Package org.apache.lucene.util

# Class VectorUtil

java.lang.Object
org.apache.lucene.util.VectorUtil

public final class VectorUtil extends Object
Utilities for computations with numeric arrays, especially algebraic operations like vector dot products. This class uses SIMD vectorization if the corresponding Java module is available and enabled. To enable vectorized code, pass `--add-modules jdk.incubator.vector` to Java's command line.

It will use CPU's FMA instructions if it is known to perform faster than separate multiply+add. This requires at least Hotspot C2 enabled, which is the default for OpenJDK based JVMs.

To explicitly disable or enable FMA usage, pass the following system properties:

- `-Dlucene.useScalarFMA=(auto|true|false)` for scalar operations
- `-Dlucene.useVectorFMA=(auto|true|false)` for vectorized operations (with vector incubator module)

The default is `auto`, which enables this for known CPU types and JVM settings. If Hotspot C2 is disabled, FMA and vectorization are not used.

Vectorization and FMA is only supported for Hotspot-based JVMs; it won't work on OpenJ9-based JVMs unless they provide `HotSpotDiagnosticMXBean`. Please also make sure that you have the `jdk.management` module enabled in modularized applications.

- ## Method Summary

  Modifier and Type
  Method
  Description
  `static void`
  `add(float[] u, float[] v)`
  Adds the second argument to the first
  `static float[]`
  `checkFinite(float[] v)`
  Checks if a float vector only has finite components.
  `static float`
  `cosine(byte[] a, byte[] b)`
  Returns the cosine similarity between the two vectors.
  `static float`
  `cosine(float[] a, float[] b)`
  Returns the cosine similarity between the two vectors.
  `static int`
  `dotProduct(byte[] a, byte[] b)`
  Dot product computed over signed bytes.
  `static float`
  `dotProduct(float[] a, float[] b)`
  Returns the vector dot product of the two vectors.
  `static float`
  `dotProductScore(byte[] a, byte[] b)`
  Dot product score computed over signed bytes, scaled to be in [0, 1].
  `static int`
  `filterByScore(int[] docBuffer, double[] scoreBuffer, double minScoreInclusive, int upTo)`
  filter both `docBuffer` and `scoreBuffer` with `minScoreInclusive`, each `docBuffer` and `scoreBuffer` of the same index forms a pair, pairs with score not greater than or equal to `minScoreInclusive` will be filtered out from the array.
  `static int`
  `findNextGEQ(int[] buffer, int target, int from, int to)`
  Given an array `buffer` that is sorted between indexes `0` inclusive and `to` exclusive, find the first array index whose value is greater than or equal to `target`.
  `static long`
  `int4BitDotProduct(byte[] q, byte[] d)`
  Dot product computed over int4 (values between [0,15]) bytes and a binary vector.
  `static int`
  `int4DotProduct(byte[] a, byte[] b)`
  `static int`
  `int4DotProductPacked(byte[] unpacked, byte[] packed)`
  Dot product computed over int4 (values between [0,15]) bytes.
  `static boolean`
  `isUnitVector(float[] v)`
  `static float[]`
  `l2normalize(float[] v)`
  Modifies the argument to be unit length, dividing by its l2-norm.
  `static float[]`
  `l2normalize(float[] v, boolean throwOnZero)`
  Modifies the argument to be unit length, dividing by its l2-norm.
  `static float`
  `minMaxScalarQuantize(float[] vector, byte[] dest, float scale, float alpha, float minQuantile, float maxQuantile)`
  Scalar quantizes `vector`, putting the result into `dest`.
  `static float`
  `normalizeDistanceToUnitInterval(float squaredDistance)`
  Maps a non-negative squared distance to a similarity score in the range (0, 1].
  `static float`
  `normalizeToUnitInterval(float value)`
  Converts a dot product or cosine similarity value to a normalized score in the [0, 1] range.
  `static float`
  `recalculateOffset(byte[] vector, float oldAlpha, float oldMinQuantile, float scale, float alpha, float minQuantile, float maxQuantile)`
  Recalculates the offset for `vector`.
  `static float`
  `scaleMaxInnerProductScore(float vectorDotProductSimilarity)`
  `static int`
  `squareDistance(byte[] a, byte[] b)`
  Returns the sum of squared differences of the two vectors.
  `static float`
  `squareDistance(float[] a, float[] b)`
  Returns the sum of squared differences of the two vectors.
  `static int`
  `xorBitCount(byte[] a, byte[] b)`
  XOR bit count computed over signed bytes.

  ### Methods inherited from class java.lang.Object

  `clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

- ## Method Details

  - ### dotProduct

    public static float dotProduct(float[] a, float[] b)
    Returns the vector dot product of the two vectors.
    Throws:
      `IllegalArgumentException` - if the vectors' dimensions differ.

  - ### cosine

    public static float cosine(float[] a, float[] b)
    Returns the cosine similarity between the two vectors.
    Throws:
      `IllegalArgumentException` - if the vectors' dimensions differ.

  - ### cosine

    public static float cosine(byte[] a, byte[] b)
    Returns the cosine similarity between the two vectors.

  - ### squareDistance

    public static float squareDistance(float[] a, float[] b)
    Returns the sum of squared differences of the two vectors.
    Throws:
      `IllegalArgumentException` - if the vectors' dimensions differ.

  - ### squareDistance

    public static int squareDistance(byte[] a, byte[] b)
    Returns the sum of squared differences of the two vectors.

  - ### l2normalize

    public static float[] l2normalize(float[] v)
    Modifies the argument to be unit length, dividing by its l2-norm. IllegalArgumentException is thrown for zero vectors.
    Returns:
      the input array after normalization

  - ### isUnitVector

    public static boolean isUnitVector(float[] v)

  - ### l2normalize

    public static float[] l2normalize(float[] v, boolean throwOnZero)
    Modifies the argument to be unit length, dividing by its l2-norm.
    Parameters:
      `v` - the vector to normalize
      `throwOnZero` - whether to throw an exception when `v` has all zeros
    Returns:
      the input array after normalization
    Throws:
      `IllegalArgumentException` - when the vector is all zero and throwOnZero is true

  - ### add

    public static void add(float[] u, float[] v)
    Adds the second argument to the first
    Parameters:
      `u` - the destination
      `v` - the vector to add to the destination

  - ### dotProduct

    public static int dotProduct(byte[] a, byte[] b)
    Dot product computed over signed bytes.
    Parameters:
      `a` - bytes containing a vector
      `b` - bytes containing another vector, of the same dimension
    Returns:
      the value of the dot product of the two vectors

  - ### int4DotProduct

    public static int int4DotProduct(byte[] a, byte[] b)

  - ### int4DotProductPacked

    public static int int4DotProductPacked(byte[] unpacked, byte[] packed)
    Dot product computed over int4 (values between [0,15]) bytes. The second vector is considered "packed" (i.e. every byte representing two values). The following packing is assumed:

           packed[0] = (raw[0] * 16) | raw[packed.length];
           packed[1] = (raw[1] * 16) | raw[packed.length + 1];
           ...
           packed[packed.length - 1] = (raw[packed.length - 1] * 16) | raw[2 * packed.length - 1];

    Parameters:
      `unpacked` - the unpacked vector, of even length
      `packed` - the packed vector, of length `(unpacked.length + 1) / 2`
    Returns:
      the value of the dot product of the two vectors

  - ### int4BitDotProduct

    public static long int4BitDotProduct(byte[] q, byte[] d)
    Dot product computed over int4 (values between [0,15]) bytes and a binary vector.
    Parameters:
      `q` - the int4 query vector
      `d` - the binary document vector
    Returns:
      the dot product

  - ### xorBitCount

    public static int xorBitCount(byte[] a, byte[] b)
    XOR bit count computed over signed bytes.
    Parameters:
      `a` - bytes containing a vector
      `b` - bytes containing another vector, of the same dimension
    Returns:
      the value of the XOR bit count of the two vectors

  - ### dotProductScore

    public static float dotProductScore(byte[] a, byte[] b)
    Dot product score computed over signed bytes, scaled to be in [0, 1].
    Parameters:
      `a` - bytes containing a vector
      `b` - bytes containing another vector, of the same dimension
    Returns:
      the value of the similarity function applied to the two vectors

  - ### scaleMaxInnerProductScore

    public static float scaleMaxInnerProductScore(float vectorDotProductSimilarity)
    Parameters:
      `vectorDotProductSimilarity` - the raw similarity between two vectors
    Returns:
      A scaled score preventing negative scores for maximum-inner-product

  - ### normalizeToUnitInterval

    public static float normalizeToUnitInterval(float value)
    Converts a dot product or cosine similarity value to a normalized score in the [0, 1] range.

    This transformation is necessary when consistent non-negative scores are required. It maps input values from [-1, 1] to [0, 1] using the formula: (1 + value) / 2. Any result below 0 is clamped to 0 for numerical safety.

    Parameters:
      `value` - the similarity value (dot product or cosine), typically in the range [-1, 1]
    Returns:
      a normalized score between 0 and 1

  - ### normalizeDistanceToUnitInterval

    public static float normalizeDistanceToUnitInterval(float squaredDistance)
    Maps a non-negative squared distance to a similarity score in the range (0, 1].

    Uses the transformation: `similarity = 1 / (1 + squaredDistance)`. Smaller distances yield scores closer to 1; larger distances approach 0.

    Parameters:
      `squaredDistance` - squared Euclidean distance (must be ≥ 0)
    Returns:
      similarity score in (0, 1]

  - ### checkFinite

    public static float[] checkFinite(float[] v)
    Checks if a float vector only has finite components.
    Parameters:
      `v` - bytes containing a vector
    Returns:
      the vector for call-chaining
    Throws:
      `IllegalArgumentException` - if any component of vector is not finite

  - ### findNextGEQ

    public static int findNextGEQ(int[] buffer, int target, int from, int to)
    Given an array `buffer` that is sorted between indexes `0` inclusive and `to` exclusive, find the first array index whose value is greater than or equal to `target`. This index is guaranteed to be at least `from`. If there is no such array index, `to` is returned.

  - ### minMaxScalarQuantize

    public static float minMaxScalarQuantize(float[] vector, byte[] dest, float scale, float alpha, float minQuantile, float maxQuantile)
    Scalar quantizes `vector`, putting the result into `dest`.
    Parameters:
      `vector` - the vector to quantize
      `dest` - the destination vector
      `scale` - the scaling factor
      `alpha` - the alpha value
      `minQuantile` - the lower quantile of the distribution
      `maxQuantile` - the upper quantile of the distribution
    Returns:
      the corrective offset that needs to be applied to the score

  - ### recalculateOffset

    public static float recalculateOffset(byte[] vector, float oldAlpha, float oldMinQuantile, float scale, float alpha, float minQuantile, float maxQuantile)
    Recalculates the offset for `vector`.
    Parameters:
      `vector` - the vector to quantize
      `oldAlpha` - the previous alpha value
      `oldMinQuantile` - the previous lower quantile
      `scale` - the scaling factor
      `alpha` - the alpha value
      `minQuantile` - the lower quantile of the distribution
      `maxQuantile` - the upper quantile of the distribution
    Returns:
      the new corrective offset

  - ### filterByScore

    public static int filterByScore(int[] docBuffer, double[] scoreBuffer, double minScoreInclusive, int upTo)
    filter both `docBuffer` and `scoreBuffer` with `minScoreInclusive`, each `docBuffer` and `scoreBuffer` of the same index forms a pair, pairs with score not greater than or equal to `minScoreInclusive` will be filtered out from the array.
    Parameters:
      `docBuffer` - doc buffer contains docs (or some other value forms a pair with `scoreBuffer`)
      `scoreBuffer` - score buffer contains scores to be compared with `minScoreInclusive`
      `minScoreInclusive` - minimal required score to not be filtered out
      `upTo` - where the filter should end
    Returns:
      how many pairs left after filter