Merge branch 'main' into esql_ts_agg_detect

Author: nik9000

docs/changelog/132162.yaml

@@ -0,0 +1,6 @@
+pr: 132162
+summary: Fix default missing index sort value of `data_nanos` pre 7.14
+area: Search
+type: bug
+issues:
+ - 132040

docs/release-notes/index.md

@@ -361,6 +361,7 @@ Machine Learning:
 * Mark token pruning for sparse vector as GA [#128854](https://github.com/elastic/elasticsearch/pull/128854)
 * Move to the Cohere V2 API for new inference endpoints [#129884](https://github.com/elastic/elasticsearch/pull/129884)
 * Semantic Text Chunking Indexing Pressure [#125517](https://github.com/elastic/elasticsearch/pull/125517)
+* Track memory used in the hierarchical results normalizer [#2831](https://github.com/elastic/ml-cpp/pull/2831)
 * Upgrade AWS v2 SDK to 2.30.38 [#124738](https://github.com/elastic/elasticsearch/pull/124738)
 * [Inference API] Propagate product use case http header to EIS [#124025](https://github.com/elastic/elasticsearch/pull/124025)
 * [ML] Add HuggingFace Chat Completion support to the Inference Plugin [#127254](https://github.com/elastic/elasticsearch/pull/127254)

libs/simdvec/src/main/java/org/elasticsearch/simdvec/ES92Int7VectorsScorer.java

@@ -41,6 +41,13 @@ public ES92Int7VectorsScorer(IndexInput in, int dimensions) {
         this.dimensions = dimensions;
     }
 
+    /**
+     * Checks if the current implementation supports fast native access.
+     */
+    public boolean hasNativeAccess() {
+        return false; // This class does not support native access
+    }
+
     /**
      * compute the quantize distance between the provided quantized query and the quantized vector
      * that is read from the wrapped {@link IndexInput}.

libs/simdvec/src/main21/java/org/elasticsearch/simdvec/internal/MemorySegmentES92Int7VectorsScorer.java

@@ -8,255 +8,32 @@
  */
 package org.elasticsearch.simdvec.internal;
 
-import jdk.incubator.vector.ByteVector;
-import jdk.incubator.vector.FloatVector;
-import jdk.incubator.vector.IntVector;
-import jdk.incubator.vector.ShortVector;
-import jdk.incubator.vector.Vector;
-import jdk.incubator.vector.VectorOperators;
-import jdk.incubator.vector.VectorShape;
-import jdk.incubator.vector.VectorSpecies;
-
 import org.apache.lucene.index.VectorSimilarityFunction;
 import org.apache.lucene.store.IndexInput;
-import org.apache.lucene.util.VectorUtil;
-import org.elasticsearch.simdvec.ES92Int7VectorsScorer;
 
 import java.io.IOException;
 import java.lang.foreign.MemorySegment;
-import java.nio.ByteOrder;
-
-import static java.nio.ByteOrder.LITTLE_ENDIAN;
-import static jdk.incubator.vector.VectorOperators.ADD;
-import static jdk.incubator.vector.VectorOperators.B2I;
-import static jdk.incubator.vector.VectorOperators.B2S;
-import static jdk.incubator.vector.VectorOperators.S2I;
-import static org.apache.lucene.index.VectorSimilarityFunction.EUCLIDEAN;
-import static org.apache.lucene.index.VectorSimilarityFunction.MAXIMUM_INNER_PRODUCT;
 
 /** Panamized scorer for 7-bit quantized vectors stored as an {@link IndexInput}. **/
-public final class MemorySegmentES92Int7VectorsScorer extends ES92Int7VectorsScorer {
-
-    private static final VectorSpecies<Byte> BYTE_SPECIES_64 = ByteVector.SPECIES_64;
-    private static final VectorSpecies<Byte> BYTE_SPECIES_128 = ByteVector.SPECIES_128;
-
-    private static final VectorSpecies<Short> SHORT_SPECIES_128 = ShortVector.SPECIES_128;
-    private static final VectorSpecies<Short> SHORT_SPECIES_256 = ShortVector.SPECIES_256;
-
-    private static final VectorSpecies<Integer> INT_SPECIES_128 = IntVector.SPECIES_128;
-    private static final VectorSpecies<Integer> INT_SPECIES_256 = IntVector.SPECIES_256;
-    private static final VectorSpecies<Integer> INT_SPECIES_512 = IntVector.SPECIES_512;
-
-    private static final int VECTOR_BITSIZE;
-    private static final VectorSpecies<Float> FLOAT_SPECIES;
-    private static final VectorSpecies<Integer> INT_SPECIES;
-
-    static {
-        // default to platform supported bitsize
-        VECTOR_BITSIZE = VectorShape.preferredShape().vectorBitSize();
-        FLOAT_SPECIES = VectorSpecies.of(float.class, VectorShape.forBitSize(VECTOR_BITSIZE));
-        INT_SPECIES = VectorSpecies.of(int.class, VectorShape.forBitSize(VECTOR_BITSIZE));
-    }
-
-    private final MemorySegment memorySegment;
+public final class MemorySegmentES92Int7VectorsScorer extends MemorySegmentES92PanamaInt7VectorsScorer {
 
     public MemorySegmentES92Int7VectorsScorer(IndexInput in, int dimensions, MemorySegment memorySegment) {
-        super(in, dimensions);
-        this.memorySegment = memorySegment;
+        super(in, dimensions, memorySegment);
     }
 
     @Override
-    public long int7DotProduct(byte[] q) throws IOException {
-        assert dimensions == q.length;
-        int i = 0;
-        int res = 0;
-        // only vectorize if we'll at least enter the loop a single time
-        if (dimensions >= 16) {
-            // compute vectorized dot product consistent with VPDPBUSD instruction
-            if (VECTOR_BITSIZE >= 512) {
-                i += BYTE_SPECIES_128.loopBound(dimensions);
-                res += dotProductBody512(q, i);
-            } else if (VECTOR_BITSIZE == 256) {
-                i += BYTE_SPECIES_64.loopBound(dimensions);
-                res += dotProductBody256(q, i);
-            } else {
-                // tricky: we don't have SPECIES_32, so we workaround with "overlapping read"
-                i += BYTE_SPECIES_64.loopBound(dimensions - BYTE_SPECIES_64.length());
-                res += dotProductBody128(q, i);
-            }
-            // scalar tail
-            while (i < dimensions) {
-                res += in.readByte() * q[i++];
-            }
-            return res;
-        } else {
-            return super.int7DotProduct(q);
-        }
-    }
-
-    private int dotProductBody512(byte[] q, int limit) throws IOException {
-        IntVector acc = IntVector.zero(INT_SPECIES_512);
-        long offset = in.getFilePointer();
-        for (int i = 0; i < limit; i += BYTE_SPECIES_128.length()) {
-            ByteVector va8 = ByteVector.fromArray(BYTE_SPECIES_128, q, i);
-            ByteVector vb8 = ByteVector.fromMemorySegment(BYTE_SPECIES_128, memorySegment, offset + i, LITTLE_ENDIAN);
-
-            // 16-bit multiply: avoid AVX-512 heavy multiply on zmm
-            Vector<Short> va16 = va8.convertShape(B2S, SHORT_SPECIES_256, 0);
-            Vector<Short> vb16 = vb8.convertShape(B2S, SHORT_SPECIES_256, 0);
-            Vector<Short> prod16 = va16.mul(vb16);
-
-            // 32-bit add
-            Vector<Integer> prod32 = prod16.convertShape(S2I, INT_SPECIES_512, 0);
-            acc = acc.add(prod32);
-        }
-
-        in.seek(offset + limit); // advance the input stream
-        // reduce
-        return acc.reduceLanes(ADD);
+    public boolean hasNativeAccess() {
+        return false; // This class does not support native access
     }
 
-    private int dotProductBody256(byte[] q, int limit) throws IOException {
-        IntVector acc = IntVector.zero(INT_SPECIES_256);
-        long offset = in.getFilePointer();
-        for (int i = 0; i < limit; i += BYTE_SPECIES_64.length()) {
-            ByteVector va8 = ByteVector.fromArray(BYTE_SPECIES_64, q, i);
-            ByteVector vb8 = ByteVector.fromMemorySegment(BYTE_SPECIES_64, memorySegment, offset + i, LITTLE_ENDIAN);
-
-            // 32-bit multiply and add into accumulator
-            Vector<Integer> va32 = va8.convertShape(B2I, INT_SPECIES_256, 0);
-            Vector<Integer> vb32 = vb8.convertShape(B2I, INT_SPECIES_256, 0);
-            acc = acc.add(va32.mul(vb32));
-        }
-        in.seek(offset + limit);
-        // reduce
-        return acc.reduceLanes(ADD);
-    }
-
-    private int dotProductBody128(byte[] q, int limit) throws IOException {
-        IntVector acc = IntVector.zero(IntVector.SPECIES_128);
-        long offset = in.getFilePointer();
-        // 4 bytes at a time (re-loading half the vector each time!)
-        for (int i = 0; i < limit; i += ByteVector.SPECIES_64.length() >> 1) {
-            // load 8 bytes
-            ByteVector va8 = ByteVector.fromArray(BYTE_SPECIES_64, q, i);
-            ByteVector vb8 = ByteVector.fromMemorySegment(BYTE_SPECIES_64, memorySegment, offset + i, LITTLE_ENDIAN);
-
-            // process first "half" only: 16-bit multiply
-            Vector<Short> va16 = va8.convert(B2S, 0);
-            Vector<Short> vb16 = vb8.convert(B2S, 0);
-            Vector<Short> prod16 = va16.mul(vb16);
-
-            // 32-bit add
-            acc = acc.add(prod16.convertShape(S2I, IntVector.SPECIES_128, 0));
-        }
-        in.seek(offset + limit);
-        // reduce
-        return acc.reduceLanes(ADD);
+    @Override
+    public long int7DotProduct(byte[] q) throws IOException {
+        return panamaInt7DotProduct(q);
     }
 
     @Override
     public void int7DotProductBulk(byte[] q, int count, float[] scores) throws IOException {
-        assert dimensions == q.length;
-        // only vectorize if we'll at least enter the loop a single time
-        if (dimensions >= 16) {
-            // compute vectorized dot product consistent with VPDPBUSD instruction
-            if (VECTOR_BITSIZE >= 512) {
-                dotProductBody512Bulk(q, count, scores);
-            } else if (VECTOR_BITSIZE == 256) {
-                dotProductBody256Bulk(q, count, scores);
-            } else {
-                // tricky: we don't have SPECIES_32, so we workaround with "overlapping read"
-                dotProductBody128Bulk(q, count, scores);
-            }
-        } else {
-            int7DotProductBulk(q, count, scores);
-        }
-    }
-
-    private void dotProductBody512Bulk(byte[] q, int count, float[] scores) throws IOException {
-        int limit = BYTE_SPECIES_128.loopBound(dimensions);
-        for (int iter = 0; iter < count; iter++) {
-            IntVector acc = IntVector.zero(INT_SPECIES_512);
-            long offset = in.getFilePointer();
-            int i = 0;
-            for (; i < limit; i += BYTE_SPECIES_128.length()) {
-                ByteVector va8 = ByteVector.fromArray(BYTE_SPECIES_128, q, i);
-                ByteVector vb8 = ByteVector.fromMemorySegment(BYTE_SPECIES_128, memorySegment, offset + i, LITTLE_ENDIAN);
-
-                // 16-bit multiply: avoid AVX-512 heavy multiply on zmm
-                Vector<Short> va16 = va8.convertShape(B2S, SHORT_SPECIES_256, 0);
-                Vector<Short> vb16 = vb8.convertShape(B2S, SHORT_SPECIES_256, 0);
-                Vector<Short> prod16 = va16.mul(vb16);
-
-                // 32-bit add
-                Vector<Integer> prod32 = prod16.convertShape(S2I, INT_SPECIES_512, 0);
-                acc = acc.add(prod32);
-            }
-
-            in.seek(offset + limit); // advance the input stream
-            // reduce
-            long res = acc.reduceLanes(ADD);
-            for (; i < dimensions; i++) {
-                res += in.readByte() * q[i];
-            }
-            scores[iter] = res;
-        }
-    }
-
-    private void dotProductBody256Bulk(byte[] q, int count, float[] scores) throws IOException {
-        int limit = BYTE_SPECIES_128.loopBound(dimensions);
-        for (int iter = 0; iter < count; iter++) {
-            IntVector acc = IntVector.zero(INT_SPECIES_256);
-            long offset = in.getFilePointer();
-            int i = 0;
-            for (; i < limit; i += BYTE_SPECIES_64.length()) {
-                ByteVector va8 = ByteVector.fromArray(BYTE_SPECIES_64, q, i);
-                ByteVector vb8 = ByteVector.fromMemorySegment(BYTE_SPECIES_64, memorySegment, offset + i, LITTLE_ENDIAN);
-
-                // 32-bit multiply and add into accumulator
-                Vector<Integer> va32 = va8.convertShape(B2I, INT_SPECIES_256, 0);
-                Vector<Integer> vb32 = vb8.convertShape(B2I, INT_SPECIES_256, 0);
-                acc = acc.add(va32.mul(vb32));
-            }
-            in.seek(offset + limit);
-            // reduce
-            long res = acc.reduceLanes(ADD);
-            for (; i < dimensions; i++) {
-                res += in.readByte() * q[i];
-            }
-            scores[iter] = res;
-        }
-    }
-
-    private void dotProductBody128Bulk(byte[] q, int count, float[] scores) throws IOException {
-        int limit = BYTE_SPECIES_64.loopBound(dimensions - BYTE_SPECIES_64.length());
-        for (int iter = 0; iter < count; iter++) {
-            IntVector acc = IntVector.zero(IntVector.SPECIES_128);
-            long offset = in.getFilePointer();
-            // 4 bytes at a time (re-loading half the vector each time!)
-            int i = 0;
-            for (; i < limit; i += ByteVector.SPECIES_64.length() >> 1) {
-                // load 8 bytes
-                ByteVector va8 = ByteVector.fromArray(BYTE_SPECIES_64, q, i);
-                ByteVector vb8 = ByteVector.fromMemorySegment(BYTE_SPECIES_64, memorySegment, offset + i, LITTLE_ENDIAN);
-
-                // process first "half" only: 16-bit multiply
-                Vector<Short> va16 = va8.convert(B2S, 0);
-                Vector<Short> vb16 = vb8.convert(B2S, 0);
-                Vector<Short> prod16 = va16.mul(vb16);
-
-                // 32-bit add
-                acc = acc.add(prod16.convertShape(S2I, IntVector.SPECIES_128, 0));
-            }
-            in.seek(offset + limit);
-            // reduce
-            long res = acc.reduceLanes(ADD);
-            for (; i < dimensions; i++) {
-                res += in.readByte() * q[i];
-            }
-            scores[iter] = res;
-        }
+        panamaInt7DotProductBulk(q, count, scores);
     }
 
     @Override
@@ -281,72 +58,4 @@ public void scoreBulk(
             scores
         );
     }
-
-    private void applyCorrectionsBulk(
-        float queryLowerInterval,
-        float queryUpperInterval,
-        int queryComponentSum,
-        float queryAdditionalCorrection,
-        VectorSimilarityFunction similarityFunction,
-        float centroidDp,
-        float[] scores
-    ) throws IOException {
-        int limit = FLOAT_SPECIES.loopBound(BULK_SIZE);
-        int i = 0;
-        long offset = in.getFilePointer();
-        float ay = queryLowerInterval;
-        float ly = (queryUpperInterval - ay) * SEVEN_BIT_SCALE;
-        float y1 = queryComponentSum;
-        for (; i < limit; i += FLOAT_SPECIES.length()) {
-            var ax = FloatVector.fromMemorySegment(FLOAT_SPECIES, memorySegment, offset + i * Float.BYTES, ByteOrder.LITTLE_ENDIAN);
-            var lx = FloatVector.fromMemorySegment(
-                FLOAT_SPECIES,
-                memorySegment,
-                offset + 4 * BULK_SIZE + i * Float.BYTES,
-                ByteOrder.LITTLE_ENDIAN
-            ).sub(ax).mul(SEVEN_BIT_SCALE);
-            var targetComponentSums = IntVector.fromMemorySegment(
-                INT_SPECIES,
-                memorySegment,
-                offset + 8 * BULK_SIZE + i * Integer.BYTES,
-                ByteOrder.LITTLE_ENDIAN
-            ).convert(VectorOperators.I2F, 0);
-            var additionalCorrections = FloatVector.fromMemorySegment(
-                FLOAT_SPECIES,
-                memorySegment,
-                offset + 12 * BULK_SIZE + i * Float.BYTES,
-                ByteOrder.LITTLE_ENDIAN
-            );
-            var qcDist = FloatVector.fromArray(FLOAT_SPECIES, scores, i);
-            // ax * ay * dimensions + ay * lx * (float) targetComponentSum + ax * ly * y1 + lx * ly *
-            // qcDist;
-            var res1 = ax.mul(ay).mul(dimensions);
-            var res2 = lx.mul(ay).mul(targetComponentSums);
-            var res3 = ax.mul(ly).mul(y1);
-            var res4 = lx.mul(ly).mul(qcDist);
-            var res = res1.add(res2).add(res3).add(res4);
-            // For euclidean, we need to invert the score and apply the additional correction, which is
-            // assumed to be the squared l2norm of the centroid centered vectors.
-            if (similarityFunction == EUCLIDEAN) {
-                res = res.mul(-2).add(additionalCorrections).add(queryAdditionalCorrection).add(1f);
-                res = FloatVector.broadcast(FLOAT_SPECIES, 1).div(res).max(0);
-                res.intoArray(scores, i);
-            } else {
-                // For cosine and max inner product, we need to apply the additional correction, which is
-                // assumed to be the non-centered dot-product between the vector and the centroid
-                res = res.add(queryAdditionalCorrection).add(additionalCorrections).sub(centroidDp);
-                if (similarityFunction == MAXIMUM_INNER_PRODUCT) {
-                    res.intoArray(scores, i);
-                    // not sure how to do it better
-                    for (int j = 0; j < FLOAT_SPECIES.length(); j++) {
-                        scores[i + j] = VectorUtil.scaleMaxInnerProductScore(scores[i + j]);
-                    }
-                } else {
-                    res = res.add(1f).mul(0.5f).max(0);
-                    res.intoArray(scores, i);
-                }
-            }
-        }
-        in.seek(offset + 16L * BULK_SIZE);
-    }
 }