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‎benchmarks/src/main/java/org/elasticsearch/benchmark/vector/JDKVectorFloat32Benchmark.java‎

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/*
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* Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
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* or more contributor license agreements. Licensed under the "Elastic License
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* 2.0", the "GNU Affero General Public License v3.0 only", and the "Server Side
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* Public License v 1"; you may not use this file except in compliance with, at
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* your election, the "Elastic License 2.0", the "GNU Affero General Public
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* License v3.0 only", or the "Server Side Public License, v 1".
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*/
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package org.elasticsearch.benchmark.vector;
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import org.apache.lucene.util.VectorUtil;
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import org.elasticsearch.common.logging.LogConfigurator;
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import org.elasticsearch.common.logging.NodeNamePatternConverter;
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import org.elasticsearch.nativeaccess.NativeAccess;
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import org.elasticsearch.nativeaccess.VectorSimilarityFunctions;
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import org.openjdk.jmh.annotations.Benchmark;
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import org.openjdk.jmh.annotations.BenchmarkMode;
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import org.openjdk.jmh.annotations.Fork;
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import org.openjdk.jmh.annotations.Level;
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import org.openjdk.jmh.annotations.Measurement;
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import org.openjdk.jmh.annotations.Mode;
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import org.openjdk.jmh.annotations.OutputTimeUnit;
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import org.openjdk.jmh.annotations.Param;
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import org.openjdk.jmh.annotations.Scope;
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import org.openjdk.jmh.annotations.Setup;
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import org.openjdk.jmh.annotations.State;
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import org.openjdk.jmh.annotations.TearDown;
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import org.openjdk.jmh.annotations.Warmup;
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import java.lang.foreign.Arena;
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import java.lang.foreign.MemorySegment;
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import java.lang.foreign.ValueLayout;
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import java.nio.ByteOrder;
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import java.util.concurrent.ThreadLocalRandom;
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import java.util.concurrent.TimeUnit;
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@BenchmarkMode(Mode.AverageTime)
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@OutputTimeUnit(TimeUnit.NANOSECONDS)
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@State(Scope.Benchmark)
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@Warmup(iterations = 3, time = 1)
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@Measurement(iterations = 5, time = 1)
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public class JDKVectorFloat32Benchmark {
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static {
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NodeNamePatternConverter.setGlobalNodeName("foo");
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LogConfigurator.loadLog4jPlugins();
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LogConfigurator.configureESLogging(); // native access requires logging to be initialized
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}
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static final ValueLayout.OfFloat LAYOUT_LE_FLOAT = ValueLayout.JAVA_FLOAT_UNALIGNED.withOrder(ByteOrder.LITTLE_ENDIAN);
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float[] floatsA;
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float[] floatsB;
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float[] scratch;
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MemorySegment heapSegA, heapSegB;
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MemorySegment nativeSegA, nativeSegB;
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Arena arena;
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@Param({ "1", "128", "207", "256", "300", "512", "702", "1024", "1536", "2048" })
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public int size;
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@Setup(Level.Iteration)
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public void init() {
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ThreadLocalRandom random = ThreadLocalRandom.current();
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floatsA = new float[size];
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floatsB = new float[size];
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scratch = new float[size];
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for (int i = 0; i < size; ++i) {
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floatsA[i] = random.nextFloat();
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floatsB[i] = random.nextFloat();
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}
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heapSegA = MemorySegment.ofArray(floatsA);
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heapSegB = MemorySegment.ofArray(floatsB);
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arena = Arena.ofConfined();
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nativeSegA = arena.allocate((long) floatsA.length * Float.BYTES);
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MemorySegment.copy(MemorySegment.ofArray(floatsA), LAYOUT_LE_FLOAT, 0L, nativeSegA, LAYOUT_LE_FLOAT, 0L, floatsA.length);
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nativeSegB = arena.allocate((long) floatsB.length * Float.BYTES);
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MemorySegment.copy(MemorySegment.ofArray(floatsB), LAYOUT_LE_FLOAT, 0L, nativeSegB, LAYOUT_LE_FLOAT, 0L, floatsB.length);
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}
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@TearDown
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public void teardown() {
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arena.close();
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}
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// -- cosine
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@Benchmark
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@Fork(value = 3, jvmArgsPrepend = { "--add-modules=jdk.incubator.vector" })
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public float cosineLucene() {
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return VectorUtil.cosine(floatsA, floatsB);
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}
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@Benchmark
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@Fork(value = 3, jvmArgsPrepend = { "--add-modules=jdk.incubator.vector" })
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public float cosineLuceneWithCopy() {
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// add a copy to better reflect what Lucene has to do to get the target vector on-heap
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MemorySegment.copy(nativeSegB, LAYOUT_LE_FLOAT, 0L, scratch, 0, scratch.length);
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return VectorUtil.cosine(floatsA, scratch);
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}
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@Benchmark
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@Fork(value = 3, jvmArgsPrepend = { "--add-modules=jdk.incubator.vector" })
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public float cosineNativeWithNativeSeg() {
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return cosineFloat32(nativeSegA, nativeSegB, size);
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}
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@Benchmark
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@Fork(value = 3, jvmArgsPrepend = { "--add-modules=jdk.incubator.vector" })
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public float cosineNativeWithHeapSeg() {
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return cosineFloat32(heapSegA, heapSegB, size);
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}
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// -- dot product
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@Benchmark
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@Fork(value = 3, jvmArgsPrepend = { "--add-modules=jdk.incubator.vector" })
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public float dotProductLucene() {
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return VectorUtil.dotProduct(floatsA, floatsB);
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}
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@Benchmark
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@Fork(value = 3, jvmArgsPrepend = { "--add-modules=jdk.incubator.vector" })
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public float dotProductLuceneWithCopy() {
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// add a copy to better reflect what Lucene has to do to get the target vector on-heap
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MemorySegment.copy(nativeSegB, LAYOUT_LE_FLOAT, 0L, scratch, 0, scratch.length);
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return VectorUtil.dotProduct(floatsA, scratch);
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}
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@Benchmark
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@Fork(value = 3, jvmArgsPrepend = { "--add-modules=jdk.incubator.vector" })
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public float dotProductNativeWithNativeSeg() {
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return dotProductFloat32(nativeSegA, nativeSegB, size);
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}
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@Benchmark
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@Fork(value = 3, jvmArgsPrepend = { "--add-modules=jdk.incubator.vector" })
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public float dotProductNativeWithHeapSeg() {
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return dotProductFloat32(heapSegA, heapSegB, size);
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}
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// -- square distance
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@Benchmark
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@Fork(value = 3, jvmArgsPrepend = { "--add-modules=jdk.incubator.vector" })
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public float squareDistanceLucene() {
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return VectorUtil.squareDistance(floatsA, floatsB);
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}
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@Benchmark
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@Fork(value = 3, jvmArgsPrepend = { "--add-modules=jdk.incubator.vector" })
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public float squareDistanceLuceneWithCopy() {
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// add a copy to better reflect what Lucene has to do to get the target vector on-heap
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MemorySegment.copy(nativeSegB, LAYOUT_LE_FLOAT, 0L, scratch, 0, scratch.length);
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return VectorUtil.squareDistance(floatsA, scratch);
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}
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@Benchmark
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@Fork(value = 3, jvmArgsPrepend = { "--add-modules=jdk.incubator.vector" })
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public float squareDistanceNativeWithNativeSeg() {
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return squareDistanceFloat32(nativeSegA, nativeSegB, size);
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}
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@Benchmark
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@Fork(value = 3, jvmArgsPrepend = { "--add-modules=jdk.incubator.vector" })
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public float squareDistanceNativeWithHeapSeg() {
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return squareDistanceFloat32(heapSegA, heapSegB, size);
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}
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static final VectorSimilarityFunctions vectorSimilarityFunctions = vectorSimilarityFunctions();
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static VectorSimilarityFunctions vectorSimilarityFunctions() {
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return NativeAccess.instance().getVectorSimilarityFunctions().get();
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}
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float cosineFloat32(MemorySegment a, MemorySegment b, int length) {
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try {
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return (float) vectorSimilarityFunctions.cosineHandleFloat32().invokeExact(a, b, length);
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} catch (Throwable e) {
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if (e instanceof Error err) {
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throw err;
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} else if (e instanceof RuntimeException re) {
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throw re;
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} else {
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throw new RuntimeException(e);
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}
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}
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}
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float dotProductFloat32(MemorySegment a, MemorySegment b, int length) {
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try {
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return (float) vectorSimilarityFunctions.dotProductHandleFloat32().invokeExact(a, b, length);
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} catch (Throwable e) {
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if (e instanceof Error err) {
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throw err;
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} else if (e instanceof RuntimeException re) {
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throw re;
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} else {
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throw new RuntimeException(e);
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}
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}
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}
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float squareDistanceFloat32(MemorySegment a, MemorySegment b, int length) {
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try {
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return (float) vectorSimilarityFunctions.squareDistanceHandleFloat32().invokeExact(a, b, length);
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} catch (Throwable e) {
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if (e instanceof Error err) {
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throw err;
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} else if (e instanceof RuntimeException re) {
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throw re;
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} else {
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throw new RuntimeException(e);
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}
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}
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}
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}

‎benchmarks/src/test/java/org/elasticsearch/benchmark/vector/JDKVectorFloat32BenchmarkTests.java‎

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/*
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* Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
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* or more contributor license agreements. Licensed under the "Elastic License
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* 2.0", the "GNU Affero General Public License v3.0 only", and the "Server Side
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* Public License v 1"; you may not use this file except in compliance with, at
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* your election, the "Elastic License 2.0", the "GNU Affero General Public
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* License v3.0 only", or the "Server Side Public License, v 1".
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*/
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package org.elasticsearch.benchmark.vector;
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import com.carrotsearch.randomizedtesting.annotations.ParametersFactory;
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import org.apache.lucene.util.Constants;
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import org.elasticsearch.test.ESTestCase;
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import org.junit.BeforeClass;
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import org.openjdk.jmh.annotations.Param;
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import java.util.Arrays;
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public class JDKVectorFloat32BenchmarkTests extends ESTestCase {
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final double delta;
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final int size;
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public JDKVectorFloat32BenchmarkTests(int size) {
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this.size = size;
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delta = 1e-3 * size;
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}
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@BeforeClass
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public static void skipWindows() {
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assumeFalse("doesn't work on windows yet", Constants.WINDOWS);
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}
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static boolean supportsHeapSegments() {
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return Runtime.version().feature() >= 22;
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}
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public void testCosine() {
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for (int i = 0; i < 100; i++) {
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var bench = new JDKVectorFloat32Benchmark();
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bench.size = size;
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bench.init();
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try {
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float expected = cosineFloat32Scalar(bench.floatsA, bench.floatsB);
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assertEquals(expected, bench.cosineLucene(), delta);
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assertEquals(expected, bench.cosineLuceneWithCopy(), delta);
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assertEquals(expected, bench.cosineNativeWithNativeSeg(), delta);
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if (supportsHeapSegments()) {
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assertEquals(expected, bench.cosineNativeWithHeapSeg(), delta);
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}
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} finally {
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bench.teardown();
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}
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}
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}
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public void testDotProduct() {
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for (int i = 0; i < 100; i++) {
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var bench = new JDKVectorFloat32Benchmark();
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bench.size = size;
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bench.init();
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try {
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float expected = dotProductFloat32Scalar(bench.floatsA, bench.floatsB);
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assertEquals(expected, bench.dotProductLucene(), delta);
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assertEquals(expected, bench.dotProductLuceneWithCopy(), delta);
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assertEquals(expected, bench.dotProductNativeWithNativeSeg(), delta);
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if (supportsHeapSegments()) {
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assertEquals(expected, bench.dotProductNativeWithHeapSeg(), delta);
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}
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} finally {
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bench.teardown();
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}
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}
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}
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public void testSquareDistance() {
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for (int i = 0; i < 100; i++) {
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var bench = new JDKVectorFloat32Benchmark();
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bench.size = size;
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bench.init();
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try {
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float expected = squareDistanceFloat32Scalar(bench.floatsA, bench.floatsB);
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assertEquals(expected, bench.squareDistanceLucene(), delta);
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assertEquals(expected, bench.squareDistanceLuceneWithCopy(), delta);
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assertEquals(expected, bench.squareDistanceNativeWithNativeSeg(), delta);
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if (supportsHeapSegments()) {
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assertEquals(expected, bench.squareDistanceNativeWithHeapSeg(), delta);
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}
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} finally {
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bench.teardown();
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}
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}
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}
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@ParametersFactory
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public static Iterable<Object[]> parametersFactory() {
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try {
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var params = JDKVectorFloat32Benchmark.class.getField("size").getAnnotationsByType(Param.class)[0].value();
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return () -> Arrays.stream(params).map(Integer::parseInt).map(i -> new Object[] { i }).iterator();
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} catch (NoSuchFieldException e) {
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throw new AssertionError(e);
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}
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}
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/** Computes the cosine of the given vectors a and b. */
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static float cosineFloat32Scalar(float[] a, float[] b) {
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float dot = 0, normA = 0, normB = 0;
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for (int i = 0; i < a.length; i++) {
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dot += a[i] * b[i];
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normA += a[i] * a[i];
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normB += b[i] * b[i];
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}
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double normAA = Math.sqrt(normA);
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double normBB = Math.sqrt(normB);
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if (normAA == 0.0f || normBB == 0.0f) return 0.0f;
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return (float) (dot / (normAA * normBB));
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}
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/** Computes the dot product of the given vectors a and b. */
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static float dotProductFloat32Scalar(float[] a, float[] b) {
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float res = 0;
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for (int i = 0; i < a.length; i++) {
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res += a[i] * b[i];
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}
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return res;
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}
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/** Computes the dot product of the given vectors a and b. */
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static float squareDistanceFloat32Scalar(float[] a, float[] b) {
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float squareSum = 0;
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for (int i = 0; i < a.length; i++) {
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float diff = a[i] - b[i];
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squareSum += diff * diff;
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}
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return squareSum;
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}
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}

‎docs/changelog/130448.yaml‎

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pr: 130448
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summary: Fix wildcard drop after lookup join
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area: ES|QL
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type: bug
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issues:
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- 129561

‎docs/changelog/130635.yaml‎

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pr: 130635
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summary: "Add low-level optimized Neon, AVX2, and AVX 512 float32 vector operations"
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area: Vector Search
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type: enhancement
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issues: []

‎docs/changelog/130638.yaml‎

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pr: 130638
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summary: Prevent search functions work with a non-STANDARD index
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area: ES|QL
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type: bug
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issues:
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- 130561
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- 129778

‎libs/native/libraries/build.gradle‎

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}
2020

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var zstdVersion = "1.5.5"
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var vecVersion = "1.0.11"
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var vecVersion = "1.0.13"
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repositories {
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exclusiveContent {

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