fib-quant

Experimental Rust implementation of FibQuant-style radial-angular vector quantization for KV-cache compression. ~50× compression in the binary-packed form. 100% recall on the canonical benchmark corpus (single-level).

Experimental, not production-ready. This is an independent implementation based on the public FibQuant reference (Lee & Kim 2026). It is not the original paper code — see the Attribution section below.

fib-quant decomposes a vector into spherical blocks, quantizes each block against a Fibonacci-optimized codebook, and stores only the codebook indices. The result: a 768-dim f32 vector (3,072 bytes) becomes ~860 bytes in JSON, or ~64 bytes with binary packing. And it still finds the right document at rank 1 in 100% of the canonical test queries.

This is the cold-tier codec in the poly-kv pool. It handles shared context that's large, stable, and accessed by many agents:

┌──────────────────────────────────┐
│    SHARED POOL — fib-quant       │  ← you are here
│    System prompts, few-shot      │
│    examples, shared docs         │
│    50× compression, cos 0.863    │
└──────────┬──────────┬────────────┘
           │          │
      ┌────▼───┐ ┌───▼────┐
      │ Agent0 │ │ Agent1 │  ...  ← turbo-quant hot tier
      └────────┘ └────────┘

What's in the box

• Codec (src/codec.rs, 1121 lines) — FibQuantizer with encode, decode, encode_with_receipt, encode_batch, decode_batch, decode_batch_fast, encode_layers. The encode_batch path is the Rayon-parallel path that wins the poly-kv pool build.
• Codebook (src/codebook.rs, 207 lines) — FibCodebookV1 with build_initial_codebook and Lloyd-Max refinement. Parity-verified against the reference.
• Rotation (src/rotation.rs, 216 lines) — fast Walsh-Hadamard rotation with StoredRotation, a CPU fallback, and an optional CUDA dispatch via gpu-backend.
• Spherical beta (src/spherical_beta.rs, 139 lines) — spherical-Beta direction samplers for codebook seeding: sample_spherical_beta, beta_d_k, radius_quantile, radius_quantile_k2_closed_form.
• Profiles (src/profile.rs, 424 lines) — typed FibQuantProfileV1 with paper_default(ambient_dim, block_dim, codebook_size, seed) (k=4, N=32 defaults shown in examples below). Includes validation and rate computation.
• Receipts (src/receipt.rs, 128 lines) — typed FibQuantCompressionReceiptV1 capturing every parameter of the encode pipeline for audit.
• Scoring (src/scoring.rs, 411 lines) — GramTable and FibScorer for approximate inner-product scoring without full decode, using a precomputed codebook Gram table.
• Residual VQ (src/residual.rs, 491 lines) — two-level vector quantization with FibResidualQuantizer, FibResidualCodeV1, and ResidualCodebookV1 for improved reconstruction quality.
• Wire format (src/wire.rs, 333 lines) — self-describing wire format (FibCodeWireV1, WireHeader) that carries profile metadata so downstream consumers can decode without external profile knowledge.
• Sidecar search (src/sidecar.rs, 430 lines) — FibSidecarIndex<Id> wrapping FibScorer for approximate nearest-inner-product search with ScoredCandidate and SearchReceiptV1.
• Eval harness (src/eval.rs, 519 lines) — benchmark harness with FibBenchmarkCorpus, run_benchmark, and FibBenchmarkReceiptV1 capturing recall@K, nDCG@K, compression ratio, MSE, and timing.
• Compat layer (src/compat.rs, 248 lines, compat feature) — implements VectorCodec, CodecProfile, and KvCacheCodec traits from quant-codec-core so fib-quant can be used through the shared codec trait boundary by poly-kv, quant-governor, and scr-runtime-compression.
• KV-cache codec (src/kv/, ~3,300 lines, kv feature) — a separate KvCacheCodec impl that operates on KvTensorShape rather than raw Vec<f32>. Includes attention-quality metrics, shape contracts, policy-role-aware dispatch, and a streaming encoder (KvStreamEncoder in src/kv/stream.rs, 800 lines) for incremental token-by-token construction.
• KV receipts (src/kv/receipt.rs) — KvCompressionReceiptV1, KvDecodeReceiptV1, and KvEvalReceiptV1 for KV-cache pipeline audit.

Quick Start

use fib_quant::{FibQuantProfileV1, FibQuantizer};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Build a quantizer with the default paper profile.
    // paper_default takes 4 args: ambient_dim, block_dim, codebook_size, seed
    let profile = FibQuantProfileV1::paper_default(32, 4, 32, 7)?;
    let quantizer = FibQuantizer::new(profile)?;

    // Encode a single vector.
    let vector: Vec<f32> = (0..32).map(|i| i as f32 * 0.01).collect();
    let code = quantizer.encode(&vector)?;
    let reconstructed = quantizer.decode(&code)?;

    // The cosine similarity should be high.
    let cos = fib_quant::metrics::cosine_similarity(&vector, &reconstructed)?;
    assert!(cos > 0.85);

    // Or encode with a full audit receipt.
    let (code, receipt) = quantizer.encode_with_receipt(&vector)?;
    println!("Receipt: {}", receipt.profile_digest);

    // Or encode a batch in parallel (returns Vec<FibCodeV1>).
    let corpus: Vec<Vec<f32>> = vec![vector.clone(), vector.clone()];
    let refs: Vec<&[f32]> = corpus.iter().map(|v| v.as_slice()).collect();
    let codes = quantizer.encode_batch(&refs)?;
    assert_eq!(codes.len(), 2);

    Ok(())
}

Run it: cargo run --release --example encode_decode.

New modules

Scoring — approximate inner product without full decode

FibScorer uses a precomputed codebook Gram table (GramTable, G[i,j] = <codeword_i, codeword_j>) to estimate the inner product of a query vector against a stored FibCodeV1 without running the full decode pipeline (rotation inverse + norm scaling). The estimate is approximate — bounded by codebook quantization noise — and avoids decoding the stored vector entirely. For a codebook of size N=32, the Gram table is 4 KB.

use fib_quant::{FibScorer, FibQuantProfileV1, FibQuantizer};

let profile = FibQuantProfileV1::paper_default(32, 4, 32, 7)?;
let quantizer = FibQuantizer::new(profile)?;
// FibScorer::new takes ownership of the quantizer.
let scorer = FibScorer::new(quantizer)?;

let query = vec![0.1; 32];
// Encode before moving the quantizer into the scorer, or use
// a separate quantizer instance for encoding.

Residual — two-level VQ for better fidelity

After encoding with the main codebook, the quantization residual still contains signal. FibResidualQuantizer encodes this residual with a second, smaller codebook (typically 4–8 codewords), adding 2–3 bits per block. Cosine fidelity improves from ~0.863 (single-level) to ~0.93+ (two-level) for typical nomic-768 workloads, at the cost of ~10–15% size increase.

use fib_quant::{FibQuantProfileV1, FibQuantizer, FibResidualQuantizer};

let profile = FibQuantProfileV1::paper_default(768, 4, 32, 7)?;
let quantizer = FibQuantizer::new(profile)?;
let residual = FibResidualQuantizer::build(&quantizer.profile().clone(), quantizer.codebook())?;

Wire format — self-describing codes

FibCodeWireV1 wraps a FibCodeV1 with a 59-byte header carrying seed, dim, k, N, norm_format, and profile digest. This enables decode without any external profile knowledge — the compact FB1/FB2 formats strip all metadata, forcing consumers to hardcode profile fields.

use fib_quant::{FibCodeWireV1, FibQuantProfileV1, FibQuantizer};

let profile = FibQuantProfileV1::paper_default(32, 4, 32, 7)?;
let quantizer = FibQuantizer::new(profile)?;
let code = quantizer.encode(&[0.1; 32])?;
let wire_bytes = FibCodeWireV1::to_wire_bytes(&code, quantizer.profile())?;
// Decode without needing the original profile — from_wire_bytes
// returns (FibCodeV1, FibQuantProfileV1):
let (decoded_code, recovered_profile) = FibCodeWireV1::from_wire_bytes(&wire_bytes)?;

Sidecar — approximate search index

FibSidecarIndex<Id> stores caller-owned IDs alongside encoded FibCodeV1 artifacts and provides approximate nearest-inner-product search via the Gram-table estimator. The index is a sidecar: it produces approximate candidates that callers must rerank with an exact inner-product computation against the original (un-encoded) vectors before acting on the result.

use fib_quant::{FibScorer, FibSidecarIndex, FibQuantProfileV1, FibQuantizer};

let profile = FibQuantProfileV1::paper_default(32, 4, 32, 7)?;
// Encode first, then build the scorer for search.
let quantizer = FibQuantizer::new(profile)?;
let code = quantizer.encode(&[0.1; 32])?;

// Build a fresh quantizer to move into the scorer.
let profile2 = FibQuantProfileV1::paper_default(32, 4, 32, 7)?;
let scorer = FibScorer::new(FibQuantizer::new(profile2)?)?;
let mut index = FibSidecarIndex::new(scorer);

index.add(42, code);

let candidates = index.search(&[0.12; 32], 10, 4)?;
// Rerank candidates against original vectors before use.

Eval — benchmark harness

run_benchmark takes a FibBenchmarkCorpus (database vectors, queries, ground-truth top-K) and a &FibQuantizer, then returns a FibBenchmarkReceiptV1 with recall@K, nDCG@K, compression ratio, cosine similarity, MSE, and timing. Standalone helpers recall_at_k and ndcg_at_k are also exported.

use fib_quant::{FibBenchmarkCorpus, FibQuantProfileV1, FibQuantizer, run_benchmark};

let corpus = FibBenchmarkCorpus { /* ... */ };
let profile = FibQuantProfileV1::paper_default(768, 4, 32, 7)?;
let quantizer = FibQuantizer::new(profile)?;
let receipt = run_benchmark(&corpus, &quantizer)?;
println!("recall@10: {}", receipt.recall_at_k);

Streaming KV encoder

KvStreamEncoder (behind the kv feature) allows building a KvEncodedTensorV1 one token at a time instead of requiring the full tensor upfront. Pages are flushed automatically when they reach tokens_per_page from the page geometry. The output is identical to the batch encode_kv_tensor for the same input.

Currently supports batch = 1, layers = 1, kv_heads = 1 shapes (the common single-stream inference case).

Benchmarks — measured

Compression ratios (768-dim nomic-embed-v1.5)

The "theoretical 50×" is the binary-packed compact form. The "JSON 3.6×" is what you get with the default wire format — the JSON envelope is 12× bigger than the actual codebook indices. If you're optimizing for storage, use the binary wire format.

Retrieval quality (P26 measurement, semantic-memory harness)

8 queries, 200 docs, 768-dim, k=10, oversample=4:

Cosine fidelity: 0.863 (single vector), 0.9996 (after turbo-quant rerank in poly-kv).

The benchmark numbers above are from the P26 measurement run and are valid for single-level quantization. Two-level residual VQ improves cosine fidelity to ~0.93+ but has not been benchmarked for retrieval quality yet.

Encode_batch throughput — "Do All" perf pass 2026-06-01

The encode_batch loop is the dominant cost in the poly-kv pool build. After the June 1 perf pass (AVX2+FMA SIMD + Rayon):

Numbers from poly-kv/benchmarks/DO_ALL_PERF_PASS_2026-06-01.md.

GPU path — measured

Honest takeaway: fib-quant's encode_batch is 2-7% faster on a real GPU (msi i7-6700HQ + GTX 1070) with the Hadamard path engaged. The codebook_lookup kernel exists and is parity-verified, but the per-call H2D/D2H overhead currently negates its win. A device-side pipeline is the next step.

Test coverage

• 23 integration test files in tests/:
  • bitpack_indices, codebook_determinism, compact_bytes_roundtrip, corruption_rejection, decode_batch_fast_parity, direction_generators, encode_decode_roundtrip, kv_attention_quality, kv_corruption_rejection, kv_encode_decode_reference, kv_policy_role_aware, kv_property_shapes, kv_shape_contracts, lloyd_refinement, norm_payload_rejection, paper_k2_radius_closed_form, paper_smoke_regression, profile_digest, profile_resource_bounds, property_bitpack, property_codec, rotation_identity, spherical_beta_sampler.
• 4 examples: build_codebook, encode_batch_microbench, encode_decode, test_compact_decode.
• 4 benches (criterion): codebook_build, encode_decode, kv_attention_ref, kv_encode_decode.
• 105 tests pass with cargo test --all-features (was 47 before the new module additions).
• cargo clippy --all-features --all-targets -- -D warnings clean (modulo upstream gpu-backend lints on newer toolchains).

MSRV

Rust 1.75 (2021 edition). #![forbid(unsafe_code)] at the crate level.

Dependencies

• serde (with derive).
• serde_json.
• blake3.
• half (f16 support for scoring/scaling).
• nalgebra (with serde-serialize).
• rand + rand_chacha (dev).
• gpu-backend (optional) — for the GPU Hadamard dispatch.
• rayon (optional, behind the parallel feature) — for parallel batch encoding.
• quant-codec-core (optional, behind the compat feature) — for the shared codec trait boundary.
• proptest (dev).
• criterion (dev).

License

Apache-2.0. See LICENSE-APACHE for the full text.

Changelog

See CHANGELOG.md for the release history.

Attribution

This crate is an independent Rust implementation of the FibQuant compression technique described in the public literature (Lee & Kim, 2026). It is not the original authors' reference code, and does not claim affiliation with the FibQuant paper authors. The mathematical approach (radial-angular block decomposition with Fibonacci-sampled codebook seeding) follows the published specification.

The kv-cache codec profile, the parallel encode pipeline, the GPU dispatch path, the receipt infrastructure, and the test suite are original to this implementation.

Where it's used

fib-quant is the cold-tier codec for:

• poly-kv — the shared pool is fib-quant compressed. Every shared system prompt, every shared few-shot example, every shared doc goes through encode_batch.
• semantic-memory — when AdmissibilityClass::Standard is selected by quant-governor, semantic-memory can route to the fib-quant sidecar for candidate generation.
• scr-runtime-compression — the fib feature is the fib-quant adapter for the runtime.

Any system that needs a high-ratio, medium-fidelity vector codec — search-only recall, cold tier, ~50× storage savings — can adopt fib-quant directly.