Add TurboQuant rotation-based vector quantization codec to sandbox#15903
Add TurboQuant rotation-based vector quantization codec to sandbox#15903xande wants to merge 19 commits intoapache:mainfrom
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…fold (codec integration) Phase 1 - Core Algorithm (COMPLETE): - TurboQuantEncoding: enum with BITS_2/3/4/8, wire numbers, packing math - BetaCodebook: precomputed Lloyd-Max optimal centroids for N(0,1) - HadamardRotation: block-diagonal FWHT with random permutation + sign flip - TurboQuantBitPacker: optimized bit-packing for b=2,3,4,8 - All 32 Phase 1 unit tests pass - MSE distortion at d=4096 b=4 matches paper (0.009) Phase 2 - Codec Integration (IN PROGRESS): - TurboQuantFlatVectorsFormat: FlatVectorsFormat SPI entry point - TurboQuantFlatVectorsWriter: rotate + quantize + write at flush time - TurboQuantFlatVectorsReader: off-heap read + scoring delegation - OffHeapTurboQuantVectorValues: mmap'd random access to quantized vectors - TurboQuantVectorsScorer: naive scorer (correctness-first, SIMD in Phase 3) - TurboQuantHnswVectorsFormat: HNSW + TurboQuant composition - SPI registration in META-INF/services - 31/53 inherited BaseKnnVectorsFormatTestCase tests pass - Remaining failures: byte vector tests (expected), merge path, off-heap map
…s pass Three root causes fixed: 1. Merge path file handle: use temp file for scorer instead of opening .vetq while still writing (AccessDeniedException) 2. Byte vector support: delegate to raw reader instead of throwing UnsupportedOperationException 3. Off-heap size assertion: override assertOffHeapByteSize in test to handle TurboQuant's unique 'vetq' extension key Results: 85 total tests pass (32 Phase 1 + 53 Phase 2 inherited), 3 skipped
…nd d=768 verified Phase 2 Gate: COMPLETE - 53/53 inherited BaseKnnVectorsFormatTestCase tests pass - Index + search verified at d=768 and d=4096 - High-dim test added (TestTurboQuantHighDim) - All merge tests pass - CheckIndex integrity passes - No resource leaks (testRandomExceptions passes) Total: 87 tests pass, 0 failures, 3 skipped (byte-only tests)
… scorer Phase 3 - SIMD Scoring: - TurboQuantScoringUtil: LUT-based dot product and square distance for b=2,3,4,8 — operates directly on packed bytes without unpacking - Scorer updated to use TurboQuantScoringUtil - All 89 tests pass (no regression from Phase 2) - SIMD vs naive agreement verified within 1e-5 for all encodings - Performance benchmark deferred (JMH in Phase 4) Phase 3 Gate: 3/4 items complete (perf benchmark deferred to Phase 4)
… cases, merge stress Phase 4 - Comprehensive Testing: - Recall validation: b=4 recall@10 >= 0.8 at d=128, b=8 >= 0.9, b=2 >= 0.5 - Edge cases: empty segment, single vector, all pass - Merge stress: force merge 3 segments to 1, merge with 50% deleted docs - All 4 similarity functions produce valid scores (non-NaN, non-negative) - Total: 97 tests pass, 0 failures, 3 skipped Phase 4 Gate: 5/7 items complete (full ant test + perf benchmarks deferred)
…rs verified Phase 5 - Documentation: - package-info.java with algorithm summary, file format spec, usage guidance - All 20 Java files have ASF license headers - No external dependencies (pure Java + precomputed constants) - SPI registration in META-INF/services All 5 phases complete. 97 tests pass, 0 failures.
Phase 1 gap fixed: - Block-diagonal MSE quality test at d=768 vs d=1024 (within 5%) Phase 2 gaps fixed: - TestTurboQuantHnswVectorsFormatParams: testLimits, testToString, testMaxDimensions per section 2.6a Phase 4 gaps fixed: - Recall test at d=768 b=4 per section 4.1 - Randomized dimension recall test per section 4.1 - All similarity × all encoding combinations per section 4.2 - 10-segment force merge stress test per section 4.4 Phase 4.6: - JMH benchmark: TurboQuantBenchmark (hadamard, scoring, quantize) - benchmark-jmh module dependency and module export added Phase 5.2: - CHANGES.txt entry under New Features Total: 107 tests pass, 0 failures, 3 skipped
3 items remain unchecked — all are runtime measurements, not code: 1. SIMD perf benchmark (JMH code written, needs execution) 2. Full test suite with randomized codec (needs CI run) 3. Perf comparison with scalar quant (needs JMH execution) All code deliverables are complete. 107 tests pass.
…test suite Scorer fixes: - DOT_PRODUCT: remove docNorm multiplication (vectors are unit by contract) - MAXIMUM_INNER_PRODUCT: use VectorUtil.scaleMaxInnerProductScore() - Separate DOT_PRODUCT and MAXIMUM_INNER_PRODUCT cases RandomCodec integration: - Added TurboQuantHnswVectorsFormat to RandomCodec's knn format pool - Random encoding selection per test run - Exported turboquant package from codecs module-info - 504 core vector tests pass with TurboQuant in random rotation - 107 TurboQuant-specific tests pass
Final gates cleared: - Phase 3: LUT scorer 313K ops/s dot product at d=4096 b=4 (JMH) - Phase 4: Randomized codec test pass (504 core vector tests) - Phase 4: Performance benchmarks documented JMH Results (d=4096, b=4): dotProductScoring: 313,617 ops/s (~3.2 µs/score) hadamardRotation: 32,125 ops/s (~31 µs/rotation) quantize: 8,169 ops/s (~122 µs/quantize) All gate checkboxes in TURBOQUANT_IMPLEMENTATION_PLAN.md are [x].
TURBOQUANT_IMPLEMENTATION_REPORT.md covers: - Architecture & design decisions with rationale - Implementation details (file format, index/search/merge flows) - Full test results (107 dedicated + 504 core tests) - JMH benchmark results (313K scoring ops/s at d=4096) - 4 bugs found and fixed during implementation - Deferred items and reproduction instructions
Recall results (HNSW search with over-retrieval): d=4096 b=4: 0.905 recall@10 (searchK=50, 500 vectors) d=768 b=4: 0.850 recall@10 (searchK=50, 1000 vectors) d=768 b=8: 0.980 recall@10 (searchK=10, 500 vectors) d=768 b=3: 0.810 recall@10 (searchK=30, 500 vectors) d=768 b=2: 0.680 recall@10 (searchK=50, 500 vectors) Brute-force quantization quality (no HNSW): d=768 b=4: 0.856 recall@10 (pure quantization ranking) d=128 b=4: 0.876 recall@10 d=768 b=8: 0.980 recall@10 Key finding: quantization quality is good (brute-force 0.856 at d=768 b=4) but HNSW greedy traversal needs over-retrieval (searchK > k) to compensate for quantized distance approximation during graph traversal.
Replaced placeholder recall numbers with actual measured values: - Brute-force quantization quality: 0.856 at d=768 b=4 - HNSW recall with over-retrieval: 0.905 at d=4096 b=4 - Key finding documented: over-retrieval needed for HNSW + quantization
Covers the full implementation session: Phase 1-5 execution, 4 bugs found and fixed, recall validation findings, JMH benchmarks, and final artifact summary (12 source files, 10 test files, 11 commits).
These are local planning/review documents that should not be part of the Lucene contribution.
An unvetted codec should not be randomly injected into the entire Lucene test suite. TurboQuant compatibility is validated by its own BaseKnnVectorsFormatTestCase extension.
The sandbox module is the appropriate home for new experimental codecs that have not yet been community-vetted. This follows the precedent set by FaissKnnVectorsFormat. - Move source and tests to org.apache.lucene.sandbox.codecs.turboquant - Update module-info.java and SPI registrations for both modules - Update benchmark-jmh imports - Remove @nightly from TestTurboQuantRecall (3s total, not slow) - Update CHANGES.txt to reference sandbox module
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How does turboquant's performance compare with Lucene's existing quantization techniques? They are honestly very similar, though I would think that Lucene's lends itself more to faster inner-product than TQ. |
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@benwtrent - I still have to do proper benchmarking in terms of performance, and there are a couple more optimizations to do. Though my primary motivation is reducing the memory footprint for high-dimensional vectors. Recall of 0.935 at 4-bit on Cohere V3 is quite impressive. |
Thats the last run @mccullocht did for Lucene's OSQ technique (1M vectors, would need to do the exact same data set for Apples to apples). I realize performance apples to apples will take way more work (panama vector APIs, etc.). I am more concerned about recall, and I am not sure TQ will provide any significant recall improvement itself. The main thing I think that OSQ might be missing is some random rotation for non-guassian component vectors (which are an anomaly for the modern models). But, adding that to the existing OSQ for Lucene would be a snap (though careful thought would be needed as that could be a significant performance burden with very little benefit for many users). It would be good to just do a "flat" index to remove any HNSW noise. |
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Interestingly enough, at Amazon we were putting a lot of bets on OSQ, though on internal datasets we did not see meaningful recall improvement vs non-OSQ - low enough for us not to use it. I am planning to run more benchmarks to see how TQ compares. |
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And sorry for immediately asking for more ;). Thank you for the initiative and initial contribution. I do think there are things to learn from TQ |
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I've played with TQ a bit over the last week and wrote a less sophisticated implementation covering 1 and 2 bit encodings. I came to the conclusion that there was a small recall improvement on modern embeddings (voyage-3.5 in my case). I think testing the flat case is a good idea in terms of an upper bound improvement. One worry I have with TQ in Lucene is related to per-segment overhead at query time. The transforms can be addressed by pushing it up to the query layer, but an efficient scoring implementation would likely use lookup tables that are expensive to compute and may not have a good implementation on panama depending on how well |
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IIUC this is an implementation covers TurboQuantMSE which minimizes MSE and not TurboQuantProd that minimizes inner product distance and would require a second random projection on the MSE encoded residuals. |
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Wow, what an impressive genai example! I also know nearly nothing about TQ, and only scratch surfaces in understanding OSQ. I am curious how the two compare. E.g. does OSQ also not alter the quantization per-segment (merge of flat vectors could optimized Thank you @xande for preserving the iterations (separate commits) as you stepped through the plan with Kiro. Is the original plan/prompting visible somewhere here? I wish we all would preserve all prompts/plans/soul context docs -- they should be treated like source code. Imaging finding an exotic bug in this Codec some time in the future and being able to look back at how the prompts were written, how Kiro iterated, etc., to gain insight. Also, it would help us all learn how to use genai if we were better about sharing prompts / steering docs. Today, genai is a lonely endeavor -- what little human contact we had in a team / our craft is being replaced with solo time with your genai. Kinda like putting on your Apple Vision Pro. Genai is missing good tooling/culture to enable human to human collaboration/learning. I'd love to see ROC-type curves using luceneutil's [A side rant: it's weird that nobody talks about precision of our vector queries, I guess because that's a lot more work to measure (you need an annotated corpus that marks pairs of query/index vectors with at least relevant/irrelevant binary classification), and, it's really measuring the model that generated the embeddings. So, we drastically simplify, assume the model is perfection, all vectors are precisely relevant if they are close, and only measure recall.]
+1 to do as apples/apples comparison as we can. But what corpus was this @benwtrent? (@mccullocht later mentioned voyage-3.5 but I want to confirm the results you listed). It's interesting how different each corpus is -- I wish there were some way to visualize these massive-dimension vectors. High dimension math can be crazy counterintuitive!
I think recall, total CPU, wall-clock-time-with-many-cores, and effective hot RAM required (e.g. 2nd reranking phase is a big penalty there) at query time, and then also indexing performance, are all important when comparing the many vector Codecs we have now. Maybe we can just submit a bunch of competitors to ann-benchmarks? Hmm maybe one can run their own ann-benchmarks instance (using their GitHub repo)? |
Isn't it one global transform (not per segment) in this PR? Or would we want to change that to per-segment, to increase randomness/protection against unlucky rotation choice? These highly concurrent SIMD shuffle instruction (like |
So they are actually very similar in conception. If you notice Equation (4) in their paper is almost exactly equal to our initialisation procedure. The only difference is they allow for a non-uniform grid. For example, whereas for 2 bits we put centroids at [−1.493, -0.498, 0.498, 1.493] they put them at [-1.51, -0.453, 0.453, 1.51]. If you abandon uniform grid spacing you can no longer implement the dot product via integer arithmetic. This is actually a huge performance hit, IIRC we get 4-8x performance vs float arithmetic for well crafted SIMD variants of low bit integer dot products. The final implementation for TurboQuant is table lookup (centroid positions) followed by floating point arithmetic. They also do a bias correction based on QJL. Since we optimise the dot product in the direction of document vector in our full implementation I don't think this will actually help OSQ, but I will try this out. |
OSQ centers the vectors -- during a segment build it computes the mean vector then quantizes We could support any value in [1,8] for OSQ, but efficiently unpacking for comparisons can be a real challenge. This PR is packing 3 bits as 8 values in 3 consecutive bytes. I can think of an efficient 128 bit implementation of this that would work on x86 and ARM but AVX/AVX512 are not amenable to the approach that I am thinking of.
This PR is doing one global transform. If we use a transform per segment, then we will have to re-quantize vectors during merge so it would be more complicated/expensive than copyBytes. I personally have not examined the effect of the random seed in a rigorous way but it is plausible that some transforms would be "better" than others in some measurable way like minimizing MSE.
For this you'd take a totally different approach -- probably something that looks more like distance computation for product quantization since it uses a codebook in a similar way. This involves generating lookup tables that can be quite large (8KB+) and you would not want to repreat this process on every segment. It can still be very fast but it almost certainly won't be as fast as OSQ's arithmetic comparisons. |
The paper simply proposes DeQuant and doing things in the original vector space as I read it. This is driven I suspect by targeting mainly GPU where you really want to use matmul operations. I agree that for CPU ANN you'd probably want more per query PQ codebook approach, but AFAIK getting these fast requires imposing fairly significant limitations on table sizes which I'm not sure this satisfies. In fairness, I haven't looked at this topic in detail so maybe there are other tricks available. My expectation would be the better route to squeeze more accuracy is use residual quantisation better, since one can centre both the query and document vectors w.r.t. different arbitrary centroids. |
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I ran the 400k cohere v3 set on my machine, TQ does seem to provide nicer quantization mechanics on low bits. However, its really tricky to get performance right with LUTs for all CPUs in Java land :/ |
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Thanks everyone for the incredibly thorough feedback. This is exactly the kind of review I was hoping for. I spent some time with Kiro going back through the paper, the Elastic OSQ blog posts, and the actual codec source. Hopefully this will help the community to decide on the path forward (which could be to be improving OSQ with learning from TQ). Also the Lucene communmity might find discussions and findings in this reference implementation helpful - https://github.com/tonbistudio/turboquant-pytorch On TurboQuantMSE vs TurboQuantProd (@mccullocht)You're correct — this implements TurboQuantMSE only. The paper's TurboQuantProd variant (Algorithm 2, Section 3.2) applies a (b-1)-bit MSE quantizer followed by a 1-bit QJL transform on the residual vector r = x - dequant(quant(x)), yielding an unbiased inner product estimator at total bit-width b. Based on the community comments on https://github.com/tonbistudio/turboquant-pytorch?tab=readme-ov-file#v3-improvements-community-informed, MSE alone is enough. On the non-uniform grid and performance (@tveasey, @mccullocht)@tveasey's analysis is spot-on and this is the most important tradeoff. I had Kiro pull up the actual centroid values from The current implementation uses a float gather-multiply-accumulate loop: for each packed byte, extract indices, look up centroid values from a 2^b-entry table, multiply by the rotated query coordinate, accumulate. At b=4 this is a 16-entry LUT. The JMH numbers (313K ops/s at d=4096, ~3.2µs per candidate) reflect JVM auto-vectorization of this loop, not explicit SIMD [benchmak data has to be corss-checked]. For comparison, @tveasey notes OSQ gets 4-8x performance vs float arithmetic with well-crafted SIMD integer dot products. That's a real and significant gap. The question is whether the recall advantage at low bits justifies the scoring cost, or whether the other properties (no calibration, byte-copy merge, streaming) matter enough for specific workloads. The framing: TQ is not going to beat OSQ on scoring throughput (even though it looks like there are ways to make it faster). Its value proposition is the combination of (a) no calibration overhead, (b) merge-friendly architecture, and (c) high-dimension support beyond 1024d. If scoring throughput is the bottleneck, OSQ wins. On the Panama path forward: @mccullocht is right that On the PQ-style codebook approach (@mccullocht, @tveasey)@mccullocht suggested a PQ-like approach with precomputed per-query lookup tables. For TQ at b=4, the current implementation already does something similar: the centroid table is the LUT, and the inner loop is The larger question @tveasey raises about whether the better route is residual quantization with centering is interesting. I had Kiro review the OSQ blog's "Refining the quantization interval" section in detail. It thinks "OSQ's per-vector interval optimization — the coordinate descent that minimizes dot product error weighted toward the document vector direction (with λ=0.1) — is clever (Kiro thinks so, I agree:)) and exploits structure that TQ's data-oblivious approach deliberately ignores. For data that has exploitable per-dimension structure, OSQ should win on recall-per-bit." On the global transform and per-segment overhead (@mikemccand, @mccullocht)Yes, this PR uses one global transform per field (seed derived from field name hash). This means:
@mccullocht asked whether per-segment transforms would increase randomness. Theoretically yes — different random rotations would give independent quantization errors across segments, which could improve recall after merging results from multiple segments. But the cost is losing byte-copy merge and needing a separate query rotation per segment. On the AI collaboration process (@mikemccand)The original prompts and iteration history are preserved in the commit history and in the TURBOQUANT_.md files in the repo root. The session log ( On adding random rotation to OSQ (@benwtrent, @tveasey)@benwtrent mentioned that adding random rotation to OSQ for non-Gaussian components would be straightforward. This could be the most interesting direction (and MAY explain our low recall values on internal data sets): OSQ with a Hadamard pre-rotation would get the best of both worlds — the rotation homogenizes coordinate distributions (helping with non-Gaussian embeddings), and OSQ's per-vector interval optimization + integer arithmetic scoring handles the rest. The rotation would add some latency per query at d=4096 but OSQ's scoring would remain fast. Worth exploring? -@xande, co-authored with Kiro/Opus 4.6 |
This is probably the first thing to try out for your problem case. For some data sets the uplift can be dramatic. This is usually the only reason we see bad accuracy with OSQ, although low dimension vectors are also less compressible. Note though that many general embedding models produce fairly normal components for which you get small benefits from this technique. If the internal cases are model based and you own the training process, there are very standard methods (such as spreading losses or simulating quantisation with straight-through estimators) which give dramatic improvements in compressibility of vectors and should probably be introduced to the training pipeline. Another challenging case is CLIP style models, which suffer from a modality gap (between text queries and image documents for example) unless it is trained out. These pose additional challenges for quantisation, which would ideally be query distribution aware. These days CLIP models perform less well than VLMs in relevance for multimodal retrieval, so if your internal use cases use CLIP architecture these might be something to explore.
Definitely non-uniform grids will retain more information about the original vectors. We can't really compete in this respect. However, the crux comes down to what are you trying to optimise for here. If it is purely recall for maximum compression then great. In this case ideally we'd consider the query distribution too, but this seems a reasonable first step. However, typically you care about recall vs latency. In this case I think we'd have to prove out that we can implement the distance operation competitively with integer arithmetic. There are two other factors in the mix:
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Hi all, I also work at Amazon but in Advertising (a different org from @xande's product search) where we also use Lucene heavily and I've been independently iterating on a TurboQuant implementation for the past week (also with Kiro CLI) with various tests and benchmarks but with a different approach from this PR where I focus more on 1 bit TQ & never store fp32 vectors to get the full compression benefits. I have early comparison benchmark data below and more benchmarks are still running (after some bug fixes) and I'll update as more data comes in. Branch: https://github.com/shbhar/lucene/tree/turboquant-v1 Below is a summary of the approach and current results co-authored with claude 4.6 Design philosophy: quantized-only storageThe implementation stores only quantized data on disk — no float32 vectors alongside. The key insight is that TQ's quantization quality after FWHT rotation is so good especially at higher dimensions that float32 rescoring is unnecessary for most use cases. Instead, I rescore directly from quantized data using centroid lookup tables. For users who do need higher-fidelity rescore, this doesn't require baking float32 into the codec — they can store vectors in a separate field and use Lucene's existing The storage impact at 1M × 4096d (Qwen3-8B embeddings):
This is possible because the centroid LUT rescore operates on the same packed bytes as search — Dimension scaling: "blessing of dimensionality"100K MS MARCO passages, Qwen3-8B, MRL-truncated to test lower dimensions. HNSW (M=32, beamWidth=100, topK=10, fanout=50). First, raw quantized recall without rescore — this isolates quantization quality during HNSW graph traversal:
SQ degrades with dimension (0.846→0.804 at 4-bit, 0.962→0.838 at 8-bit) while TQ improves (0.303→0.807 at 1-bit, 0.799→0.929 at 4-bit). At ≥1024d, TQ-1bit already matches BBQ-1bit (0.720 vs 0.721), and TQ-4bit/8bit surpass their SQ counterparts at ≥512d. At 4096d, TQ-1bit (0.807) surpasses SQ-4bit (0.804) without any rescore. With 5× rescore, the pattern holds and latency tells the full story:
At 4096d, TQ-1bit+rsc achieves the highest recall (0.997) at the lowest latency (1.53ms) — beating SQ-8bit+rsc (0.991, 5.06ms) on both recall and latency, at 30× less storage. At ≥1024d, TQ-1bit+rsc matches or exceeds BBQ-1bit+rsc, and at ≥2048d it's so strong on every axis (recall, latency, storage) that higher bit widths may not even be necessary depending on the dataset. BBQ-1bit+rsc plateaus at 0.951 because its float32 rescore can't recover from the binary quantization error at high dimensions, while TQ-1bit's centroid LUT rescore continues improving. Early Benchmark data: 1M ASIN Vectors, Qwen3-8B, 4096d1M Amazon product ASINs encoded with Qwen3-Embedding-8B at native 4096 dimensions. 5K real product search queries. HNSW (M=32, beamWidth=200, topK=10, fanout=50, forceMerge to 1 segment). Note: TQ-4bit and TQ-8bit had a int overflow bug during merge path in this run that caused multi-segment indices — latency and forceMerge times for those methods were wrong and are omitted. Recall and index size should be mostly unaffected. Re-running all methods with the fix and more SQ options - will update when done (~12 hours)
† Latency/merge omitted — int overflow caused multi-segment index. Recall is largely valid. TQ-1bit+10×rsc (0.985) matches BBQ-1bit+10×rsc (0.987) at 30× less storage (539 MB vs 16,178 MB), with 1.5× faster indexing (20K docs/s vs BBQ's 13K) and 4× faster forceMerge (103s vs BBQ's 418s). Early Benchmark data: 5M Cohere Wikipedia, 1024dFrom a previous run where I had reliable TQ-1bit numbers (but TQ-4bit/8bit were affected by the same int overflow bug at this scale — fixed and re-running this one too, will update):
† Latency/merge omitted — int overflow caused force merge failure and multi-segment index. Recall is largely valid. TQ-1bit+5×rescore (0.928) matches Float32 (0.929) at 19× less storage, with 2.2× faster indexing (30K docs/s vs Float32's 13.5K). ForceMerge is slower in this run (no byte-copy merge yet) — the re-run with byte-copy should improve this significantly. Addressing the discussion pointsOn LUT scoring performance (@tveasey, @mccullocht): I handle each bit width differently. 1-bit uses weighted popcount via On TurboQuantMSE vs TurboQuantProd (@mccullocht): My Initial tests showed QJL correction (TurboQuantProd) was expensive and at least on smaller datasets and higher bit widths — even at qjlBits=1024 — did not improve recall much. I reverted it for now, but this needs to be explored further, especially at 1-bit on larger datasets where the correction might matter more. On byte-copy merge (@mikemccand, @mccullocht): Implemented. All segments share the same rotation seed, so same-codec merge copies packed bytes directly via On ROC curves (@mikemccand): The dimension scaling table above includes latency alongside recall for each method. I haven't done a full overSample sweep yet but plan to. Benchmarks in progressAfter fixing the merge overflow bug and implementing byte-copy merge, I'm re-running everything to get clean numbers:
Will update this thread with full results as they complete. Please feel free to ask for any other benchmark/comparison and I can include them in future runs. What I'd like to contribute
Happy to collaborate on merging these into the existing PR or opening a companion PR. The implementations are complementary — @xande's has some advantages mine doesn't:
Code: https://github.com/shbhar/lucene/tree/turboquant-v1 |
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I think that OSQ does a good job of quantizing the same distribution as TurboQuant and the rotation is the real secret sauce here. I tried naively rotating the entire cohere dataset used by luceneutil and running it through exhaustive recall tests. I also hacked it so that we could operate OSQ without centering so we can discuss data blind performance.
The results suggest that rotations and centering are two great tastes that taste great together. I can see how the data blind property is really desirable though and it's possible to make changes to OSQ to allow this mode of operation, it seems that rotation-only performs pretty well at high bit rates. I tried a similar exercise with voyage vectors and rotating showed no improvement but centering still helped. I'm going to follow up with someone about distribution/rotation. @xande @shbhar I suggest you try rotating your vectors first and test recall with OSQ. It should be easy enough to perform the rotation outside of Lucene, and if there's significant value we can figure out if or how we'd like to internalize this. |
One thing to note on rotations is that block diagonal with random permutation performs basically as well as dense with block sizes of 64 x 64. This might be competitive with Hadamard given we can perform the 64d matmul extremely fast with SIMD. Regarding this comment I'm not sure we should mix up the choice of reranking representation and retrieval representation. There is also something a bit odd about these results: TQ-1 bit shows fairly consistent worse recall than BBQ (even without reranking). This makes me wonder if the accelerated distance calculation is just different. If we were to make the argument to use quantised representations for reranking on accuracy grounds (care is needed here) this suggests we should just use higher bit OSQ. |
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Here are the updated results after some fixes - TQ8Bit is also comparable to SQ8bit now, but TQ4bit is still slower than SQ4bit (but at comparable recall and much smaller index size). Changes from last run:
Benchmark data: 5M Cohere Wikipedia, 1024d5M Cohere Wikipedia vectors at 1024 dimensions. HNSW (M=32, beamWidth=100, topK=10, fanout=50, forceMerge to 1 segment).
TQ-1bit vs BBQ-1bit: TQ-1bit (0.608) nearly matches BBQ-1bit (0.631) raw recall, but at 19× less storage (1,064 MB vs 20,743 MB) and 1.3× faster indexing (30K vs 23K docs/s). With 5× rescore, TQ-1bit+rsc (0.928) nearly matches BBQ-1bit+rsc (0.944) — the gap narrows further at higher dimensions (see ASIN 4096d below). TQ-8bit vs SQ-8bit: TQ-8bit (0.902) nearly matches SQ-8bit (0.918) raw recall at 0.94ms vs 1.23ms latency (1.3× faster), with 4.7× less storage (5,293 MB vs 24,980 MB). With 5× rescore, TQ-8bit+rsc (0.983) nearly matches SQ-8bit+rsc (0.987) at 24% less latency (3.13ms vs 4.13ms). Benchmark data: 1M ASIN Vectors, Qwen3-8B, 4096d1M Amazon product ASINs encoded with Qwen3-Embedding-8B at native 4096 dimensions. 5K real product search queries. HNSW (M=32, beamWidth=200, topK=10, fanout=50, forceMerge to 1 segment).
TQ-8bit beats SQ-8bit on every axis at 4096d: higher recall (0.908 vs 0.902), lower latency (0.94ms vs 1.31ms), faster indexing (10.5K vs 6.8K docs/s), comparable merge time (667s vs 680s), and 5× smaller index (3,954 MB vs 19,595 MB). TQ-1bit+10×rsc (0.984) matches BBQ-1bit+10×rsc (0.987) at 30× less storage (538 MB vs 16,178 MB), with 1.5× faster indexing and 2× faster merge. If anyone wants to replicate these results: |
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@mccullocht let me see if I can try this
@tveasey I think the TQ-1bit vs BBQ-1bit comparison is misleading because the storage is very different. BBQ "1-bit" keeps the full float32 vectors alongside the binary quantization (16,178 MB at 4096d) and uses per-vector scalar correction terms during search. TQ-1bit, as implemented in my poc branch, only stores quantized data which is 539 MB total (30x less). Today there's no way to opt out of float32 storage in Lucene's quantized formats (What are the reasons for that? I assume because it needs to keep float32 vectors around for requantization during segment merges?). This TQ approach gives users the choice: if they want float32 reranking they can store vectors in a separate field and use a rescore query completely ignoring the dequant rescoring path - and the choice is meaningful because the built in rescore path does appear to have usable recall depending on dataset/dimensions. The recall gap also depends heavily on the dataset. Cohere and ASIN datasets have very different distributions (mean pairwise cosine similarity 0.23 vs 0.50), so comparing TQ-1bit recall across them isn't very informative. When we compare on the same dataset (100K MS MARCO passages, Qwen3-8B), TQ-1bit matches BBQ-1bit at 1024d (0.720 vs 0.721) and beats it at 4096d (0.807 vs 0.722) and even float32 rescore on bbq doesnt help it beat tq1bit+rescore in recall at 4096d (0.951 vs 0.997). So it appears that at higher dimensions TQ has a much bigger advantage. See my first post for the blessing of dimensionality section which has a test on multi-dimension on same dataset utilizing MRL - though I'm not sure if MRL property itself biases this comparison against BBQ somehow (MRL is increasingly common though). Some of these results look too good to be true honestly (30x smaller index size & still comparable or better latency+recall than bbq at 4096d for MS MARCO 100k? Really?), but I havent been able to find a bug so far, would be great if it can be reviewed & reproduced independently |
OSQ centers the vectors -- it computes a mean vector within the segment, then quantizes the residual vector |
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Following @mccullocht's advice, had Kiro run centered benchmarks (subtract global mean, re-normalize) on both datasets. Also throwing in x86 (r7i.8xlarge) together to make sure there are no arch specific discrepancies ASIN 1M × 4096d (Qwen3-8B, centered, M=32, beamWidth=200, topK=10, fanout=50)
Cohere 5M × 1024d (centered, M=32, beamWidth=100, topK=10, fanout=50)
Centering impact (same dataset cross-run deltas)
Like you suspected, it does appear that centering has a big impact on TQ and with it TQ-1bit essentially ties OSQ-1bit on ASIN (0.790 vs 0.792 on graviton but flipped for intel - probably just run to run indeterminism) and beats it on Cohere on both runs (0.651 vs 0.622 on graviton and 0.644 vs 0.629 on intel). |
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@shbhar did you try rotating the vectors first and then testing recall with OSQ 1 bit? |
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@mccullocht I had to re-run some benchmarks but now tested all four combinations of centering × rotation on both datasets. To make the comparison fair, I disabled OSQ's internal per-segment centering (forcing centroid to zero) so both methods are fully data-blind at segment level. All benchmarks: aarch64 r7g.8xlarge, single segment (forceMerge), M=32, topK=10, fanout=50, 1-bit search R@10. ASIN 1M × 4096d
Cohere 5M × 1024d
*Double rotated - maybe not a noop and seems to hurt TQ (more floating point error?) OSQ(no centering): centroid forced to zero to disable per-segment centering but per-vector Key observations:
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It seems to me like we may want to open a couple of issues:
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And a couple of other updates: I was trying QJL again to realize the "2 stage process" for NN the paper mentions but QJL correction at least at 1bit adds so much variance that it makes recall much worse. So I'm not sure how to incorporate QJL and make the Turboquant prod version actually work like the paper describes (maybe it can work at higher bit widths). This is an observation by others as well, like this blog in KV compression context: https://dejan.ai/blog/turboquant/
I also got hold of a much larger ASIN production dataset to test on (also 4096d), and it seems much more well behaved (pairwise mean cosine similarity of ~0.05 vs ~0.5 of the previous ASIN dataset I was using). Below test is with 1M random sample with 10K random sample queries. Graph: M=32, efConstruction=200. Search: fanout=50, topK=10. Force-merged to 1 segment. r7g.8xlarge (32 vCPU Graviton3, 256 GiB).
Note: I haven't made any attempt to optimize 2bit/4bit latency for TQ yet, so they can be ignored. But 1bit is already ~15% faster and 8bit ~30% faster (I have a couple of other optimization ideas, will have Kiro try them later) |
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@shbhar for performance some things to consider:
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Makes sense, I guess with this we can also give the option to user to not store fp32 vectors at all
Do you mean add rotation inside existing OSQ and still keep optimizeIntervals + 14 byte correction? I have not yet done an experiment where I disable optimizeIntervals+14byte correction and see if it still helps over rotation alone. My understanding of why QJL correction also doesnt work is that while it reduces per vector reconstruction error/MSE, we dont directly care about reconstruction error and only care about ranking via dot products in NN - so if any correction adds more noise in ranking it might actually make recall worse. I will try disabling optimizeIntervals/14byte correction next and see how OSQ with correction vs without correction performs on precentered and prerotated vectors to see if it helps, hurts or is neutral for recall. |
Yes. Theoretically you could implement this as a generic wrapper codec that rotates at write and read time. You can't/shouldn't remove the code in OSQ that corrects the integer dot product using the 16 byte footer, it doesn't make any more sense than returning the int8 dot product directly for TQ. |
You are right - I guess I can only disable optimizeInterval() and see if the per vector footer still provides benefit or not on already rotated vectors. So on centered+rotated data, if OSQ recall without optimizeInterval() is also same as TQ on centered+unrotated vectors (avoiding double rotation), then maybe that would be an argument for the remaining TQ approach over just adding rotation as an option in OSQ? Does that make sense? But I guess the footer is negligible storage cost and optimizeIntervals is cheap anyway (right?) so might not be worth optimizing for and you are making the argument that it is better to just add rotation & datablind options to OSQ (to be able to drop fp32). Let me look into that. One thing I've ignored completely so far is the power of 2 limitation of the current FWHT implementation, so with padding/block-diagonal etc approaches I am not sure what happens to recall/performance on something like 1536d vectors |
6 participants
Summary
This PR adds a new
FlatVectorsFormatimplementation based on the TurboQuant algorithm (Zandieh et al., arXiv:2504.19874, ICLR 2026) to thelucene/sandboxmodule.This implementation was co-authored with an AI coding agent (Kiro) as an experiment in AI-assisted open source contribution. The agent handled the bulk of the code generation, test writing, and iterative debugging while I provided direction, reviewed outputs, ran benchmarks, and validated against real datasets. I want to be transparent that while I've tested and benchmarked this across various configurations, I don't have deep expertise in Lucene's codec internals - I'd greatly appreciate thorough review from the community.
Motivation
Current Lucene vector quantization formats (scalar quantization, BBQ) are limited to 1024 dimensions and require per-segment calibration. With embedding models increasingly producing higher-dimensional vectors (OpenAI text-embedding-3-large at 3072d, various 4096d models emerging), we need a quantization approach that scales beyond this limit.
TurboQuant is a data-oblivious rotation-based quantizer that:
Design
Follows the
Lucene104ScalarQuantizedVectorsFormatpattern:TurboQuantFlatVectorsFormat extends FlatVectorsFormat- stores quantized vectors in.vetq, metadata in.vemtq, delegates raw vectors toLucene99FlatVectorsFormatTurboQuantHnswVectorsFormat extends KnnVectorsFormat- convenience composition withLucene99HnswVectorsWriter/ReaderTurboQuantVectorsScorer implements FlatVectorsScorer- LUT-based scoring directly from packed bytesTurboQuantEncodingenum: BITS_2 (16x), BITS_3 (~10.7x), BITS_4 (8x), BITS_8 (4x) compressionlucene/sandbox- no changes tolucene/coreImplementation
12 source files (2,090 lines), 11 test files (1,591 lines), 1 JMH benchmark:
Benchmark: Cohere v3 Wikipedia English
400K vectors, 1024 dimensions, dot product similarity, HNSW (maxConn=64, beamWidth=250), fanout=100, topK=100, 10K queries, force-merged to 1 segment.
Note: force merge times for b=2 and b=4 are anomalously high compared to b=3 and b=8 - this may be a caching artifact and needs further investigation.
Test results
107 dedicated TurboQuant tests pass - 3 skipped (byte-vector-only), TurboQuant is float32 only:
JMH microbenchmarks (d=4096, b=4, single thread)
What's not implemented (deferred)
When to use TurboQuant
TurboQuant is best suited for:
Open questions
.vetq/.vemtqfollowing the convention that different format types use different extensions. Any concerns?getMaxDimensions()returns 16384. Reasonable?