kaivalnp commented on code in PR #16790:
URL: https://github.com/apache/lucene/pull/16790#discussion_r4446889958
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lucene/core/src/java/org/apache/lucene/util/quantization/OptimizedScalarQuantizer.java:
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@@ -192,25 +176,15 @@ public QuantizationResult scalarQuantize(
assert bits > 0 && bits <= 8;
float[] intervalScratch = new float[2];
int points = 1 << bits;
- double vecMean = 0;
- double vecVar = 0;
- float norm2 = 0;
- float centroidDot = 0;
- float min = Float.MAX_VALUE;
- float max = -Float.MAX_VALUE;
- for (int i = 0; i < vector.length; ++i) {
- if (similarityFunction != EUCLIDEAN) {
- centroidDot += vector[i] * centroid[i];
- }
- vector[i] = vector[i] - centroid[i];
- min = Math.min(min, vector[i]);
- max = Math.max(max, vector[i]);
- norm2 += (vector[i] * vector[i]);
- double delta = vector[i] - vecMean;
- vecMean += delta / (i + 1);
- vecVar += delta * (vector[i] - vecMean);
- }
- vecVar /= vector.length;
+ float[] centerStats = new float[5];
+ VectorUtil.osqCenter(vector, centroid, centerStats);
+ float centroidDot = centerStats[0];
+ float min = centerStats[1];
+ float max = centerStats[2];
+ float norm2 = centerStats[3];
+ double vecMean = centerStats[4] / (double) vector.length;
+ // Rounding can push the mean of squares minus the squared mean just below
zero.
+ double vecVar = Math.max(0, norm2 / (double) vector.length - vecMean *
vecMean);
Review Comment:
The Cohere dataset may be unit-norm / centered, so the simple method works
well.
Other datasets might have larger norms (i.e. using `MAXIMUM_INNER_PRODUCT`)
and / or off-center vectors (e.g. a data-blind assumption), which might cause
issues?
I feel like the 6-8% performance drop is worth the broader correctness
support (on par with `main`).
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