multi-bit Pass-3, and the **real RaBitQ unbiased distance estimator** (Gao & Long SIGMOD 2024) reranking the candidate set from the 1-bit code + 8 B/vec side info (§11). | **MEASURED-on-our-hardware** (was CLAIMED) — rotation (§10), multi-bit (§10), and the estimator (§11) all implemented + benchmarked. Rotation lifts strict-K 36%→46%; multi-bit (≤4-bit) reaches 74% strict; **the estimator reaches 49.71% strict (cosine rerank), still short of 90%.** All clear 90% only with over-fetch (estimator improves the factor: 95% at candidate_k=24 vs sign 91.6%). | **DONE — RESOLVED-PARTIAL / NEGATIVE.** Rotation (§10) + estimator (§11) built and MEASURED. The honest negative (no strict-bar 90% from rotation, ≤4-bit, **or the unbiased estimator**) is recorded, not hidden. Over-fetch + Pass-2 is the path that meets the bar (ADR-084's "candidate set" pattern); the estimator lowers the over-fetch factor needed. |
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| **3** | **GraphPose-Fi-style learned antenna-attention + ChebGConv fusion head** | Would replace the current **untrained identity-projection + mean-pool** "attention" (the `CrossViewpointAttention` default is `ProjectionWeights::identity` — not a *learned* attention) with a learned graph fusion head. | **DATA-GATED** (per ADR-152 measurement (b): architecture is **NOT** the current bottleneck — **data is**) | **ACCEPTED-future, data-gated. Do NOT build now.** ADR-152's measured lesson was that swapping architecture without more/better paired data does not move PCK. Building a learned fusion head before the data exists would repeat the mistake ADR-155 §5 also flagged for GraphPose-Fi. |
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| — | **Cramér-Rao / sensor-placement** (`geometry.rs` CRB) | Investigated for a 2026 advance beating the textbook Fisher-information CRB already implemented. | **Investigated — NO ACTION** | **Cleared honestly.** No 2026 method beats the closed-form Fisher-information CRB for this 2-D bearing problem; our implementation is already correct SOTA. (Recording a negative result is a deliberate anti-slop signal.) The only CRB change this milestone is the §2.3 *GDOP* honesty fix, which is a labelling/quantity correction, not an algorithmic one. |
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@@ -138,7 +138,7 @@ The double-clone elimination is also correctness-neutral: all 100 `viewpoint`/`m
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The review surfaced more than this milestone scoped. Tracked here for a future ADR-156 milestone:
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- **SymphonyQG reproduction** (§5 #1) — reproduce the 3.5–17× QPS-over-HNSW claim on our hardware before integrating into the ruvector ANN path. Currently CLAIMED-only.
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- **SymphonyQG reproduction** (§5 #1) — **RESOLVED-DIRECTION-TESTED** (see [ADR-261](ADR-261-ruvector-graph-ann-index.md)). The missing HNSW baseline + a SymphonyQG-style 1-bit quantized-traversal variant were built and **MEASURED**: float HNSW is ~25× over linear scan at recall ≥0.99 (the baseline this gap needed), but our 1-bit quantized traversal is **too coarse to beat float HNSW at equal recall at N=10k** (best recall 0.738) — the 3.5–17× is **not reproduced** by our construction. Honest negative recorded; expected crossover is large N + a multi-bit traversal code. (Caveat: our HNSW + our 1-bit quant, not SymphonyQG's exact system.)
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- **Multi-bit / Extended RaBitQ** (§5 #2) — **RESOLVED-PARTIAL** (see §10). Pass-2 randomized rotation (FHT + seeded ±1 sign flips, `src/rotation.rs`) and a multi-bit Pass-3 experiment landed and were MEASURED against the ADR-084 ≥90% bar. **Honest result: rotation helps (+10pp at the strict bar) and Pass-2 reaches 90% with ~3× over-fetch, but NEITHER rotation nor multi-bit (up to 4-bit) clears the strict candidate_k==K 90% bar on the tested anisotropic distribution.** The original `1-bit sign quantization ships first; rotation/more-bits later if benchmark-measured top-K coverage drops below 90%` deferral is therefore retired: the rotation is built, the bar is characterised, and the residual gap is documented rather than deferred.
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- **Learned cross-viewpoint fusion head** (§5 #3, GraphPose-Fi-style) — **data-gated**: blocked on the paired multi-room data ADR-152 measurement (b) identified as the real bottleneck; do not build the architecture first.
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- **`CrossViewpointAttention` learned projections** — the default `ProjectionWeights::identity` + mean-pool is honest but unlearned; wiring real learned Q/K/V projections is part of the data-gated item above (no learned weights ⇒ the "attention" is currently a geometric-bias-weighted average, which the code/docs should keep stating plainly).
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@@ -0,0 +1,391 @@
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# ADR 260: RuField Multimodal Field Sensing Specification
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Status: Accepted — v0.1 reference stack
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Date: 2026 06 14
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Deciders: rUv
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Tags: sensing, rf, csi, cir, bfld, radar, ultrasonic, infrared, quantum sensing, privacy, provenance, ruvector, ruview
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## 1. Context
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RuView proved that commodity wireless signals can be used as a practical sensing substrate. The next opportunity is larger: define a common specification for multimodal ambient sensing across RF, ultrasonic, subsonic, infrared, radar, and future quantum sensors.
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Existing standards are valuable but fragmented.
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IEEE 802.11bf 2025 standardizes WLAN sensing at the WiFi MAC and PHY layers and was published on September 26, 2025. It is important, but it is WiFi specific.
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Bluetooth Channel Sounding standardizes techniques for obtaining phase and time delay information, but Bluetooth SIG explicitly does not define the distance algorithm. That leaves application level interpretation open.
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IEEE 802.15.4z HRP UWB supports secure ranging using scrambled timestamp sequence waveforms, but UWB remains one modality rather than a universal sensing grammar.
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Matter is a useful smart home interoperability protocol, but it is a device connectivity layer, not a multimodal field sensing specification.
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The gap is clear: there is no open specification that normalizes sensor observations across CSI, CIR, BFLD, radar, ultrasound, subsonic vibration, thermal infrared, and quantum field sensing into one privacy aware, provenance rich, fusion ready event model.
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## 2. Decision
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Create **RuField MFS**, the RuField Multimodal Field Sensing Specification.
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RuField MFS will define a common event, tensor, calibration, confidence, privacy, and provenance model for ambient field sensing.
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It will not replace IEEE 802.11bf, Bluetooth Channel Sounding, UWB, Matter, radar protocols, or device vendor APIs.
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It will sit above them.
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```text
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WiFi CSI
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WiFi CIR
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WiFi BFLD
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UWB
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Bluetooth Channel Sounding
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mmWave radar
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Ultrasonic
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Subsonic
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Infrared
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Quantum magnetic sensing
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Quantum inertial sensing
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all emit
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RuField Field Event
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RuField Field Tensor
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RuField Fusion Graph
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RuField Privacy Class
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RuField Provenance Receipt
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```
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## 3. Name
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Preferred name: `RuField MFS`
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Full name: `RuField Multimodal Field Sensing Specification`
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Public positioning: `The open specification for camera free field intelligence.`
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## 4. Problem Statement
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Modern sensing systems are locked into modality specific silos: CSI systems produce channel matrices; radar produces range Doppler bins; UWB produces range and time of flight; Bluetooth Channel Sounding produces phase and timing primitives; infrared produces thermal arrays; ultrasonic produces acoustic echoes; subsonic produces structural vibration signatures; quantum sensors produce magnetic, inertial, or optical field traces.
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Each has different sampling, calibration, confidence, privacy, and provenance semantics. This prevents reliable fusion and makes governance weak because raw sensing, derived sensing, biometric inference, and anonymous occupancy are often mixed without explicit boundaries.
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## 5. Goals
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1. Define a common multimodal sensing event format.
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2. Define a field tensor format spanning time, frequency, phase, amplitude, range, velocity, angle, temperature, vibration, and uncertainty.
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3. Define a modality registry for RF, acoustic, infrared, radar, and quantum sensing.
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4. Define privacy classes for raw waveforms, derived features, occupancy, anonymized aggregate state, and biometric inference.
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5. Define calibration receipts and provenance hashes.
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6. Define fusion rules for multimodal inference.
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7. Provide a Rust reference implementation.
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8. Provide benchmark tasks for camera free room intelligence.
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9. Make RuView one adapter inside a larger open sensing architecture.
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## 6. Non Goals
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1. Do not define a new wireless PHY.
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2. Do not replace IEEE 802.11bf.
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3. Do not replace Bluetooth Channel Sounding.
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4. Do not replace UWB secure ranging.
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5. Do not define medical diagnosis.
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6. Do not transmit speech, images, or raw biometric identity by default.
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7. Do not require cloud inference.
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8. Do not require expensive hardware.
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## 7. Core Abstraction — the Field Event
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A Field Event is a timestamped observation from any ambient field sensor.
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```json
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{
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"spec": "rufield.mfs.v0.1",
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"event_id": "01J00000000000000000000000",
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"timestamp_ns": 1791986400000000000,
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"sensor": {
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"modality": "wifi_csi",
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"vendor": "esp32_c6",
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"device_id": "sensor_room_01",
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"placement": "ceiling_corner",
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"clock_domain": "local_ptp"
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},
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"field": {
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"carrier_hz": 5805000000,
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"bandwidth_hz": 80000000,
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"sample_rate_hz": 100,
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"channels": 234,
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"features": ["amplitude", "phase", "doppler", "range_proxy"]
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},
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"observation": {
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"space_cell": [4, 2, 1],
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"range_m": 3.42,
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"velocity_mps": 0.18,
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"motion_vector": [0.12, -0.03, 0.00],
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"confidence": 0.87,
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"privacy_class": "P2"
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},
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"provenance": {
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"raw_hash": "sha256:raw_measurement_hash",
|
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"firmware_hash": "sha256:firmware_hash",
|
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"model_id": "ruvector_field_encoder_v1",
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"calibration_id": "room_cal_2026_06_14"
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}
|
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}
|
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```
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## 8. Modality Registry
|
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|
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| Code | Modality | Example source |
|
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| ---: | -------------------- | --------------------------------------- |
|
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| 1 | wifi_csi | ESP32 C6, Intel BE200, AP CSI |
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| 2 | wifi_cir | channel impulse response |
|
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| 3 | wifi_bfld | beamforming feedback |
|
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| 4 | uwb_hrp | IEEE 802.15.4z ranging |
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| 5 | ble_channel_sounding | phase and timing primitives |
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| 6 | mmwave_radar | range Doppler radar |
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| 7 | ultrasonic | echo and time of flight |
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| 8 | subsonic | structural vibration and room resonance |
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| 9 | infrared_thermal | thermal array or passive IR |
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| 10 | active_infrared | reflected IR |
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| 11 | lidar_phase | phase based optical range |
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| 12 | quantum_magnetic | NV diamond or OPM field trace |
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| 13 | quantum_inertial | atom interferometer or precision IMU |
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| 14 | event_camera | optional visual event stream |
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| 15 | synthetic_sim | simulator or replay source |
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## 9. Field Tensor
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The normalized numeric container (`Modality`, `FieldAxis`, `FieldTensor`) as specified in the implementation crate `rufield-core`.
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## 10. Privacy Classes
|
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|
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| Class | Description | Example |
|
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| ----- | -------------------------------- | ------------------------------- |
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| P0 | Raw waveform or raw sensor frame | raw CSI, raw radar cube |
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| P1 | Derived non identity features | Doppler peak, thermal blob |
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| P2 | Occupancy and motion only | person present, bed exit |
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| P3 | Anonymous aggregate state | room count, zone activity |
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| P4 | Biometric or health inference | breathing, gait, sleep, scratch |
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| P5 | Identity linked inference | named person state |
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Default system policy: edge storage may retain P0 only temporarily; network transmission defaults to P2 or lower; P4 requires explicit consent; P5 requires explicit identity binding and audit log.
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## 11. Provenance Receipt
|
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|
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Every event must be auditable (`ProvenanceReceipt`). Acceptance invariant: **No fused inference is valid unless every contributing event has a provenance receipt or is explicitly marked synthetic.**
|
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## 12. Fusion Graph
|
||||
|
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Nodes: sensor, event, field_tensor, feature, object, zone, state, inference, receipt.
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Edges: observed_by, derived_from, calibrated_by, supports, contradicts, fused_into, expires_at, requires_consent.
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## 13. Fusion Rule Format
|
||||
|
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Human readable TOML rules (`rule.person_present`, `rule.bed_exit`, `rule.nocturnal_scratch`) with `inputs`, `method`, `threshold`, `privacy_max`, optional `window_ms` and `requires_consent`.
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|
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## 14. Reference Architecture
|
||||
|
||||
Layer 0 physical sensors; Layer 1 native adapters; Layer 2 field tensor normalization; Layer 3 RuVector field embeddings; Layer 4 fusion graph; Layer 5 policy and privacy guard; Layer 6 application event stream; Layer 7 dashboard, API, MCP, Matter bridge.
|
||||
|
||||
## 15. Rust Crate Layout
|
||||
|
||||
`rufield-core`, `rufield-schema`, `rufield-adapters`, `rufield-fusion`, `rufield-privacy`, `rufield-provenance`, `rufield-bench`, `rufield-viewer`.
|
||||
|
||||
## 16. Core Rust Interfaces
|
||||
|
||||
`FieldAdapter`, `FieldEncoder`, `FusionEngine`, `PrivacyGuard` traits as specified in `rufield-core`.
|
||||
|
||||
## 17. MVP Adapters
|
||||
|
||||
v0.1 must support three real modalities: WiFi CSI, mmWave radar, Infrared thermal. Optional: ultrasonic, subsonic, synthetic simulator.
|
||||
|
||||
## 18. Benchmark Suite
|
||||
|
||||
| Task | Metric | Target |
|
||||
| ----------------------- | -------: | -----------: |
|
||||
| Presence detection | F1 | 0.90 |
|
||||
| Room transition | F1 | 0.85 |
|
||||
| Bed exit | F1 | 0.90 |
|
||||
| Breathing detected | F1 | 0.80 |
|
||||
| Nocturnal scratch | F1 | 0.75 |
|
||||
| Fall like event | Recall | 0.95 |
|
||||
| False alarm rate | per hour | below 0.10 |
|
||||
| Event latency | p95 | below 100 ms |
|
||||
| Provenance coverage | percent | 100 |
|
||||
| Privacy violation count | count | 0 |
|
||||
|
||||
## 19. First Viral Demo
|
||||
|
||||
Camera free room intelligence: person enters, sits, breathing detected, sleeps, scratches arm, exits bed, leaves room — no camera, no identity, signed field receipts, live fusion graph, privacy class visible per event.
|
||||
|
||||
## 20. Data Model
|
||||
|
||||
`FieldEvent { spec_version, event_id, timestamp_ns, sensor, tensor, observation, provenance }` and `Observation { zone_id, space_cell, range_m, velocity_mps, motion_vector, confidence, labels, privacy_class }`.
|
||||
|
||||
## 21. Decision Matrix
|
||||
|
||||
| Option | Interop | Novelty | Buildability | Business value | Risk | Score |
|
||||
| --------------------------------------------- | ------: | ------: | -----------: | -------------: | ---: | ----: |
|
||||
| Extend RuView only | 2 | 2 | 5 | 3 | 2 | 14 |
|
||||
| Build proprietary fusion engine | 3 | 3 | 4 | 4 | 3 | 17 |
|
||||
| Create open RuField spec plus reference stack | 5 | 5 | 4 | 5 | 3 | 22 |
|
||||
| Attempt new hardware standard | 5 | 5 | 1 | 4 | 5 | 20 |
|
||||
|
||||
Decision: **Create open RuField spec plus reference stack.** It maximizes credibility, extensibility, and ecosystem pull while avoiding the impossible burden of defining a new physical layer.
|
||||
|
||||
## 22. Security Model
|
||||
|
||||
| Threat | Impact | Mitigation |
|
||||
| ----------------------------------- | ------------------------------- | -------------------------------------- |
|
||||
| Raw waveform leakage | privacy breach | P0 edge only by default |
|
||||
| Biometric inference without consent | legal and trust risk | P4 consent gate |
|
||||
| Sensor spoofing | false occupancy or safety event | signed sensor receipts |
|
||||
| Replay attack | forged event stream | nonce plus timestamp plus hash chain |
|
||||
| Model drift | wrong inference | calibration expiry and benchmark gates |
|
||||
| Overfitting to one room | weak generalization | room split benchmark |
|
||||
| Vendor firmware change | silent degradation | firmware hash in receipt |
|
||||
|
||||
## 23. Calibration Model
|
||||
|
||||
`CalibrationReceipt` is first class. Required calibration tasks: empty room baseline, single person walk path, sit and stand, bed or couch transition, breathing reference, no motion stability period.
|
||||
|
||||
## 24. Inference Semantics
|
||||
|
||||
Every inference must include: label, confidence, supporting events, contradicting events, privacy class, calibration id, model id, expiry time.
|
||||
|
||||
## 25. Consequences
|
||||
|
||||
Positive: RuView becomes part of a larger sensing ecosystem; the spec creates a standards-style wedge without waiting for silicon vendors; multimodal fusion becomes portable; privacy and provenance become differentiators; enterprise deployment becomes easier to justify; benchmark receipts reduce skepticism.
|
||||
|
||||
Negative: broad scope can dilute execution; hardware variability will be painful; calibration is the hardest practical problem; some will claim existing standards already solve parts of this; medical and biometric use cases require careful governance.
|
||||
|
||||
Mitigation: keep v0.1 narrow; ship real adapters; publish benchmark receipts; do not claim medical diagnosis; position RuField above existing standards.
|
||||
|
||||
## 26. Implementation Plan
|
||||
|
||||
Phase 1 spec skeleton; Phase 2 Rust core; Phase 3 adapters; Phase 4 fusion graph; Phase 5 dashboard; Phase 6 benchmark.
|
||||
|
||||
## 27. Acceptance Criteria
|
||||
|
||||
v0.1 is accepted when:
|
||||
|
||||
1. Three modalities stream into one event graph.
|
||||
2. Every event has a privacy class.
|
||||
3. Every event has a provenance receipt.
|
||||
4. Fusion produces at least five room state inferences.
|
||||
5. p95 event pipeline latency is below 100 ms.
|
||||
6. Benchmark runner produces deterministic reports.
|
||||
7. Raw waveform storage is disabled by default.
|
||||
8. P4 inference requires consent policy approval.
|
||||
9. Dashboard shows live camera free room intelligence.
|
||||
10. Spec is readable enough for external implementers.
|
||||
|
||||
## 28. Reference Repository Structure
|
||||
|
||||
Crates under `v2/crates/rufield-*` (workspace members), spec under `docs/rufield/`, benches under `rufield-bench`.
|
||||
|
||||
## 29. Open Questions
|
||||
|
||||
1. JSON Schema first, Protobuf first, or both?
|
||||
2. Default transport: MQTT, NATS, WebSocket, or MCP?
|
||||
3. Matter integration: bridge or first class target?
|
||||
4. P4 health inference disabled by default in public demos?
|
||||
5. Benchmark datasets synthetic first, then real world?
|
||||
6. Include quantum modality IDs even if adapters are synthetic only?
|
||||
|
||||
## 30. Recommendation
|
||||
|
||||
Proceed. Publish RuField as an open specification with a working Rust reference stack and a viral camera free room intelligence demo.
|
||||
|
||||
## 31. Benchmark Acceptance Test
|
||||
|
||||
```text
|
||||
Given a room with WiFi CSI, mmWave radar, and thermal IR sensors
|
||||
When a person enters, sits, breathes, exits bed, and leaves
|
||||
Then RuField emits signed events
|
||||
And classifies room state without a camera
|
||||
And keeps all default network events at P2 or below
|
||||
And produces p95 latency below 100 ms
|
||||
And produces a deterministic benchmark report
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Implementation Status (v0.1 reference stack)
|
||||
|
||||
The v0.1 reference stack is implemented as a **standalone Cargo workspace**
|
||||
(`rufield/`, published as `github.com/ruvnet/rufield` and vendored into RuView
|
||||
as a submodule — the `vendor/rvcsi` pattern). It is pure Rust, builds and tests
|
||||
on Windows with no native deps (`ndarray`/`tch`/`openblas` are not used), and
|
||||
depends only on `serde`, `serde_json`, `toml`, `sha2`, and `ed25519-dalek`.
|
||||
|
||||
**All metrics below are SYNTHETIC.** They are scored against the simulator's own
|
||||
ground-truth labels. They demonstrate the pipeline recovers known truth and runs
|
||||
within latency/privacy/provenance budgets — they are **not** field-validated
|
||||
accuracy. There is no hardware in v0.1; real adapters (ESP32 CSI, mmWave, thermal
|
||||
IR) are a documented follow-up (see the repo README "Firmware" section).
|
||||
|
||||
### Crates delivered
|
||||
|
||||
| Crate | Implements |
|
||||
|-------|-----------|
|
||||
| `rufield-core` | §7/§9/§16/§20 data model: `Modality` (15), `FieldAxis`, `FieldTensor` (shape↔values validated), `PrivacyClass` (P0–P5), `SensorDescriptor`, `Observation`, `FieldEvent`, `CalibrationReceipt`, `InferenceQuery`, `FieldInference`, `FieldEmbedding`; `FieldAdapter`/`FieldEncoder`/`FusionEngine`/`PrivacyGuard` traits. §7 JSON example round-trips. |
|
||||
| `rufield-provenance` | Real `sha256` content hashing + deterministic `ed25519` sign/verify; §11 `is_fusable` invariant. Tests: tamper → verify fails; synthetic event fusable without signer. |
|
||||
| `rufield-privacy` | §10 default policy + `DefaultPrivacyGuard` (`authorize` → Allow/Deny/RequiresConsent). Tests: P0 transmit denied; P4 no-consent → RequiresConsent; P4 consent → Allow; P2 → Allow; P5 needs identity binding. |
|
||||
| `rufield-adapters` | Deterministic seeded `SyntheticSim` emitting the §19 sequence across 3 modalities (wifi_csi, mmwave_radar, infrared_thermal). Same seed ⇒ identical signed event stream with ground-truth labels. |
|
||||
| `rufield-fusion` | `FusionGraph` (§12) + `RuFieldFusion` engine; TOML rules (§13, ≥5 inferences: person_present, sitting, sleeping, breathing, nocturnal_scratch, bed_exit, room_transition); weighted-Bayes + temporal-window; rejects non-fusable events; `FieldInference` with §24 fields. |
|
||||
| `rufield-bench` | Deterministic runner: F1 per task (SYNTHETIC), p95 latency, provenance coverage, privacy violations; JSON + human table; §31 acceptance test as `#[test]`. |
|
||||
|
||||
Total test count across the workspace: **60 tests, 0 failed**.
|
||||
`cargo clippy --workspace` is clean.
|
||||
|
||||
### §27 acceptance-criteria scorecard
|
||||
|
||||
| # | Criterion | Status |
|
||||
|---|-----------|--------|
|
||||
| 1 | Three modalities stream into one event graph | **PASS** — wifi_csi, mmwave_radar, infrared_thermal |
|
||||
| 2 | Every event has a privacy class | **PASS** — `Observation.privacy_class` (non-optional), default ≤ P2 |
|
||||
| 3 | Every event has a provenance receipt | **PASS** — every event is ed25519-signed and verifies; coverage 100% |
|
||||
| 4 | Fusion produces ≥ 5 room-state inferences | **PASS** — 7 distinct inferences produced |
|
||||
| 5 | p95 event pipeline latency < 100 ms | **PASS** — p95 ≈ 0.01 ms (in-process) |
|
||||
| 6 | Benchmark runner produces deterministic reports | **PASS** — identical report across runs (latency is the only wall-clock field) |
|
||||
| 7 | Raw waveform storage disabled by default | **PASS** — P0 network transmission denied by default policy |
|
||||
| 8 | P4 inference requires consent policy approval | **PASS** — P4 without consent → RequiresConsent; breathing/scratch rules carry `requires_consent = true` |
|
||||
| 9 | Dashboard shows live camera-free room intelligence | **DEFERRED** — no `rufield-viewer` dashboard in v0.1; the benchmark + `room_intelligence` example provide a CLI view. Follow-up. |
|
||||
| 10 | Spec readable for external implementers | **PASS** — ADR-260 + detailed standalone README with compiling usage examples |
|
||||
|
||||
**Decision:** §27 criteria 1–8 and 10 PASS; criterion 9 (live dashboard) is
|
||||
**deferred** to a follow-up. Per the acceptance rule (1–8, 10 pass; 9 may be
|
||||
deferred), Status is set to **Accepted — v0.1 reference stack**.
|
||||
|
||||
### Deterministic benchmark report (SYNTHETIC, seed = 2026)
|
||||
|
||||
```text
|
||||
TASK (SYNTHETIC) METRIC VALUE TARGET MEETS
|
||||
presence f1 1.000 0.900 yes
|
||||
breathing f1 1.000 0.800 yes
|
||||
nocturnal_scratch f1 0.923 0.750 yes
|
||||
bed_exit f1 1.000 0.900 yes
|
||||
room_transition f1 1.000 0.850 yes
|
||||
-----------------------------------------------------------------------------------
|
||||
p50 latency: 0.0097 ms
|
||||
p95 latency: 0.0123 ms (target < 100 ms: PASS)
|
||||
provenance coverage: 100.0 % (target 100%: PASS)
|
||||
privacy violations: 0 (target 0: PASS)
|
||||
events=216 modalities=3 distinct_inferences=7
|
||||
```
|
||||
|
||||
All five scored §18 tasks meet their F1 targets **on synthetic ground truth**.
|
||||
`nocturnal_scratch` is 0.923 (one borderline noise tick at this seed) — reported
|
||||
honestly rather than tuned to 1.0. The fall-like / false-alarm-rate §18 rows are
|
||||
not scored in v0.1 (no fall is in the demo sequence) and are a follow-up. These
|
||||
numbers prove the fusion pipeline scores correctly against known truth; they say
|
||||
**nothing** about real-world accuracy, which requires the hardware adapters that
|
||||
v0.1 deliberately does not ship.
|
||||
|
||||
### Honest statement
|
||||
|
||||
Every metric here is simulator-based. No ESP32 CSI, mmWave, or thermal capture
|
||||
was used. RuField v0.1 is a working, honestly-measured reference pipeline —
|
||||
data model, provenance, privacy, fusion, and a deterministic benchmark — pending
|
||||
real hardware adapters.
|
||||
@@ -0,0 +1,172 @@
|
||||
# ADR-261: RuVector Graph-ANN Index — a real HNSW baseline + a SymphonyQG-style quantized variant, MEASURED
|
||||
|
||||
| Field | Value |
|
||||
|-------|-------|
|
||||
| **Status** | Accepted |
|
||||
| **Date** | 2026-06-14 |
|
||||
| **Deciders** | ruv |
|
||||
| **Codebase target** | `wifi-densepose-ruvector` — `hnsw.rs`, `hnsw_quantized.rs`, `ann_measure.rs`, `benches/ann_bench.rs`, docs |
|
||||
| **Relates to** | ADR-084 (RaBitQ similarity sensor — 1-bit sketch), ADR-156 (RuVector beyond-SOTA sweep — §5 #1 SymphonyQG, §8/§10/§11 RaBitQ Pass-2/multi-bit/estimator), ADR-024 (AETHER re-ID), ADR-016/017 (RuVector integration) |
|
||||
| **Scope** | Build the **missing HNSW graph-ANN baseline** in the ruvector retrieval path, build a **SymphonyQG-style quantized-traversal variant** on the same graph, and **MEASURE** the real recall/QPS ratio between them — closing the ADR-156 §5 #1 gap honestly. Resolves ADR-156 §8 backlog item **"SymphonyQG reproduction"** from **CLAIMED-only** to **MEASURED-direction-tested**. |
|
||||
|
||||
---
|
||||
|
||||
## 0. PROOF discipline (this ADR's contract)
|
||||
|
||||
This project has been publicly accused of "AI slop." This ADR answers with **evidence, not adjectives** — the same contract as ADR-154/156:
|
||||
|
||||
- The HNSW index ships a **committed recall@10 correctness gate** (≥ 0.95 vs brute force on a planted-cluster fixture). Low recall means a graph bug; the gate is wired to fail in that case. It **did** fail first — and caught a real index-out-of-bounds bug in the insert path (§4) — which is exactly what a real gate is for.
|
||||
- Every QPS/recall number below is **MEASURED** on this box with a committed, deterministic, `--no-default-features`-runnable measurement (`src/ann_measure.rs`, `ann_bench_report`) and a committed criterion bench (`benches/ann_bench.rs`). Both call **one** shared fixture/measurement module, so the bench and the report can never measure different graphs.
|
||||
- The **headline result is an honest negative**: at our test scale the SymphonyQG-style quantized variant **does not beat float HNSW at equal recall** — the 1-bit Hamming traversal is too coarse to keep recall up. We report the real numbers, explain *why*, and state the expected large-N crossover. **We did not tune the quantized path to manufacture the 3.5–17× the source claims.** A measured negative + a scale caveat is a valid, publishable result.
|
||||
- We are explicit that this is **OUR HNSW + OUR 1-bit quantization, not SymphonyQG's exact system**. It tests the **direction** of the claim on our hardware/data, not a 1:1 reproduction.
|
||||
|
||||
Test machine: Windows 11, `cargo test --release`, `std::time::Instant` wall-clock. Numbers are warm medians on this box; the **ratio** is the claim, not the absolute QPS.
|
||||
|
||||
Reproduce:
|
||||
```bash
|
||||
cd v2 && cargo test -p wifi-densepose-ruvector --no-default-features --release \
|
||||
ann_bench_report -- --nocapture
|
||||
# Larger N: ANN_BENCH_N=50000 cargo test ... --release ann_bench_report -- --nocapture
|
||||
cargo bench -p wifi-densepose-ruvector --bench ann_bench
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 1. Context
|
||||
|
||||
The ruvector crate's retrieval path — AETHER re-ID hot-cache (ADR-024), the `sketch.rs` 1-bit prefilter (ADR-084), room fingerprinting — is, at its core, an **approximate nearest-neighbour (ANN)** problem: dense float embedding in, top-K similar ids out. But **the crate had no graph index**. Every `topk` was either a linear scan (`O(N·d)` per query) or a 1-bit Hamming prefilter over a linear scan. That is `O(N)` per query and does not scale.
|
||||
|
||||
[ADR-156 §5 #1](ADR-156-ruvector-fusion-beyond-sota.md) graded **SymphonyQG** (SIGMOD 2025) the **lead beyond-SOTA ANN candidate**, citing the source's claim of **3.5–17× QPS over HNSW at equal recall**, but marked it **CLAIMED**:
|
||||
|
||||
> *"author-measured; **not reproduced on our hardware** — reproduction is future work."*
|
||||
|
||||
And ADR-156 §8 was blunt about *why* it could not be reproduced: **there was no HNSW baseline to compare against.** You cannot measure a ratio against a baseline that does not exist. This ADR builds that missing baseline, builds the quantized variant that tests the direction of the SymphonyQG bet, and measures the real ratio.
|
||||
|
||||
---
|
||||
|
||||
## 2. Decision
|
||||
|
||||
1. Add a correct, dependency-free **float HNSW** graph index (`hnsw.rs`): the real Malkov & Yashunin (TPAMI 2018) algorithm — multi-layer navigable small-world graph, `ef_construction` / `ef_search`, the Algorithm-4 neighbour-selection heuristic, seeded-deterministic level assignment, L2 + cosine. This is the **baseline** ADR-156 said was missing.
|
||||
2. Add a **SymphonyQG-style quantized-traversal variant** (`hnsw_quantized.rs`): the *same* graph (same seed, same structure), but the beam search scores candidates with a **cheap 1-bit Hamming distance** over the RaBitQ Pass-2 rotated sign code (reusing `rotation.rs` + the sign-quantization of `sketch.rs`), then **exact-float reranks** the final candidate set. This is the SymphonyQG bet — cheaper per-node scoring, recovered by a final exact rerank.
|
||||
3. **Measure** linear vs float-HNSW vs quantized-HNSW (recall@10, QPS, equal-recall ratios) on one deterministic planted-cluster fixture, and record the honest verdict against the SymphonyQG 3.5–17× claim.
|
||||
|
||||
### Why 1-bit Hamming for the quantized traversal
|
||||
|
||||
The crate already had the exact pieces SymphonyQG fuses: a deterministic orthogonal rotation (`rotation.rs`, RaBitQ Pass-2) and sign-quantization (`sketch.rs`). A 1-bit code compares by POPCNT Hamming — a few machine words, no per-dimension float work — so it is the cheapest possible traversal score and the most direct test of "can a quantized score keep the beam on the right path." The cost (measured below): the 1-bit code is a *coarse* angle proxy (ADR-156 §10 measured ~46% strict-K coverage for sign-only), and that coarseness is what limits recall here.
|
||||
|
||||
---
|
||||
|
||||
## 3. Design
|
||||
|
||||
### 3.1 `hnsw.rs` — float HNSW (the baseline)
|
||||
|
||||
- **Graph.** `links[id][layer]` adjacency; layer 0 holds every node, higher layers exponentially sparser. `m_max` is `2·M` on layer 0, `M` above (the paper's asymmetric degree cap).
|
||||
- **Insert.** Greedy-descend the upper layers to a good entry point, then for each layer from the node's level down to 0: `search_layer` for `ef_construction` candidates, `select_neighbours` (Algorithm 4 — keep a candidate only if it is closer to the new node than to any already-selected neighbour, giving diverse navigable edges), wire bidirectional edges, re-prune any neighbour that overflows `m_max`. The node is pushed into the arrays **before** wiring so every `links[*]` index is valid mid-insert (§4 — the bug the gate caught).
|
||||
- **Search.** Greedy-descend layers `>0`, then best-first beam search of width `ef` on layer 0; return the closest `k`. Iterative (explicit heaps + visited set) — **no recursion**, bounded by the beam and the visited set.
|
||||
- **Determinism.** Level assignment is the only randomness and is driven by a **seeded SplitMix64** (the exact pattern from `rotation.rs`) — never `Date::now`/OS RNG/unseeded `rand`. Same `(seed, params, insertion order)` ⇒ bit-identical graph and search (pinned by `hnsw_is_deterministic_for_seed`).
|
||||
- **Robustness.** Empty index, `k==0`, `k>n`, single node, zero-dim, ragged query, `ef<k` all return cleanly — pinned by `*_no_panic` tests.
|
||||
|
||||
### 3.2 `hnsw_quantized.rs` — the SymphonyQG-style variant
|
||||
|
||||
Same graph as the float index (identical seed/structure — the **only** variable is the scoring), plus a per-node `ceil(D/8)`-byte 1-bit Pass-2 sign code (`D = next_pow2(dim)`). `search_quantized(query, k, ef, rerank)`:
|
||||
1. Encode the query to its 1-bit code (one rotation + sign pack).
|
||||
2. Greedy-descend + beam-search the graph scoring every visited node by **POPCNT Hamming** (query-code XOR node-code) — no per-dim float work.
|
||||
3. **Exact-float rerank** the top `rerank` Hamming candidates with the true L2/cosine metric, return the best `k`.
|
||||
|
||||
### 3.3 Security / robustness
|
||||
|
||||
Both indices: bounded **iterative** traversal (no unbounded recursion), no panic on empty/degenerate/ragged/zero-dim input (the metric compares over the shorter prefix; zero-norm cosine returns max distance, not NaN). The 1-bit encode handles padded dims via the existing `Rotation::apply_padded`.
|
||||
|
||||
---
|
||||
|
||||
## 4. The bug the correctness gate caught (disclosed, not hidden)
|
||||
|
||||
The first run of the recall@10 gate **panicked**: `index out of bounds: the len is 33 but the index is 33` in `search_layer`. Root cause: `insert` wired bidirectional edges (`links[nbr][l].push(id)`) **before** pushing the new node's own `links[id]` row into the array. A later traversal step in the *same* insert could hop to a neighbour that now pointed at `id` and read `links[id]` — which did not exist yet. Fix: push the node (with empty per-layer link lists) into `vectors`/`links`/`levels` **up front**, then wire edges into its existing slot. The new node has no incoming edges and empty outgoing lists until wiring, so it is unreachable by the searches that run first — pushing early is safe and keeps every index valid. This is exactly why the recall gate exists: a silent low-recall graph and an out-of-bounds panic are both "slop" the gate forces into the open.
|
||||
|
||||
---
|
||||
|
||||
## 5. The SymphonyQG claim being tested
|
||||
|
||||
| Source | Claim | Grade (before this ADR) |
|
||||
|--------|-------|-------------------------|
|
||||
| SymphonyQG, SIGMOD 2025 | **3.5–17× QPS over HNSW at equal recall**, via quantization unified with graph traversal, pure-CPU/edge-portable | **CLAIMED** — author-measured, *not reproduced on our hardware (no HNSW baseline existed)* |
|
||||
|
||||
The bet: a quantized traversal score is cheap enough — and accurate enough to keep the beam on-path — that you pay far less per visited node and recover the small recall loss with a final exact rerank.
|
||||
|
||||
---
|
||||
|
||||
## 6. MEASURED results
|
||||
|
||||
Fixture: planted-cluster synthetic, **dim=128, N=10,000, 64 clusters, 200 queries, K=10, noise=0.35**, L2 metric, `M=16`, `ef_construction=200`. Graph seed `0x6261524741484E53`, rotation seed `0x5EEDC0DE12345678`. `--release`, warm wall-clock on the test machine. (The fixture and both indices are shared by the criterion bench.)
|
||||
|
||||
| Method | recall@10 | QPS | latency (µs) |
|
||||
|--------|-----------|-----|--------------|
|
||||
| **linear scan (brute force)** | 1.0000 | 1,022 | 978 |
|
||||
| **float-HNSW** ef=16 | 0.9945 | **25,744** | 39 |
|
||||
| float-HNSW ef=32 | 0.9990 | 21,470 | 47 |
|
||||
| float-HNSW ef=64 | 1.0000 | 18,779 | 53 |
|
||||
| float-HNSW ef=128 | 1.0000 | 12,722 | 79 |
|
||||
| float-HNSW ef=256 | 1.0000 | 5,742 | 174 |
|
||||
| quant-HNSW ef=32 rr=20 | 0.1620 | 30,005 | 33 |
|
||||
| quant-HNSW ef=32 rr=100 | 0.2615 | 36,388 | 28 |
|
||||
| quant-HNSW ef=64 rr=100 | 0.4865 | 20,603 | 49 |
|
||||
| quant-HNSW ef=128 rr=100 | 0.6785 | 13,718 | 73 |
|
||||
| quant-HNSW ef=256 rr=100 | **0.7380** | 6,578 | 152 |
|
||||
|
||||
### Equal-recall QPS ratios
|
||||
|
||||
| Target recall | Fastest float-HNSW | Fastest quant-HNSW meeting it | quant/float | float/linear |
|
||||
|---------------|--------------------|-------------------------------|-------------|--------------|
|
||||
| ≥ 0.90 | ef=16 → 25,744 QPS | **none** (best quant recall = 0.738) | — | **25.19×** |
|
||||
| ≥ 0.95 | ef=16 → 25,744 QPS | **none** | — | **25.19×** |
|
||||
| ≥ 0.99 | ef=16 → 25,744 QPS | **none** | — | **25.19×** |
|
||||
|
||||
---
|
||||
|
||||
## 7. Honest verdict
|
||||
|
||||
**The HNSW baseline is a decisive win over linear scan: ~25× QPS at recall ≥ 0.99** (ef=16: 0.9945 recall, 25,744 QPS vs linear 1,022 QPS). The correctness gate (recall@10 ≥ 0.95 vs brute force, both L2 and cosine) holds. This is the baseline ADR-156 §5 #1 said did not exist — it now does.
|
||||
|
||||
**The SymphonyQG-style quantized variant does NOT beat float HNSW at our scale — direction REFUTED at N=10k.** The 1-bit Hamming traversal is too coarse: its best achievable recall is **0.738** (ef=256, rr=100), and it never reaches even the 0.90 equal-recall point where a fair QPS comparison could be made. Where the quantized score *is* faster (ef=32: ~30–36k QPS, beating float's 25.7k), its recall collapses to 0.16–0.26 — a meaningless win. There is **no equal-recall operating point** at which quantized is faster, so the SymphonyQG 3.5–17× claim is **not reproduced** by our 1-bit construction here.
|
||||
|
||||
**Why** (so the negative is understood, not just stated):
|
||||
1. The 1-bit sign code is a **coarse angle proxy** — ADR-156 §10 already measured it at ~46% strict-K coverage. Driving graph *traversal* by that coarse score steers the beam onto the wrong nodes, and the exact-float rerank can only recover what the beam actually visited. At N=10k, near-neighbours have nearly-identical sign codes, so Hamming cannot separate them.
|
||||
2. At this scale **float distance is already cheap**: one 128-d L2 is a handful of µs; the per-node float compute the quantization saves is small relative to the recall it costs. SymphonyQG's win shows up at **much larger N** (millions), where (a) the float-distance fraction of query time dominates and (b) their *multi-bit RaBitQ-fused* code (not our 1-bit sign code) keeps recall high. **Expected crossover: large N + a higher-bit code.** ADR-156 §10 already measured that a ≤4-bit code reaches ~74% strict coverage vs 1-bit's ~46%, so a multi-bit traversal score is the obvious next lever — deferred, not claimed.
|
||||
|
||||
**Caveat (stated plainly):** this is **our** HNSW + **our** 1-bit quantization, not SymphonyQG's system. We tested the *direction* of the claim ("does quantized traversal + rerank beat float HNSW at equal recall?") on our hardware/data and got a **measured no at N=10k**. That neither confirms nor refutes SymphonyQG's own published numbers on their system/scale — it refutes the direction *for our construction at our scale*, and identifies the two levers (scale, code bit-depth) a real reproduction would need.
|
||||
|
||||
---
|
||||
|
||||
## 8. Validation
|
||||
|
||||
- **`cd v2 && cargo test -p wifi-densepose-ruvector --no-default-features --lib`** — **151 passed / 0 failed** (was 131; +20 new tests: 10 `hnsw`, 7 `hnsw_quantized`, 3 `ann_measure`).
|
||||
- **`cargo test --workspace --no-default-features`** — GREEN (see §10 for the count).
|
||||
- **Correctness gate verified to bite:** the recall@10 gate **panicked** on the first (buggy) insert path (§4); after the fix it passes at 0.99+ recall (L2 and cosine).
|
||||
- **`cargo test -p wifi-densepose-ruvector --no-default-features --release ann_bench_report -- --nocapture`** — prints the §6 table; the numbers above are copied verbatim from that run.
|
||||
- **`cargo bench -p wifi-densepose-ruvector --bench ann_bench`** — compiles and runs the same fixture through criterion.
|
||||
- **`python archive/v1/data/proof/verify.py`** — **VERDICT: PASS** (the Rust ANN work is independent of the Python signal-proof pipeline; hash unchanged).
|
||||
|
||||
---
|
||||
|
||||
## 9. Consequences
|
||||
|
||||
**Positive.** ruvector now has a real, deterministic, pure-Rust HNSW graph index (25× over linear scan at high recall) usable by the AETHER re-ID / sketch-prefilter path — the ANN substrate ADR-156 §5 #1 wanted. The SymphonyQG claim is no longer CLAIMED-only: we built the missing baseline and **measured** the direction, with the bug-caught-by-the-gate disclosed.
|
||||
|
||||
**Negative / honest.** The 1-bit quantized variant is **not** an equal-recall QPS win at our scale; it is shipped as a measured experiment with a clearly-stated ceiling, not as a recommended default. Anyone reaching for it must read §7.
|
||||
|
||||
**Deferred (not silently dropped).**
|
||||
- **Multi-bit / RaBitQ-estimator traversal score.** Replace 1-bit Hamming traversal with a ≤4-bit code or the `estimator.rs` unbiased rescale (ADR-156 §10/§11) — the lever most likely to lift quantized recall to the equal-recall regime.
|
||||
- **Large-N crossover measurement.** Re-run §6 at N=100k–1M (`ANN_BENCH_N`) to find where quantization's per-node saving starts to dominate.
|
||||
- **Wiring HNSW into the live re-ID path** (AETHER hot-cache / sketch prefilter) behind a flag.
|
||||
|
||||
---
|
||||
|
||||
## 10. What changed, file by file
|
||||
|
||||
- `hnsw.rs` (new) — float HNSW: graph, seeded-deterministic level assignment, Algorithm-2 beam search, Algorithm-4 neighbour selection, L2/cosine, brute-force ground truth, full degenerate-case guards; 10 tests incl. the recall@10 correctness gate (L2 + cosine) and determinism. The insert-order bug fix (§4).
|
||||
- `hnsw_quantized.rs` (new) — SymphonyQG-style quantized-traversal index over the shared graph: 1-bit Pass-2 code per node, Hamming-scored greedy + beam, exact-float rerank; 7 tests incl. the rerank-recall gate and determinism.
|
||||
- `ann_measure.rs` (new) — shared deterministic fixture + recall/QPS measurement for linear / float-HNSW / quant-HNSW, the `ann_bench_report` test (the §6 source of truth), `ANN_BENCH_N` override.
|
||||
- `benches/ann_bench.rs` (new) + `Cargo.toml` `[[bench]]` — criterion bench over the same fixture/indices.
|
||||
- `lib.rs` — `pub mod hnsw / hnsw_quantized / ann_measure`; re-export `HnswIndex`, `HnswParams`, `Metric`, `QuantizedHnswIndex`.
|
||||
- `ADR-156-ruvector-fusion-beyond-sota.md` §5 #1 + §8 backlog — SymphonyQG regraded **CLAIMED → MEASURED-direction-tested (refuted at N=10k for our 1-bit construction)**, pointing here.
|
||||
- `CHANGELOG.md` — `[Unreleased]` entry.
|
||||
@@ -47,3 +47,7 @@ harness = false
|
||||
[[bench]]
|
||||
name = "fusion_bench"
|
||||
harness = false
|
||||
|
||||
[[bench]]
|
||||
name = "ann_bench"
|
||||
harness = false
|
||||
|
||||
@@ -0,0 +1,74 @@
|
||||
//! Criterion bench for the ADR-261 graph-ANN index: linear scan vs float HNSW
|
||||
//! vs quantized HNSW, on the shared `ann_measure` fixture.
|
||||
//!
|
||||
//! The authoritative recall/QPS numbers in ADR-261 come from the
|
||||
//! `--no-default-features --release` test report
|
||||
//! (`ann_bench_report` in `src/ann_measure.rs`), which is deterministic and
|
||||
//! gate-runnable. This criterion bench times the same operations through the
|
||||
//! criterion harness for stable per-op medians:
|
||||
//!
|
||||
//! ```text
|
||||
//! cargo bench -p wifi-densepose-ruvector --bench ann_bench
|
||||
//! ```
|
||||
//!
|
||||
//! Build is excluded from the timed region (done once in setup); only the query
|
||||
//! path is measured. The fixture and both indices are identical to the report's,
|
||||
//! so the bench and the report can never measure different graphs.
|
||||
|
||||
use criterion::{black_box, criterion_group, criterion_main, Criterion};
|
||||
use wifi_densepose_ruvector::ann_measure::{build_indices, queries, AnnBenchParams};
|
||||
|
||||
fn bench_ann(c: &mut Criterion) {
|
||||
// Modest N so the bench builds quickly; the report covers the larger N.
|
||||
let p = AnnBenchParams::default_fixture(10_000);
|
||||
let (float_idx, quant_idx, _v) = build_indices(p);
|
||||
let qs = queries(p);
|
||||
let k = p.k;
|
||||
|
||||
let mut group = c.benchmark_group("ann_query");
|
||||
group.sample_size(20);
|
||||
|
||||
// Linear scan (brute force) — the no-index baseline.
|
||||
group.bench_function("linear_scan", |b| {
|
||||
b.iter(|| {
|
||||
let mut sink = 0u64;
|
||||
for q in &qs {
|
||||
sink = sink.wrapping_add(float_idx.brute_force(black_box(q), k).len() as u64);
|
||||
}
|
||||
black_box(sink)
|
||||
})
|
||||
});
|
||||
|
||||
// Float HNSW at a mid beam width.
|
||||
for &ef in &[64usize, 128] {
|
||||
group.bench_function(format!("float_hnsw_ef{ef}"), |b| {
|
||||
b.iter(|| {
|
||||
let mut sink = 0u64;
|
||||
for q in &qs {
|
||||
sink = sink.wrapping_add(float_idx.search(black_box(q), k, ef).len() as u64);
|
||||
}
|
||||
black_box(sink)
|
||||
})
|
||||
});
|
||||
}
|
||||
|
||||
// Quantized HNSW at matched beam widths + rerank.
|
||||
for &ef in &[64usize, 128] {
|
||||
let rr = k * 5;
|
||||
group.bench_function(format!("quant_hnsw_ef{ef}_rr{rr}"), |b| {
|
||||
b.iter(|| {
|
||||
let mut sink = 0u64;
|
||||
for q in &qs {
|
||||
sink = sink
|
||||
.wrapping_add(quant_idx.search_quantized(black_box(q), k, ef, rr).len() as u64);
|
||||
}
|
||||
black_box(sink)
|
||||
})
|
||||
});
|
||||
}
|
||||
|
||||
group.finish();
|
||||
}
|
||||
|
||||
criterion_group!(benches, bench_ann);
|
||||
criterion_main!(benches);
|
||||
@@ -0,0 +1,400 @@
|
||||
//! Deterministic, `--no-default-features`-runnable **ANN benchmark measurement**
|
||||
//! for ADR-261 — the single source of truth for the QPS/recall numbers the ADR
|
||||
//! quotes for **linear scan**, **float HNSW**, and **quantized HNSW**.
|
||||
//!
|
||||
//! Both the criterion bench (`benches/ann_bench.rs`) and the in-crate report test
|
||||
//! ([`tests::ann_bench_report`]) call into here, so they can never silently
|
||||
//! measure different things. The numbers in ADR-261 §6 come from running:
|
||||
//!
|
||||
//! ```text
|
||||
//! cd v2 && cargo test -p wifi-densepose-ruvector --no-default-features --release \
|
||||
//! ann_bench_report -- --nocapture
|
||||
//! ```
|
||||
//!
|
||||
//! # What is measured, and the honesty contract
|
||||
//!
|
||||
//! On one fixed planted-cluster fixture (documented dim/N/K/seed), for each
|
||||
//! method we measure:
|
||||
//! - **recall@10** vs the brute-force exact top-10 (the ground truth),
|
||||
//! - **QPS** = queries / total wall-clock query time (warm; build excluded),
|
||||
//! at matched recall operating points found by sweeping `ef` (HNSW) and
|
||||
//! `(ef, rerank)` (quantized).
|
||||
//!
|
||||
//! The reported **ratio** is the claim, not the absolute QPS (which is
|
||||
//! machine-specific). We do **not** tune the quantized path to manufacture a
|
||||
//! win: if at our scale quantized does not beat float HNSW, the report says so
|
||||
//! and the ADR records the honest negative + the expected larger-N crossover.
|
||||
|
||||
use std::collections::HashSet;
|
||||
use std::time::Instant;
|
||||
|
||||
use crate::hnsw::{HnswIndex, HnswParams, Metric};
|
||||
use crate::hnsw_quantized::QuantizedHnswIndex;
|
||||
|
||||
/// SplitMix64 — the crate-wide deterministic PRNG (mirrors `coverage.rs`).
|
||||
#[inline]
|
||||
fn split_mix64(state: &mut u64) -> u64 {
|
||||
*state = state.wrapping_add(0x9E37_79B9_7F4A_7C15);
|
||||
let mut z = *state;
|
||||
z = (z ^ (z >> 30)).wrapping_mul(0xBF58_476D_1CE4_E5B9);
|
||||
z = (z ^ (z >> 27)).wrapping_mul(0x94D0_49BB_1331_11EB);
|
||||
z ^ (z >> 31)
|
||||
}
|
||||
#[inline]
|
||||
fn unif01(state: &mut u64) -> f32 {
|
||||
((split_mix64(state) >> 40) as f32) / ((1u64 << 24) as f32)
|
||||
}
|
||||
#[inline]
|
||||
fn gauss(state: &mut u64) -> f32 {
|
||||
let u1 = unif01(state).max(1e-7);
|
||||
let u2 = unif01(state);
|
||||
(-2.0 * u1.ln()).sqrt() * (std::f32::consts::TAU * u2).cos()
|
||||
}
|
||||
|
||||
/// ANN benchmark fixture parameters, documented in the ADR-261 report.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct AnnBenchParams {
|
||||
/// Embedding dimension.
|
||||
pub dim: usize,
|
||||
/// Number of indexed vectors (N).
|
||||
pub n: usize,
|
||||
/// Number of planted clusters (near-neighbour structure).
|
||||
pub clusters: usize,
|
||||
/// Number of queries timed.
|
||||
pub n_queries: usize,
|
||||
/// Top-K.
|
||||
pub k: usize,
|
||||
/// Intra-cluster Gaussian jitter.
|
||||
pub noise: f32,
|
||||
/// Master fixture seed.
|
||||
pub seed: u64,
|
||||
/// Graph construction/level seed.
|
||||
pub graph_seed: u64,
|
||||
/// Rotation seed for the quantized 1-bit codes.
|
||||
pub rot_seed: u64,
|
||||
}
|
||||
|
||||
impl AnnBenchParams {
|
||||
/// The default ADR-261 fixture: AETHER-shape 128-d, planted clusters.
|
||||
pub fn default_fixture(n: usize) -> Self {
|
||||
Self {
|
||||
dim: 128,
|
||||
n,
|
||||
clusters: 64,
|
||||
n_queries: 200,
|
||||
k: 10,
|
||||
noise: 0.35,
|
||||
seed: 0xADADADAD_0000_0261,
|
||||
graph_seed: 0x6261_5247_4148_4E53,
|
||||
rot_seed: 0x5EED_C0DE_1234_5678,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// The fixture vectors for `p` (deterministic planted clusters).
|
||||
pub fn fixture(p: AnnBenchParams) -> Vec<Vec<f32>> {
|
||||
let centres: Vec<Vec<f32>> = (0..p.clusters)
|
||||
.map(|c| {
|
||||
let mut s = p.seed ^ (0xC0FFEE_u64.wrapping_mul(c as u64 + 1));
|
||||
(0..p.dim).map(|_| gauss(&mut s) * 3.0).collect()
|
||||
})
|
||||
.collect();
|
||||
(0..p.n)
|
||||
.map(|i| {
|
||||
let c = i % p.clusters;
|
||||
let mut s = p.seed ^ (i as u64).wrapping_mul(0x9E37);
|
||||
(0..p.dim)
|
||||
.map(|d| centres[c][d] + gauss(&mut s) * p.noise)
|
||||
.collect()
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
|
||||
/// The timed query set for `p` (drawn from the same clusters, disjoint seed).
|
||||
pub fn queries(p: AnnBenchParams) -> Vec<Vec<f32>> {
|
||||
let centres: Vec<Vec<f32>> = (0..p.clusters)
|
||||
.map(|c| {
|
||||
let mut s = p.seed ^ (0xC0FFEE_u64.wrapping_mul(c as u64 + 1));
|
||||
(0..p.dim).map(|_| gauss(&mut s) * 3.0).collect()
|
||||
})
|
||||
.collect();
|
||||
(0..p.n_queries)
|
||||
.map(|q| {
|
||||
let c = q % p.clusters;
|
||||
let mut s = p.seed ^ 0xDEAD_0000_0000 ^ (q as u64).wrapping_mul(0x2545_F491);
|
||||
(0..p.dim)
|
||||
.map(|d| centres[c][d] + gauss(&mut s) * p.noise)
|
||||
.collect()
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
|
||||
/// Per-method measurement: recall@K and QPS.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct MethodResult {
|
||||
/// Mean recall@K vs brute-force ground truth.
|
||||
pub recall: f64,
|
||||
/// Queries per second (warm wall-clock).
|
||||
pub qps: f64,
|
||||
/// Mean query latency in microseconds.
|
||||
pub latency_us: f64,
|
||||
}
|
||||
|
||||
/// Ground-truth brute-force top-K id sets for every query (computed once).
|
||||
/// Public so the criterion bench and the report test share one definition.
|
||||
pub fn ground_truth(idx: &HnswIndex, queries: &[Vec<f32>], k: usize) -> Vec<HashSet<u32>> {
|
||||
queries
|
||||
.iter()
|
||||
.map(|q| idx.brute_force(q, k).into_iter().map(|(id, _)| id).collect())
|
||||
.collect()
|
||||
}
|
||||
|
||||
/// Measure **linear scan** (brute force): recall is 1.0 by definition; QPS is the
|
||||
/// timed exact scan. This is the no-index baseline.
|
||||
pub fn measure_linear(
|
||||
idx: &HnswIndex,
|
||||
queries: &[Vec<f32>],
|
||||
truth: &[HashSet<u32>],
|
||||
k: usize,
|
||||
) -> MethodResult {
|
||||
let mut recall_acc = 0.0f64;
|
||||
let start = Instant::now();
|
||||
let mut sink = 0u64;
|
||||
for (qi, q) in queries.iter().enumerate() {
|
||||
let got = idx.brute_force(q, k);
|
||||
let hit = got.iter().filter(|(id, _)| truth[qi].contains(id)).count();
|
||||
recall_acc += hit as f64 / k as f64;
|
||||
sink = sink.wrapping_add(got.len() as u64);
|
||||
}
|
||||
let elapsed = start.elapsed().as_secs_f64();
|
||||
std::hint::black_box(sink);
|
||||
MethodResult {
|
||||
recall: recall_acc / queries.len() as f64,
|
||||
qps: queries.len() as f64 / elapsed,
|
||||
latency_us: elapsed / queries.len() as f64 * 1e6,
|
||||
}
|
||||
}
|
||||
|
||||
/// Measure **float HNSW** at a given beam width `ef`.
|
||||
pub fn measure_float_hnsw(
|
||||
idx: &HnswIndex,
|
||||
queries: &[Vec<f32>],
|
||||
truth: &[HashSet<u32>],
|
||||
k: usize,
|
||||
ef: usize,
|
||||
) -> MethodResult {
|
||||
let mut recall_acc = 0.0f64;
|
||||
let start = Instant::now();
|
||||
let mut sink = 0u64;
|
||||
for (qi, q) in queries.iter().enumerate() {
|
||||
let got = idx.search(q, k, ef);
|
||||
let hit = got.iter().filter(|(id, _)| truth[qi].contains(id)).count();
|
||||
recall_acc += hit as f64 / k as f64;
|
||||
sink = sink.wrapping_add(got.len() as u64);
|
||||
}
|
||||
let elapsed = start.elapsed().as_secs_f64();
|
||||
std::hint::black_box(sink);
|
||||
MethodResult {
|
||||
recall: recall_acc / queries.len() as f64,
|
||||
qps: queries.len() as f64 / elapsed,
|
||||
latency_us: elapsed / queries.len() as f64 * 1e6,
|
||||
}
|
||||
}
|
||||
|
||||
/// Measure **quantized HNSW** at a given `(ef, rerank)`.
|
||||
pub fn measure_quantized_hnsw(
|
||||
qidx: &QuantizedHnswIndex,
|
||||
queries: &[Vec<f32>],
|
||||
truth: &[HashSet<u32>],
|
||||
k: usize,
|
||||
ef: usize,
|
||||
rerank: usize,
|
||||
) -> MethodResult {
|
||||
let mut recall_acc = 0.0f64;
|
||||
let start = Instant::now();
|
||||
let mut sink = 0u64;
|
||||
for (qi, q) in queries.iter().enumerate() {
|
||||
let got = qidx.search_quantized(q, k, ef, rerank);
|
||||
let hit = got.iter().filter(|(id, _)| truth[qi].contains(id)).count();
|
||||
recall_acc += hit as f64 / k as f64;
|
||||
sink = sink.wrapping_add(got.len() as u64);
|
||||
}
|
||||
let elapsed = start.elapsed().as_secs_f64();
|
||||
std::hint::black_box(sink);
|
||||
MethodResult {
|
||||
recall: recall_acc / queries.len() as f64,
|
||||
qps: queries.len() as f64 / elapsed,
|
||||
latency_us: elapsed / queries.len() as f64 * 1e6,
|
||||
}
|
||||
}
|
||||
|
||||
/// Build both indices for `p` (shared insertion order + graph seed so the float
|
||||
/// and quantized graphs are identical — the only variable is scoring).
|
||||
pub fn build_indices(p: AnnBenchParams) -> (HnswIndex, QuantizedHnswIndex, Vec<Vec<f32>>) {
|
||||
let vectors = fixture(p);
|
||||
let params = HnswParams {
|
||||
m: 16,
|
||||
ef_construction: 200,
|
||||
ef_search: 64,
|
||||
seed: p.graph_seed,
|
||||
};
|
||||
let mut float_idx = HnswIndex::new(p.dim, Metric::L2, params);
|
||||
for v in &vectors {
|
||||
float_idx.insert(v);
|
||||
}
|
||||
let quant_idx =
|
||||
QuantizedHnswIndex::build(&vectors, p.dim, Metric::L2, params, p.rot_seed, p.k * 4);
|
||||
(float_idx, quant_idx, vectors)
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn fixture_and_queries_are_deterministic() {
|
||||
let p = AnnBenchParams::default_fixture(500);
|
||||
assert_eq!(fixture(p), fixture(p));
|
||||
assert_eq!(queries(p), queries(p));
|
||||
let p2 = AnnBenchParams {
|
||||
seed: p.seed ^ 1,
|
||||
..p
|
||||
};
|
||||
assert_ne!(fixture(p)[0], fixture(p2)[0]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn linear_recall_is_one() {
|
||||
// Linear scan IS the ground truth, so recall must be exactly 1.0.
|
||||
let p = AnnBenchParams::default_fixture(800);
|
||||
let (float_idx, _q, _v) = build_indices(p);
|
||||
let qs = queries(p);
|
||||
let truth = ground_truth(&float_idx, &qs, p.k);
|
||||
let r = measure_linear(&float_idx, &qs, &truth, p.k);
|
||||
assert!((r.recall - 1.0).abs() < 1e-9, "linear recall {} != 1.0", r.recall);
|
||||
assert!(r.qps > 0.0);
|
||||
}
|
||||
|
||||
/// The ADR-261 measurement report. Prints the linear / float-HNSW /
|
||||
/// quantized-HNSW recall@10 + QPS table and the QPS ratios at matched recall.
|
||||
/// Run with `--release --nocapture` for the numbers the ADR quotes.
|
||||
#[test]
|
||||
fn ann_bench_report() {
|
||||
// N here is the small/CI-friendly default so the standard (debug) test
|
||||
// gate stays fast; the ADR's headline numbers are taken at the larger N
|
||||
// under --release (documented in the ADR with the exact command). This
|
||||
// test asserts only structural invariants so it is gate-safe at any N.
|
||||
let n: usize = std::env::var("ANN_BENCH_N")
|
||||
.ok()
|
||||
.and_then(|s| s.parse().ok())
|
||||
.unwrap_or(10_000);
|
||||
let p = AnnBenchParams::default_fixture(n);
|
||||
let (float_idx, quant_idx, _v) = build_indices(p);
|
||||
let qs = queries(p);
|
||||
let truth = ground_truth(&float_idx, &qs, p.k);
|
||||
|
||||
println!("\n=== ADR-261 ANN benchmark (planted-cluster synthetic) ===");
|
||||
println!(
|
||||
"dim={} N={} clusters={} queries={} K={} noise={} graph_seed=0x{:X} rot_seed=0x{:X}",
|
||||
p.dim, p.n, p.clusters, p.n_queries, p.k, p.noise, p.graph_seed, p.rot_seed
|
||||
);
|
||||
println!("metric=L2 M=16 ef_construction=200 (debug build unless --release)");
|
||||
println!(
|
||||
"{:<28} {:>9} {:>12} {:>12}",
|
||||
"method", "recall@10", "QPS", "lat(us)"
|
||||
);
|
||||
|
||||
let lin = measure_linear(&float_idx, &qs, &truth, p.k);
|
||||
println!(
|
||||
"{:<28} {:>8.4} {:>12.1} {:>12.1}",
|
||||
"linear scan (brute)", lin.recall, lin.qps, lin.latency_us
|
||||
);
|
||||
|
||||
// Float HNSW across an ef sweep.
|
||||
let mut float_ops: Vec<(usize, MethodResult)> = Vec::new();
|
||||
for &ef in &[16usize, 32, 64, 128, 256] {
|
||||
let r = measure_float_hnsw(&float_idx, &qs, &truth, p.k, ef);
|
||||
println!(
|
||||
"{:<28} {:>8.4} {:>12.1} {:>12.1}",
|
||||
format!("float-HNSW ef={ef}"),
|
||||
r.recall,
|
||||
r.qps,
|
||||
r.latency_us
|
||||
);
|
||||
float_ops.push((ef, r));
|
||||
}
|
||||
|
||||
// Quantized HNSW across (ef, rerank) sweep.
|
||||
let mut quant_ops: Vec<((usize, usize), MethodResult)> = Vec::new();
|
||||
for &ef in &[32usize, 64, 128, 256] {
|
||||
for &rr in &[p.k * 2, p.k * 5, p.k * 10] {
|
||||
let r = measure_quantized_hnsw(&quant_idx, &qs, &truth, p.k, ef, rr);
|
||||
println!(
|
||||
"{:<28} {:>8.4} {:>12.1} {:>12.1}",
|
||||
format!("quant-HNSW ef={ef} rr={rr}"),
|
||||
r.recall,
|
||||
r.qps,
|
||||
r.latency_us
|
||||
);
|
||||
quant_ops.push(((ef, rr), r));
|
||||
}
|
||||
}
|
||||
|
||||
// Equal-recall comparison: pick, for a target recall, the FASTEST op of
|
||||
// each method that meets it, then report the QPS ratios.
|
||||
println!("\n--- equal-recall QPS ratios ---");
|
||||
for &target in &[0.90f64, 0.95, 0.99] {
|
||||
let best_float = float_ops
|
||||
.iter()
|
||||
.filter(|(_, r)| r.recall >= target)
|
||||
.max_by(|a, b| a.1.qps.partial_cmp(&b.1.qps).unwrap());
|
||||
let best_quant = quant_ops
|
||||
.iter()
|
||||
.filter(|(_, r)| r.recall >= target)
|
||||
.max_by(|a, b| a.1.qps.partial_cmp(&b.1.qps).unwrap());
|
||||
match (best_float, best_quant) {
|
||||
(Some((fef, fr)), Some(((qef, qrr), qr))) => {
|
||||
let ratio = qr.qps / fr.qps;
|
||||
let hnsw_vs_lin = fr.qps / lin.qps;
|
||||
println!(
|
||||
"recall>={:.2}: float ef={} {:.0} QPS | quant ef={} rr={} {:.0} QPS | quant/float={:.2}x | float/linear={:.2}x",
|
||||
target, fef, fr.qps, qef, qrr, qr.qps, ratio, hnsw_vs_lin
|
||||
);
|
||||
}
|
||||
(Some((fef, fr)), None) => {
|
||||
let hnsw_vs_lin = fr.qps / lin.qps;
|
||||
println!(
|
||||
"recall>={:.2}: float ef={} {:.0} QPS | quant: NO op met this recall | float/linear={:.2}x",
|
||||
target, fef, fr.qps, hnsw_vs_lin
|
||||
);
|
||||
}
|
||||
_ => {
|
||||
println!("recall>={:.2}: neither method met this recall at the swept ops", target);
|
||||
}
|
||||
}
|
||||
}
|
||||
println!("=========================================================\n");
|
||||
|
||||
// Structural assertions (gate-safe, any N):
|
||||
// - linear scan is exact,
|
||||
// - the best float-HNSW op clears the correctness gate,
|
||||
// - quantized's best op is at least useful (recall well above random).
|
||||
assert!((lin.recall - 1.0).abs() < 1e-9);
|
||||
let best_float_recall = float_ops.iter().map(|(_, r)| r.recall).fold(0.0, f64::max);
|
||||
assert!(
|
||||
best_float_recall >= 0.95,
|
||||
"best float-HNSW recall {best_float_recall:.4} below 0.95 gate"
|
||||
);
|
||||
let best_quant_recall = quant_ops.iter().map(|(_, r)| r.recall).fold(0.0, f64::max);
|
||||
// Honest floor: the 1-bit Hamming traversal is a COARSE angle proxy, so
|
||||
// at large N its best recall lands well below the float gate (MEASURED
|
||||
// ~0.74 at N=10k — see ADR-261 §6). We assert only that it is clearly
|
||||
// useful (>> random: random top-10 of N=10k is ~0.001), which catches a
|
||||
// fully-broken traversal/rerank without pretending the quantized variant
|
||||
// matches float HNSW. The honest negative IS the result.
|
||||
assert!(
|
||||
best_quant_recall >= 0.30,
|
||||
"best quant-HNSW recall {best_quant_recall:.4} below the 0.30 not-broken floor"
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,826 @@
|
||||
//! A correct, dependency-free **float HNSW** graph-ANN index — ADR-261.
|
||||
//!
|
||||
//! # Why this exists
|
||||
//!
|
||||
//! The ruvector crate's retrieval path (AETHER re-ID hot-cache, the `sketch.rs`
|
||||
//! 1-bit prefilter, room fingerprinting) is, at its core, an **approximate
|
||||
//! nearest-neighbour** problem: dense float embedding in, top-K similar ids out.
|
||||
//! Until now the crate had **no graph index** — every `topk` was a linear scan
|
||||
//! (`O(N·d)` per query) or a 1-bit Hamming prefilter over a linear scan. That is
|
||||
//! fine at the small N the unit fixtures use, but it is `O(N)` per query and does
|
||||
//! not scale.
|
||||
//!
|
||||
//! [ADR-156 §5 #1](../../../../../docs/adr/ADR-156-ruvector-fusion-beyond-sota.md)
|
||||
//! lists **SymphonyQG** (SIGMOD 2025) as the lead beyond-SOTA ANN candidate,
|
||||
//! claiming **3.5–17× QPS over HNSW at equal recall** — but graded that claim
|
||||
//! **CLAIMED**, *"not reproduced on our hardware (no HNSW baseline exists to
|
||||
//! compare against)."* You cannot measure a ratio against a baseline you do not
|
||||
//! have. This module **builds that missing HNSW baseline**; [`crate::hnsw_quantized`]
|
||||
//! builds the quantized-rerank variant that tests the *direction* of the
|
||||
//! SymphonyQG bet. ADR-261 reports the **measured** ratio.
|
||||
//!
|
||||
//! # The algorithm (Malkov & Yashunin, TPAMI 2018)
|
||||
//!
|
||||
//! HNSW = a multi-layer navigable small-world graph. Each inserted point gets a
|
||||
//! random **level** `ℓ` (geometrically distributed, mean `1/ln(M)`); it appears
|
||||
//! in all layers `0..=ℓ`. Layer 0 holds every point; higher layers are
|
||||
//! exponentially sparser "express lanes". A search:
|
||||
//!
|
||||
//! 1. Enters at the top layer's single entry point.
|
||||
//! 2. **Greedy-descends** each layer above 0: repeatedly hop to the neighbour
|
||||
//! closest to the query until no neighbour is closer, then drop a layer.
|
||||
//! 3. At layer 0, runs a **best-first beam search** with beam width `ef`,
|
||||
//! keeping the `ef` closest candidates seen, and returns the closest `k`.
|
||||
//!
|
||||
//! Construction inserts each point by searching for its `ef_construction`
|
||||
//! nearest existing neighbours at each of its layers, then connecting it to a
|
||||
//! pruned subset chosen by the **neighbour-selection heuristic** (Algorithm 4 in
|
||||
//! the paper): prefer neighbours that are closer to the new point than to any
|
||||
//! already-selected neighbour, which keeps the graph navigable (diverse edges)
|
||||
//! instead of clumping all edges toward one cluster.
|
||||
//!
|
||||
//! # Determinism (the proof contract)
|
||||
//!
|
||||
//! Level assignment is the only randomness, and it is driven by a **seeded
|
||||
//! SplitMix64** PRNG (the exact pattern from [`crate::rotation`]) — never
|
||||
//! `Date::now`, an OS RNG, or `rand` without a seed. Two indices built from the
|
||||
//! same `(seed, params, insertion order)` are bit-identical, pinned by
|
||||
//! [`tests::hnsw_is_deterministic_for_seed`]. This matters for reproducible
|
||||
//! benchmarks: the recall/QPS numbers in ADR-261 must be regenerable.
|
||||
//!
|
||||
//! # Robustness (no panic on degenerate input)
|
||||
//!
|
||||
//! Empty index, `k > n`, `k == 0`, a single node, zero-dimension vectors,
|
||||
//! ragged-length queries, and `ef < k` are all handled without panicking —
|
||||
//! pinned by the `*_no_panic` / degenerate tests. Graph traversal is bounded by
|
||||
//! the visited-set and the candidate beam, so there is no unbounded recursion
|
||||
//! (the search is iterative, using explicit heaps).
|
||||
|
||||
use std::cmp::Ordering;
|
||||
use std::collections::{BinaryHeap, HashSet};
|
||||
|
||||
/// Distance metric for the index. Both are computed over `Vec<f32>` with an
|
||||
/// `f64` accumulator for numerical stability on long vectors.
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
|
||||
pub enum Metric {
|
||||
/// Squared euclidean distance `Σ (a_i − b_i)²`. Monotone in euclidean
|
||||
/// distance, so top-K ranking is identical; we skip the sqrt.
|
||||
L2,
|
||||
/// Cosine **distance** `1 − cos(a, b)`. Smaller = more similar. This is
|
||||
/// AETHER's actual angular metric and what the `sketch.rs` sign code
|
||||
/// approximates, so it is the default for ruvector re-ID.
|
||||
Cosine,
|
||||
}
|
||||
|
||||
impl Metric {
|
||||
/// Distance between two equal-length slices under this metric.
|
||||
///
|
||||
/// Ragged lengths are handled charitably (compared over the shorter prefix);
|
||||
/// a degenerate (zero-norm) cosine input yields the maximum cosine distance
|
||||
/// `1.0` rather than a NaN. Never panics.
|
||||
#[inline]
|
||||
pub fn distance(self, a: &[f32], b: &[f32]) -> f32 {
|
||||
let n = a.len().min(b.len());
|
||||
match self {
|
||||
Metric::L2 => {
|
||||
let mut acc = 0.0f64;
|
||||
for i in 0..n {
|
||||
let d = a[i] as f64 - b[i] as f64;
|
||||
acc += d * d;
|
||||
}
|
||||
acc as f32
|
||||
}
|
||||
Metric::Cosine => {
|
||||
let mut dot = 0.0f64;
|
||||
let mut na = 0.0f64;
|
||||
let mut nb = 0.0f64;
|
||||
for i in 0..n {
|
||||
let (x, y) = (a[i] as f64, b[i] as f64);
|
||||
dot += x * y;
|
||||
na += x * x;
|
||||
nb += y * y;
|
||||
}
|
||||
let denom = (na * nb).sqrt();
|
||||
if denom < 1e-12 {
|
||||
1.0
|
||||
} else {
|
||||
(1.0 - dot / denom) as f32
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Construction / search hyper-parameters for an [`HnswIndex`].
|
||||
///
|
||||
/// Defaults follow the paper's recommended starting points (`M = 16`,
|
||||
/// `ef_construction = 200`). `ef_search` is the query-time beam width; larger
|
||||
/// `ef_search` trades QPS for recall — the knob the ADR-261 benchmark sweeps to
|
||||
/// find the equal-recall operating point.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct HnswParams {
|
||||
/// Max neighbours per node on layers ≥ 1. Layer 0 uses `2·M` (`m_max0`),
|
||||
/// the paper's standard asymmetry (the base layer needs higher degree).
|
||||
pub m: usize,
|
||||
/// Candidate list size during construction (`efConstruction`). Larger =
|
||||
/// better-connected graph, slower build.
|
||||
pub ef_construction: usize,
|
||||
/// Default beam width at query time (`ef`). Overridable per-query in
|
||||
/// [`HnswIndex::search`].
|
||||
pub ef_search: usize,
|
||||
/// Seed for the level-assignment PRNG. Fixed ⇒ reproducible graph.
|
||||
pub seed: u64,
|
||||
}
|
||||
|
||||
impl Default for HnswParams {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
m: 16,
|
||||
ef_construction: 200,
|
||||
ef_search: 64,
|
||||
seed: 0x1157_0000_0000_0001u64,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// A min-distance ordering wrapper: a `BinaryHeap<Candidate>` is a **max-heap**,
|
||||
/// so we negate the comparison to make `peek()` the *closest* candidate when we
|
||||
/// want a min-heap, or use it directly for a max-heap of the *farthest*. We keep
|
||||
/// two explicit newtypes to make the intent unmistakable at each call site.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
struct Scored {
|
||||
dist: f32,
|
||||
id: u32,
|
||||
}
|
||||
|
||||
impl PartialEq for Scored {
|
||||
fn eq(&self, other: &Self) -> bool {
|
||||
self.dist == other.dist && self.id == other.id
|
||||
}
|
||||
}
|
||||
impl Eq for Scored {}
|
||||
|
||||
/// Max-heap ordering: larger `dist` is "greater" ⇒ at the top. Ties broken by
|
||||
/// id so the order is total and deterministic.
|
||||
impl Ord for Scored {
|
||||
fn cmp(&self, other: &Self) -> Ordering {
|
||||
self.dist
|
||||
.partial_cmp(&other.dist)
|
||||
.unwrap_or(Ordering::Equal)
|
||||
.then(self.id.cmp(&other.id))
|
||||
}
|
||||
}
|
||||
impl PartialOrd for Scored {
|
||||
fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
|
||||
Some(self.cmp(other))
|
||||
}
|
||||
}
|
||||
|
||||
/// `Reverse`-equivalent for a min-heap (closest at top) without pulling in
|
||||
/// `std::cmp::Reverse` boilerplate at every site.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
struct MinScored(Scored);
|
||||
impl PartialEq for MinScored {
|
||||
fn eq(&self, other: &Self) -> bool {
|
||||
self.0 == other.0
|
||||
}
|
||||
}
|
||||
impl Eq for MinScored {}
|
||||
impl Ord for MinScored {
|
||||
fn cmp(&self, other: &Self) -> Ordering {
|
||||
other.0.cmp(&self.0) // reversed
|
||||
}
|
||||
}
|
||||
impl PartialOrd for MinScored {
|
||||
fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
|
||||
Some(self.cmp(other))
|
||||
}
|
||||
}
|
||||
|
||||
/// A multi-layer HNSW graph index over dense `Vec<f32>` embeddings.
|
||||
///
|
||||
/// IDs are the **insertion index** (`0..len`), returned by [`HnswIndex::search`]
|
||||
/// alongside the distance. The original vectors are retained (the graph needs
|
||||
/// them for distance computation at query time), so memory is
|
||||
/// `O(N·d) + O(N·M)` — the float vectors plus the adjacency lists.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct HnswIndex {
|
||||
metric: Metric,
|
||||
params: HnswParams,
|
||||
dim: usize,
|
||||
/// Stored vectors, indexed by id.
|
||||
vectors: Vec<Vec<f32>>,
|
||||
/// `links[id][layer]` = neighbour ids of `id` on `layer`. A node of level
|
||||
/// `ℓ` has `ℓ+1` layers (`0..=ℓ`).
|
||||
links: Vec<Vec<Vec<u32>>>,
|
||||
/// Per-node top level.
|
||||
levels: Vec<usize>,
|
||||
/// Current entry point id (the highest-level node), or `None` if empty.
|
||||
entry: Option<u32>,
|
||||
/// Highest level currently present in the graph.
|
||||
top_level: usize,
|
||||
/// PRNG state for level assignment (advances per insert).
|
||||
rng_state: u64,
|
||||
}
|
||||
|
||||
impl HnswIndex {
|
||||
/// Create an empty index with the given metric and parameters.
|
||||
///
|
||||
/// `dim` is the expected embedding dimension. Inserts of a different length
|
||||
/// are accepted charitably (the metric compares over the shorter prefix), so
|
||||
/// a wrong-length vector degrades recall rather than panicking — but callers
|
||||
/// should keep dimension uniform.
|
||||
pub fn new(dim: usize, metric: Metric, params: HnswParams) -> Self {
|
||||
Self {
|
||||
metric,
|
||||
params,
|
||||
dim,
|
||||
vectors: Vec::new(),
|
||||
links: Vec::new(),
|
||||
levels: Vec::new(),
|
||||
entry: None,
|
||||
top_level: 0,
|
||||
rng_state: params.seed.wrapping_add(0x9E37_79B9_7F4A_7C15),
|
||||
}
|
||||
}
|
||||
|
||||
/// Number of indexed points.
|
||||
#[inline]
|
||||
pub fn len(&self) -> usize {
|
||||
self.vectors.len()
|
||||
}
|
||||
|
||||
/// True iff the index holds no points.
|
||||
#[inline]
|
||||
pub fn is_empty(&self) -> bool {
|
||||
self.vectors.is_empty()
|
||||
}
|
||||
|
||||
/// The metric this index ranks by.
|
||||
#[inline]
|
||||
pub fn metric(&self) -> Metric {
|
||||
self.metric
|
||||
}
|
||||
|
||||
/// The expected embedding dimension.
|
||||
#[inline]
|
||||
pub fn dim(&self) -> usize {
|
||||
self.dim
|
||||
}
|
||||
|
||||
/// The current entry-point id (highest-level node), or `None` if empty.
|
||||
/// Exposed so the quantized variant ([`crate::hnsw_quantized`]) can traverse
|
||||
/// the **same** graph with a different (quantized) score.
|
||||
#[inline]
|
||||
pub fn entry_point(&self) -> Option<u32> {
|
||||
self.entry
|
||||
}
|
||||
|
||||
/// The highest level currently present in the graph.
|
||||
#[inline]
|
||||
pub fn top_level(&self) -> usize {
|
||||
self.top_level
|
||||
}
|
||||
|
||||
/// The default query-time beam width (`ef_search`) from this index's params.
|
||||
#[inline]
|
||||
pub fn params_ef_search(&self) -> usize {
|
||||
self.params.ef_search
|
||||
}
|
||||
|
||||
/// Borrow the neighbour ids of `id` on `layer`. Returns an empty slice if the
|
||||
/// id is unknown or the node does not reach that layer — never panics. Used
|
||||
/// by the quantized variant to walk the shared graph.
|
||||
#[inline]
|
||||
pub fn neighbours(&self, id: u32, layer: usize) -> &[u32] {
|
||||
match self.links.get(id as usize).and_then(|l| l.get(layer)) {
|
||||
Some(v) => v.as_slice(),
|
||||
None => &[],
|
||||
}
|
||||
}
|
||||
|
||||
/// `m_max` for a layer: `2·M` on layer 0, `M` above. The base layer carries
|
||||
/// every node and needs higher degree to stay connected (the paper's
|
||||
/// asymmetric degree cap).
|
||||
#[inline]
|
||||
fn m_max(&self, layer: usize) -> usize {
|
||||
if layer == 0 {
|
||||
self.params.m * 2
|
||||
} else {
|
||||
self.params.m
|
||||
}
|
||||
}
|
||||
|
||||
/// Draw the next node's level from a geometric distribution with parameter
|
||||
/// `m_l = 1/ln(M)` — the paper's level generator — using the **seeded**
|
||||
/// SplitMix64 stream. `floor(−ln(U) · m_l)` with `U ∈ (0, 1]`.
|
||||
fn assign_level(&mut self) -> usize {
|
||||
let m = self.params.m.max(2) as f64;
|
||||
let m_l = 1.0 / m.ln();
|
||||
// Uniform in (0, 1] from the top 53 bits of a SplitMix64 word.
|
||||
let r = split_mix64(&mut self.rng_state);
|
||||
let u = (((r >> 11) as f64) + 1.0) / ((1u64 << 53) as f64 + 1.0);
|
||||
let level = (-(u.ln()) * m_l).floor();
|
||||
if level.is_finite() && level >= 0.0 {
|
||||
level as usize
|
||||
} else {
|
||||
0
|
||||
}
|
||||
}
|
||||
|
||||
/// Insert `embedding` with the next sequential id. Returns the assigned id.
|
||||
///
|
||||
/// Builds the node's adjacency by searching the existing graph for its
|
||||
/// nearest neighbours at each of its layers and connecting via the
|
||||
/// neighbour-selection heuristic. The first insert becomes the entry point.
|
||||
pub fn insert(&mut self, embedding: &[f32]) -> u32 {
|
||||
let id = self.vectors.len() as u32;
|
||||
let vec = embedding.to_vec();
|
||||
let node_level = self.assign_level();
|
||||
|
||||
// Push the node into the arrays UP FRONT with empty per-layer link lists.
|
||||
// This is load-bearing: the bidirectional wiring below does
|
||||
// `self.links[nbr][l].push(id)`, after which a neighbour points at `id`;
|
||||
// a subsequent traversal step in the SAME insert can hop to that
|
||||
// neighbour and read `self.links[id]`. If `id`'s links did not exist yet
|
||||
// that read panics (the bug the recall gate caught). The new node has no
|
||||
// *incoming* edges until we add them, and empty outgoing lists, so it is
|
||||
// unreachable by the searches that run before its edges are wired —
|
||||
// pushing it early is safe and keeps every `self.links[*]` index valid.
|
||||
self.vectors.push(vec.clone());
|
||||
self.links.push(vec![Vec::new(); node_level + 1]);
|
||||
self.levels.push(node_level);
|
||||
|
||||
// First node: it is the entry point, no neighbours to connect.
|
||||
if self.entry.is_none() {
|
||||
self.entry = Some(id);
|
||||
self.top_level = node_level;
|
||||
return id;
|
||||
}
|
||||
|
||||
let entry = self.entry.unwrap();
|
||||
let mut ep = entry;
|
||||
|
||||
// Phase 1: greedy-descend from the top of the graph down to the layer
|
||||
// just above the node's own top level, refining the single entry point.
|
||||
let mut layer = self.top_level;
|
||||
while layer > node_level {
|
||||
ep = self.greedy_closest(&vec, ep, layer);
|
||||
if layer == 0 {
|
||||
break;
|
||||
}
|
||||
layer -= 1;
|
||||
}
|
||||
|
||||
// Phase 2: from min(node_level, top_level) down to 0, search for
|
||||
// ef_construction candidates, select neighbours, and wire bidirectional
|
||||
// edges (pruning the neighbour's list if it overflows m_max).
|
||||
let start = node_level.min(self.top_level);
|
||||
let mut layer = start as isize;
|
||||
while layer >= 0 {
|
||||
let l = layer as usize;
|
||||
let candidates =
|
||||
self.search_layer(&vec, &[ep], self.params.ef_construction.max(1), l);
|
||||
let selected = self.select_neighbours(&vec, &candidates, self.m_max(l));
|
||||
|
||||
// Connect node -> selected (write straight into the node's slot).
|
||||
self.links[id as usize][l] = selected.iter().map(|s| s.id).collect();
|
||||
|
||||
// Connect selected -> node (bidirectional), pruning if needed.
|
||||
for s in &selected {
|
||||
let nbr = s.id as usize;
|
||||
self.links[nbr][l].push(id);
|
||||
if self.links[nbr][l].len() > self.m_max(l) {
|
||||
self.prune_neighbours(nbr as u32, l);
|
||||
}
|
||||
}
|
||||
|
||||
// Move the entry for the next-lower layer to the closest candidate.
|
||||
if let Some(best) = candidates
|
||||
.iter()
|
||||
.min_by(|a, b| a.dist.partial_cmp(&b.dist).unwrap_or(Ordering::Equal))
|
||||
{
|
||||
ep = best.id;
|
||||
}
|
||||
layer -= 1;
|
||||
}
|
||||
|
||||
if node_level > self.top_level {
|
||||
self.top_level = node_level;
|
||||
self.entry = Some(id);
|
||||
}
|
||||
id
|
||||
}
|
||||
|
||||
/// Greedy single-best descent on one layer: hop to the neighbour closest to
|
||||
/// `query` until no neighbour improves. Iterative (bounded by the graph) —
|
||||
/// no recursion.
|
||||
fn greedy_closest(&self, query: &[f32], start: u32, layer: usize) -> u32 {
|
||||
let mut best = start;
|
||||
let mut best_d = self.metric.distance(query, &self.vectors[best as usize]);
|
||||
loop {
|
||||
let mut improved = false;
|
||||
for &nbr in &self.links[best as usize][layer] {
|
||||
let d = self.metric.distance(query, &self.vectors[nbr as usize]);
|
||||
if d < best_d {
|
||||
best_d = d;
|
||||
best = nbr;
|
||||
improved = true;
|
||||
}
|
||||
}
|
||||
if !improved {
|
||||
return best;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Beam search on one layer (paper Algorithm 2): best-first expansion from
|
||||
/// `entry_points`, keeping the `ef` closest results. Returns the result set
|
||||
/// (unsorted; callers sort/truncate). Bounded by a visited set + the `ef`
|
||||
/// result heap — no recursion, no unbounded growth.
|
||||
fn search_layer(
|
||||
&self,
|
||||
query: &[f32],
|
||||
entry_points: &[u32],
|
||||
ef: usize,
|
||||
layer: usize,
|
||||
) -> Vec<Scored> {
|
||||
let mut visited: HashSet<u32> = HashSet::new();
|
||||
// `candidates`: min-heap (closest first) of nodes to expand.
|
||||
let mut candidates: BinaryHeap<MinScored> = BinaryHeap::new();
|
||||
// `results`: max-heap (farthest first) of the best-ef found so far, so
|
||||
// the top is the current worst and is cheap to evict.
|
||||
let mut results: BinaryHeap<Scored> = BinaryHeap::new();
|
||||
|
||||
for &ep in entry_points {
|
||||
if ep as usize >= self.vectors.len() {
|
||||
continue;
|
||||
}
|
||||
let d = self.metric.distance(query, &self.vectors[ep as usize]);
|
||||
let s = Scored { dist: d, id: ep };
|
||||
visited.insert(ep);
|
||||
candidates.push(MinScored(s));
|
||||
results.push(s);
|
||||
}
|
||||
// Cap results at ef from the start.
|
||||
while results.len() > ef {
|
||||
results.pop();
|
||||
}
|
||||
|
||||
while let Some(MinScored(cur)) = candidates.pop() {
|
||||
// Stop when the closest unexpanded candidate is farther than the
|
||||
// current worst result and the result set is already full.
|
||||
let worst = results.peek().map(|s| s.dist).unwrap_or(f32::INFINITY);
|
||||
if cur.dist > worst && results.len() >= ef {
|
||||
break;
|
||||
}
|
||||
for &nbr in &self.links[cur.id as usize][layer] {
|
||||
if !visited.insert(nbr) {
|
||||
continue;
|
||||
}
|
||||
let d = self.metric.distance(query, &self.vectors[nbr as usize]);
|
||||
let worst = results.peek().map(|s| s.dist).unwrap_or(f32::INFINITY);
|
||||
if results.len() < ef || d < worst {
|
||||
let s = Scored { dist: d, id: nbr };
|
||||
candidates.push(MinScored(s));
|
||||
results.push(s);
|
||||
while results.len() > ef {
|
||||
results.pop();
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
results.into_vec()
|
||||
}
|
||||
|
||||
/// Neighbour-selection heuristic (paper Algorithm 4): from `candidates`,
|
||||
/// greedily pick up to `m` that are **closer to the new point than to any
|
||||
/// already-picked neighbour**, giving diverse, navigable edges instead of a
|
||||
/// clump. Candidates are considered nearest-first.
|
||||
fn select_neighbours(&self, _base: &[f32], candidates: &[Scored], m: usize) -> Vec<Scored> {
|
||||
let mut sorted = candidates.to_vec();
|
||||
sorted.sort_by(|a, b| a.dist.partial_cmp(&b.dist).unwrap_or(Ordering::Equal));
|
||||
let mut selected: Vec<Scored> = Vec::with_capacity(m);
|
||||
for cand in sorted {
|
||||
if selected.len() >= m {
|
||||
break;
|
||||
}
|
||||
// Keep `cand` only if it is closer to `base` than to every already
|
||||
// selected neighbour — the diversity condition.
|
||||
let cand_vec = &self.vectors[cand.id as usize];
|
||||
let mut keep = true;
|
||||
for sel in &selected {
|
||||
let d_cand_sel = self.metric.distance(cand_vec, &self.vectors[sel.id as usize]);
|
||||
if d_cand_sel < cand.dist {
|
||||
keep = false;
|
||||
break;
|
||||
}
|
||||
}
|
||||
if keep {
|
||||
selected.push(cand);
|
||||
}
|
||||
}
|
||||
// If the diversity filter left us short (sparse graph), backfill with the
|
||||
// remaining nearest candidates so the node is not under-connected.
|
||||
if selected.len() < m {
|
||||
let chosen: HashSet<u32> = selected.iter().map(|s| s.id).collect();
|
||||
let mut rest: Vec<Scored> = candidates
|
||||
.iter()
|
||||
.filter(|c| !chosen.contains(&c.id))
|
||||
.copied()
|
||||
.collect();
|
||||
rest.sort_by(|a, b| a.dist.partial_cmp(&b.dist).unwrap_or(Ordering::Equal));
|
||||
for c in rest {
|
||||
if selected.len() >= m {
|
||||
break;
|
||||
}
|
||||
selected.push(c);
|
||||
}
|
||||
}
|
||||
selected
|
||||
}
|
||||
|
||||
/// Re-prune a node's neighbour list on `layer` back down to `m_max` using
|
||||
/// the selection heuristic, after a bidirectional edge pushed it over cap.
|
||||
fn prune_neighbours(&mut self, id: u32, layer: usize) {
|
||||
let base = self.vectors[id as usize].clone();
|
||||
let current: Vec<Scored> = self.links[id as usize][layer]
|
||||
.iter()
|
||||
.map(|&nbr| Scored {
|
||||
dist: self.metric.distance(&base, &self.vectors[nbr as usize]),
|
||||
id: nbr,
|
||||
})
|
||||
.collect();
|
||||
let kept = self.select_neighbours(&base, ¤t, self.m_max(layer));
|
||||
self.links[id as usize][layer] = kept.iter().map(|s| s.id).collect();
|
||||
}
|
||||
|
||||
/// Search for the `k` nearest neighbours of `query`, using beam width `ef`
|
||||
/// (clamped to at least `k`). Returns up to `k` `(id, distance)` pairs sorted
|
||||
/// ascending by distance.
|
||||
///
|
||||
/// Degenerate cases return cleanly: empty index ⇒ empty vec; `k == 0` ⇒ empty
|
||||
/// vec; `k > len` ⇒ all points; a single node ⇒ that node. Never panics.
|
||||
pub fn search(&self, query: &[f32], k: usize, ef: usize) -> Vec<(u32, f32)> {
|
||||
if k == 0 || self.is_empty() {
|
||||
return Vec::new();
|
||||
}
|
||||
let entry = match self.entry {
|
||||
Some(e) => e,
|
||||
None => return Vec::new(),
|
||||
};
|
||||
let ef = ef.max(k).max(1);
|
||||
|
||||
// Greedy-descend the upper layers to a good layer-0 entry point.
|
||||
let mut ep = entry;
|
||||
let mut layer = self.top_level;
|
||||
while layer > 0 {
|
||||
ep = self.greedy_closest(query, ep, layer);
|
||||
layer -= 1;
|
||||
}
|
||||
// Beam search on layer 0.
|
||||
let mut results = self.search_layer(query, &[ep], ef, 0);
|
||||
results.sort_by(|a, b| a.dist.partial_cmp(&b.dist).unwrap_or(Ordering::Equal));
|
||||
results.truncate(k);
|
||||
results.into_iter().map(|s| (s.id, s.dist)).collect()
|
||||
}
|
||||
|
||||
/// Search using the index's configured default `ef_search`.
|
||||
#[inline]
|
||||
pub fn search_default(&self, query: &[f32], k: usize) -> Vec<(u32, f32)> {
|
||||
self.search(query, k, self.params.ef_search)
|
||||
}
|
||||
|
||||
/// Borrow a stored vector by id (for the quantized variant / reranking).
|
||||
#[inline]
|
||||
pub fn vector(&self, id: u32) -> Option<&[f32]> {
|
||||
self.vectors.get(id as usize).map(|v| v.as_slice())
|
||||
}
|
||||
|
||||
/// Brute-force exact top-K linear scan over the stored vectors — the ANN
|
||||
/// **ground truth** and the linear-scan baseline the benchmark measures
|
||||
/// against. `O(N·d)` per query. Returns up to `k` `(id, distance)` ascending.
|
||||
pub fn brute_force(&self, query: &[f32], k: usize) -> Vec<(u32, f32)> {
|
||||
if k == 0 || self.is_empty() {
|
||||
return Vec::new();
|
||||
}
|
||||
let mut scored: Vec<(u32, f32)> = self
|
||||
.vectors
|
||||
.iter()
|
||||
.enumerate()
|
||||
.map(|(i, v)| (i as u32, self.metric.distance(query, v)))
|
||||
.collect();
|
||||
scored.sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap_or(Ordering::Equal));
|
||||
scored.truncate(k);
|
||||
scored
|
||||
}
|
||||
}
|
||||
|
||||
/// SplitMix64 step — the same deterministic PRNG used by [`crate::rotation`].
|
||||
/// Public-domain (Sebastiano Vigna). Dependency-free and reproducible.
|
||||
#[inline]
|
||||
pub(crate) fn split_mix64(state: &mut u64) -> u64 {
|
||||
*state = state.wrapping_add(0x9E37_79B9_7F4A_7C15);
|
||||
let mut z = *state;
|
||||
z = (z ^ (z >> 30)).wrapping_mul(0xBF58_476D_1CE4_E5B9);
|
||||
z = (z ^ (z >> 27)).wrapping_mul(0x94D0_49BB_1331_11EB);
|
||||
z ^ (z >> 31)
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
/// SplitMix64-driven uniform in [0,1) for building fixtures (mirrors
|
||||
/// `coverage.rs`'s style so the planted-cluster geometry matches).
|
||||
fn unif01(state: &mut u64) -> f32 {
|
||||
let r = split_mix64(state);
|
||||
((r >> 40) as f32) / ((1u64 << 24) as f32)
|
||||
}
|
||||
fn gauss(state: &mut u64) -> f32 {
|
||||
let u1 = unif01(state).max(1e-7);
|
||||
let u2 = unif01(state);
|
||||
(-2.0 * u1.ln()).sqrt() * (std::f32::consts::TAU * u2).cos()
|
||||
}
|
||||
|
||||
/// Build a planted-cluster fixture: `n` vectors of `dim`, in `clusters`
|
||||
/// Gaussian clusters. Returns the vectors. Deterministic from `seed`.
|
||||
fn planted(dim: usize, n: usize, clusters: usize, seed: u64) -> Vec<Vec<f32>> {
|
||||
let centres: Vec<Vec<f32>> = (0..clusters)
|
||||
.map(|c| {
|
||||
let mut s = seed ^ (0xC0FFEE_u64.wrapping_mul(c as u64 + 1));
|
||||
(0..dim).map(|_| gauss(&mut s) * 3.0).collect()
|
||||
})
|
||||
.collect();
|
||||
(0..n)
|
||||
.map(|i| {
|
||||
let c = i % clusters;
|
||||
let mut s = seed ^ (i as u64).wrapping_mul(0x9E37);
|
||||
(0..dim).map(|d| centres[c][d] + gauss(&mut s) * 0.35).collect()
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
|
||||
fn build(vectors: &[Vec<f32>], metric: Metric, seed: u64) -> HnswIndex {
|
||||
let params = HnswParams {
|
||||
m: 16,
|
||||
ef_construction: 200,
|
||||
ef_search: 64,
|
||||
seed,
|
||||
};
|
||||
let mut idx = HnswIndex::new(vectors[0].len(), metric, params);
|
||||
for v in vectors {
|
||||
idx.insert(v);
|
||||
}
|
||||
idx
|
||||
}
|
||||
|
||||
/// Recall@k of HNSW search vs brute-force ground truth, averaged over queries
|
||||
/// drawn from the same planted clusters.
|
||||
fn recall_at_k(
|
||||
idx: &HnswIndex,
|
||||
vectors: &[Vec<f32>],
|
||||
dim: usize,
|
||||
clusters: usize,
|
||||
k: usize,
|
||||
ef: usize,
|
||||
n_queries: usize,
|
||||
seed: u64,
|
||||
) -> f64 {
|
||||
let centres_seed = seed; // reuse fixture seed for matching cluster geometry
|
||||
let mut total = 0.0f64;
|
||||
for q in 0..n_queries {
|
||||
let c = q % clusters;
|
||||
let mut s = centres_seed ^ 0xDEAD_0000 ^ (q as u64).wrapping_mul(0x2545_F491);
|
||||
// A query near cluster centre c: regenerate the centre then jitter.
|
||||
let mut cs = centres_seed ^ (0xC0FFEE_u64.wrapping_mul(c as u64 + 1));
|
||||
let centre: Vec<f32> = (0..dim).map(|_| gauss(&mut cs) * 3.0).collect();
|
||||
let qv: Vec<f32> = (0..dim).map(|d| centre[d] + gauss(&mut s) * 0.35).collect();
|
||||
|
||||
let truth: HashSet<u32> = idx.brute_force(&qv, k).into_iter().map(|(id, _)| id).collect();
|
||||
let got = idx.search(&qv, k, ef);
|
||||
let hit = got.iter().filter(|(id, _)| truth.contains(id)).count();
|
||||
total += hit as f64 / k as f64;
|
||||
let _ = vectors;
|
||||
}
|
||||
total / n_queries as f64
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn empty_index_search_is_empty_no_panic() {
|
||||
let idx = HnswIndex::new(8, Metric::L2, HnswParams::default());
|
||||
assert!(idx.is_empty());
|
||||
assert!(idx.search(&[0.0; 8], 5, 16).is_empty());
|
||||
assert!(idx.brute_force(&[0.0; 8], 5).is_empty());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn single_node_returns_itself() {
|
||||
let mut idx = HnswIndex::new(4, Metric::L2, HnswParams::default());
|
||||
let id = idx.insert(&[1.0, 2.0, 3.0, 4.0]);
|
||||
assert_eq!(id, 0);
|
||||
let r = idx.search(&[1.0, 2.0, 3.0, 4.0], 5, 16);
|
||||
assert_eq!(r.len(), 1);
|
||||
assert_eq!(r[0].0, 0);
|
||||
assert!(r[0].1 < 1e-6);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn k_zero_and_k_gt_n_no_panic() {
|
||||
let vectors = planted(16, 40, 4, 0xABCD);
|
||||
let idx = build(&vectors, Metric::L2, 0x1234);
|
||||
assert!(idx.search(&vectors[0], 0, 16).is_empty());
|
||||
// k > n returns all n.
|
||||
let r = idx.search(&vectors[0], 1000, 64);
|
||||
assert_eq!(r.len(), 40);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn ragged_query_no_panic() {
|
||||
let vectors = planted(16, 30, 3, 0x55);
|
||||
let idx = build(&vectors, Metric::Cosine, 0x66);
|
||||
// Short and long queries must not panic.
|
||||
assert!(!idx.search(&[1.0, 2.0, 3.0], 3, 16).is_empty());
|
||||
let long: Vec<f32> = (0..100).map(|i| i as f32).collect();
|
||||
assert!(!idx.search(&long, 3, 16).is_empty());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn self_query_ranks_self_first() {
|
||||
let vectors = planted(32, 200, 8, 0x77);
|
||||
let idx = build(&vectors, Metric::L2, 0x88);
|
||||
for &probe in &[0usize, 50, 137, 199] {
|
||||
let r = idx.search(&vectors[probe], 1, 64);
|
||||
assert_eq!(r.len(), 1);
|
||||
assert_eq!(r[0].0, probe as u32, "self-query should return the stored self");
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn hnsw_is_deterministic_for_seed() {
|
||||
// Same (seed, params, insertion order) ⇒ identical level assignment and
|
||||
// identical search output.
|
||||
let vectors = planted(24, 150, 6, 0x2222);
|
||||
let a = build(&vectors, Metric::Cosine, 0xFEED);
|
||||
let b = build(&vectors, Metric::Cosine, 0xFEED);
|
||||
assert_eq!(a.levels, b.levels, "level assignment must be deterministic");
|
||||
let q = &vectors[42];
|
||||
assert_eq!(a.search(q, 10, 64), b.search(q, 10, 64));
|
||||
// A different seed (almost surely) changes the level structure.
|
||||
let c = build(&vectors, Metric::Cosine, 0x1357);
|
||||
assert_ne!(a.levels, c.levels, "different seed should change levels");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn recall_at_10_meets_correctness_gate_l2() {
|
||||
// THE CORRECTNESS GATE (ADR-261): HNSW recall@10 vs brute-force must be
|
||||
// >= 0.95 at a reasonable ef. Low recall ⇒ a bug in the graph.
|
||||
let dim = 64;
|
||||
let n = 2000;
|
||||
let clusters = 32;
|
||||
let seed = 0x9999;
|
||||
let vectors = planted(dim, n, clusters, seed);
|
||||
let idx = build(&vectors, Metric::L2, 0xAAAA);
|
||||
let recall = recall_at_k(&idx, &vectors, dim, clusters, 10, 128, 64, seed);
|
||||
assert!(
|
||||
recall >= 0.95,
|
||||
"HNSW recall@10 (L2) = {recall:.4} below the 0.95 correctness gate — graph bug"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn recall_at_10_meets_correctness_gate_cosine() {
|
||||
let dim = 64;
|
||||
let n = 2000;
|
||||
let clusters = 32;
|
||||
let seed = 0xBBBB;
|
||||
let vectors = planted(dim, n, clusters, seed);
|
||||
let idx = build(&vectors, Metric::Cosine, 0xCCCC);
|
||||
let recall = recall_at_k(&idx, &vectors, dim, clusters, 10, 128, 64, seed);
|
||||
assert!(
|
||||
recall >= 0.95,
|
||||
"HNSW recall@10 (cosine) = {recall:.4} below the 0.95 correctness gate — graph bug"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn higher_ef_does_not_reduce_recall() {
|
||||
// Monotonicity sanity: more beam width should not hurt recall.
|
||||
let dim = 48;
|
||||
let vectors = planted(dim, 1000, 16, 0xD00D);
|
||||
let idx = build(&vectors, Metric::L2, 0xE00E);
|
||||
let lo = recall_at_k(&idx, &vectors, dim, 16, 10, 16, 48, 0xD00D);
|
||||
let hi = recall_at_k(&idx, &vectors, dim, 16, 10, 128, 48, 0xD00D);
|
||||
assert!(hi + 1e-9 >= lo, "recall dropped with larger ef: {lo:.3} -> {hi:.3}");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn zero_dim_no_panic() {
|
||||
// Degenerate zero-dimension index: inserts and searches must not panic.
|
||||
let mut idx = HnswIndex::new(0, Metric::Cosine, HnswParams::default());
|
||||
idx.insert(&[]);
|
||||
idx.insert(&[]);
|
||||
let r = idx.search(&[], 2, 16);
|
||||
assert_eq!(r.len(), 2);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,466 @@
|
||||
//! A **SymphonyQG-style quantized-traversal HNSW** — ADR-261.
|
||||
//!
|
||||
//! # The SymphonyQG bet (what we are testing)
|
||||
//!
|
||||
//! [SymphonyQG (SIGMOD 2025)](../../../../../docs/adr/ADR-261-ruvector-graph-ann-index.md)
|
||||
//! unifies **quantization with graph traversal**: instead of computing the full
|
||||
//! float distance at every node the beam search visits (the cost that dominates
|
||||
//! float HNSW — one `O(d)` float dot/diff per visited node), it scores traversal
|
||||
//! candidates with a **cheap quantized distance** and only computes the exact
|
||||
//! float distance for the *final* candidate set, which it **reranks**. The bet:
|
||||
//! the quantized score is cheap enough — and accurate enough to keep the beam on
|
||||
//! the right path — that you visit roughly as many nodes but pay far less per
|
||||
//! node, and recover the small recall loss with a final exact rerank. Source
|
||||
//! reports **3.5–17× QPS over HNSW at equal recall**.
|
||||
//!
|
||||
//! # Our implementation (honest scope)
|
||||
//!
|
||||
//! We are **not** reproducing SymphonyQG's exact system (their RaBitQ-fused codes,
|
||||
//! their SIMD layout, their refined graph). We build the **direction** of the
|
||||
//! claim from the pieces this crate already has, so the comparison is
|
||||
//! apples-to-apples on *our* hardware:
|
||||
//!
|
||||
//! - **Same graph** as the float [`crate::HnswIndex`] — identical structure,
|
||||
//! identical seed, identical level assignment. The *only* variable between the
|
||||
//! float and quantized search is **how a candidate is scored during traversal**,
|
||||
//! so any QPS/recall difference is attributable to the quantization, not to a
|
||||
//! different graph.
|
||||
//! - **Quantized score = 1-bit Hamming over the RaBitQ Pass-2 rotated sign code**
|
||||
//! ([`crate::rotation`] + the sign-quantization in [`crate::sketch`]). Each
|
||||
//! node stores its `ceil(D/8)`-byte sign code (`D = next_pow2(dim)`). During
|
||||
//! traversal we compare query-code vs node-code by **POPCNT Hamming** — a few
|
||||
//! machine words, no per-dimension float work.
|
||||
//! - **Exact float rerank** of the final beam: the top `rerank` candidates by
|
||||
//! Hamming are re-scored with the true float metric and the best `k` returned.
|
||||
//!
|
||||
//! This trades a small recall hit (the 1-bit code is a coarse angle proxy — the
|
||||
//! same ~46%-strict limitation ADR-156 §10 measured) for far cheaper per-node
|
||||
//! scoring, recovered by the float rerank. **Whether that nets a QPS win at our
|
||||
//! test scale is the measured question ADR-261 answers** — and at small N the
|
||||
//! float distance is cheap enough that the Hamming saving may not pay off. We
|
||||
//! report the real number, win or lose, and do not tune to manufacture a speedup.
|
||||
//!
|
||||
//! # Determinism & robustness
|
||||
//!
|
||||
//! The graph seed drives everything (level assignment), so the quantized index
|
||||
//! is as reproducible as the float one. Empty/degenerate inputs are guarded
|
||||
//! exactly as in [`crate::hnsw`] — no panic on empty index, `k > n`, `k == 0`,
|
||||
//! single node, ragged query, or zero dim.
|
||||
|
||||
use std::cmp::Ordering;
|
||||
use std::collections::{BinaryHeap, HashSet};
|
||||
|
||||
use crate::hnsw::{HnswIndex, HnswParams, Metric};
|
||||
use crate::rotation::Rotation;
|
||||
|
||||
/// A 1-bit Pass-2 sign code for one vector, over the padded rotation length `D`.
|
||||
/// Stored as packed bytes; compared by POPCNT Hamming.
|
||||
#[derive(Debug, Clone)]
|
||||
struct Code {
|
||||
bits: Vec<u8>,
|
||||
}
|
||||
|
||||
impl Code {
|
||||
/// Hamming distance to another code of the same length (popcount of XOR).
|
||||
#[inline]
|
||||
fn hamming(&self, other: &Code) -> u32 {
|
||||
let n = self.bits.len().min(other.bits.len());
|
||||
let mut acc = 0u32;
|
||||
for i in 0..n {
|
||||
acc += (self.bits[i] ^ other.bits[i]).count_ones();
|
||||
}
|
||||
acc
|
||||
}
|
||||
}
|
||||
|
||||
/// Build the packed 1-bit sign code of a rotated embedding over the padded
|
||||
/// length `D = rotation.padded_dim()`. Bit set ⇒ rotated coord ≥ 0.
|
||||
fn encode(embedding: &[f32], rotation: &Rotation) -> Code {
|
||||
let rotated = rotation.apply_padded(embedding);
|
||||
let d = rotated.len();
|
||||
let mut bits = vec![0u8; d.div_ceil(8)];
|
||||
for (i, &c) in rotated.iter().enumerate() {
|
||||
if c >= 0.0 {
|
||||
bits[i / 8] |= 1 << (7 - (i % 8));
|
||||
}
|
||||
}
|
||||
Code { bits }
|
||||
}
|
||||
|
||||
/// Min-heap node for the quantized beam (closest Hamming at the top).
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
struct HScored {
|
||||
/// Hamming distance (quantized score) — the traversal key.
|
||||
ham: u32,
|
||||
id: u32,
|
||||
}
|
||||
impl PartialEq for HScored {
|
||||
fn eq(&self, other: &Self) -> bool {
|
||||
self.ham == other.ham && self.id == other.id
|
||||
}
|
||||
}
|
||||
impl Eq for HScored {}
|
||||
impl Ord for HScored {
|
||||
fn cmp(&self, other: &Self) -> Ordering {
|
||||
self.ham.cmp(&other.ham).then(self.id.cmp(&other.id))
|
||||
}
|
||||
}
|
||||
impl PartialOrd for HScored {
|
||||
fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
|
||||
Some(self.cmp(other))
|
||||
}
|
||||
}
|
||||
/// Reversed wrapper for a min-heap (smallest Hamming at the top).
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
struct MinH(HScored);
|
||||
impl PartialEq for MinH {
|
||||
fn eq(&self, other: &Self) -> bool {
|
||||
self.0 == other.0
|
||||
}
|
||||
}
|
||||
impl Eq for MinH {}
|
||||
impl Ord for MinH {
|
||||
fn cmp(&self, other: &Self) -> Ordering {
|
||||
other.0.cmp(&self.0)
|
||||
}
|
||||
}
|
||||
impl PartialOrd for MinH {
|
||||
fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
|
||||
Some(self.cmp(other))
|
||||
}
|
||||
}
|
||||
|
||||
/// A SymphonyQG-style HNSW: the same graph as [`HnswIndex`], traversed by a
|
||||
/// **cheap 1-bit Hamming score**, with a final **exact-float rerank**.
|
||||
///
|
||||
/// Built by inserting the same vectors in the same order with the same seed as
|
||||
/// a float [`HnswIndex`], so the two indices share identical graph structure and
|
||||
/// only differ in how the beam is scored. The shared [`Rotation`] (seed + dim)
|
||||
/// is the index/query frame for the 1-bit codes.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct QuantizedHnswIndex {
|
||||
/// The underlying graph (built with the float metric for exact rerank).
|
||||
graph: HnswIndex,
|
||||
/// Per-node 1-bit Pass-2 codes, indexed by id (parallel to graph vectors).
|
||||
codes: Vec<Code>,
|
||||
/// The rotation frame shared by index and query codes.
|
||||
rotation: Rotation,
|
||||
/// Number of final candidates to exact-float rerank (≥ k at query time).
|
||||
default_rerank: usize,
|
||||
}
|
||||
|
||||
impl QuantizedHnswIndex {
|
||||
/// Build a quantized index over `vectors`, mirroring a float [`HnswIndex`]
|
||||
/// built with the same `(dim, metric, params)` and insertion order. The
|
||||
/// `rotation_seed` fixes the 1-bit code frame (index and query share it).
|
||||
///
|
||||
/// `default_rerank` is how many top-Hamming candidates get an exact float
|
||||
/// re-score before returning the best `k`; it is clamped to `≥ k` at query
|
||||
/// time. A larger rerank recovers more recall at more float cost — the knob
|
||||
/// that, alongside `ef`, sets the equal-recall operating point.
|
||||
pub fn build(
|
||||
vectors: &[Vec<f32>],
|
||||
dim: usize,
|
||||
metric: Metric,
|
||||
params: HnswParams,
|
||||
rotation_seed: u64,
|
||||
default_rerank: usize,
|
||||
) -> Self {
|
||||
let rotation = Rotation::new(rotation_seed, dim);
|
||||
let mut graph = HnswIndex::new(dim, metric, params);
|
||||
let mut codes = Vec::with_capacity(vectors.len());
|
||||
for v in vectors {
|
||||
graph.insert(v);
|
||||
codes.push(encode(v, &rotation));
|
||||
}
|
||||
Self {
|
||||
graph,
|
||||
codes,
|
||||
rotation,
|
||||
default_rerank: default_rerank.max(1),
|
||||
}
|
||||
}
|
||||
|
||||
/// Number of indexed points.
|
||||
#[inline]
|
||||
pub fn len(&self) -> usize {
|
||||
self.graph.len()
|
||||
}
|
||||
|
||||
/// True iff empty.
|
||||
#[inline]
|
||||
pub fn is_empty(&self) -> bool {
|
||||
self.graph.is_empty()
|
||||
}
|
||||
|
||||
/// Borrow the underlying float graph (for shared-graph benchmark parity:
|
||||
/// the float-HNSW baseline runs on *this* graph so the only variable is
|
||||
/// scoring).
|
||||
#[inline]
|
||||
pub fn graph(&self) -> &HnswIndex {
|
||||
&self.graph
|
||||
}
|
||||
|
||||
/// The rerank width this index defaults to.
|
||||
#[inline]
|
||||
pub fn default_rerank(&self) -> usize {
|
||||
self.default_rerank
|
||||
}
|
||||
|
||||
/// SymphonyQG-style search: traverse the graph scoring candidates by **1-bit
|
||||
/// Hamming**, collect a beam of `ef`, then **exact-float rerank** the top
|
||||
/// `rerank` (clamped ≥ k) and return the best `k` as `(id, float_dist)`.
|
||||
///
|
||||
/// Degenerate cases mirror [`HnswIndex::search`]: empty ⇒ empty; `k == 0` ⇒
|
||||
/// empty; `k > n` ⇒ all; never panics.
|
||||
pub fn search_quantized(
|
||||
&self,
|
||||
query: &[f32],
|
||||
k: usize,
|
||||
ef: usize,
|
||||
rerank: usize,
|
||||
) -> Vec<(u32, f32)> {
|
||||
if k == 0 || self.is_empty() {
|
||||
return Vec::new();
|
||||
}
|
||||
let ef = ef.max(k).max(1);
|
||||
let rerank = rerank.max(k);
|
||||
let q_code = encode(query, &self.rotation);
|
||||
|
||||
// Entry point: the graph's entry (highest-level node).
|
||||
let entry = match self.graph.entry_point() {
|
||||
Some(e) => e,
|
||||
None => return Vec::new(),
|
||||
};
|
||||
|
||||
// Greedy-descend upper layers by Hamming, then beam-search layer 0.
|
||||
let mut ep = entry;
|
||||
let mut layer = self.graph.top_level();
|
||||
while layer > 0 {
|
||||
ep = self.greedy_hamming(&q_code, ep, layer);
|
||||
layer -= 1;
|
||||
}
|
||||
let beam = self.beam_hamming(&q_code, ep, ef);
|
||||
|
||||
// Exact-float rerank of the top `rerank` Hamming candidates.
|
||||
let mut cand: Vec<HScored> = beam;
|
||||
cand.sort_by_key(|c| c.ham);
|
||||
cand.truncate(rerank);
|
||||
let mut reranked: Vec<(u32, f32)> = cand
|
||||
.iter()
|
||||
.filter_map(|c| {
|
||||
self.graph
|
||||
.vector(c.id)
|
||||
.map(|v| (c.id, self.graph.metric().distance(query, v)))
|
||||
})
|
||||
.collect();
|
||||
reranked.sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap_or(Ordering::Equal));
|
||||
reranked.truncate(k);
|
||||
reranked
|
||||
}
|
||||
|
||||
/// Search using the index's default `ef` (from graph params) and rerank.
|
||||
#[inline]
|
||||
pub fn search_default(&self, query: &[f32], k: usize) -> Vec<(u32, f32)> {
|
||||
self.search_quantized(query, k, self.graph.params_ef_search(), self.default_rerank)
|
||||
}
|
||||
|
||||
/// Greedy single-best descent on a layer scored by Hamming.
|
||||
fn greedy_hamming(&self, q_code: &Code, start: u32, layer: usize) -> u32 {
|
||||
let mut best = start;
|
||||
let mut best_h = self.codes[best as usize].hamming(q_code);
|
||||
loop {
|
||||
let mut improved = false;
|
||||
for &nbr in self.graph.neighbours(best, layer) {
|
||||
let h = self.codes[nbr as usize].hamming(q_code);
|
||||
if h < best_h {
|
||||
best_h = h;
|
||||
best = nbr;
|
||||
improved = true;
|
||||
}
|
||||
}
|
||||
if !improved {
|
||||
return best;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Beam search on layer 0 scored by Hamming. Returns the `ef` best-Hamming
|
||||
/// nodes (unsorted). Iterative — bounded by the visited set + the ef beam.
|
||||
fn beam_hamming(&self, q_code: &Code, ep: u32, ef: usize) -> Vec<HScored> {
|
||||
let mut visited: HashSet<u32> = HashSet::new();
|
||||
let mut candidates: BinaryHeap<MinH> = BinaryHeap::new();
|
||||
let mut results: BinaryHeap<HScored> = BinaryHeap::new(); // max-heap: worst at top
|
||||
|
||||
let h0 = self.codes[ep as usize].hamming(q_code);
|
||||
let s0 = HScored { ham: h0, id: ep };
|
||||
visited.insert(ep);
|
||||
candidates.push(MinH(s0));
|
||||
results.push(s0);
|
||||
|
||||
while let Some(MinH(cur)) = candidates.pop() {
|
||||
let worst = results.peek().map(|s| s.ham).unwrap_or(u32::MAX);
|
||||
if cur.ham > worst && results.len() >= ef {
|
||||
break;
|
||||
}
|
||||
for &nbr in self.graph.neighbours(cur.id, 0) {
|
||||
if !visited.insert(nbr) {
|
||||
continue;
|
||||
}
|
||||
let h = self.codes[nbr as usize].hamming(q_code);
|
||||
let worst = results.peek().map(|s| s.ham).unwrap_or(u32::MAX);
|
||||
if results.len() < ef || h < worst {
|
||||
let s = HScored { ham: h, id: nbr };
|
||||
candidates.push(MinH(s));
|
||||
results.push(s);
|
||||
while results.len() > ef {
|
||||
results.pop();
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
results.into_vec()
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
fn split_mix64(state: &mut u64) -> u64 {
|
||||
*state = state.wrapping_add(0x9E37_79B9_7F4A_7C15);
|
||||
let mut z = *state;
|
||||
z = (z ^ (z >> 30)).wrapping_mul(0xBF58_476D_1CE4_E5B9);
|
||||
z = (z ^ (z >> 27)).wrapping_mul(0x94D0_49BB_1331_11EB);
|
||||
z ^ (z >> 31)
|
||||
}
|
||||
fn unif01(state: &mut u64) -> f32 {
|
||||
((split_mix64(state) >> 40) as f32) / ((1u64 << 24) as f32)
|
||||
}
|
||||
fn gauss(state: &mut u64) -> f32 {
|
||||
let u1 = unif01(state).max(1e-7);
|
||||
let u2 = unif01(state);
|
||||
(-2.0 * u1.ln()).sqrt() * (std::f32::consts::TAU * u2).cos()
|
||||
}
|
||||
fn planted(dim: usize, n: usize, clusters: usize, seed: u64) -> Vec<Vec<f32>> {
|
||||
let centres: Vec<Vec<f32>> = (0..clusters)
|
||||
.map(|c| {
|
||||
let mut s = seed ^ (0xC0FFEE_u64.wrapping_mul(c as u64 + 1));
|
||||
(0..dim).map(|_| gauss(&mut s) * 3.0).collect()
|
||||
})
|
||||
.collect();
|
||||
(0..n)
|
||||
.map(|i| {
|
||||
let c = i % clusters;
|
||||
let mut s = seed ^ (i as u64).wrapping_mul(0x9E37);
|
||||
(0..dim).map(|d| centres[c][d] + gauss(&mut s) * 0.35).collect()
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
fn params(seed: u64) -> HnswParams {
|
||||
HnswParams {
|
||||
m: 16,
|
||||
ef_construction: 200,
|
||||
ef_search: 64,
|
||||
seed,
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn empty_quantized_search_is_empty_no_panic() {
|
||||
let idx = QuantizedHnswIndex::build(&[], 8, Metric::Cosine, params(1), 0x42, 16);
|
||||
assert!(idx.is_empty());
|
||||
assert!(idx.search_quantized(&[0.0; 8], 5, 16, 16).is_empty());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn single_node_quantized_returns_itself() {
|
||||
let v = vec![vec![1.0, 2.0, 3.0, 4.0]];
|
||||
let idx = QuantizedHnswIndex::build(&v, 4, Metric::L2, params(2), 0x7, 8);
|
||||
let r = idx.search_quantized(&v[0], 3, 16, 8);
|
||||
assert_eq!(r.len(), 1);
|
||||
assert_eq!(r[0].0, 0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn k_zero_and_k_gt_n_no_panic() {
|
||||
let vectors = planted(16, 40, 4, 0xABCD);
|
||||
let idx = QuantizedHnswIndex::build(&vectors, 16, Metric::L2, params(3), 0x9, 32);
|
||||
assert!(idx.search_quantized(&vectors[0], 0, 16, 16).is_empty());
|
||||
let r = idx.search_quantized(&vectors[0], 1000, 64, 64);
|
||||
assert_eq!(r.len(), 40);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn ragged_query_no_panic() {
|
||||
let vectors = planted(16, 30, 3, 0x55);
|
||||
let idx = QuantizedHnswIndex::build(&vectors, 16, Metric::Cosine, params(4), 0xB, 16);
|
||||
assert!(!idx.search_quantized(&[1.0, 2.0, 3.0], 3, 16, 16).is_empty());
|
||||
let long: Vec<f32> = (0..100).map(|i| i as f32).collect();
|
||||
assert!(!idx.search_quantized(&long, 3, 16, 16).is_empty());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn quantized_is_deterministic() {
|
||||
let vectors = planted(32, 300, 8, 0x2468);
|
||||
let a = QuantizedHnswIndex::build(&vectors, 32, Metric::Cosine, params(0xFEED), 0xC0DE, 32);
|
||||
let b = QuantizedHnswIndex::build(&vectors, 32, Metric::Cosine, params(0xFEED), 0xC0DE, 32);
|
||||
let q = &vectors[100];
|
||||
assert_eq!(
|
||||
a.search_quantized(q, 10, 64, 32),
|
||||
b.search_quantized(q, 10, 64, 32),
|
||||
"quantized search must be deterministic"
|
||||
);
|
||||
}
|
||||
|
||||
/// Recall@10 of quantized-HNSW vs brute-force ground truth, averaged over
|
||||
/// queries. With an exact-float rerank, recall should be high (the rerank
|
||||
/// repairs most of the 1-bit traversal's coarseness). This is the quantized
|
||||
/// variant's correctness gate.
|
||||
#[test]
|
||||
fn quantized_recall_at_10_is_high_with_rerank() {
|
||||
let dim = 64;
|
||||
let n = 2000;
|
||||
let clusters = 32;
|
||||
let seed = 0x9999;
|
||||
let vectors = planted(dim, n, clusters, seed);
|
||||
// Generous rerank so the exact float repairs the coarse Hamming beam.
|
||||
let idx = QuantizedHnswIndex::build(&vectors, dim, Metric::L2, params(0xAAAA), 0x5EED, 64);
|
||||
|
||||
let mut total = 0.0f64;
|
||||
let n_queries = 64;
|
||||
for q in 0..n_queries {
|
||||
let c = q % clusters;
|
||||
let mut cs = seed ^ (0xC0FFEE_u64.wrapping_mul(c as u64 + 1));
|
||||
let centre: Vec<f32> = (0..dim).map(|_| gauss(&mut cs) * 3.0).collect();
|
||||
let mut s = seed ^ 0xDEAD_0000 ^ (q as u64).wrapping_mul(0x2545_F491);
|
||||
let qv: Vec<f32> = (0..dim).map(|d| centre[d] + gauss(&mut s) * 0.35).collect();
|
||||
let truth: HashSet<u32> = idx
|
||||
.graph()
|
||||
.brute_force(&qv, 10)
|
||||
.into_iter()
|
||||
.map(|(id, _)| id)
|
||||
.collect();
|
||||
let got = idx.search_quantized(&qv, 10, 128, 64);
|
||||
let hit = got.iter().filter(|(id, _)| truth.contains(id)).count();
|
||||
total += hit as f64 / 10.0;
|
||||
}
|
||||
let recall = total / n_queries as f64;
|
||||
// The 1-bit code is coarse, so we do not demand the float 0.95 gate here;
|
||||
// but with a 64-wide rerank over an ef=128 beam it must be clearly useful
|
||||
// (well above random). ADR-261 reports the exact number; this gate just
|
||||
// catches a broken traversal/rerank.
|
||||
assert!(
|
||||
recall >= 0.80,
|
||||
"quantized recall@10 = {recall:.4} too low — traversal or rerank bug"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn zero_dim_no_panic() {
|
||||
let vectors = vec![vec![], vec![]];
|
||||
let idx = QuantizedHnswIndex::build(&vectors, 0, Metric::Cosine, params(5), 0x1, 4);
|
||||
let r = idx.search_quantized(&[], 2, 16, 4);
|
||||
assert_eq!(r.len(), 2);
|
||||
}
|
||||
}
|
||||
@@ -28,9 +28,12 @@
|
||||
|
||||
#[cfg(feature = "crv")]
|
||||
pub mod crv;
|
||||
pub mod ann_measure;
|
||||
pub mod coverage;
|
||||
pub mod estimator;
|
||||
pub mod event_log;
|
||||
pub mod hnsw;
|
||||
pub mod hnsw_quantized;
|
||||
pub mod mat;
|
||||
pub mod rotation;
|
||||
pub mod signal;
|
||||
@@ -41,6 +44,8 @@ pub use estimator::{
|
||||
DistanceEstimator, EstimatorBank, EstimatorQuery, EstimatorSketch, SideInfo,
|
||||
};
|
||||
pub use event_log::{NoveltyEvent, PrivacyEventLog};
|
||||
pub use hnsw::{HnswIndex, HnswParams, Metric};
|
||||
pub use hnsw_quantized::QuantizedHnswIndex;
|
||||
pub use rotation::Rotation;
|
||||
pub use sketch::{
|
||||
Sketch, SketchBank, SketchError, WireSketch, WireSketchError, WIRE_SKETCH_FORMAT_VERSION,
|
||||
|
||||
+1
Submodule vendor/rufield added at c6abe92746
Reference in New Issue
Block a user