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Add support for bitwise inner-product in painless #116082
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Original file line number | Diff line number | Diff line change |
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@@ -0,0 +1,5 @@ | ||
pr: 116082 | ||
summary: Add support for bitwise inner-product in painless | ||
area: Vector Search | ||
type: enhancement | ||
issues: [] |
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@@ -332,6 +332,9 @@ When using `bit` vectors, not all the vector functions are available. The suppor | |
* <<vector-functions-hamming,`hamming`>> – calculates Hamming distance, the sum of the bitwise XOR of the two vectors | ||
* <<vector-functions-l1,`l1norm`>> – calculates L^1^ distance, this is simply the `hamming` distance | ||
* <<vector-functions-l2,`l2norm`>> - calculates L^2^ distance, this is the square root of the `hamming` distance | ||
* <<vector-functions-dot-product,`dotProduct`>> – calculates dot product. When comparing two `bit` vectors, | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. May be we can add that queryVector can be byte[] (of the same dims as docs or dims *8), or also can be a string, and can be of float[] There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. ++ |
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this is the sum of the bitwise AND of the two vectors. If providing `float[]` as a query vector, the `dotProduct` is | ||
the sum of the floating point values using the stored `bit` vector as a mask. | ||
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Currently, the `cosineSimilarity` and `dotProduct` functions are not supported for `bit` vectors. | ||
Currently, the `cosineSimilarity` function is not supported for `bit` vectors. | ||
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@@ -9,13 +9,23 @@ | |
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package org.elasticsearch.simdvec; | ||
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import org.apache.lucene.util.BitUtil; | ||
import org.apache.lucene.util.Constants; | ||
import org.elasticsearch.simdvec.internal.vectorization.ESVectorUtilSupport; | ||
import org.elasticsearch.simdvec.internal.vectorization.ESVectorizationProvider; | ||
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import static org.elasticsearch.simdvec.internal.vectorization.ESVectorUtilSupport.B_QUERY; | ||
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public class ESVectorUtil { | ||
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/** | ||
* For xorBitCount we stride over the values as either 64-bits (long) or 32-bits (int) at a time. | ||
* On ARM Long::bitCount is not vectorized, and therefore produces less than optimal code, when | ||
* compared to Integer::bitCount. While Long::bitCount is optimal on x64. See | ||
* https://bugs.openjdk.org/browse/JDK-8336000 | ||
*/ | ||
static final boolean XOR_BIT_COUNT_STRIDE_AS_INT = Constants.OS_ARCH.equals("aarch64"); | ||
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private static final ESVectorUtilSupport IMPL = ESVectorizationProvider.getInstance().getVectorUtilSupport(); | ||
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public static long ipByteBinByte(byte[] q, byte[] d) { | ||
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@@ -24,4 +34,95 @@ public static long ipByteBinByte(byte[] q, byte[] d) { | |
} | ||
return IMPL.ipByteBinByte(q, d); | ||
} | ||
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/** | ||
* Compute the inner product of two vectors, where the query vector is a byte vector and the document vector is a bit vector. | ||
* This will return the sum of the query vector values using the document vector as a mask. | ||
* @param q the query vector | ||
* @param d the document vector | ||
* @return the inner product of the two vectors | ||
*/ | ||
public static int ipByteBit(byte[] q, byte[] d) { | ||
if (q.length != d.length * Byte.SIZE) { | ||
throw new IllegalArgumentException("vector dimensions incompatible: " + q.length + "!= " + Byte.SIZE + " x " + d.length); | ||
} | ||
int result = 0; | ||
// now combine the two vectors, summing the byte dimensions where the bit in d is `1` | ||
for (int i = 0; i < d.length; i++) { | ||
byte mask = d[i]; | ||
for (int j = 0; j < Byte.SIZE; j++) { | ||
if ((mask & (1 << j)) != 0) { | ||
result += q[i * Byte.SIZE + j]; | ||
} | ||
} | ||
} | ||
return result; | ||
} | ||
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/** | ||
* Compute the inner product of two vectors, where the query vector is a float vector and the document vector is a bit vector. | ||
* This will return the sum of the query vector values using the document vector as a mask. | ||
* @param q the query vector | ||
* @param d the document vector | ||
* @return the inner product of the two vectors | ||
*/ | ||
public static float ipFloatBit(float[] q, byte[] d) { | ||
if (q.length != d.length * Byte.SIZE) { | ||
throw new IllegalArgumentException("vector dimensions incompatible: " + q.length + "!= " + Byte.SIZE + " x " + d.length); | ||
} | ||
float result = 0; | ||
for (int i = 0; i < d.length; i++) { | ||
byte mask = d[i]; | ||
for (int j = 0; j < Byte.SIZE; j++) { | ||
if ((mask & (1 << j)) != 0) { | ||
result += q[i * Byte.SIZE + j]; | ||
} | ||
} | ||
} | ||
return result; | ||
} | ||
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/** | ||
* AND bit count computed over signed bytes. | ||
* | ||
* @param a bytes containing a vector | ||
* @param b bytes containing another vector, of the same dimension | ||
* @return the value of the AND bit count of the two vectors | ||
*/ | ||
public static int andBitCount(byte[] a, byte[] b) { | ||
if (a.length != b.length) { | ||
throw new IllegalArgumentException("vector dimensions differ: " + a.length + "!=" + b.length); | ||
} | ||
if (XOR_BIT_COUNT_STRIDE_AS_INT) { | ||
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return andBitCountInt(a, b); | ||
} else { | ||
return andBitCountLong(a, b); | ||
} | ||
} | ||
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/** AND bit count striding over 4 bytes at a time. */ | ||
static int andBitCountInt(byte[] a, byte[] b) { | ||
int distance = 0, i = 0; | ||
for (final int upperBound = a.length & -Integer.BYTES; i < upperBound; i += Integer.BYTES) { | ||
distance += Integer.bitCount((int) BitUtil.VH_NATIVE_INT.get(a, i) & (int) BitUtil.VH_NATIVE_INT.get(b, i)); | ||
} | ||
// tail: | ||
for (; i < a.length; i++) { | ||
distance += Integer.bitCount((a[i] & b[i]) & 0xFF); | ||
} | ||
return distance; | ||
} | ||
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/** AND bit count striding over 8 bytes at a time. */ | ||
static int andBitCountLong(byte[] a, byte[] b) { | ||
int distance = 0, i = 0; | ||
for (final int upperBound = a.length & -Long.BYTES; i < upperBound; i += Long.BYTES) { | ||
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distance += Long.bitCount((long) BitUtil.VH_NATIVE_LONG.get(a, i) & (long) BitUtil.VH_NATIVE_LONG.get(b, i)); | ||
} | ||
// tail: | ||
for (; i < a.length; i++) { | ||
distance += Integer.bitCount((a[i] & b[i]) & 0xFF); | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Could the tail be done with a single There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Possibly? But I didn't want to bother with over optimizing. Especially since these methods are effectively copy-pastes of what exists in Lucene for xor (just changing to |
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} | ||
return distance; | ||
} | ||
} |
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On line 19 we also say that
dot_product
is not supported for bit vectors.