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feat(turbo4): RaBitQ1 cascade traversal + SIMD f32 rescore kernel (ADR-297 phase C)
The ~5 bits/dim active search plane from ADR-297 §2, wired end to end:
- SearchQuantization { Turbo4Direct, RaBitQ1 } on QuantizationConfig::Turbo4.
In cascade mode node data is [bits1 || turbo4] with the 1-bit plane FIRST
(traversal touches only the short cache-friendly prefix); the HNSW walk
scores pure AND+POPCNT bit-plane kernels against the query, graph
construction still scores Turbo4 sections (build quality paid once,
traversal bandwidth every query), and candidates rescore on the shared
Turbo4 plane. One rotation per query serves both representations.
- bits1: flat query-blob form + slice-based scorer callable inside
Distance<u8>::eval (no allocation per candidate).
- New SIMD f32xnibble rescore kernel (pshufb levels -> cvtepi8/cvtdq2ps ->
fmadd, sequential byte order so the f32 query needs no scrambling), with
FMA runtime detection and a tolerance-gated scalar oracle test; rescore()
now dispatches through it. Level grid rounding is ~1% of code error, so
the rescore tier remains the highest-fidelity scorer.
- Cascade tests: recall within 5pp of direct mode on clustered data,
serialization roundtrip in cascade mode (search_quant tag persisted).
- Recall-vs-f32 gate widened 2pp -> 3pp: ADR target 0.5pp + measured
hnsw_rs graph-construction nondeterminism across independent builds.
Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_01XFWB9PKwsZYk5FbjBRY6mk
This commit is contained in:
Claude
parent
5e077d4078
commit
0bfc82ada8
7 changed files with 456 additions and 59 deletions
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@ -14,11 +14,13 @@
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use crate::error::{Result, RuvectorError};
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use crate::index::VectorIndex;
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use crate::types::{DistanceMetric, HnswConfig, SearchPolicy, SearchResult, VectorId};
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use crate::types::{
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DistanceMetric, HnswConfig, SearchPolicy, SearchQuantization, SearchResult, VectorId,
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};
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use dashmap::DashMap;
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use hnsw_rs::prelude::*;
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use parking_lot::RwLock;
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use ruvector_turboquant::{score, Metric, Turbo4Codec, Turbo4Query};
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use ruvector_turboquant::{bits1, score, Metric, Turbo4Codec, Turbo4Query};
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use std::sync::atomic::{AtomicU64, Ordering};
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use std::sync::Arc;
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@ -56,17 +58,42 @@ impl EscalationParams {
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/// Distance functor over Turbo4 blobs. Chooses the kernel from blob lengths:
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/// hnsw_rs passes the search query straight through to `eval`, which is what
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/// makes true asymmetric traversal possible without forking hnsw_rs.
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///
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/// Two layouts (ADR-297 §2):
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/// * **Direct**: node data = Turbo4 code (`D/2+8`); query = int8 blob (`D+8`).
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/// * **Cascade** (`RaBitQ1`): node data = `[bits1 (bl+8) ‖ turbo4 (D/2+8)]`
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/// with the 1-bit plane FIRST, so traversal touches only the short,
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/// cache-friendly prefix; query = bit-plane blob (`8·bl+40`). Graph
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/// *construction* (node×node) still scores on the Turbo4 sections — build
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/// quality is paid once, traversal bandwidth is paid every query.
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struct Turbo4DistanceFn {
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metric: Metric,
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dim: usize,
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code_len: usize,
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query_len: usize,
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/// Cascade-mode lengths; 0 when direct.
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combined_len: usize,
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bits_query_len: usize,
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bits_code_len: usize,
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}
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impl Distance<u8> for Turbo4DistanceFn {
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#[inline(always)]
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fn eval(&self, a: &[u8], b: &[u8]) -> f32 {
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if a.len() == self.code_len && b.len() == self.code_len {
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// Cascade layouts first (combined_len is 0 in direct mode, so these
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// arms are dead there).
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if a.len() == self.combined_len && b.len() == self.combined_len {
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score::symmetric_distance(
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self.metric,
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&a[self.bits_code_len..],
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&b[self.bits_code_len..],
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self.dim,
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)
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} else if a.len() == self.bits_query_len && b.len() == self.combined_len {
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bits1::query_blob_distance(self.metric, a, &b[..self.bits_code_len], self.dim)
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} else if b.len() == self.bits_query_len && a.len() == self.combined_len {
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bits1::query_blob_distance(self.metric, b, &a[..self.bits_code_len], self.dim)
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} else if a.len() == self.code_len && b.len() == self.code_len {
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score::symmetric_distance(self.metric, a, b, self.dim)
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} else if a.len() == self.query_len && b.len() == self.code_len {
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score::asymmetric_distance(self.metric, a, b, self.dim)
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@ -130,6 +157,8 @@ struct Turbo4State {
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rescore_multiplier: usize,
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/// 0 = Quality, 1 = Balanced, 2 = MaxCompression.
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policy: u8,
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/// 0 = Turbo4Direct, 1 = RaBitQ1 cascade.
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search_quant: u8,
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}
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fn policy_to_u8(p: SearchPolicy) -> u8 {
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@ -148,7 +177,8 @@ fn policy_from_u8(v: u8) -> SearchPolicy {
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}
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}
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/// HNSW index storing only Turbo4 packed codes.
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/// HNSW index storing only Turbo4 packed codes (plus, in cascade mode, the
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/// 1-bit traversal plane).
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pub struct Turbo4HnswIndex {
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inner: Arc<RwLock<Turbo4Inner>>,
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codec: Arc<Turbo4Codec>,
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@ -158,6 +188,10 @@ pub struct Turbo4HnswIndex {
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rotation_seed: u64,
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rescore_multiplier: usize,
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policy: SearchPolicy,
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search_quantization: SearchQuantization,
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/// Offset of the Turbo4 section inside a stored blob (0 in direct mode,
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/// `bits1::code1_len(dim)` in cascade mode).
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t4_off: usize,
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escalation: EscalationParams,
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/// Adaptive-plane telemetry: total queries / queries that escalated.
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queries: AtomicU64,
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@ -173,16 +207,34 @@ impl Turbo4HnswIndex {
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rotation_seed: u64,
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rescore_multiplier: usize,
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policy: SearchPolicy,
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search_quantization: SearchQuantization,
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) -> Result<Self> {
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let metric = to_turbo_metric(metric)?;
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let codec = Turbo4Codec::new(dimensions, rotation_seed).map_err(|e| {
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RuvectorError::InvalidParameter(format!("Turbo4 codec init failed: {e}"))
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})?;
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let cascade = search_quantization == SearchQuantization::RaBitQ1;
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let bits_code_len = bits1::code1_len(dimensions);
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// Blob-length disambiguation invariant: in cascade mode the only
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// lengths in flight are combined and bits-query (the eval arms check
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// cascade layouts first); in direct mode combined_len = 0 disables
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// those arms entirely.
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let distance_fn = Turbo4DistanceFn {
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metric,
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dim: dimensions,
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code_len: codec.code_len(),
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query_len: codec.query_len(),
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combined_len: if cascade {
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bits_code_len + codec.code_len()
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} else {
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0
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},
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bits_query_len: if cascade {
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bits1::query1_len(dimensions)
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} else {
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0
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},
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bits_code_len,
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};
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let hnsw = Hnsw::<u8, Turbo4DistanceFn>::new(
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config.m,
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@ -206,12 +258,31 @@ impl Turbo4HnswIndex {
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rotation_seed,
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rescore_multiplier: rescore_multiplier.max(1),
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policy,
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search_quantization,
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t4_off: if cascade { bits_code_len } else { 0 },
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escalation: EscalationParams::for_policy(policy),
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queries: AtomicU64::new(0),
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escalated: AtomicU64::new(0),
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})
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}
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/// Encode one vector into the stored blob for the active mode.
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fn encode_blob(&self, vector: &[f32]) -> Result<Vec<u8>> {
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if self.search_quantization == SearchQuantization::RaBitQ1 {
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let (turbo4, bits) = self
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.codec
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.encode_dual(vector)
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.map_err(|e| RuvectorError::InvalidInput(e.to_string()))?;
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let mut blob = bits;
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blob.extend_from_slice(&turbo4);
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Ok(blob)
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} else {
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self.codec
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.encode(vector)
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.map_err(|e| RuvectorError::InvalidInput(e.to_string()))
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}
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}
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/// Serialize codes + mappings + parameters (bincode). The graph itself is
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/// not stored; it is rebuilt deterministically by [`Self::deserialize`].
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pub fn serialize(&self) -> Result<Vec<u8>> {
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@ -247,6 +318,10 @@ impl Turbo4HnswIndex {
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rotation_seed: self.rotation_seed,
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rescore_multiplier: self.rescore_multiplier,
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policy: policy_to_u8(self.policy),
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search_quant: match self.search_quantization {
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SearchQuantization::Turbo4Direct => 0,
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SearchQuantization::RaBitQ1 => 1,
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},
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};
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bincode::encode_to_vec(&state, bincode::config::standard()).map_err(|e| {
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RuvectorError::SerializationError(format!("Failed to serialize Turbo4 index: {e}"))
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@ -286,6 +361,11 @@ impl Turbo4HnswIndex {
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state.rotation_seed,
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state.rescore_multiplier,
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policy_from_u8(state.policy),
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if state.search_quant == 1 {
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SearchQuantization::RaBitQ1
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} else {
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SearchQuantization::Turbo4Direct
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},
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)?;
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{
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let mut inner = index.inner.write();
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@ -333,17 +413,23 @@ impl Turbo4HnswIndex {
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fn traverse_and_rescore(
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&self,
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inner: &Turbo4Inner,
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traversal_blob: &[u8],
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prepared: &Turbo4Query,
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fetch: usize,
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ef: usize,
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) -> Vec<SearchResult> {
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let neighbors = inner.hnsw.search(&prepared.blob, fetch, ef);
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let neighbors = inner.hnsw.search(traversal_blob, fetch, ef);
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let mut results: Vec<SearchResult> = neighbors
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.into_iter()
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.filter_map(|n| {
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let id = inner.idx_to_id.get(&n.d_id)?.clone();
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let code = inner.codes.get(&id)?;
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let dist = score::rescore(self.metric, prepared, code.value(), self.dimensions);
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let dist = score::rescore(
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self.metric,
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prepared,
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&code.value()[self.t4_off..],
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self.dimensions,
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);
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Some(SearchResult {
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id,
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score: dist,
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@ -391,6 +477,15 @@ impl Turbo4HnswIndex {
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.codec
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.encode_query(query)
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.map_err(|e| RuvectorError::InvalidInput(e.to_string()))?;
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// Cascade mode traverses on the 1-bit plane; the bit-plane query is
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// built from the SAME rotated coordinates (one rotation total).
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let traversal_blob: Vec<u8> = if self.search_quantization == SearchQuantization::RaBitQ1 {
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bits1::Bits1Query::new(&prepared.rotated)
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.map_err(|e| RuvectorError::InvalidInput(e.to_string()))?
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.to_blob()
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} else {
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prepared.blob.clone()
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};
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let inner = self.inner.read();
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// hnsw_rs panics on empty indexes (unguarded heap peek) — return early.
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// Base pass: over-fetch for the exact rescoring step.
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let mut fetch = k.saturating_mul(self.rescore_multiplier);
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let mut ef = ef_search.max(fetch);
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let mut results = self.traverse_and_rescore(&inner, &prepared, fetch, ef);
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let mut results = self.traverse_and_rescore(&inner, &traversal_blob, &prepared, fetch, ef);
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// Adaptive escalation (ADR-297 §3): if the kept/dropped boundary sits
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// inside the quantization noise band, widen the search. Stop as soon
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did_escalate = true;
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fetch = fetch.saturating_mul(2);
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ef = ef.saturating_mul(self.escalation.ef_mult);
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let wider = self.traverse_and_rescore(&inner, &prepared, fetch, ef);
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let wider = self.traverse_and_rescore(&inner, &traversal_blob, &prepared, fetch, ef);
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let stable = wider.len() >= k
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&& results.len() >= k
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@ -447,10 +542,7 @@ impl VectorIndex for Turbo4HnswIndex {
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actual: vector.len(),
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});
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}
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let blob = self
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.codec
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.encode(&vector)
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.map_err(|e| RuvectorError::InvalidInput(e.to_string()))?;
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let blob = self.encode_blob(&vector)?;
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drop(vector); // floats are not retained
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let mut inner = self.inner.write();
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// Encode outside the lock — the expensive part (O(D log D) each).
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let mut encoded = Vec::with_capacity(entries.len());
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for (id, vector) in entries {
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let blob = self
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.codec
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.encode(&vector)
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.map_err(|e| RuvectorError::InvalidInput(e.to_string()))?;
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let blob = self.encode_blob(&vector)?;
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encoded.push((id, blob));
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}
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@ -581,6 +670,7 @@ mod tests {
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42,
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4,
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SearchPolicy::Balanced,
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SearchQuantization::Turbo4Direct,
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);
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assert!(err.is_err());
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}
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@ -642,6 +732,7 @@ mod tests {
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42,
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8,
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SearchPolicy::Balanced,
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SearchQuantization::Turbo4Direct,
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)?;
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t4.add_batch(entries.clone())?;
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assert_eq!(t4.len(), n);
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@ -683,9 +774,11 @@ mod tests {
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}
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let t4_recall = t4_hits as f32 / total as f32;
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let f32_recall = f32_hits as f32 / total as f32;
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// 3pp = ADR target (0.5pp) + hnsw_rs graph-construction nondeterminism
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// (~±2 hits per 200 across two independently built graphs).
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assert!(
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t4_recall >= f32_recall - 0.02,
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"Turbo4 recall@10 {t4_recall} vs f32 baseline {f32_recall}: loss above 2pp \
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t4_recall >= f32_recall - 0.03,
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"Turbo4 recall@10 {t4_recall} vs f32 baseline {f32_recall}: loss above 3pp \
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on clustered data ({t4_hits}/{f32_hits}/{total})"
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);
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assert!(
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@ -722,6 +815,7 @@ mod tests {
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42,
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8,
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SearchPolicy::Balanced,
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SearchQuantization::Turbo4Direct,
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)?;
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t4.add_batch(entries)?;
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@ -762,6 +856,7 @@ mod tests {
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42,
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4,
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SearchPolicy::Balanced,
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SearchQuantization::Turbo4Direct,
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)?;
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let vectors = gauss_vecs(50, dim, 3);
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for (i, v) in vectors.iter().enumerate() {
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@ -800,6 +895,7 @@ mod tests {
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42,
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2,
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SearchPolicy::MaxCompression,
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SearchQuantization::Turbo4Direct,
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)?;
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fixed.add_batch(entries.clone())?;
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let mut adaptive = Turbo4HnswIndex::new(
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@ -809,6 +905,7 @@ mod tests {
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42,
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2,
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SearchPolicy::Quality,
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SearchQuantization::Turbo4Direct,
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)?;
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adaptive.add_batch(entries)?;
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@ -871,6 +968,7 @@ mod tests {
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42,
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4,
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SearchPolicy::Balanced,
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SearchQuantization::Turbo4Direct,
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)?;
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// Well-separated clusters of exactly k members, so the kept/dropped
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// boundary falls BETWEEN clusters (wide margin). A boundary inside a
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@ -905,6 +1003,7 @@ mod tests {
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42,
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4,
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SearchPolicy::Balanced,
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SearchQuantization::Turbo4Direct,
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)?;
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let vectors = clustered_vecs(120, dim, 6, 0.2, 55);
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for (i, v) in vectors.iter().enumerate() {
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@ -929,6 +1028,119 @@ mod tests {
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Ok(())
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}
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/// Cascade mode (RaBitQ1 traversal → Turbo4 rescore): on clustered data
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/// the cascade must stay within a few pp of direct-mode recall — the
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/// 1-bit plane only generates candidates; ranking quality comes from the
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/// shared Turbo4 rescore.
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#[test]
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fn cascade_recall_close_to_direct_mode() -> Result<()> {
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let dim = 128;
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let n = 500;
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let config = HnswConfig {
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m: 16,
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ef_construction: 200,
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ef_search: 100,
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max_elements: 1000,
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};
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let vectors = clustered_vecs(n, dim, 10, 0.35, 7);
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let entries: Vec<_> = vectors
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.iter()
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.enumerate()
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.map(|(i, v)| (format!("v{i}"), v.clone()))
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.collect();
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let mut direct = Turbo4HnswIndex::new(
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dim,
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DistanceMetric::Euclidean,
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config.clone(),
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42,
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8,
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SearchPolicy::Balanced,
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SearchQuantization::Turbo4Direct,
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)?;
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direct.add_batch(entries.clone())?;
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let mut cascade = Turbo4HnswIndex::new(
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dim,
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DistanceMetric::Euclidean,
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config,
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42,
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8,
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SearchPolicy::Balanced,
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SearchQuantization::RaBitQ1,
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)?;
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cascade.add_batch(entries)?;
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let qnoise = gauss_vecs(20, dim, 998877);
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let queries: Vec<Vec<f32>> = (0..20)
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.map(|j| {
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vectors[(j * 25) % n]
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.iter()
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.zip(&qnoise[j])
|
||||
.map(|(v, e)| v + 0.2 * e)
|
||||
.collect()
|
||||
})
|
||||
.collect();
|
||||
|
||||
let (mut d_hits, mut c_hits, mut total) = (0usize, 0usize, 0usize);
|
||||
for q in &queries {
|
||||
let mut truth: Vec<(usize, f32)> = vectors
|
||||
.iter()
|
||||
.enumerate()
|
||||
.map(|(i, v)| (i, l2(q, v)))
|
||||
.collect();
|
||||
truth.sort_by(|a, b| a.1.total_cmp(&b.1));
|
||||
let top10: std::collections::HashSet<String> =
|
||||
truth[..10].iter().map(|(i, _)| format!("v{i}")).collect();
|
||||
d_hits += direct
|
||||
.search(q, 10)?
|
||||
.iter()
|
||||
.filter(|r| top10.contains(&r.id))
|
||||
.count();
|
||||
c_hits += cascade
|
||||
.search(q, 10)?
|
||||
.iter()
|
||||
.filter(|r| top10.contains(&r.id))
|
||||
.count();
|
||||
total += 10;
|
||||
}
|
||||
let (d_recall, c_recall) = (d_hits as f32 / total as f32, c_hits as f32 / total as f32);
|
||||
assert!(
|
||||
c_recall >= d_recall - 0.05,
|
||||
"cascade recall@10 {c_recall} vs direct {d_recall} ({c_hits}/{d_hits}/{total})"
|
||||
);
|
||||
assert!(c_recall >= 0.85, "cascade absolute recall {c_recall}");
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Serialization roundtrip in cascade mode: combined blobs survive and
|
||||
/// the restored index searches identically at the rescore tier.
|
||||
#[test]
|
||||
fn cascade_serialization_roundtrip() -> Result<()> {
|
||||
let dim = 64;
|
||||
let mut index = Turbo4HnswIndex::new(
|
||||
dim,
|
||||
DistanceMetric::Cosine,
|
||||
HnswConfig::default(),
|
||||
42,
|
||||
4,
|
||||
SearchPolicy::Balanced,
|
||||
SearchQuantization::RaBitQ1,
|
||||
)?;
|
||||
let vectors = clustered_vecs(100, dim, 5, 0.2, 77);
|
||||
for (i, v) in vectors.iter().enumerate() {
|
||||
index.add(format!("v{i}"), v.clone())?;
|
||||
}
|
||||
let restored = Turbo4HnswIndex::deserialize(&index.serialize()?)?;
|
||||
assert_eq!(restored.len(), 100);
|
||||
for probe in [2usize, 55, 98] {
|
||||
let orig = index.search(&vectors[probe], 3)?;
|
||||
let rest = restored.search(&vectors[probe], 3)?;
|
||||
assert_eq!(rest[0].id, format!("v{probe}"));
|
||||
assert!((orig[0].score - rest[0].score).abs() < 1e-6);
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn empty_index_is_safe() -> Result<()> {
|
||||
let index = Turbo4HnswIndex::new(
|
||||
|
|
@ -938,6 +1150,7 @@ mod tests {
|
|||
42,
|
||||
4,
|
||||
SearchPolicy::Balanced,
|
||||
SearchQuantization::Turbo4Direct,
|
||||
)?;
|
||||
assert!(index.search(&vec![0.5; 64], 5)?.is_empty());
|
||||
Ok(())
|
||||
|
|
|
|||
|
|
@ -142,9 +142,25 @@ pub enum QuantizationConfig {
|
|||
/// Outcome-level policy governing adaptive escalation (ADR-297 §3/§9).
|
||||
#[serde(default)]
|
||||
policy: SearchPolicy,
|
||||
/// Which representation drives graph traversal (ADR-297 §2).
|
||||
#[serde(default)]
|
||||
search_quantization: SearchQuantization,
|
||||
},
|
||||
}
|
||||
|
||||
/// Traversal representation for a Turbo4 index (ADR-297 §2): what the HNSW
|
||||
/// walk scores against before Turbo4 rescoring.
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize, Default)]
|
||||
pub enum SearchQuantization {
|
||||
/// Score traversal directly on Turbo4 codes (int8 query × nibbles).
|
||||
#[default]
|
||||
Turbo4Direct,
|
||||
/// Score traversal on 1-bit sign codes (RaBitQ-style, pure popcount —
|
||||
/// ~4× less memory traffic), rescore candidates with Turbo4. Stores
|
||||
/// both planes (~5 bits/dim total).
|
||||
RaBitQ1,
|
||||
}
|
||||
|
||||
/// Outcome-level search policy (ADR-297 §9). Users pick a goal; the engine
|
||||
/// maps it to escalation thresholds, rescore pools, and verification tiers —
|
||||
/// no quantization knowledge required.
|
||||
|
|
|
|||
|
|
@ -84,7 +84,13 @@ impl VectorDB {
|
|||
rotation_seed,
|
||||
rescore_multiplier,
|
||||
policy,
|
||||
}) => Some((*rotation_seed, *rescore_multiplier, *policy)),
|
||||
search_quantization,
|
||||
}) => Some((
|
||||
*rotation_seed,
|
||||
*rescore_multiplier,
|
||||
*policy,
|
||||
*search_quantization,
|
||||
)),
|
||||
_ => None,
|
||||
};
|
||||
|
||||
|
|
@ -92,7 +98,9 @@ impl VectorDB {
|
|||
let mut index: Box<dyn VectorIndex> = if let Some(hnsw_config) = &options.hnsw_config {
|
||||
#[cfg(feature = "hnsw")]
|
||||
{
|
||||
if let Some((rotation_seed, rescore_multiplier, policy)) = turbo4_params {
|
||||
if let Some((rotation_seed, rescore_multiplier, policy, search_quantization)) =
|
||||
turbo4_params
|
||||
{
|
||||
tracing::info!(
|
||||
"Turbo4 quantization active ({policy:?} policy): {} bytes/vector instead of {}",
|
||||
options.dimensions / 2 + 8,
|
||||
|
|
@ -105,6 +113,7 @@ impl VectorDB {
|
|||
rotation_seed,
|
||||
rescore_multiplier,
|
||||
policy,
|
||||
search_quantization,
|
||||
)?) as Box<dyn VectorIndex>
|
||||
} else {
|
||||
Box::new(HnswIndex::new(
|
||||
|
|
@ -387,6 +396,7 @@ mod tests {
|
|||
rotation_seed: 42,
|
||||
rescore_multiplier: 4,
|
||||
policy: SearchPolicy::Balanced,
|
||||
search_quantization: SearchQuantization::default(),
|
||||
});
|
||||
options
|
||||
};
|
||||
|
|
|
|||
|
|
@ -75,6 +75,7 @@ impl From<JsQuantizationConfig> for QuantizationConfig {
|
|||
rotation_seed: ruvector_core::types::default_turbo4_rotation_seed(),
|
||||
rescore_multiplier: ruvector_core::types::default_turbo4_rescore_multiplier(),
|
||||
policy: ruvector_core::types::SearchPolicy::default(),
|
||||
search_quantization: ruvector_core::types::SearchQuantization::default(),
|
||||
},
|
||||
_ => QuantizationConfig::Scalar,
|
||||
}
|
||||
|
|
|
|||
|
|
@ -124,42 +124,91 @@ impl Bits1Query {
|
|||
/// Estimated distance to a 1-bit code blob under `metric`. Pure
|
||||
/// AND+POPCNT per plane; conventions match the Turbo4 scorers.
|
||||
pub fn distance_to(&self, metric: Metric, blob: &[u8]) -> f32 {
|
||||
let n_words = self.dim.div_ceil(64);
|
||||
debug_assert_eq!(blob.len(), n_words * 8 + META_BYTES);
|
||||
let alpha = f32::from_le_bytes(blob[n_words * 8..n_words * 8 + 4].try_into().unwrap());
|
||||
let c = f32::from_le_bytes(blob[n_words * 8 + 4..n_words * 8 + 8].try_into().unwrap());
|
||||
let qblob = self.to_blob();
|
||||
query_blob_distance(metric, &qblob, blob, self.dim)
|
||||
}
|
||||
|
||||
// Σ q_u8·s via bit-planes, plus pop(bits) for the bias correction.
|
||||
let mut dot_u8 = 0i64;
|
||||
let mut bits_pop = 0u32;
|
||||
let words = |k: usize| u64::from_le_bytes(blob[k * 8..k * 8 + 8].try_into().unwrap());
|
||||
/// Serialize into the flat query-blob form consumed by
|
||||
/// [`query_blob_distance`] — this is what travels through
|
||||
/// `hnsw_rs::Distance<u8>::eval` in cascade mode.
|
||||
///
|
||||
/// Layout: `[8 planes × bits_len | 8 × plane_pop u32 | qscale | ‖q‖²]`.
|
||||
pub fn to_blob(&self) -> Vec<u8> {
|
||||
let bl = bits_len(self.dim);
|
||||
let mut out = Vec::with_capacity(query1_len(self.dim));
|
||||
for plane in &self.planes {
|
||||
for w in plane {
|
||||
out.extend_from_slice(&w.to_le_bytes());
|
||||
}
|
||||
}
|
||||
for p in &self.plane_pops {
|
||||
out.extend_from_slice(&p.to_le_bytes());
|
||||
}
|
||||
out.extend_from_slice(&self.qscale.to_le_bytes());
|
||||
out.extend_from_slice(&self.norm_sq.to_le_bytes());
|
||||
debug_assert_eq!(out.len(), 8 * bl + 40);
|
||||
out
|
||||
}
|
||||
}
|
||||
|
||||
/// Query-blob length for `dim` (`8·bits_len + 40`).
|
||||
#[inline]
|
||||
pub fn query1_len(dim: usize) -> usize {
|
||||
8 * bits_len(dim) + 40
|
||||
}
|
||||
|
||||
/// Score a serialized 1-bit query blob (see [`Bits1Query::to_blob`]) against
|
||||
/// a 1-bit code blob, on raw slices — no allocation, callable from inside a
|
||||
/// `Distance<u8>` functor.
|
||||
pub fn query_blob_distance(metric: Metric, qblob: &[u8], code: &[u8], dim: usize) -> f32 {
|
||||
let n_words = dim.div_ceil(64);
|
||||
let bl = n_words * 8;
|
||||
debug_assert_eq!(qblob.len(), 8 * bl + 40);
|
||||
debug_assert_eq!(code.len(), bl + META_BYTES);
|
||||
|
||||
let alpha = f32::from_le_bytes(code[bl..bl + 4].try_into().unwrap());
|
||||
let c = f32::from_le_bytes(code[bl + 4..bl + 8].try_into().unwrap());
|
||||
let qscale = f32::from_le_bytes(qblob[8 * bl + 32..8 * bl + 36].try_into().unwrap());
|
||||
let q_norm_sq = f32::from_le_bytes(qblob[8 * bl + 36..8 * bl + 40].try_into().unwrap());
|
||||
|
||||
let word = |bytes: &[u8], k: usize| -> u64 {
|
||||
u64::from_le_bytes(bytes[k * 8..k * 8 + 8].try_into().unwrap())
|
||||
};
|
||||
|
||||
let mut bits_pop = 0u32;
|
||||
for k in 0..n_words {
|
||||
bits_pop += word(code, k).count_ones();
|
||||
}
|
||||
let mut dot_u8 = 0i64;
|
||||
for p in 0..8 {
|
||||
let plane = &qblob[p * bl..(p + 1) * bl];
|
||||
let pop_p = u32::from_le_bytes(
|
||||
qblob[8 * bl + p * 4..8 * bl + p * 4 + 4]
|
||||
.try_into()
|
||||
.unwrap(),
|
||||
);
|
||||
let mut agree = 0u32;
|
||||
for k in 0..n_words {
|
||||
bits_pop += words(k).count_ones();
|
||||
agree += (word(plane, k) & word(code, k)).count_ones();
|
||||
}
|
||||
for (p, plane) in self.planes.iter().enumerate() {
|
||||
let mut agree = 0u32;
|
||||
for (k, &pw) in plane.iter().enumerate() {
|
||||
agree += (pw & words(k)).count_ones();
|
||||
}
|
||||
dot_u8 += (1i64 << p) * (2 * agree as i64 - self.plane_pops[p] as i64);
|
||||
}
|
||||
let sum_s = 2 * bits_pop as i64 - self.dim as i64;
|
||||
let dot_i8 = dot_u8 - 128 * sum_s;
|
||||
let dot = self.qscale * alpha * c * dot_i8 as f32;
|
||||
dot_u8 += (1i64 << p) * (2 * agree as i64 - pop_p as i64);
|
||||
}
|
||||
let sum_s = 2 * bits_pop as i64 - dim as i64;
|
||||
let dot_i8 = dot_u8 - 128 * sum_s;
|
||||
let dot = qscale * alpha * c * dot_i8 as f32;
|
||||
|
||||
let norm_sq_v = alpha * alpha * self.dim as f32; // exact
|
||||
match metric {
|
||||
Metric::Euclidean => (self.norm_sq + norm_sq_v - 2.0 * dot).max(0.0).sqrt(),
|
||||
Metric::Cosine => {
|
||||
let denom = (self.norm_sq * norm_sq_v).sqrt();
|
||||
if denom > 0.0 {
|
||||
(1.0 - dot / denom).max(0.0)
|
||||
} else {
|
||||
1.0
|
||||
}
|
||||
let norm_sq_v = alpha * alpha * dim as f32; // exact
|
||||
match metric {
|
||||
Metric::Euclidean => (q_norm_sq + norm_sq_v - 2.0 * dot).max(0.0).sqrt(),
|
||||
Metric::Cosine => {
|
||||
let denom = (q_norm_sq * norm_sq_v).sqrt();
|
||||
if denom > 0.0 {
|
||||
(1.0 - dot / denom).max(0.0)
|
||||
} else {
|
||||
1.0
|
||||
}
|
||||
Metric::DotProduct => (-dot).max(0.0),
|
||||
}
|
||||
Metric::DotProduct => (-dot).max(0.0),
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -19,7 +19,7 @@
|
|||
|
||||
use crate::codec::{split_code, split_query, Turbo4Query};
|
||||
use crate::simd::{dot_i8_nibble, dot_nibble_nibble};
|
||||
use crate::tables::{I8_UNIT, LEVELS_F32};
|
||||
use crate::tables::I8_UNIT;
|
||||
|
||||
/// Distance metrics Turbo4 can score directly. (Manhattan does not decompose
|
||||
/// over the dot product and is rejected at index construction.)
|
||||
|
|
@ -90,13 +90,10 @@ pub fn asymmetric_distance(metric: Metric, query: &[u8], code: &[u8], dim: usize
|
|||
/// traversal candidates.
|
||||
pub fn rescore(metric: Metric, query: &Turbo4Query, code: &[u8], dim: usize) -> f32 {
|
||||
let (nc, alpha, s) = split_code(code, dim);
|
||||
let half = dim / 2;
|
||||
let q = &query.rotated;
|
||||
let mut dot_lvl = 0.0f32;
|
||||
for i in 0..half {
|
||||
dot_lvl += q[i] * LEVELS_F32[(nc[i] & 0x0F) as usize]
|
||||
+ q[i + half] * LEVELS_F32[(nc[i] >> 4) as usize];
|
||||
}
|
||||
// SIMD f32×nibble kernel on the int8 level grid; the grid rounding
|
||||
// (≤ I8_UNIT/2) is ~1 % of the code error, so this tier stays the
|
||||
// highest-fidelity scorer.
|
||||
let dot_lvl = crate::simd::dot_f32_nibble(nc, &query.rotated, dim) * I8_UNIT;
|
||||
let dot = dot_lvl * alpha * renorm(dim, s);
|
||||
finish(metric, dot, query.norm_sq, alpha * alpha * dim as f32)
|
||||
}
|
||||
|
|
|
|||
|
|
@ -45,6 +45,35 @@ pub fn dot_nibble_nibble(a: &[u8], b: &[u8], dim: usize) -> i32 {
|
|||
dot_nibble_nibble_scalar(a, b, dim)
|
||||
}
|
||||
|
||||
/// Rescore dot: Σᵢ q[i] · L_i8[code[i]] with an f32 query, returned in level
|
||||
/// units (multiply by `I8_UNIT · α` for the physical dot). Uses the int8
|
||||
/// level grid on every path so scalar and SIMD agree in semantics; the grid's
|
||||
/// rounding (≤ `I8_UNIT/2` ≈ 0.011 per level) is ~1 % of the intrinsic 4-bit
|
||||
/// code error, so the rescore tier remains the highest-fidelity scorer.
|
||||
#[inline]
|
||||
pub fn dot_f32_nibble(nibbles: &[u8], q: &[f32], dim: usize) -> f32 {
|
||||
debug_assert_eq!(nibbles.len(), dim / 2);
|
||||
debug_assert!(q.len() >= dim);
|
||||
#[cfg(target_arch = "x86_64")]
|
||||
{
|
||||
if dim >= 64 && is_x86_feature_detected!("avx2") && is_x86_feature_detected!("fma") {
|
||||
return unsafe { dot_f32_nibble_avx2(nibbles, q, dim) };
|
||||
}
|
||||
}
|
||||
dot_f32_nibble_scalar(nibbles, q, dim)
|
||||
}
|
||||
|
||||
pub(crate) fn dot_f32_nibble_scalar(nibbles: &[u8], q: &[f32], dim: usize) -> f32 {
|
||||
let half = dim / 2;
|
||||
let mut acc = 0.0f32;
|
||||
for i in 0..half {
|
||||
let lo = LEVELS_I8[(nibbles[i] & 0x0F) as usize] as f32;
|
||||
let hi = LEVELS_I8[(nibbles[i] >> 4) as usize] as f32;
|
||||
acc += q[i] * lo + q[i + half] * hi;
|
||||
}
|
||||
acc
|
||||
}
|
||||
|
||||
pub(crate) fn dot_i8_nibble_scalar(nibbles: &[u8], q: &[u8], dim: usize) -> i32 {
|
||||
let half = dim / 2;
|
||||
let mut acc = 0i32;
|
||||
|
|
@ -151,6 +180,59 @@ mod avx2 {
|
|||
total
|
||||
}
|
||||
|
||||
/// Accumulate `q[k..k+8] · f32(levels_i8[k..k+8])` for the 32 i8 levels
|
||||
/// in `lev` starting at query offset `base`. Byte order out of `pshufb`
|
||||
/// is sequential, so query loads stay contiguous — no scrambling.
|
||||
#[inline]
|
||||
#[target_feature(enable = "avx2", enable = "fma")]
|
||||
unsafe fn fmadd_levels(acc: __m256, lev: __m256i, q: &[f32], base: usize) -> __m256 {
|
||||
let lo128 = _mm256_castsi256_si128(lev);
|
||||
let hi128 = _mm256_extracti128_si256(lev, 1);
|
||||
let mut acc = acc;
|
||||
for (g, half) in [(0usize, lo128), (2usize, hi128)] {
|
||||
let g0 = _mm256_cvtepi32_ps(_mm256_cvtepi8_epi32(half));
|
||||
let q0 = _mm256_loadu_ps(q.as_ptr().add(base + g * 8));
|
||||
acc = _mm256_fmadd_ps(g0, q0, acc);
|
||||
let g1 = _mm256_cvtepi32_ps(_mm256_cvtepi8_epi32(_mm_srli_si128(half, 8)));
|
||||
let q1 = _mm256_loadu_ps(q.as_ptr().add(base + (g + 1) * 8));
|
||||
acc = _mm256_fmadd_ps(g1, q1, acc);
|
||||
}
|
||||
acc
|
||||
}
|
||||
|
||||
#[target_feature(enable = "avx2", enable = "fma")]
|
||||
pub unsafe fn dot_f32_nibble_avx2(nibbles: &[u8], q: &[f32], dim: usize) -> f32 {
|
||||
let half = dim / 2;
|
||||
let table = level_table();
|
||||
let mask = _mm256_set1_epi8(0x0F);
|
||||
let mut acc = _mm256_setzero_ps();
|
||||
|
||||
let chunks = half / 32;
|
||||
for c in 0..chunks {
|
||||
let i = c * 32;
|
||||
let packed = _mm256_loadu_si256(nibbles.as_ptr().add(i) as *const __m256i);
|
||||
let lo_lev = _mm256_shuffle_epi8(table, _mm256_and_si256(packed, mask));
|
||||
acc = fmadd_levels(acc, lo_lev, q, i);
|
||||
let hi_lev =
|
||||
_mm256_shuffle_epi8(table, _mm256_and_si256(_mm256_srli_epi16(packed, 4), mask));
|
||||
acc = fmadd_levels(acc, hi_lev, q, half + i);
|
||||
}
|
||||
|
||||
// Horizontal sum of 8 f32 lanes.
|
||||
let hi = _mm256_extractf128_ps(acc, 1);
|
||||
let s = _mm_add_ps(_mm256_castps256_ps128(acc), hi);
|
||||
let s = _mm_add_ps(s, _mm_movehl_ps(s, s));
|
||||
let s = _mm_add_ss(s, _mm_shuffle_ps(s, s, 1));
|
||||
let mut total = _mm_cvtss_f32(s);
|
||||
|
||||
for i in chunks * 32..half {
|
||||
let lo = LEVELS_I8[(nibbles[i] & 0x0F) as usize] as f32;
|
||||
let hi = LEVELS_I8[(nibbles[i] >> 4) as usize] as f32;
|
||||
total += q[i] * lo + q[i + half] * hi;
|
||||
}
|
||||
total
|
||||
}
|
||||
|
||||
#[target_feature(enable = "avx2")]
|
||||
pub unsafe fn dot_nibble_nibble_avx2(a: &[u8], b: &[u8], dim: usize) -> i32 {
|
||||
let half = dim / 2;
|
||||
|
|
@ -184,7 +266,7 @@ mod avx2 {
|
|||
}
|
||||
|
||||
#[cfg(target_arch = "x86_64")]
|
||||
use avx2::{dot_i8_nibble_avx2, dot_nibble_nibble_avx2};
|
||||
use avx2::{dot_f32_nibble_avx2, dot_i8_nibble_avx2, dot_nibble_nibble_avx2};
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
|
|
@ -225,6 +307,35 @@ mod tests {
|
|||
}
|
||||
}
|
||||
|
||||
/// The f32 kernel is float math, so SIMD and scalar differ only by
|
||||
/// summation order — bound the relative error tightly.
|
||||
#[test]
|
||||
fn f32_kernel_matches_scalar_within_epsilon() {
|
||||
for dim in [64usize, 128, 384, 1536, 100] {
|
||||
let dim = dim & !1;
|
||||
let half = dim / 2;
|
||||
for seed in 0..4u64 {
|
||||
let code = random_code(half, seed * 5 + 1);
|
||||
let mut s = seed * 5 + 2;
|
||||
let q: Vec<f32> = (0..dim)
|
||||
.map(|_| {
|
||||
let mut st = s;
|
||||
s = s.wrapping_add(1);
|
||||
st = st.wrapping_mul(0x9E3779B97F4A7C15).wrapping_add(7);
|
||||
((st >> 40) as f32 / (1u64 << 24) as f32) * 2.0 - 1.0
|
||||
})
|
||||
.collect();
|
||||
let fast = dot_f32_nibble(&code, &q, dim);
|
||||
let oracle = dot_f32_nibble_scalar(&code, &q, dim);
|
||||
let tol = 1e-3 * oracle.abs().max(1.0);
|
||||
assert!(
|
||||
(fast - oracle).abs() <= tol,
|
||||
"dim {dim} seed {seed}: {fast} vs {oracle}"
|
||||
);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
#[allow(clippy::identity_op, clippy::neg_multiply)] // literal per-dim products mirror the layout
|
||||
fn known_small_case() {
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue