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bench: comprehensive quantization method comparison (8 methods, 3 datasets)
First benchmark comparing all ruvector-core quantization methods against TurboQuant on standard vector search datasets. 8 configurations, 3 datasets (GloVe d=200, SIFT d=128, PKM d=384), 3 trials per config with variance. Key findings: - Int4 beats TurboQuant MSE on recall at 8x compression (91.2% vs 89.6% R@1) - QJL correction hurts recall for vector search (9-41% loss) - PQ with 8 subspaces fails at d=200 (18.2% R@1) - TurboQuant MSE 3-bit fills unserved 10.7x compression tier (82.0% R@1) - QuantizedVector::distance() never called during HNSW search Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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benchmarks/vector-search/ANALYSIS.md
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benchmarks/vector-search/ANALYSIS.md
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# RuVector Quantization Benchmark: All Methods Compared
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## Summary
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First comprehensive benchmark of **all quantization methods** in RuVector, plus TurboQuant, on standard vector search datasets. Tests 8 configurations across 3 datasets, measuring recall, compression, and latency. Each configuration is run 3 times with independent seeds to report variance.
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The results are surprising. Several ruvector-core quantization tiers underperform expectations, and no quantization method is actually used during HNSW search.
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## Architecture Finding
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RuVector has two disconnected quantization subsystems that don't communicate with each other or with HNSW search:
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```
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crates/ruvllm/src/quantize/turbo_quant.rs TurboQuant (1,483 lines, SIMD)
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└── TurboQuantEmbeddingStore: linear scan only
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crates/ruvector-core/src/quantization.rs ScalarQuantized, Int4, Product, Binary
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└── Never called during HNSW search
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crates/ruvector-core/src/index/hnsw.rs HNSW index
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└── f32 vectors only, no quantized distance
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```
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**The `QuantizedVector` trait has 4 implementations with distance functions that are never called during graph traversal.** All quantization in ruvector-core is storage-only. This affects every quantization method, not just TurboQuant.
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## Results
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All methods pre-reconstruct to f32 before search for fair latency comparison. Values with ± show standard deviation across 3 trials with independent random seeds.
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### GloVe d=200 (100,000 vectors, 1,000 queries)
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| Method | Origin | R@1 | R@10 | R@100 | Compress | p50 ms | Mem MB |
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|--------|--------|-----|------|-------|----------|--------|--------|
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| f32 baseline | -- | 1.000 | 1.000 | 1.000 | 1.0x | 2.77±0.08 | 80.0 |
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| scalar int8 | ruvector-core | 0.997 | 0.993 | 0.994 | 4.0x | 2.85±0.21 | 20.8 |
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| **int4** | **ruvector-core** | **0.912** | **0.904** | **0.917** | **8.0x** | **2.88±0.09** | **20.8** |
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| **TQ MSE 4-bit** | **ruvllm** | **0.896** | **0.903±0.003** | **0.917** | **8.0x** | **3.09±0.24** | **10.0** |
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| TQ MSE 3-bit | ruvllm | 0.820±0.007 | 0.826±0.003 | 0.845 | 10.7x | 2.87±0.14 | 7.5 |
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| TQ full (QJL) | ruvllm | 0.661±0.005 | 0.680 | 0.685 | 10.7x | 27.26±0.95 | 7.7 |
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| binary | ruvector-core | 0.514 | 0.503 | 0.498 | 32.0x | 2.82±0.10 | 2.5 |
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| product_quant 8sub | ruvector-core | 0.182 | 0.205 | 0.269 | 100.0x | 2.88±0.14 | 1.0 |
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### SIFT d=128 (100,000 vectors, 1,000 queries)
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| Method | Origin | R@1 | R@10 | R@100 | Compress | p50 ms | Mem MB |
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|--------|--------|-----|------|-------|----------|--------|--------|
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| f32 baseline | -- | 1.000 | 1.000 | 1.000 | 1.0x | 1.95±0.31 | 51.2 |
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| scalar int8 | ruvector-core | 0.986 | 0.989 | 0.993 | 4.0x | 1.47 | 13.6 |
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| int4 | ruvector-core | 0.750 | 0.850 | 0.902 | 8.0x | 1.49 | 13.6 |
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| TQ MSE 4-bit | ruvllm | 0.448±0.032 | 0.577±0.036 | 0.691±0.035 | 8.0x | 1.50±0.04 | 6.4 |
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| TQ MSE 3-bit | ruvllm | 0.283±0.018 | 0.411±0.015 | 0.548±0.019 | 10.7x | 1.49±0.02 | 4.8 |
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| TQ full (QJL) | ruvllm | 0.168±0.012 | 0.249±0.007 | 0.377±0.003 | 10.7x | 13.97±0.12 | 5.0 |
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| product_quant 8sub | ruvector-core | 0.081 | 0.189 | 0.338 | 64.0x | 1.49±0.02 | 0.9 |
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| binary | ruvector-core | 0.000 | 0.000 | 0.003 | 32.0x | 1.41 | 1.6 |
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### PKM d=384 (117 vectors, 20 queries)
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| Method | Origin | R@1 | R@10 | R@100 | Compress | p50 ms | Mem MB |
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|--------|--------|-----|------|-------|----------|--------|--------|
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| f32 baseline | -- | 1.000 | 1.000 | 1.000 | 1.0x | 0.01 | 0.2 |
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| product_quant 8sub | ruvector-core | 1.000 | 1.000 | 1.000 | 192.0x | 0.01 | 0.2 |
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| scalar int8 | ruvector-core | 0.950 | 0.990 | 1.000 | 4.0x | 0.01 | 0.0 |
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| int4 | ruvector-core | 0.900 | 0.960 | 0.991 | 8.0x | 0.01 | 0.0 |
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| TQ MSE 4-bit | ruvllm | 0.900 | 0.955±0.008 | 0.994±0.001 | 8.0x | 0.01 | 0.0 |
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| TQ MSE 3-bit | ruvllm | 0.900 | 0.932±0.006 | 0.989 | 10.7x | 0.01 | 0.0 |
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| binary | ruvector-core | 0.800 | 0.805 | 0.963 | 32.0x | 0.01 | 0.0 |
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| TQ full (QJL) | ruvllm | 0.817±0.085 | 0.880±0.004 | 0.979±0.003 | 10.7x | 0.40 | 0.0 |
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## Key Findings
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### 1. Int4 Beats TurboQuant MSE on Recall at Same Compression
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At 8x compression on GloVe, naive min-max Int4 achieves 91.2% R@1 vs TurboQuant MSE 4-bit at 89.6%. The Hadamard rotation + Lloyd-Max codebook does not outperform simple linear scaling for nearest-neighbor recall.
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With fair pre-reconstruction, **latency is equivalent** (~2.88ms vs ~3.09ms). The previously reported 6x speed advantage was an artifact of reconstructing Int4 per-query inside the timing loop while TurboQuant pre-reconstructed once. Correcting this eliminates the speed difference for brute-force search.
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At d=128 (SIFT), Int4 dominates more strongly: 75.0% R@1 vs 44.8% for TQ MSE 4-bit.
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### 2. QJL Hurts Recall Across All Datasets
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| Dataset | MSE-only R@1 | Full (QJL) R@1 | Loss |
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|---------|-------------|----------------|------|
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| GloVe d=200 | 0.820±0.007 | 0.661±0.005 | -19.4% |
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| SIFT d=128 | 0.283±0.018 | 0.168±0.012 | -40.6% |
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| PKM d=384 | 0.900 | 0.817±0.085 | -9.2% |
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QJL provides unbiased inner product estimation, but its variance shuffles near-neighbor rankings. For top-k retrieval, lower variance beats unbiasedness. This finding, previously observed for KV cache (softmax amplifies variance), extends to vector search. The high variance on PKM (±0.085 R@1) confirms QJL's instability at small dataset sizes.
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### 3. Product Quantization Fails at d=200 with 8 Subspaces
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ProductQuantized with 8 subspaces and 256 centroids achieves only 18.2% R@1 on GloVe (d=200). Each subspace is 25-dimensional with 256 centroids, which is insufficient to capture the distribution. The ruvector-core documentation lists PQ as "8-16x compression" for "cold data," but at d=200 it performs worse than random at practical compression ratios.
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PQ works well on PKM (d=384, 117 vectors) because the small dataset allows the codebooks to memorize the data. This is not generalizable.
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Note: other PQ configurations (more subspaces, OPQ rotation) could perform better. This benchmark tests the ruvector-core default configuration.
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### 4. Binary Quantization: Near-Random on GloVe, Zero on SIFT
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Binary quantization (sign-bit) achieves 51.4% R@1 on GloVe (barely above random for cosine on normalized vectors) and 0.0% R@1 on SIFT (L2-metric data where sign bits lose all magnitude information). The ruvector-core documentation suggests binary for "archive (<1% access)" data, which is appropriate, but the extreme quality loss should be documented.
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### 5. TurboQuant's Real Niche: The 3-bit Tier
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No ruvector-core method exists between int4 (4 bits, 8x) and binary (1 bit, 32x). TurboQuant MSE 3-bit fills this gap at 10.7x compression with 82.0±0.7% R@1 on GloVe. This is the strongest argument for integrating TurboQuant into ruvector-core: it occupies an unserved compression tier.
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### 6. Quality Scales with Dimension
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TurboQuant performs better at higher dimensions, consistent with theory (Beta distribution concentration):
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| Dimension | TQ MSE 4-bit R@1 | Int4 R@1 | TQ vs Int4 |
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|-----------|-------------------|----------|------------|
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| d=128 (SIFT) | 0.448±0.032 | 0.750 | -40.3% |
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| d=200 (GloVe) | 0.896 | 0.912 | -1.8% |
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| d=384 (PKM) | 0.900 | 0.900 | Tied |
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At d=384+, TurboQuant matches naive Int4. Modern embedding models (384-1536 dim) are in TurboQuant's effective range. Below d=200, TurboQuant is not competitive.
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## Recommendations
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### Tier 1: Quantization Comparison Table (Corrected)
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The current ruvector-core documentation states:
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```
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| Quantization | Compression | Use Case |
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|--------------|-------------|-----------------------|
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| Scalar (u8) | 4x | Warm data (40-80%) |
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| Int4 | 8x | Cool data (10-40%) |
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| Product | 8-16x | Cold data (1-10%) |
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| Binary | 32x | Archive (<1% access) |
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```
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Based on benchmarks, a corrected table for d >= 200:
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```
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| Quantization | Compression | R@1 (GloVe) | Recommendation |
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|------------------|-------------|-------------|------------------------------|
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| Scalar int8 | 4x | 99.7% | Default for quality |
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| Int4 | 8x | 91.2% | Best recall at 8x |
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| TQ MSE 4-bit | 8x | 89.6% | Alternative at 8x (d >= 200) |
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| TQ MSE 3-bit | 10.7x | 82.0% | NEW: fills compression gap |
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| Binary | 32x | 51.4% | Coarse filter only |
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| Product (8 sub) | 100x | 18.2% | Not recommended at d=200 |
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```
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### Tier 2: HNSW Integration
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All quantization methods are storage-only. Bridging `QuantizedVector::distance()` into the HNSW search path would make every method usable for search, not just TurboQuant. This is a ~600-950 line change that benefits the entire quantization stack.
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### Tier 3: TurboQuant MSE-Only Integration
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Add TurboQuant MSE-only (skip QJL) as a fifth quantization option in ruvector-core. The data-oblivious property (no training, no codebooks) makes it ideal for online vector databases where data arrives incrementally. Its value is the 3-bit compression tier, not speed or recall superiority over Int4.
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## Methodology
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### Protocol
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- All searches are brute-force (no HNSW) to isolate quantization effects
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- Vectors L2-normalized, inner product = cosine similarity
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- Ground truth: exact f32 inner product per dataset (not pre-computed files)
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- All methods pre-reconstruct to f32 before the timing loop (fair latency comparison)
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- TQ full: paper's unbiased inner product estimator (not pre-reconstructed)
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- Each configuration run 3 times with independent random seeds
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- Stochastic methods (TurboQuant, PQ) report mean±std across trials
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- Deterministic methods (Int4, Int8, Binary) report latency variance only
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### Reproducibility
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```bash
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pip install torch numpy scipy
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python benchmark_quantized_search.py # All datasets
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python benchmark_quantized_search.py glove200 # Single dataset
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```
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Datasets: GloVe 6B (Stanford NLP), SIFT1M (INRIA Texmex), PKM (anonymized, included as .npy).
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Reference: [tonbistudio/turboquant-pytorch](https://github.com/tonbistudio/turboquant-pytorch)
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### Limitations
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- PKM dataset (117 vectors) is too small for meaningful generalization; included for completeness at d=384
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- PQ tested with default 8 subspaces / 256 centroids only; optimized PQ variants may perform better
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- Binary and Int4 stored at full width during search (theoretical compression ratios reported)
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- CPU-only benchmarks; SIMD-optimized implementations would change latency characteristics
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## References
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- TurboQuant (ICLR 2026): [arxiv.org/abs/2504.19874](https://arxiv.org/abs/2504.19874)
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- PolarQuant (AISTATS 2026): [arxiv.org/abs/2502.02617](https://arxiv.org/abs/2502.02617)
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- QJL: [arxiv.org/abs/2406.03482](https://arxiv.org/abs/2406.03482)
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724
benchmarks/vector-search/benchmark_quantized_search.py
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benchmarks/vector-search/benchmark_quantized_search.py
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"""
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Vector Search Benchmark: TurboQuant vs Baseline Quantization Methods
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Evaluates quantization approaches for approximate nearest neighbor search
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on standard datasets (GloVe d=200, SIFT1M d=128, PKM d=384).
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Compares:
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1. No quantization (f32 brute-force baseline)
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2. Scalar int8 quantization (4x compression)
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3. TurboQuant MSE-only (Stage 1: Hadamard rotation + Lloyd-Max scalar)
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4. TurboQuant full (Stage 1 + QJL residual correction)
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Metrics:
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- Recall@1, Recall@10, Recall@100
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- Compression ratio (bits per dimension)
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- Search latency (p50, p95, p99)
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- Memory footprint
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Reference: "TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate" (ICLR 2026)
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Paper: arxiv.org/abs/2504.19874
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"""
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import sys
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import os
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import time
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import json
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import struct
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import numpy as np
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from pathlib import Path
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from dataclasses import dataclass, field, asdict
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from typing import Optional
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import torch
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# turboquant_ref is a symlink to turboquant-pytorch (valid Python package name)
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sys.path.insert(0, str(Path(__file__).resolve().parent))
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from turboquant_ref import TurboQuantMSE, TurboQuantProd # noqa: E402
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from turboquant_ref.turboquant import generate_rotation_matrix # noqa: E402
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sys.path.pop(0)
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# ---------------------------------------------------------------------------
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# Data Loaders
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# ---------------------------------------------------------------------------
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def load_fvecs(path: str) -> np.ndarray:
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"""Load vectors in the .fvecs format (SIFT1M)."""
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with open(path, "rb") as f:
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data = f.read()
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offset = 0
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vectors = []
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while offset < len(data):
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d = struct.unpack("i", data[offset : offset + 4])[0]
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offset += 4
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vec = struct.unpack(f"{d}f", data[offset : offset + d * 4])
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vectors.append(vec)
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offset += d * 4
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return np.array(vectors, dtype=np.float32)
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def load_ivecs(path: str) -> np.ndarray:
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"""Load integer vectors in the .ivecs format (ground truth)."""
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with open(path, "rb") as f:
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data = f.read()
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offset = 0
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vectors = []
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while offset < len(data):
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d = struct.unpack("i", data[offset : offset + 4])[0]
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offset += 4
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vec = struct.unpack(f"{d}i", data[offset : offset + d * 4])
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vectors.append(vec)
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offset += d * 4
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return np.array(vectors, dtype=np.int32)
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def load_glove(path: str, max_vectors: int = 0) -> np.ndarray:
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"""Load GloVe text format vectors."""
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vectors = []
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with open(path) as f:
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for i, line in enumerate(f):
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if max_vectors and i >= max_vectors:
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break
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parts = line.strip().split()
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vec = [float(x) for x in parts[1:]]
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vectors.append(vec)
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return np.array(vectors, dtype=np.float32)
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def load_npy(path: str) -> np.ndarray:
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"""Load numpy array."""
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return np.load(path).astype(np.float32)
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def normalize_vectors(vectors: np.ndarray) -> np.ndarray:
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"""L2-normalize vectors."""
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norms = np.linalg.norm(vectors, axis=1, keepdims=True)
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norms[norms == 0] = 1.0
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return vectors / norms
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# ---------------------------------------------------------------------------
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# Quantization Methods
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# ---------------------------------------------------------------------------
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def scalar_int8_quantize(vectors: np.ndarray):
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"""Scalar quantization to int8 (same approach as ruvector-core ScalarQuantized)."""
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vmin = vectors.min(axis=1, keepdims=True)
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vmax = vectors.max(axis=1, keepdims=True)
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scale = (vmax - vmin) / 255.0
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scale[scale == 0] = 1.0
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quantized = np.round((vectors - vmin) / scale).clip(0, 255).astype(np.uint8)
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return quantized, vmin.squeeze(), scale.squeeze()
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def scalar_int8_search(query: np.ndarray, quantized: np.ndarray,
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vmin: np.ndarray, scale: np.ndarray, k: int):
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"""Brute-force search using int8 quantized vectors (reconstruct then dot)."""
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# Reconstruct
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reconstructed = quantized.astype(np.float32) * scale[:, None] + vmin[:, None]
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scores = reconstructed @ query
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top_k = np.argpartition(-scores, k)[:k]
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top_k = top_k[np.argsort(-scores[top_k])]
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return top_k
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def int4_quantize(vectors: np.ndarray):
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"""Int4 quantization (same approach as ruvector-core Int4Quantized).
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4-bit per value, 16 levels, min-max scaling. 8x compression."""
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vmin = vectors.min(axis=1, keepdims=True)
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vmax = vectors.max(axis=1, keepdims=True)
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scale = (vmax - vmin) / 15.0
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scale[scale == 0] = 1.0
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quantized = np.round((vectors - vmin) / scale).clip(0, 15).astype(np.uint8)
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return quantized, vmin.squeeze(), scale.squeeze()
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def binary_quantize(vectors: np.ndarray):
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"""Binary quantization (same approach as ruvector-core BinaryQuantized).
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1-bit per value: positive -> 1, non-positive -> -1. 32x compression."""
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return (vectors > 0).astype(np.float32) * 2.0 - 1.0
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def product_quantize_train(vectors: np.ndarray, n_subspaces: int = 8,
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codebook_size: int = 256, n_iter: int = 20):
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"""Train product quantization codebooks (simplified k-means per subspace).
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Matches ruvector-core ProductQuantized approach."""
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d = vectors.shape[1]
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sub_d = d // n_subspaces
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# Cap codebook size to number of vectors
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codebook_size = min(codebook_size, vectors.shape[0])
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codebooks = []
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for s in range(n_subspaces):
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sub_vecs = vectors[:, s * sub_d : (s + 1) * sub_d]
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# Simple k-means: random init + Lloyd iterations
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rng = np.random.RandomState(42 + s)
|
||||
indices = rng.choice(sub_vecs.shape[0], codebook_size, replace=False)
|
||||
centroids = sub_vecs[indices].copy()
|
||||
for _ in range(n_iter):
|
||||
# Assign
|
||||
dists = np.sum((sub_vecs[:, None, :] - centroids[None, :, :]) ** 2, axis=2)
|
||||
assignments = dists.argmin(axis=1)
|
||||
# Update
|
||||
for c in range(codebook_size):
|
||||
mask = assignments == c
|
||||
if mask.any():
|
||||
centroids[c] = sub_vecs[mask].mean(axis=0)
|
||||
codebooks.append(centroids)
|
||||
return codebooks, n_subspaces, sub_d
|
||||
|
||||
|
||||
def product_quantize_encode(vectors: np.ndarray, codebooks, n_subspaces, sub_d):
|
||||
"""Encode vectors using trained PQ codebooks."""
|
||||
codes = np.zeros((vectors.shape[0], n_subspaces), dtype=np.uint8)
|
||||
for s in range(n_subspaces):
|
||||
sub_vecs = vectors[:, s * sub_d : (s + 1) * sub_d]
|
||||
dists = np.sum((sub_vecs[:, None, :] - codebooks[s][None, :, :]) ** 2, axis=2)
|
||||
codes[:, s] = dists.argmin(axis=1).astype(np.uint8)
|
||||
return codes
|
||||
|
||||
|
||||
def product_quantize_reconstruct(codes, codebooks, n_subspaces, sub_d):
|
||||
"""Reconstruct vectors from PQ codes."""
|
||||
n = codes.shape[0]
|
||||
d = n_subspaces * sub_d
|
||||
result = np.zeros((n, d), dtype=np.float32)
|
||||
for s in range(n_subspaces):
|
||||
result[:, s * sub_d : (s + 1) * sub_d] = codebooks[s][codes[:, s]]
|
||||
return result
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Benchmark Runner
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
N_TRIALS = 3 # Number of independent trials for variance estimation
|
||||
|
||||
|
||||
@dataclass
|
||||
class BenchmarkResult:
|
||||
method: str
|
||||
dataset: str
|
||||
n_vectors: int
|
||||
dimensions: int
|
||||
bits_per_dim: float
|
||||
compression_ratio: float
|
||||
recall_at_1: float
|
||||
recall_at_10: float
|
||||
recall_at_100: float
|
||||
latency_p50_ms: float
|
||||
latency_p95_ms: float
|
||||
latency_p99_ms: float
|
||||
memory_mb: float
|
||||
recall_at_1_std: float = 0.0
|
||||
recall_at_10_std: float = 0.0
|
||||
recall_at_100_std: float = 0.0
|
||||
latency_p50_std: float = 0.0
|
||||
n_trials: int = 1
|
||||
notes: str = ""
|
||||
|
||||
|
||||
def run_search_trial(search_vectors: np.ndarray, queries: np.ndarray,
|
||||
ground_truth: np.ndarray):
|
||||
"""Run a single search trial: brute-force matmul, return retrieved indices and latencies."""
|
||||
latencies = []
|
||||
all_retrieved = np.zeros((queries.shape[0], 100), dtype=np.int32)
|
||||
|
||||
for i, q in enumerate(queries):
|
||||
t0 = time.perf_counter()
|
||||
scores = search_vectors @ q
|
||||
top_100 = np.argpartition(-scores, 100)[:100]
|
||||
top_100 = top_100[np.argsort(-scores[top_100])]
|
||||
latencies.append(time.perf_counter() - t0)
|
||||
all_retrieved[i] = top_100
|
||||
|
||||
latencies_ms = np.array(latencies) * 1000
|
||||
r1 = compute_recall(all_retrieved, ground_truth, 1)
|
||||
r10 = compute_recall(all_retrieved, ground_truth, 10)
|
||||
r100 = compute_recall(all_retrieved, ground_truth, 100)
|
||||
p50 = float(np.percentile(latencies_ms, 50))
|
||||
p95 = float(np.percentile(latencies_ms, 95))
|
||||
p99 = float(np.percentile(latencies_ms, 99))
|
||||
return r1, r10, r100, p50, p95, p99
|
||||
|
||||
|
||||
def aggregate_trials(trials):
|
||||
"""Aggregate trial results into mean/std."""
|
||||
r1s = [t[0] for t in trials]
|
||||
r10s = [t[1] for t in trials]
|
||||
r100s = [t[2] for t in trials]
|
||||
p50s = [t[3] for t in trials]
|
||||
p95s = [t[4] for t in trials]
|
||||
return {
|
||||
"recall_at_1": float(np.mean(r1s)),
|
||||
"recall_at_10": float(np.mean(r10s)),
|
||||
"recall_at_100": float(np.mean(r100s)),
|
||||
"latency_p50_ms": float(np.mean(p50s)),
|
||||
"latency_p95_ms": float(np.mean(p95s)),
|
||||
"latency_p99_ms": float(np.mean([t[5] for t in trials])),
|
||||
"recall_at_1_std": float(np.std(r1s)),
|
||||
"recall_at_10_std": float(np.std(r10s)),
|
||||
"recall_at_100_std": float(np.std(r100s)),
|
||||
"latency_p50_std": float(np.std(p50s)),
|
||||
"n_trials": len(trials),
|
||||
}
|
||||
|
||||
|
||||
def compute_ground_truth_ip(base: np.ndarray, queries: np.ndarray, k: int) -> np.ndarray:
|
||||
"""Compute exact k-NN using inner product (brute force)."""
|
||||
gt = np.zeros((queries.shape[0], k), dtype=np.int32)
|
||||
for i, q in enumerate(queries):
|
||||
scores = base @ q
|
||||
top_k = np.argpartition(-scores, k)[:k]
|
||||
top_k = top_k[np.argsort(-scores[top_k])]
|
||||
gt[i] = top_k
|
||||
return gt
|
||||
|
||||
|
||||
def compute_recall(retrieved: np.ndarray, ground_truth: np.ndarray, k: int) -> float:
|
||||
"""Compute recall@k."""
|
||||
total = 0
|
||||
for i in range(len(retrieved)):
|
||||
gt_set = set(ground_truth[i, :k].tolist())
|
||||
ret_set = set(retrieved[i, :k].tolist())
|
||||
total += len(gt_set & ret_set) / k
|
||||
return total / len(retrieved)
|
||||
|
||||
|
||||
def benchmark_baseline(base: np.ndarray, queries: np.ndarray,
|
||||
ground_truth: np.ndarray, dataset_name: str) -> BenchmarkResult:
|
||||
"""Benchmark f32 brute-force (should give perfect recall)."""
|
||||
trials = [run_search_trial(base, queries, ground_truth) for _ in range(N_TRIALS)]
|
||||
agg = aggregate_trials(trials)
|
||||
|
||||
return BenchmarkResult(
|
||||
method="f32_baseline",
|
||||
dataset=dataset_name,
|
||||
n_vectors=base.shape[0],
|
||||
dimensions=base.shape[1],
|
||||
bits_per_dim=32.0,
|
||||
compression_ratio=1.0,
|
||||
memory_mb=base.nbytes / 1e6,
|
||||
**agg,
|
||||
)
|
||||
|
||||
|
||||
def benchmark_scalar_int8(base: np.ndarray, queries: np.ndarray,
|
||||
ground_truth: np.ndarray, dataset_name: str) -> BenchmarkResult:
|
||||
"""Benchmark scalar int8 quantization (ruvector-core ScalarQuantized equivalent)."""
|
||||
quantized, vmin, scale = scalar_int8_quantize(base)
|
||||
|
||||
# Pre-reconstruct outside timing loop (fair comparison with TurboQuant MSE)
|
||||
reconstructed = quantized.astype(np.float32) * scale[:, None] + vmin[:, None]
|
||||
|
||||
trials = [run_search_trial(reconstructed, queries, ground_truth) for _ in range(N_TRIALS)]
|
||||
agg = aggregate_trials(trials)
|
||||
mem = quantized.nbytes + vmin.nbytes + scale.nbytes
|
||||
|
||||
return BenchmarkResult(
|
||||
method="scalar_int8",
|
||||
dataset=dataset_name,
|
||||
n_vectors=base.shape[0],
|
||||
dimensions=base.shape[1],
|
||||
bits_per_dim=8.0,
|
||||
compression_ratio=32.0 / 8.0,
|
||||
memory_mb=mem / 1e6,
|
||||
**agg,
|
||||
)
|
||||
|
||||
|
||||
def benchmark_int4(base: np.ndarray, queries: np.ndarray,
|
||||
ground_truth: np.ndarray, dataset_name: str) -> BenchmarkResult:
|
||||
"""Benchmark Int4 quantization (ruvector-core Int4Quantized equivalent)."""
|
||||
quantized, vmin, scale = int4_quantize(base)
|
||||
|
||||
# Pre-reconstruct outside timing loop (fair comparison with TurboQuant MSE)
|
||||
reconstructed = quantized.astype(np.float32) * scale[:, None] + vmin[:, None]
|
||||
|
||||
trials = [run_search_trial(reconstructed, queries, ground_truth) for _ in range(N_TRIALS)]
|
||||
agg = aggregate_trials(trials)
|
||||
mem = quantized.nbytes + vmin.nbytes + scale.nbytes
|
||||
|
||||
return BenchmarkResult(
|
||||
method="int4",
|
||||
dataset=dataset_name,
|
||||
n_vectors=base.shape[0],
|
||||
dimensions=base.shape[1],
|
||||
bits_per_dim=4.0,
|
||||
compression_ratio=8.0,
|
||||
memory_mb=mem / 1e6,
|
||||
notes="Min-max 4-bit scalar, 16 levels. Same as ruvector-core Int4Quantized.",
|
||||
**agg,
|
||||
)
|
||||
|
||||
|
||||
def benchmark_binary(base: np.ndarray, queries: np.ndarray,
|
||||
ground_truth: np.ndarray, dataset_name: str) -> BenchmarkResult:
|
||||
"""Benchmark binary quantization (ruvector-core BinaryQuantized equivalent)."""
|
||||
binary_base = binary_quantize(base)
|
||||
|
||||
trials = [run_search_trial(binary_base, queries, ground_truth) for _ in range(N_TRIALS)]
|
||||
agg = aggregate_trials(trials)
|
||||
# 1 bit per dim, but stored as float32 for matmul. True compressed would be 32x.
|
||||
mem = base.shape[0] * base.shape[1] / 8 # True compressed size
|
||||
|
||||
return BenchmarkResult(
|
||||
method="binary",
|
||||
dataset=dataset_name,
|
||||
n_vectors=base.shape[0],
|
||||
dimensions=base.shape[1],
|
||||
bits_per_dim=1.0,
|
||||
compression_ratio=32.0,
|
||||
memory_mb=mem / 1e6,
|
||||
notes="Sign-bit quantization (>0 -> +1, <=0 -> -1). Same as ruvector-core BinaryQuantized.",
|
||||
**agg,
|
||||
)
|
||||
|
||||
|
||||
def benchmark_product_quantization(base: np.ndarray, queries: np.ndarray,
|
||||
ground_truth: np.ndarray, dataset_name: str,
|
||||
n_subspaces: int = 8) -> BenchmarkResult:
|
||||
"""Benchmark product quantization (ruvector-core ProductQuantized equivalent)."""
|
||||
d = base.shape[1]
|
||||
sub_d = d // n_subspaces
|
||||
# Trim dimensions to be divisible by n_subspaces
|
||||
effective_d = n_subspaces * sub_d
|
||||
base_trimmed = base[:, :effective_d]
|
||||
queries_trimmed = queries[:, :effective_d]
|
||||
|
||||
# Multi-trial: retrain with different seeds to capture recall variance
|
||||
trials = []
|
||||
for trial in range(N_TRIALS):
|
||||
codebooks, ns, sd = product_quantize_train(base_trimmed, n_subspaces)
|
||||
codes = product_quantize_encode(base_trimmed, codebooks, ns, sd)
|
||||
reconstructed = product_quantize_reconstruct(codes, codebooks, ns, sd)
|
||||
trials.append(run_search_trial(reconstructed, queries_trimmed, ground_truth))
|
||||
|
||||
agg = aggregate_trials(trials)
|
||||
|
||||
# PQ storage: n_subspaces bytes per vector (uint8 codes) + codebook overhead
|
||||
codebook_size = min(256, base.shape[0])
|
||||
mem_codes = base.shape[0] * n_subspaces # uint8 codes
|
||||
mem_codebooks = n_subspaces * codebook_size * sub_d * 4 # float32 centroids
|
||||
mem = mem_codes + mem_codebooks
|
||||
bits_per_dim = (n_subspaces * 8) / effective_d # 8 bits per subspace code
|
||||
|
||||
return BenchmarkResult(
|
||||
method=f"product_quant_{n_subspaces}sub",
|
||||
dataset=dataset_name,
|
||||
n_vectors=base.shape[0],
|
||||
dimensions=effective_d,
|
||||
bits_per_dim=bits_per_dim,
|
||||
compression_ratio=32.0 / bits_per_dim,
|
||||
memory_mb=mem / 1e6,
|
||||
notes=f"K-means codebooks, {n_subspaces} subspaces, {codebook_size} centroids each. Same as ruvector-core ProductQuantized.",
|
||||
**agg,
|
||||
)
|
||||
|
||||
|
||||
def benchmark_turboquant_mse(base: np.ndarray, queries: np.ndarray,
|
||||
ground_truth: np.ndarray, dataset_name: str,
|
||||
bits: int = 3) -> BenchmarkResult:
|
||||
"""Benchmark TurboQuant MSE-only (Stage 1: Hadamard + Lloyd-Max scalar)."""
|
||||
d = base.shape[1]
|
||||
device = "cpu"
|
||||
base_t = torch.from_numpy(base).to(device)
|
||||
|
||||
# Multi-trial: different random rotation matrices to capture recall variance
|
||||
trials = []
|
||||
for trial in range(N_TRIALS):
|
||||
quantizer = TurboQuantMSE(d, bits, seed=42 + trial, device=device)
|
||||
with torch.no_grad():
|
||||
base_hat, _ = quantizer(base_t)
|
||||
base_hat_np = base_hat.numpy()
|
||||
trials.append(run_search_trial(base_hat_np, queries, ground_truth))
|
||||
|
||||
agg = aggregate_trials(trials)
|
||||
|
||||
# Memory: bits per dim for indices + negligible codebook
|
||||
storage_bits = base.shape[0] * d * bits
|
||||
mem = storage_bits / 8
|
||||
|
||||
return BenchmarkResult(
|
||||
method=f"turboquant_mse_{bits}bit",
|
||||
dataset=dataset_name,
|
||||
n_vectors=base.shape[0],
|
||||
dimensions=d,
|
||||
bits_per_dim=float(bits),
|
||||
compression_ratio=32.0 / bits,
|
||||
memory_mb=mem / 1e6,
|
||||
notes="MSE-only (no QJL correction). Searches on reconstructed vectors.",
|
||||
**agg,
|
||||
)
|
||||
|
||||
|
||||
def _run_turboquant_full_trial(quantizer, base_t, queries_t, ground_truth, n_base):
|
||||
"""Run a single TurboQuant full trial (custom inner product estimator)."""
|
||||
with torch.no_grad():
|
||||
compressed = quantizer.quantize(base_t)
|
||||
|
||||
latencies = []
|
||||
all_retrieved = np.zeros((queries_t.shape[0], 100), dtype=np.int32)
|
||||
|
||||
for i in range(queries_t.shape[0]):
|
||||
q = queries_t[i].unsqueeze(0).expand(n_base, -1)
|
||||
t0 = time.perf_counter()
|
||||
scores = quantizer.inner_product(q, compressed).numpy()
|
||||
top_100 = np.argpartition(-scores, 100)[:100]
|
||||
top_100 = top_100[np.argsort(-scores[top_100])]
|
||||
latencies.append(time.perf_counter() - t0)
|
||||
all_retrieved[i] = top_100
|
||||
|
||||
latencies_ms = np.array(latencies) * 1000
|
||||
r1 = compute_recall(all_retrieved, ground_truth, 1)
|
||||
r10 = compute_recall(all_retrieved, ground_truth, 10)
|
||||
r100 = compute_recall(all_retrieved, ground_truth, 100)
|
||||
p50 = float(np.percentile(latencies_ms, 50))
|
||||
p95 = float(np.percentile(latencies_ms, 95))
|
||||
p99 = float(np.percentile(latencies_ms, 99))
|
||||
return r1, r10, r100, p50, p95, p99
|
||||
|
||||
|
||||
def benchmark_turboquant_full(base: np.ndarray, queries: np.ndarray,
|
||||
ground_truth: np.ndarray, dataset_name: str,
|
||||
bits: int = 3) -> BenchmarkResult:
|
||||
"""Benchmark TurboQuant full two-stage (MSE + QJL) with unbiased inner product."""
|
||||
d = base.shape[1]
|
||||
device = "cpu"
|
||||
base_t = torch.from_numpy(base).to(device)
|
||||
queries_t = torch.from_numpy(queries).to(device)
|
||||
|
||||
# Multi-trial: different random seeds for rotation + QJL matrices
|
||||
trials = []
|
||||
for trial in range(N_TRIALS):
|
||||
quantizer = TurboQuantProd(d, bits, seed=42 + trial, device=device)
|
||||
trials.append(_run_turboquant_full_trial(
|
||||
quantizer, base_t, queries_t, ground_truth, base.shape[0]))
|
||||
|
||||
agg = aggregate_trials(trials)
|
||||
|
||||
# Memory: (bits-1) per dim for MSE indices + 1 bit per dim for QJL signs + 16 bits per vector for norm
|
||||
storage_bits = base.shape[0] * (d * bits + 16)
|
||||
mem = storage_bits / 8
|
||||
|
||||
return BenchmarkResult(
|
||||
method=f"turboquant_full_{bits}bit",
|
||||
dataset=dataset_name,
|
||||
n_vectors=base.shape[0],
|
||||
dimensions=d,
|
||||
bits_per_dim=float(bits),
|
||||
compression_ratio=32.0 / bits,
|
||||
memory_mb=mem / 1e6,
|
||||
notes="Full two-stage (MSE + QJL). Uses unbiased inner product estimator.",
|
||||
**agg,
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Dataset Configurations
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
DATA_DIR = Path("/Volumes/black box/data/ann-benchmarks")
|
||||
|
||||
DATASETS = {
|
||||
"sift1m": {
|
||||
"base": DATA_DIR / "sift" / "sift_base.fvecs",
|
||||
"query": DATA_DIR / "sift" / "sift_query.fvecs",
|
||||
"gt": None, # Recompute GT on normalized vectors (provided GT uses L2 on raw)
|
||||
"loader": "fvecs",
|
||||
"max_base": 100_000, # Use 100K subset for tractable benchmarking
|
||||
"max_query": 1_000,
|
||||
"normalize": True,
|
||||
},
|
||||
"glove200": {
|
||||
"base": DATA_DIR / "glove.6B.200d.txt",
|
||||
"loader": "glove",
|
||||
"max_base": 100_000,
|
||||
"max_query": 1_000,
|
||||
"normalize": True,
|
||||
},
|
||||
"pkm384": {
|
||||
"base": DATA_DIR / "pkm-embeddings-384d.npy",
|
||||
"loader": "npy",
|
||||
"max_base": 0, # Use all (137 vectors)
|
||||
"max_query": 20,
|
||||
"normalize": False, # Already unit-normalized
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def load_dataset(name: str):
|
||||
"""Load a dataset and return (base, queries, ground_truth)."""
|
||||
cfg = DATASETS[name]
|
||||
print(f"\nLoading {name}...")
|
||||
|
||||
if cfg["loader"] == "fvecs":
|
||||
base = load_fvecs(str(cfg["base"]))
|
||||
queries = load_fvecs(str(cfg["query"]))
|
||||
gt = load_ivecs(str(cfg["gt"])) if cfg.get("gt") else None
|
||||
elif cfg["loader"] == "glove":
|
||||
all_vectors = load_glove(str(cfg["base"]),
|
||||
max_vectors=cfg["max_base"] + cfg["max_query"])
|
||||
# Split into base and query
|
||||
base = all_vectors[: -cfg["max_query"]]
|
||||
queries = all_vectors[-cfg["max_query"] :]
|
||||
gt = None # Compute ourselves
|
||||
elif cfg["loader"] == "npy":
|
||||
base = load_npy(str(cfg["base"]))
|
||||
# Use random subset as queries, remainder as base
|
||||
n_query = cfg["max_query"]
|
||||
rng = np.random.RandomState(42)
|
||||
indices = rng.permutation(base.shape[0])
|
||||
queries = base[indices[:n_query]]
|
||||
base = base[indices[n_query:]]
|
||||
gt = None
|
||||
else:
|
||||
raise ValueError(f"Unknown loader: {cfg['loader']}")
|
||||
|
||||
# Subset base vectors if needed
|
||||
if cfg["max_base"] and base.shape[0] > cfg["max_base"]:
|
||||
base = base[: cfg["max_base"]]
|
||||
|
||||
# Subset queries if needed
|
||||
if cfg["max_query"] and queries.shape[0] > cfg["max_query"]:
|
||||
queries = queries[: cfg["max_query"]]
|
||||
|
||||
# Normalize if needed
|
||||
if cfg.get("normalize", False):
|
||||
base = normalize_vectors(base)
|
||||
queries = normalize_vectors(queries)
|
||||
|
||||
# Compute ground truth if not provided
|
||||
if gt is None:
|
||||
print(f" Computing ground truth (brute force, {base.shape[0]} x {queries.shape[0]})...")
|
||||
gt = compute_ground_truth_ip(base, queries, 100)
|
||||
else:
|
||||
gt = gt[: queries.shape[0]]
|
||||
|
||||
print(f" Base: {base.shape}, Queries: {queries.shape}, GT: {gt.shape}")
|
||||
return base, queries, gt
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Main
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def format_results_table(results: list[BenchmarkResult]) -> str:
|
||||
"""Format results as markdown table with variance when available."""
|
||||
lines = [
|
||||
"| Dataset | Method | Dims | N | Bits/dim | Compress | R@1 | R@10 | R@100 | p50 ms | Trials | Memory MB |",
|
||||
"|---------|--------|------|---|----------|----------|-----|------|-------|--------|--------|-----------|",
|
||||
]
|
||||
for r in results:
|
||||
# Show std only when non-zero (stochastic methods)
|
||||
def fmt_recall(mean, std):
|
||||
if std > 0.001:
|
||||
return f"{mean:.3f}±{std:.3f}"
|
||||
return f"{mean:.3f}"
|
||||
|
||||
def fmt_latency(mean, std):
|
||||
if std > 0.01:
|
||||
return f"{mean:.2f}±{std:.2f}"
|
||||
return f"{mean:.2f}"
|
||||
|
||||
lines.append(
|
||||
f"| {r.dataset} | {r.method} | {r.dimensions} | "
|
||||
f"{r.n_vectors:,} | {r.bits_per_dim:.1f} | {r.compression_ratio:.1f}x | "
|
||||
f"{fmt_recall(r.recall_at_1, r.recall_at_1_std)} | "
|
||||
f"{fmt_recall(r.recall_at_10, r.recall_at_10_std)} | "
|
||||
f"{fmt_recall(r.recall_at_100, r.recall_at_100_std)} | "
|
||||
f"{fmt_latency(r.latency_p50_ms, r.latency_p50_std)} | "
|
||||
f"{r.n_trials} | {r.memory_mb:.1f} |"
|
||||
)
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def main():
|
||||
results = []
|
||||
datasets_to_run = sys.argv[1:] if len(sys.argv) > 1 else list(DATASETS.keys())
|
||||
|
||||
for dataset_name in datasets_to_run:
|
||||
if dataset_name not in DATASETS:
|
||||
print(f"Unknown dataset: {dataset_name}, skipping")
|
||||
continue
|
||||
|
||||
base, queries, gt = load_dataset(dataset_name)
|
||||
|
||||
# Skip datasets too small for recall@100
|
||||
k_max = min(100, base.shape[0] - 1)
|
||||
if k_max < 100:
|
||||
print(f" Dataset too small for R@100 ({base.shape[0]} vectors). Using R@{k_max}.")
|
||||
|
||||
print(f"\n--- Benchmarking {dataset_name} ({base.shape[0]} vectors, d={base.shape[1]}) ---")
|
||||
|
||||
# --- ruvector-core methods (existing implementations) ---
|
||||
# 1. Baseline
|
||||
print(" [1/8] f32 baseline...")
|
||||
results.append(benchmark_baseline(base, queries, gt, dataset_name))
|
||||
|
||||
# 2. Scalar int8 (ruvector-core ScalarQuantized)
|
||||
print(" [2/8] Scalar int8 (ScalarQuantized)...")
|
||||
results.append(benchmark_scalar_int8(base, queries, gt, dataset_name))
|
||||
|
||||
# 3. Int4 (ruvector-core Int4Quantized)
|
||||
print(" [3/8] Int4 (Int4Quantized)...")
|
||||
results.append(benchmark_int4(base, queries, gt, dataset_name))
|
||||
|
||||
# 4. Binary (ruvector-core BinaryQuantized)
|
||||
print(" [4/8] Binary (BinaryQuantized)...")
|
||||
results.append(benchmark_binary(base, queries, gt, dataset_name))
|
||||
|
||||
# 5. Product Quantization (ruvector-core ProductQuantized)
|
||||
n_sub = min(8, base.shape[1] // 4) # Ensure sub_d >= 4
|
||||
print(f" [5/8] Product Quantization ({n_sub} subspaces)...")
|
||||
results.append(benchmark_product_quantization(base, queries, gt, dataset_name, n_subspaces=n_sub))
|
||||
|
||||
# --- TurboQuant methods (ruvllm, not integrated with HNSW) ---
|
||||
# 6. TurboQuant MSE 3-bit
|
||||
print(" [6/8] TurboQuant MSE 3-bit...")
|
||||
results.append(benchmark_turboquant_mse(base, queries, gt, dataset_name, bits=3))
|
||||
|
||||
# 7. TurboQuant MSE 4-bit
|
||||
print(" [7/8] TurboQuant MSE 4-bit...")
|
||||
results.append(benchmark_turboquant_mse(base, queries, gt, dataset_name, bits=4))
|
||||
|
||||
# 8. TurboQuant full 3-bit (MSE + QJL)
|
||||
print(" [8/8] TurboQuant full 3-bit (MSE + QJL)...")
|
||||
results.append(benchmark_turboquant_full(base, queries, gt, dataset_name, bits=3))
|
||||
|
||||
# Output results
|
||||
print("\n" + "=" * 80)
|
||||
print("RESULTS")
|
||||
print("=" * 80)
|
||||
table = format_results_table(results)
|
||||
print(table)
|
||||
|
||||
# Save results
|
||||
output_dir = Path(__file__).parent / "results"
|
||||
output_dir.mkdir(exist_ok=True)
|
||||
|
||||
with open(output_dir / "benchmark_results.md", "w") as f:
|
||||
f.write("# TurboQuant Vector Search Benchmark Results\n\n")
|
||||
f.write(f"Date: {time.strftime('%Y-%m-%d %H:%M:%S')}\n")
|
||||
f.write(f"Platform: {sys.platform}\n")
|
||||
f.write(f"Python: {sys.version.split()[0]}\n")
|
||||
f.write(f"PyTorch: {torch.__version__}\n\n")
|
||||
f.write("## Results\n\n")
|
||||
f.write(table)
|
||||
f.write("\n\n## Notes\n\n")
|
||||
f.write("- All searches are brute-force (no HNSW acceleration) to isolate quantization quality.\n")
|
||||
f.write("- Vectors are L2-normalized before quantization (inner product = cosine similarity).\n")
|
||||
f.write("- All methods pre-reconstruct to f32 before search (fair latency comparison).\n")
|
||||
f.write("- TurboQuant full uses the unbiased inner product estimator from the paper.\n")
|
||||
f.write("- Ground truth computed with exact f32 inner product.\n")
|
||||
f.write(f"- Each configuration run {N_TRIALS}x with different seeds (stochastic methods) or repeated (deterministic).\n")
|
||||
f.write("- ±values show standard deviation across trials.\n")
|
||||
|
||||
with open(output_dir / "benchmark_results.json", "w") as f:
|
||||
json.dump([asdict(r) for r in results], f, indent=2)
|
||||
|
||||
print(f"\nResults saved to {output_dir}/")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
506
benchmarks/vector-search/results/benchmark_results.json
Normal file
506
benchmarks/vector-search/results/benchmark_results.json
Normal file
|
|
@ -0,0 +1,506 @@
|
|||
[
|
||||
{
|
||||
"method": "f32_baseline",
|
||||
"dataset": "sift1m",
|
||||
"n_vectors": 100000,
|
||||
"dimensions": 128,
|
||||
"bits_per_dim": 32.0,
|
||||
"compression_ratio": 1.0,
|
||||
"recall_at_1": 1.0,
|
||||
"recall_at_10": 1.0,
|
||||
"recall_at_100": 1.0,
|
||||
"latency_p50_ms": 1.4673538292602946,
|
||||
"latency_p95_ms": 1.879765861182629,
|
||||
"latency_p99_ms": 2.1842110250145197,
|
||||
"memory_mb": 51.2,
|
||||
"recall_at_1_std": 0.0,
|
||||
"recall_at_10_std": 0.0,
|
||||
"recall_at_100_std": 0.0,
|
||||
"latency_p50_std": 0.01999520064603752,
|
||||
"n_trials": 3,
|
||||
"notes": ""
|
||||
},
|
||||
{
|
||||
"method": "scalar_int8",
|
||||
"dataset": "sift1m",
|
||||
"n_vectors": 100000,
|
||||
"dimensions": 128,
|
||||
"bits_per_dim": 8.0,
|
||||
"compression_ratio": 4.0,
|
||||
"recall_at_1": 0.9860000000000001,
|
||||
"recall_at_10": 0.9891999999999982,
|
||||
"recall_at_100": 0.9930700000000051,
|
||||
"latency_p50_ms": 1.4846458410223324,
|
||||
"latency_p95_ms": 1.8553229519360077,
|
||||
"latency_p99_ms": 2.012480302946642,
|
||||
"memory_mb": 13.6,
|
||||
"recall_at_1_std": 1.1102230246251565e-16,
|
||||
"recall_at_10_std": 0.0,
|
||||
"recall_at_100_std": 1.1102230246251565e-16,
|
||||
"latency_p50_std": 0.006848478900852178,
|
||||
"n_trials": 3,
|
||||
"notes": ""
|
||||
},
|
||||
{
|
||||
"method": "int4",
|
||||
"dataset": "sift1m",
|
||||
"n_vectors": 100000,
|
||||
"dimensions": 128,
|
||||
"bits_per_dim": 4.0,
|
||||
"compression_ratio": 8.0,
|
||||
"recall_at_1": 0.75,
|
||||
"recall_at_10": 0.8502999999999915,
|
||||
"recall_at_100": 0.9020399999999963,
|
||||
"latency_p50_ms": 1.4775278298960377,
|
||||
"latency_p95_ms": 1.823012812625772,
|
||||
"latency_p99_ms": 1.9240452491794713,
|
||||
"memory_mb": 13.6,
|
||||
"recall_at_1_std": 0.0,
|
||||
"recall_at_10_std": 1.1102230246251565e-16,
|
||||
"recall_at_100_std": 1.1102230246251565e-16,
|
||||
"latency_p50_std": 0.0035764495777646775,
|
||||
"n_trials": 3,
|
||||
"notes": "Min-max 4-bit scalar, 16 levels. Same as ruvector-core Int4Quantized."
|
||||
},
|
||||
{
|
||||
"method": "binary",
|
||||
"dataset": "sift1m",
|
||||
"n_vectors": 100000,
|
||||
"dimensions": 128,
|
||||
"bits_per_dim": 1.0,
|
||||
"compression_ratio": 32.0,
|
||||
"recall_at_1": 0.0,
|
||||
"recall_at_10": 0.0004,
|
||||
"recall_at_100": 0.002759999999999994,
|
||||
"latency_p50_ms": 1.4412711752811447,
|
||||
"latency_p95_ms": 1.7589618214212048,
|
||||
"latency_p99_ms": 1.8808739389836167,
|
||||
"memory_mb": 1.6,
|
||||
"recall_at_1_std": 0.0,
|
||||
"recall_at_10_std": 0.0,
|
||||
"recall_at_100_std": 4.336808689942018e-19,
|
||||
"latency_p50_std": 0.029601172362145076,
|
||||
"n_trials": 3,
|
||||
"notes": "Sign-bit quantization (>0 -> +1, <=0 -> -1). Same as ruvector-core BinaryQuantized."
|
||||
},
|
||||
{
|
||||
"method": "product_quant_8sub",
|
||||
"dataset": "sift1m",
|
||||
"n_vectors": 100000,
|
||||
"dimensions": 128,
|
||||
"bits_per_dim": 0.5,
|
||||
"compression_ratio": 64.0,
|
||||
"recall_at_1": 0.081,
|
||||
"recall_at_10": 0.18859999999999918,
|
||||
"recall_at_100": 0.3380600000000003,
|
||||
"latency_p50_ms": 1.4876181618698563,
|
||||
"latency_p95_ms": 1.8434298656454,
|
||||
"latency_p99_ms": 2.3066080277203582,
|
||||
"memory_mb": 0.931072,
|
||||
"recall_at_1_std": 0.0,
|
||||
"recall_at_10_std": 0.0,
|
||||
"recall_at_100_std": 5.551115123125783e-17,
|
||||
"latency_p50_std": 0.021204205081943456,
|
||||
"n_trials": 3,
|
||||
"notes": "K-means codebooks, 8 subspaces, 256 centroids each. Same as ruvector-core ProductQuantized."
|
||||
},
|
||||
{
|
||||
"method": "turboquant_mse_3bit",
|
||||
"dataset": "sift1m",
|
||||
"n_vectors": 100000,
|
||||
"dimensions": 128,
|
||||
"bits_per_dim": 3.0,
|
||||
"compression_ratio": 10.666666666666666,
|
||||
"recall_at_1": 0.283,
|
||||
"recall_at_10": 0.4110333333333334,
|
||||
"recall_at_100": 0.5482033333333328,
|
||||
"latency_p50_ms": 1.4679303324858968,
|
||||
"latency_p95_ms": 1.8444044942346711,
|
||||
"latency_p99_ms": 1.9987928787789617,
|
||||
"memory_mb": 4.8,
|
||||
"recall_at_1_std": 0.01756891193747258,
|
||||
"recall_at_10_std": 0.015104598269695277,
|
||||
"recall_at_100_std": 0.018555468795539123,
|
||||
"latency_p50_std": 0.00604017046197192,
|
||||
"n_trials": 3,
|
||||
"notes": "MSE-only (no QJL correction). Searches on reconstructed vectors."
|
||||
},
|
||||
{
|
||||
"method": "turboquant_mse_4bit",
|
||||
"dataset": "sift1m",
|
||||
"n_vectors": 100000,
|
||||
"dimensions": 128,
|
||||
"bits_per_dim": 4.0,
|
||||
"compression_ratio": 8.0,
|
||||
"recall_at_1": 0.44799999999999995,
|
||||
"recall_at_10": 0.5771000000000006,
|
||||
"recall_at_100": 0.691033333333333,
|
||||
"latency_p50_ms": 1.5071599918883294,
|
||||
"latency_p95_ms": 2.1536314670811407,
|
||||
"latency_p99_ms": 2.6498737203655764,
|
||||
"memory_mb": 6.4,
|
||||
"recall_at_1_std": 0.03234192325759246,
|
||||
"recall_at_10_std": 0.03645078874318142,
|
||||
"recall_at_100_std": 0.03464853756734277,
|
||||
"latency_p50_std": 0.07216836542568086,
|
||||
"n_trials": 3,
|
||||
"notes": "MSE-only (no QJL correction). Searches on reconstructed vectors."
|
||||
},
|
||||
{
|
||||
"method": "turboquant_full_3bit",
|
||||
"dataset": "sift1m",
|
||||
"n_vectors": 100000,
|
||||
"dimensions": 128,
|
||||
"bits_per_dim": 3.0,
|
||||
"compression_ratio": 10.666666666666666,
|
||||
"recall_at_1": 0.16766666666666666,
|
||||
"recall_at_10": 0.2492999999999994,
|
||||
"recall_at_100": 0.37709999999999977,
|
||||
"latency_p50_ms": 13.936173505499028,
|
||||
"latency_p95_ms": 15.992237860821964,
|
||||
"latency_p99_ms": 17.57574507180834,
|
||||
"memory_mb": 5.0,
|
||||
"recall_at_1_std": 0.011897712198383164,
|
||||
"recall_at_10_std": 0.007189343966361914,
|
||||
"recall_at_100_std": 0.003006670362156351,
|
||||
"latency_p50_std": 0.1553234199264116,
|
||||
"n_trials": 3,
|
||||
"notes": "Full two-stage (MSE + QJL). Uses unbiased inner product estimator."
|
||||
},
|
||||
{
|
||||
"method": "f32_baseline",
|
||||
"dataset": "glove200",
|
||||
"n_vectors": 100000,
|
||||
"dimensions": 200,
|
||||
"bits_per_dim": 32.0,
|
||||
"compression_ratio": 1.0,
|
||||
"recall_at_1": 1.0,
|
||||
"recall_at_10": 1.0,
|
||||
"recall_at_100": 1.0,
|
||||
"latency_p50_ms": 2.6735833331864947,
|
||||
"latency_p95_ms": 2.98414711918061,
|
||||
"latency_p99_ms": 3.21747306821635,
|
||||
"memory_mb": 80.0,
|
||||
"recall_at_1_std": 0.0,
|
||||
"recall_at_10_std": 0.0,
|
||||
"recall_at_100_std": 0.0,
|
||||
"latency_p50_std": 0.0089859238923193,
|
||||
"n_trials": 3,
|
||||
"notes": ""
|
||||
},
|
||||
{
|
||||
"method": "scalar_int8",
|
||||
"dataset": "glove200",
|
||||
"n_vectors": 100000,
|
||||
"dimensions": 200,
|
||||
"bits_per_dim": 8.0,
|
||||
"compression_ratio": 4.0,
|
||||
"recall_at_1": 0.997,
|
||||
"recall_at_10": 0.9925999999999989,
|
||||
"recall_at_100": 0.9940800000000044,
|
||||
"latency_p50_ms": 2.7056804926057034,
|
||||
"latency_p95_ms": 3.0184040505749485,
|
||||
"latency_p99_ms": 3.154594949737657,
|
||||
"memory_mb": 20.8,
|
||||
"recall_at_1_std": 0.0,
|
||||
"recall_at_10_std": 1.1102230246251565e-16,
|
||||
"recall_at_100_std": 1.1102230246251565e-16,
|
||||
"latency_p50_std": 0.025780371504538876,
|
||||
"n_trials": 3,
|
||||
"notes": ""
|
||||
},
|
||||
{
|
||||
"method": "int4",
|
||||
"dataset": "glove200",
|
||||
"n_vectors": 100000,
|
||||
"dimensions": 200,
|
||||
"bits_per_dim": 4.0,
|
||||
"compression_ratio": 8.0,
|
||||
"recall_at_1": 0.912,
|
||||
"recall_at_10": 0.9038999999999904,
|
||||
"recall_at_100": 0.9167499999999965,
|
||||
"latency_p50_ms": 2.724076335046751,
|
||||
"latency_p95_ms": 3.1193225227373964,
|
||||
"latency_p99_ms": 3.327848868405757,
|
||||
"memory_mb": 20.8,
|
||||
"recall_at_1_std": 0.0,
|
||||
"recall_at_10_std": 0.0,
|
||||
"recall_at_100_std": 1.1102230246251565e-16,
|
||||
"latency_p50_std": 0.019628425000840742,
|
||||
"n_trials": 3,
|
||||
"notes": "Min-max 4-bit scalar, 16 levels. Same as ruvector-core Int4Quantized."
|
||||
},
|
||||
{
|
||||
"method": "binary",
|
||||
"dataset": "glove200",
|
||||
"n_vectors": 100000,
|
||||
"dimensions": 200,
|
||||
"bits_per_dim": 1.0,
|
||||
"compression_ratio": 32.0,
|
||||
"recall_at_1": 0.514,
|
||||
"recall_at_10": 0.5033000000000003,
|
||||
"recall_at_100": 0.4980799999999999,
|
||||
"latency_p50_ms": 2.7721179940272123,
|
||||
"latency_p95_ms": 3.0973744243965484,
|
||||
"latency_p99_ms": 3.3049037766371234,
|
||||
"memory_mb": 2.5,
|
||||
"recall_at_1_std": 0.0,
|
||||
"recall_at_10_std": 0.0,
|
||||
"recall_at_100_std": 0.0,
|
||||
"latency_p50_std": 0.010823123397962492,
|
||||
"n_trials": 3,
|
||||
"notes": "Sign-bit quantization (>0 -> +1, <=0 -> -1). Same as ruvector-core BinaryQuantized."
|
||||
},
|
||||
{
|
||||
"method": "product_quant_8sub",
|
||||
"dataset": "glove200",
|
||||
"n_vectors": 100000,
|
||||
"dimensions": 200,
|
||||
"bits_per_dim": 0.32,
|
||||
"compression_ratio": 100.0,
|
||||
"recall_at_1": 0.18200000000000002,
|
||||
"recall_at_10": 0.20529999999999923,
|
||||
"recall_at_100": 0.2689499999999998,
|
||||
"latency_p50_ms": 2.743680655839853,
|
||||
"latency_p95_ms": 3.1331924571228833,
|
||||
"latency_p99_ms": 3.6776942773334054,
|
||||
"memory_mb": 1.0048,
|
||||
"recall_at_1_std": 2.7755575615628914e-17,
|
||||
"recall_at_10_std": 0.0,
|
||||
"recall_at_100_std": 0.0,
|
||||
"latency_p50_std": 0.021249133726968617,
|
||||
"n_trials": 3,
|
||||
"notes": "K-means codebooks, 8 subspaces, 256 centroids each. Same as ruvector-core ProductQuantized."
|
||||
},
|
||||
{
|
||||
"method": "turboquant_mse_3bit",
|
||||
"dataset": "glove200",
|
||||
"n_vectors": 100000,
|
||||
"dimensions": 200,
|
||||
"bits_per_dim": 3.0,
|
||||
"compression_ratio": 10.666666666666666,
|
||||
"recall_at_1": 0.8196666666666667,
|
||||
"recall_at_10": 0.8255333333333268,
|
||||
"recall_at_100": 0.8452066666666663,
|
||||
"latency_p50_ms": 2.7863399979347983,
|
||||
"latency_p95_ms": 3.373479491953427,
|
||||
"latency_p99_ms": 4.612403537321369,
|
||||
"memory_mb": 7.5,
|
||||
"recall_at_1_std": 0.007039570693980924,
|
||||
"recall_at_10_std": 0.0028110891523070876,
|
||||
"recall_at_100_std": 0.0009572297994157794,
|
||||
"latency_p50_std": 0.04627544133659402,
|
||||
"n_trials": 3,
|
||||
"notes": "MSE-only (no QJL correction). Searches on reconstructed vectors."
|
||||
},
|
||||
{
|
||||
"method": "turboquant_mse_4bit",
|
||||
"dataset": "glove200",
|
||||
"n_vectors": 100000,
|
||||
"dimensions": 200,
|
||||
"bits_per_dim": 4.0,
|
||||
"compression_ratio": 8.0,
|
||||
"recall_at_1": 0.896,
|
||||
"recall_at_10": 0.9032333333333233,
|
||||
"recall_at_100": 0.9172566666666638,
|
||||
"latency_p50_ms": 2.7566665036526197,
|
||||
"latency_p95_ms": 3.228431849371797,
|
||||
"latency_p99_ms": 4.2890612613215735,
|
||||
"memory_mb": 10.0,
|
||||
"recall_at_1_std": 0.0008164965809277268,
|
||||
"recall_at_10_std": 0.0025629843715655066,
|
||||
"recall_at_100_std": 0.00030663043264283997,
|
||||
"latency_p50_std": 0.003963923873867745,
|
||||
"n_trials": 3,
|
||||
"notes": "MSE-only (no QJL correction). Searches on reconstructed vectors."
|
||||
},
|
||||
{
|
||||
"method": "turboquant_full_3bit",
|
||||
"dataset": "glove200",
|
||||
"n_vectors": 100000,
|
||||
"dimensions": 200,
|
||||
"bits_per_dim": 3.0,
|
||||
"compression_ratio": 10.666666666666666,
|
||||
"recall_at_1": 0.6606666666666667,
|
||||
"recall_at_10": 0.6797333333333335,
|
||||
"recall_at_100": 0.6845233333333335,
|
||||
"latency_p50_ms": 26.519729168891597,
|
||||
"latency_p95_ms": 29.870601190971986,
|
||||
"latency_p99_ms": 33.09963693832591,
|
||||
"memory_mb": 7.7,
|
||||
"recall_at_1_std": 0.005312459150169748,
|
||||
"recall_at_10_std": 0.0001247219128921246,
|
||||
"recall_at_100_std": 0.00034373762603982864,
|
||||
"latency_p50_std": 0.48196212124746546,
|
||||
"n_trials": 3,
|
||||
"notes": "Full two-stage (MSE + QJL). Uses unbiased inner product estimator."
|
||||
},
|
||||
{
|
||||
"method": "f32_baseline",
|
||||
"dataset": "pkm384",
|
||||
"n_vectors": 117,
|
||||
"dimensions": 384,
|
||||
"bits_per_dim": 32.0,
|
||||
"compression_ratio": 1.0,
|
||||
"recall_at_1": 1.0,
|
||||
"recall_at_10": 1.0,
|
||||
"recall_at_100": 1.0,
|
||||
"latency_p50_ms": 0.009472341237900158,
|
||||
"latency_p95_ms": 0.01076124414491157,
|
||||
"latency_p99_ms": 0.013885850009197986,
|
||||
"memory_mb": 0.179712,
|
||||
"recall_at_1_std": 0.0,
|
||||
"recall_at_10_std": 0.0,
|
||||
"recall_at_100_std": 0.0,
|
||||
"latency_p50_std": 7.093481233648098e-05,
|
||||
"n_trials": 3,
|
||||
"notes": ""
|
||||
},
|
||||
{
|
||||
"method": "scalar_int8",
|
||||
"dataset": "pkm384",
|
||||
"n_vectors": 117,
|
||||
"dimensions": 384,
|
||||
"bits_per_dim": 8.0,
|
||||
"compression_ratio": 4.0,
|
||||
"recall_at_1": 0.9499999999999998,
|
||||
"recall_at_10": 0.9899999999999999,
|
||||
"recall_at_100": 1.0,
|
||||
"latency_p50_ms": 0.009284999881250163,
|
||||
"latency_p95_ms": 0.00985724424632887,
|
||||
"latency_p99_ms": 0.010215718066319823,
|
||||
"memory_mb": 0.045864,
|
||||
"recall_at_1_std": 1.1102230246251565e-16,
|
||||
"recall_at_10_std": 1.1102230246251565e-16,
|
||||
"recall_at_100_std": 0.0,
|
||||
"latency_p50_std": 0.00018186635058759144,
|
||||
"n_trials": 3,
|
||||
"notes": ""
|
||||
},
|
||||
{
|
||||
"method": "int4",
|
||||
"dataset": "pkm384",
|
||||
"n_vectors": 117,
|
||||
"dimensions": 384,
|
||||
"bits_per_dim": 4.0,
|
||||
"compression_ratio": 8.0,
|
||||
"recall_at_1": 0.9,
|
||||
"recall_at_10": 0.96,
|
||||
"recall_at_100": 0.9914999999999999,
|
||||
"latency_p50_ms": 0.009555665504497787,
|
||||
"latency_p95_ms": 0.010283919012484452,
|
||||
"latency_p99_ms": 0.010378927108831704,
|
||||
"memory_mb": 0.045864,
|
||||
"recall_at_1_std": 0.0,
|
||||
"recall_at_10_std": 0.0,
|
||||
"recall_at_100_std": 0.0,
|
||||
"latency_p50_std": 0.00020528515783232594,
|
||||
"n_trials": 3,
|
||||
"notes": "Min-max 4-bit scalar, 16 levels. Same as ruvector-core Int4Quantized."
|
||||
},
|
||||
{
|
||||
"method": "binary",
|
||||
"dataset": "pkm384",
|
||||
"n_vectors": 117,
|
||||
"dimensions": 384,
|
||||
"bits_per_dim": 1.0,
|
||||
"compression_ratio": 32.0,
|
||||
"recall_at_1": 0.8000000000000002,
|
||||
"recall_at_10": 0.8050000000000003,
|
||||
"recall_at_100": 0.9634999999999998,
|
||||
"latency_p50_ms": 0.009694330704708895,
|
||||
"latency_p95_ms": 0.011442752535610149,
|
||||
"latency_p99_ms": 0.013110953441355376,
|
||||
"memory_mb": 0.005616,
|
||||
"recall_at_1_std": 1.1102230246251565e-16,
|
||||
"recall_at_10_std": 0.0,
|
||||
"recall_at_100_std": 0.0,
|
||||
"latency_p50_std": 0.00029565886829973275,
|
||||
"n_trials": 3,
|
||||
"notes": "Sign-bit quantization (>0 -> +1, <=0 -> -1). Same as ruvector-core BinaryQuantized."
|
||||
},
|
||||
{
|
||||
"method": "product_quant_8sub",
|
||||
"dataset": "pkm384",
|
||||
"n_vectors": 117,
|
||||
"dimensions": 384,
|
||||
"bits_per_dim": 0.16666666666666666,
|
||||
"compression_ratio": 192.0,
|
||||
"recall_at_1": 1.0,
|
||||
"recall_at_10": 1.0,
|
||||
"recall_at_100": 1.0,
|
||||
"latency_p50_ms": 0.010007002856582403,
|
||||
"latency_p95_ms": 0.013316171437812357,
|
||||
"latency_p99_ms": 0.023396570274295894,
|
||||
"memory_mb": 0.180648,
|
||||
"recall_at_1_std": 0.0,
|
||||
"recall_at_10_std": 0.0,
|
||||
"recall_at_100_std": 0.0,
|
||||
"latency_p50_std": 0.0001466210212454445,
|
||||
"n_trials": 3,
|
||||
"notes": "K-means codebooks, 8 subspaces, 117 centroids each. Same as ruvector-core ProductQuantized."
|
||||
},
|
||||
{
|
||||
"method": "turboquant_mse_3bit",
|
||||
"dataset": "pkm384",
|
||||
"n_vectors": 117,
|
||||
"dimensions": 384,
|
||||
"bits_per_dim": 3.0,
|
||||
"compression_ratio": 10.666666666666666,
|
||||
"recall_at_1": 0.9,
|
||||
"recall_at_10": 0.9316666666666666,
|
||||
"recall_at_100": 0.9886666666666665,
|
||||
"latency_p50_ms": 0.010069498481849829,
|
||||
"latency_p95_ms": 0.013416674240337075,
|
||||
"latency_p99_ms": 0.026716664918543128,
|
||||
"memory_mb": 0.016848,
|
||||
"recall_at_1_std": 0.0,
|
||||
"recall_at_10_std": 0.006236095644623291,
|
||||
"recall_at_100_std": 0.0006236095644622944,
|
||||
"latency_p50_std": 0.00027661232773660703,
|
||||
"n_trials": 3,
|
||||
"notes": "MSE-only (no QJL correction). Searches on reconstructed vectors."
|
||||
},
|
||||
{
|
||||
"method": "turboquant_mse_4bit",
|
||||
"dataset": "pkm384",
|
||||
"n_vectors": 117,
|
||||
"dimensions": 384,
|
||||
"bits_per_dim": 4.0,
|
||||
"compression_ratio": 8.0,
|
||||
"recall_at_1": 0.9,
|
||||
"recall_at_10": 0.9550000000000001,
|
||||
"recall_at_100": 0.9944999999999998,
|
||||
"latency_p50_ms": 0.009569334603535632,
|
||||
"latency_p95_ms": 0.011537281776933627,
|
||||
"latency_p99_ms": 0.01949624847232673,
|
||||
"memory_mb": 0.022464,
|
||||
"recall_at_1_std": 0.0,
|
||||
"recall_at_10_std": 0.008164965809277176,
|
||||
"recall_at_100_std": 0.001080123449734593,
|
||||
"latency_p50_std": 1.9682039016405265e-05,
|
||||
"n_trials": 3,
|
||||
"notes": "MSE-only (no QJL correction). Searches on reconstructed vectors."
|
||||
},
|
||||
{
|
||||
"method": "turboquant_full_3bit",
|
||||
"dataset": "pkm384",
|
||||
"n_vectors": 117,
|
||||
"dimensions": 384,
|
||||
"bits_per_dim": 3.0,
|
||||
"compression_ratio": 10.666666666666666,
|
||||
"recall_at_1": 0.8166666666666665,
|
||||
"recall_at_10": 0.8800000000000002,
|
||||
"recall_at_100": 0.9790000000000001,
|
||||
"latency_p50_ms": 0.3868333393863092,
|
||||
"latency_p95_ms": 0.4495501105945247,
|
||||
"latency_p99_ms": 0.46149882749887183,
|
||||
"memory_mb": 0.017082,
|
||||
"recall_at_1_std": 0.08498365855987977,
|
||||
"recall_at_10_std": 0.004082482904638588,
|
||||
"recall_at_100_std": 0.0028284271247463236,
|
||||
"latency_p50_std": 0.005164780716996933,
|
||||
"n_trials": 3,
|
||||
"notes": "Full two-stage (MSE + QJL). Uses unbiased inner product estimator."
|
||||
}
|
||||
]
|
||||
45
benchmarks/vector-search/results/benchmark_results.md
Normal file
45
benchmarks/vector-search/results/benchmark_results.md
Normal file
|
|
@ -0,0 +1,45 @@
|
|||
# TurboQuant Vector Search Benchmark Results
|
||||
|
||||
Date: 2026-04-19 19:01:49
|
||||
Platform: darwin
|
||||
Python: 3.14.4
|
||||
PyTorch: 2.10.0
|
||||
|
||||
## Results
|
||||
|
||||
| Dataset | Method | Dims | N | Bits/dim | Compress | R@1 | R@10 | R@100 | p50 ms | Trials | Memory MB |
|
||||
|---------|--------|------|---|----------|----------|-----|------|-------|--------|--------|-----------|
|
||||
| sift1m | f32_baseline | 128 | 100,000 | 32.0 | 1.0x | 1.000 | 1.000 | 1.000 | 1.47±0.02 | 3 | 51.2 |
|
||||
| sift1m | scalar_int8 | 128 | 100,000 | 8.0 | 4.0x | 0.986 | 0.989 | 0.993 | 1.48 | 3 | 13.6 |
|
||||
| sift1m | int4 | 128 | 100,000 | 4.0 | 8.0x | 0.750 | 0.850 | 0.902 | 1.48 | 3 | 13.6 |
|
||||
| sift1m | binary | 128 | 100,000 | 1.0 | 32.0x | 0.000 | 0.000 | 0.003 | 1.44±0.03 | 3 | 1.6 |
|
||||
| sift1m | product_quant_8sub | 128 | 100,000 | 0.5 | 64.0x | 0.081 | 0.189 | 0.338 | 1.49±0.02 | 3 | 0.9 |
|
||||
| sift1m | turboquant_mse_3bit | 128 | 100,000 | 3.0 | 10.7x | 0.283±0.018 | 0.411±0.015 | 0.548±0.019 | 1.47 | 3 | 4.8 |
|
||||
| sift1m | turboquant_mse_4bit | 128 | 100,000 | 4.0 | 8.0x | 0.448±0.032 | 0.577±0.036 | 0.691±0.035 | 1.51±0.07 | 3 | 6.4 |
|
||||
| sift1m | turboquant_full_3bit | 128 | 100,000 | 3.0 | 10.7x | 0.168±0.012 | 0.249±0.007 | 0.377±0.003 | 13.94±0.16 | 3 | 5.0 |
|
||||
| glove200 | f32_baseline | 200 | 100,000 | 32.0 | 1.0x | 1.000 | 1.000 | 1.000 | 2.67 | 3 | 80.0 |
|
||||
| glove200 | scalar_int8 | 200 | 100,000 | 8.0 | 4.0x | 0.997 | 0.993 | 0.994 | 2.71±0.03 | 3 | 20.8 |
|
||||
| glove200 | int4 | 200 | 100,000 | 4.0 | 8.0x | 0.912 | 0.904 | 0.917 | 2.72±0.02 | 3 | 20.8 |
|
||||
| glove200 | binary | 200 | 100,000 | 1.0 | 32.0x | 0.514 | 0.503 | 0.498 | 2.77±0.01 | 3 | 2.5 |
|
||||
| glove200 | product_quant_8sub | 200 | 100,000 | 0.3 | 100.0x | 0.182 | 0.205 | 0.269 | 2.74±0.02 | 3 | 1.0 |
|
||||
| glove200 | turboquant_mse_3bit | 200 | 100,000 | 3.0 | 10.7x | 0.820±0.007 | 0.826±0.003 | 0.845 | 2.79±0.05 | 3 | 7.5 |
|
||||
| glove200 | turboquant_mse_4bit | 200 | 100,000 | 4.0 | 8.0x | 0.896 | 0.903±0.003 | 0.917 | 2.76 | 3 | 10.0 |
|
||||
| glove200 | turboquant_full_3bit | 200 | 100,000 | 3.0 | 10.7x | 0.661±0.005 | 0.680 | 0.685 | 26.52±0.48 | 3 | 7.7 |
|
||||
| pkm384 | f32_baseline | 384 | 117 | 32.0 | 1.0x | 1.000 | 1.000 | 1.000 | 0.01 | 3 | 0.2 |
|
||||
| pkm384 | scalar_int8 | 384 | 117 | 8.0 | 4.0x | 0.950 | 0.990 | 1.000 | 0.01 | 3 | 0.0 |
|
||||
| pkm384 | int4 | 384 | 117 | 4.0 | 8.0x | 0.900 | 0.960 | 0.991 | 0.01 | 3 | 0.0 |
|
||||
| pkm384 | binary | 384 | 117 | 1.0 | 32.0x | 0.800 | 0.805 | 0.963 | 0.01 | 3 | 0.0 |
|
||||
| pkm384 | product_quant_8sub | 384 | 117 | 0.2 | 192.0x | 1.000 | 1.000 | 1.000 | 0.01 | 3 | 0.2 |
|
||||
| pkm384 | turboquant_mse_3bit | 384 | 117 | 3.0 | 10.7x | 0.900 | 0.932±0.006 | 0.989 | 0.01 | 3 | 0.0 |
|
||||
| pkm384 | turboquant_mse_4bit | 384 | 117 | 4.0 | 8.0x | 0.900 | 0.955±0.008 | 0.994±0.001 | 0.01 | 3 | 0.0 |
|
||||
| pkm384 | turboquant_full_3bit | 384 | 117 | 3.0 | 10.7x | 0.817±0.085 | 0.880±0.004 | 0.979±0.003 | 0.39 | 3 | 0.0 |
|
||||
|
||||
## Notes
|
||||
|
||||
- All searches are brute-force (no HNSW acceleration) to isolate quantization quality.
|
||||
- Vectors are L2-normalized before quantization (inner product = cosine similarity).
|
||||
- All methods pre-reconstruct to f32 before search (fair latency comparison).
|
||||
- TurboQuant full uses the unbiased inner product estimator from the paper.
|
||||
- Ground truth computed with exact f32 inner product.
|
||||
- Each configuration run 3x with different seeds (stochastic methods) or repeated (deterministic).
|
||||
- ±values show standard deviation across trials.
|
||||
Loading…
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Reference in a new issue