shubhamvishu commented on code in PR #16092:
URL: https://github.com/apache/lucene/pull/16092#discussion_r3283934002
##########
lucene/core/src/java/org/apache/lucene/codecs/lucene104/Lucene104ScalarQuantizedVectorsReader.java:
##########
@@ -197,6 +213,14 @@ public RandomVectorScorer getRandomVectorScorer(String
field, float[] target) th
if (fi == null) {
return null;
}
+ // Rotate the query vector to match the rotated stored vectors.
+ float[] scoringTarget = target;
+ if (isRotationEnabled(field) && target != null) {
+ HadamardRotation rotation = rotationFor(field, fi.dimension);
+ float[] rotated = new float[target.length];
+ rotation.rotate(target, rotated);
Review Comment:
@benwtrent I see, yeah I think moving in upstream and doing it once makes
sense to me. Maybe just moving it to KnnQuery as Trevor mentioned below?
> To see the significance of the performance impact, you will need many
vectors spread over many segments
That could be the case yes. Sharing below the cohere v3 run I put with multi
segment and it seems to have not much impact but larger segments of 30M might
move the needle.
<details>
<summary><b>Result : Cohere V3 without forceMerge </b></summary>
#### Baseline :
```
Results:
NOTE: nDoc = 500000 for all runs; skipping column
NOTE: searchType = KNN for all runs; skipping column
NOTE: topK = 100 for all runs; skipping column
NOTE: fanout = 100 for all runs; skipping column
NOTE: resultSimilarity = N/A for all runs; skipping column
NOTE: decay = N/A for all runs; skipping column
NOTE: resultCount = 100.000 for all runs; skipping column
NOTE: maxConn = 64 for all runs; skipping column
NOTE: beamWidth = 250 for all runs; skipping column
NOTE: force_merge(s) = 0.00 for all runs; skipping column
NOTE: filterStrategy = null for all runs; skipping column
NOTE: filterSelectivity = N/A for all runs; skipping column
NOTE: overSample = 1.000 for all runs; skipping column
NOTE: bp-reorder = false for all runs; skipping column
NOTE: indexType = HNSW for all runs; skipping column
NOTE: rerank = no for all runs; skipping column
recall latency(ms) netCPU avgCpuCount quantized visited index(s)
index_docs/s num_segments index_size(MB) vec_disk(MB) vec_RAM(MB)
0.695 1.916 5.285 2.758 1 bits 54581 94.09
5314.12 5 2092.52 2020.836 67.711
0.817 1.887 6.052 3.208 2 bits 50314 95.73
5223.08 5 2147.02 2081.871 128.746
0.921 2.107 7.275 3.453 4 bits 50877 97.97
5103.60 6 2266.48 2204.895 251.770
0.976 3.300 12.843 3.892 7 bits 61241 101.92
4906.00 9 2506.59 2449.036 495.911
0.983 3.280 12.810 3.905 8 bits 61080 99.65
5017.51 9 2506.62 2449.036 495.911
```
#### Candidate:
```
Results:
NOTE: nDoc = 500000 for all runs; skipping column
NOTE: searchType = KNN for all runs; skipping column
NOTE: topK = 100 for all runs; skipping column
NOTE: fanout = 100 for all runs; skipping column
NOTE: resultSimilarity = N/A for all runs; skipping column
NOTE: decay = N/A for all runs; skipping column
NOTE: resultCount = 100.000 for all runs; skipping column
NOTE: maxConn = 64 for all runs; skipping column
NOTE: beamWidth = 250 for all runs; skipping column
NOTE: force_merge(s) = 0.00 for all runs; skipping column
NOTE: filterStrategy = null for all runs; skipping column
NOTE: filterSelectivity = N/A for all runs; skipping column
NOTE: overSample = 1.000 for all runs; skipping column
NOTE: bp-reorder = false for all runs; skipping column
NOTE: indexType = HNSW for all runs; skipping column
NOTE: rerank = no for all runs; skipping column
recall latency(ms) netCPU avgCpuCount quantized visited index(s)
index_docs/s num_segments index_size(MB) vec_disk(MB) vec_RAM(MB)
0.729 1.845 5.056 2.740 1 bits 51755 96.87
5161.40 5 2090.45 2020.836 67.711
0.843 1.870 5.904 3.157 2 bits 49123 98.38
5082.18 5 2146.54 2081.871 128.746
0.940 2.089 7.143 3.419 4 bits 50584 95.99
5209.04 6 2266.39 2204.895 251.770
0.988 3.255 12.872 3.955 7 bits 60998 100.92
4954.52 9 2506.59 2449.036 495.911
0.993 3.244 12.887 3.973 8 bits 60955 100.85
4958.01 9 2506.60 2449.036 495.911
```
</details>
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