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Merge pull request #466 from mipt-npm/dev

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Dev
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Alexander Nozik 2022-03-07 22:07:41 +03:00 committed by GitHub
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18 changed files with 310 additions and 2093 deletions
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1
.gitignore vendored
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@ -19,3 +19,4 @@ out/
!/.idea/copyright/
!/.idea/scopes/
/kotlin-js-store/yarn.lock

7
benchmarks/build.gradle.kts
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@ -52,6 +52,8 @@ kotlin {
implementation(project(":kmath-viktor"))
implementation(project(":kmath-jafama"))
implementation(project(":kmath-multik"))
implementation(projects.kmath.kmathTensorflow)
implementation("org.tensorflow:tensorflow-core-platform:0.4.0")
implementation("org.nd4j:nd4j-native:1.0.0-M1")
// uncomment if your system supports AVX2
// val os = System.getProperty("os.name")
@ -122,6 +124,11 @@ benchmark {
include("JafamaBenchmark")
}
configurations.register("tensorAlgebra") {
commonConfiguration()
include("TensorAlgebraBenchmark")
}
configurations.register("viktor") {
commonConfiguration()
include("ViktorBenchmark")

27
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/DotBenchmark.kt
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@ -17,7 +17,9 @@ import space.kscience.kmath.multik.multikAlgebra
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.tensorflow.produceWithTF
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
import space.kscience.kmath.tensors.core.tensorAlgebra
import kotlin.random.Random
@State(Scope.Benchmark)
@ -34,9 +36,6 @@ internal class DotBenchmark {
random.nextDouble()
}
val tensor1 = DoubleTensorAlgebra.randomNormal(shape = intArrayOf(dim, dim), 12224)
val tensor2 = DoubleTensorAlgebra.randomNormal(shape = intArrayOf(dim, dim), 12225)
val cmMatrix1 = CMLinearSpace { matrix1.toCM() }
val cmMatrix2 = CMLinearSpace { matrix2.toCM() }
@ -44,6 +43,16 @@ internal class DotBenchmark {
val ejmlMatrix2 = EjmlLinearSpaceDDRM { matrix2.toEjml() }
}
@Benchmark
fun tfDot(blackhole: Blackhole) {
blackhole.consume(
DoubleField.produceWithTF {
matrix1 dot matrix1
}
)
}
@Benchmark
fun cmDotWithConversion(blackhole: Blackhole) = CMLinearSpace {
blackhole.consume(matrix1 dot matrix2)
@ -64,13 +73,13 @@ internal class DotBenchmark {
blackhole.consume(matrix1 dot matrix2)
}
// @Benchmark
// fun tensorDot(blackhole: Blackhole) = with(Double.tensorAlgebra) {
// blackhole.consume(matrix1 dot matrix2)
// }
@Benchmark
fun tensorDot(blackhole: Blackhole) = with(DoubleField.tensorAlgebra) {
blackhole.consume(matrix1 dot matrix2)
}
@Benchmark
fun multikDot(blackhole: Blackhole) = with(Double.multikAlgebra) {
fun multikDot(blackhole: Blackhole) = with(DoubleField.multikAlgebra) {
blackhole.consume(matrix1 dot matrix2)
}
@ -86,6 +95,6 @@ internal class DotBenchmark {
@Benchmark
fun doubleTensorDot(blackhole: Blackhole) = DoubleTensorAlgebra.invoke {
blackhole.consume(tensor1 dot tensor2)
blackhole.consume(matrix1 dot matrix2)
}
}

37
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/TensorAlgebraBenchmark.kt Normal file
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@ -0,0 +1,37 @@
/*
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.benchmarks
import kotlinx.benchmark.Benchmark
import kotlinx.benchmark.Blackhole
import kotlinx.benchmark.Scope
import kotlinx.benchmark.State
import space.kscience.kmath.linear.linearSpace
import space.kscience.kmath.linear.matrix
import space.kscience.kmath.linear.symmetric
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.tensors.core.tensorAlgebra
import kotlin.random.Random
@State(Scope.Benchmark)
internal class TensorAlgebraBenchmark {
companion object {
private val random = Random(12224)
private const val dim = 30
private val matrix = DoubleField.linearSpace.matrix(dim, dim).symmetric { _, _ -> random.nextDouble() }
}
@Benchmark
fun tensorSymEigSvd(blackhole: Blackhole) = with(Double.tensorAlgebra) {
blackhole.consume(matrix.symEigSvd(1e-10))
}
@Benchmark
fun tensorSymEigJacobi(blackhole: Blackhole) = with(Double.tensorAlgebra) {
blackhole.consume(matrix.symEigJacobi(50, 1e-10))
}
}

2
build.gradle.kts
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@ -10,7 +10,7 @@ allprojects {
}
group = "space.kscience"
version = "0.3.0-dev-18"
version = "0.3.0-dev-19"
}
subprojects {

4
examples/src/main/kotlin/space/kscience/kmath/functions/interpolateSquare.kt
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@ -5,8 +5,8 @@
package space.kscience.kmath.functions
import space.kscience.kmath.interpolation.SplineInterpolator
import space.kscience.kmath.interpolation.interpolatePolynomials
import space.kscience.kmath.interpolation.splineInterpolator
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.real.map
import space.kscience.kmath.real.step
@ -28,7 +28,7 @@ fun main() {
val xs = 0.0..100.0 step 0.5
val ys = xs.map(function)
val polynomial: PiecewisePolynomial<Double> = SplineInterpolator.double.interpolatePolynomials(xs, ys)
val polynomial: PiecewisePolynomial<Double> = DoubleField.splineInterpolator.interpolatePolynomials(xs, ys)
val polyFunction = polynomial.asFunction(DoubleField, 0.0)

0
kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/RealVector.kt → kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/DoubleVector.kt
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3
kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/SplineIntegrator.kt
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@ -28,6 +28,8 @@ public fun <T : Comparable<T>> PiecewisePolynomial<T>.integrate(algebra: Field<T
/**
* Compute definite integral of given [PiecewisePolynomial] piece by piece in a given [range]
* Requires [UnivariateIntegrationNodes] or [IntegrationRange] and [IntegrandMaxCalls]
*
* TODO use context receiver for algebra
*/
@UnstableKMathAPI
public fun <T : Comparable<T>> PiecewisePolynomial<T>.integrate(
@ -98,6 +100,7 @@ public object DoubleSplineIntegrator : UnivariateIntegrator<Double> {
}
}
@Suppress("unused")
@UnstableKMathAPI
public inline val DoubleField.splineIntegrator: UnivariateIntegrator<Double>
get() = DoubleSplineIntegrator

31
kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/Interpolator.kt
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@ -9,6 +9,7 @@ package space.kscience.kmath.interpolation
import space.kscience.kmath.data.XYColumnarData
import space.kscience.kmath.functions.PiecewisePolynomial
import space.kscience.kmath.functions.asFunction
import space.kscience.kmath.functions.value
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.operations.Ring
@ -59,3 +60,33 @@ public fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolatePolynomials(
val pointSet = XYColumnarData.of(data.map { it.first }.asBuffer(), data.map { it.second }.asBuffer())
return interpolatePolynomials(pointSet)
}
public fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolate(
x: Buffer<T>,
y: Buffer<T>,
): (T) -> T? = interpolatePolynomials(x, y).asFunction(algebra)
public fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolate(
data: Map<T, T>,
): (T) -> T? = interpolatePolynomials(data).asFunction(algebra)
public fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolate(
data: List<Pair<T, T>>,
): (T) -> T? = interpolatePolynomials(data).asFunction(algebra)
public fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolate(
x: Buffer<T>,
y: Buffer<T>,
defaultValue: T,
): (T) -> T = interpolatePolynomials(x, y).asFunction(algebra, defaultValue)
public fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolate(
data: Map<T, T>,
defaultValue: T,
): (T) -> T = interpolatePolynomials(data).asFunction(algebra, defaultValue)
public fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolate(
data: List<Pair<T, T>>,
defaultValue: T,
): (T) -> T = interpolatePolynomials(data).asFunction(algebra, defaultValue)

4
kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/LinearInterpolator.kt
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@ -22,6 +22,7 @@ internal fun <T : Comparable<T>> insureSorted(points: XYColumnarData<*, T, *>) {
* Reference JVM implementation: https://github.com/apache/commons-math/blob/master/src/main/java/org/apache/commons/math4/analysis/interpolation/LinearInterpolator.java
*/
public class LinearInterpolator<T : Comparable<T>>(override val algebra: Field<T>) : PolynomialInterpolator<T> {
@OptIn(UnstableKMathAPI::class)
override fun interpolatePolynomials(points: XYColumnarData<T, T, T>): PiecewisePolynomial<T> = algebra {
require(points.size > 0) { "Point array should not be empty" }
@ -37,3 +38,6 @@ public class LinearInterpolator<T : Comparable<T>>(override val algebra: Field<T
}
}
}
public val <T : Comparable<T>> Field<T>.linearInterpolator: LinearInterpolator<T>
get() = LinearInterpolator(this)

12
kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/SplineInterpolator.kt
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@ -72,8 +72,12 @@ public class SplineInterpolator<T : Comparable<T>>(
}
}
}
}
public companion object {
public val double: SplineInterpolator<Double> = SplineInterpolator(DoubleField, ::DoubleBuffer)
}
}
public fun <T : Comparable<T>> Field<T>.splineInterpolator(
bufferFactory: MutableBufferFactory<T>,
): SplineInterpolator<T> = SplineInterpolator(this, bufferFactory)
public val DoubleField.splineInterpolator: SplineInterpolator<Double>
get() = SplineInterpolator(this, ::DoubleBuffer)

6
kmath-functions/src/commonTest/kotlin/space/kscience/kmath/interpolation/LinearInterpolatorTest.kt
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@ -5,8 +5,6 @@
package space.kscience.kmath.interpolation
import space.kscience.kmath.functions.PiecewisePolynomial
import space.kscience.kmath.functions.asFunction
import space.kscience.kmath.operations.DoubleField
import kotlin.test.Test
import kotlin.test.assertEquals
@ -21,8 +19,8 @@ internal class LinearInterpolatorTest {
3.0 to 4.0
)
val polynomial: PiecewisePolynomial<Double> = LinearInterpolator(DoubleField).interpolatePolynomials(data)
val function = polynomial.asFunction(DoubleField)
//val polynomial: PiecewisePolynomial<Double> = DoubleField.linearInterpolator.interpolatePolynomials(data)
val function = DoubleField.linearInterpolator.interpolate(data)
assertEquals(null, function(-1.0))
assertEquals(0.5, function(0.5))
assertEquals(2.0, function(1.5))

7
kmath-functions/src/commonTest/kotlin/space/kscience/kmath/interpolation/SplineInterpolatorTest.kt
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@ -5,8 +5,6 @@
package space.kscience.kmath.interpolation
import space.kscience.kmath.functions.PiecewisePolynomial
import space.kscience.kmath.functions.asFunction
import space.kscience.kmath.operations.DoubleField
import kotlin.math.PI
import kotlin.math.sin
@ -21,9 +19,10 @@ internal class SplineInterpolatorTest {
x to sin(x)
}
val polynomial: PiecewisePolynomial<Double> = SplineInterpolator.double.interpolatePolynomials(data)
//val polynomial: PiecewisePolynomial<Double> = DoubleField.splineInterpolator.interpolatePolynomials(data)
val function = DoubleField.splineInterpolator.interpolate(data, Double.NaN)
val function = polynomial.asFunction(DoubleField, Double.NaN)
assertEquals(Double.NaN, function(-1.0))
assertEquals(sin(0.5), function(0.5), 0.1)
assertEquals(sin(1.5), function(1.5), 0.1)

25
kmath-multik/src/test/kotlin/space/kscience/kmath/multik/MultikNDTest.kt
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@ -6,13 +6,38 @@
package space.kscience.kmath.multik
import org.junit.jupiter.api.Test
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.one
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
import space.kscience.kmath.tensors.core.tensorAlgebra
import kotlin.test.assertTrue
internal class MultikNDTest {
@Test
fun basicAlgebra(): Unit = DoubleField.multikAlgebra{
one(2,2) + 1.0
}
@Test
fun dotResult(){
val dim = 100
val tensor1 = DoubleTensorAlgebra.randomNormal(shape = intArrayOf(dim, dim), 12224)
val tensor2 = DoubleTensorAlgebra.randomNormal(shape = intArrayOf(dim, dim), 12225)
val multikResult = with(DoubleField.multikAlgebra){
tensor1 dot tensor2
}
val defaultResult = with(DoubleField.tensorAlgebra){
tensor1 dot tensor2
}
assertTrue {
StructureND.contentEquals(multikResult, defaultResult)
}
}
}

5
kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/TensorFlowAlgebra.kt
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@ -199,8 +199,9 @@ public abstract class TensorFlowAlgebra<T, TT : TNumber, A : Ring<T>> internal c
override fun StructureND<T>.dot(other: StructureND<T>): TensorFlowOutput<T, TT> = operate(other) { l, r ->
ops.linalg.matMul(
if (l.asTensor().shape().numDimensions() == 1) ops.expandDims(l, ops.constant(0)) else l,
if (r.asTensor().shape().numDimensions() == 1) ops.expandDims(r, ops.constant(-1)) else r)
if (l.shape().numDimensions() == 1) ops.expandDims(l, ops.constant(0)) else l,
if (r.shape().numDimensions() == 1) ops.expandDims(r, ops.constant(-1)) else r
)
}
override fun diagonalEmbedding(

14
kmath-tensorflow/src/test/kotlin/space/kscience/kmath/tensorflow/DoubleTensorFlowOps.kt
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@ -4,6 +4,8 @@ import org.junit.jupiter.api.Test
import space.kscience.kmath.nd.get
import space.kscience.kmath.nd.structureND
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.sum
import kotlin.test.assertEquals
class DoubleTensorFlowOps {
@ -18,6 +20,18 @@ class DoubleTensorFlowOps {
assertEquals(3.0, res[0, 0])
}
@Test
fun dot(){
val dim = 1000
val tensor1 = DoubleTensorAlgebra.randomNormal(shape = intArrayOf(dim, dim), 12224)
val tensor2 = DoubleTensorAlgebra.randomNormal(shape = intArrayOf(dim, dim), 12225)
DoubleField.produceWithTF {
tensor1 dot tensor2
}.sum()
}
@Test
fun extensionOps(){
val res = DoubleField.produceWithTF {

155
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
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@ -9,10 +9,7 @@
package space.kscience.kmath.tensors.core
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.nd.MutableStructure2D
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.as1D
import space.kscience.kmath.nd.as2D
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.structures.MutableBuffer
import space.kscience.kmath.structures.indices
@ -885,7 +882,7 @@ public open class DoubleTensorAlgebra :
return Triple(uTensor.transpose(), sTensor, vTensor.transpose())
}
override fun StructureND<Double>.symEig(): Pair<DoubleTensor, DoubleTensor> = symEig(epsilon = 1e-15)
override fun StructureND<Double>.symEig(): Pair<DoubleTensor, DoubleTensor> = symEigJacobi(maxIteration = 50, epsilon = 1e-15)
/**
* Returns eigenvalues and eigenvectors of a real symmetric matrix input or a batch of real symmetric matrices,
@ -895,7 +892,7 @@ public open class DoubleTensorAlgebra :
* and when the cosine approaches 1 in the SVD algorithm.
* @return a pair `eigenvalues to eigenvectors`.
*/
public fun StructureND<Double>.symEig(epsilon: Double): Pair<DoubleTensor, DoubleTensor> {
public fun StructureND<Double>.symEigSvd(epsilon: Double): Pair<DoubleTensor, DoubleTensor> {
checkSymmetric(tensor, epsilon)
fun MutableStructure2D<Double>.cleanSym(n: Int) {
@ -922,6 +919,151 @@ public open class DoubleTensorAlgebra :
return eig to v
}
public fun StructureND<Double>.symEigJacobi(maxIteration: Int, epsilon: Double): Pair<DoubleTensor, DoubleTensor> {
checkSymmetric(tensor, epsilon)
val size = this.dimension
val eigenvectors = zeros(this.shape)
val eigenvalues = zeros(this.shape.sliceArray(0 until size - 1))
var eigenvalueStart = 0
var eigenvectorStart = 0
for (matrix in tensor.matrixSequence()) {
val matrix2D = matrix.as2D()
val (d, v) = matrix2D.jacobiHelper(maxIteration, epsilon)
for (i in 0 until matrix2D.rowNum) {
for (j in 0 until matrix2D.colNum) {
eigenvectors.mutableBuffer.array()[eigenvectorStart + i * matrix2D.rowNum + j] = v[i, j]
}
}
for (i in 0 until matrix2D.rowNum) {
eigenvalues.mutableBuffer.array()[eigenvalueStart + i] = d[i]
}
eigenvalueStart += this.shape.last()
eigenvectorStart += this.shape.last() * this.shape.last()
}
return eigenvalues to eigenvectors
}
private fun MutableStructure2D<Double>.jacobiHelper(
maxIteration: Int,
epsilon: Double
): Pair<Structure1D<Double>, Structure2D<Double>> {
val n = this.shape[0]
val A_ = this.copy()
val V = eye(n)
val D = DoubleTensor(intArrayOf(n), (0 until this.rowNum).map { this[it, it] }.toDoubleArray()).as1D()
val B = DoubleTensor(intArrayOf(n), (0 until this.rowNum).map { this[it, it] }.toDoubleArray()).as1D()
val Z = zeros(intArrayOf(n)).as1D()
// assume that buffered tensor is square matrix
operator fun BufferedTensor<Double>.get(i: Int, j: Int): Double {
return this.mutableBuffer.array()[bufferStart + i * this.shape[0] + j]
}
operator fun BufferedTensor<Double>.set(i: Int, j: Int, value: Double) {
this.mutableBuffer.array()[bufferStart + i * this.shape[0] + j] = value
}
fun maxOffDiagonal(matrix: BufferedTensor<Double>): Double {
var maxOffDiagonalElement = 0.0
for (i in 0 until n - 1) {
for (j in i + 1 until n) {
maxOffDiagonalElement = max(maxOffDiagonalElement, abs(matrix[i, j]))
}
}
return maxOffDiagonalElement
}
fun rotate(a: BufferedTensor<Double>, s: Double, tau: Double, i: Int, j: Int, k: Int, l: Int) {
val g = a[i, j]
val h = a[k, l]
a[i, j] = g - s * (h + g * tau)
a[k, l] = h + s * (g - h * tau)
}
fun jacobiIteration(
a: BufferedTensor<Double>,
v: BufferedTensor<Double>,
d: MutableStructure1D<Double>,
z: MutableStructure1D<Double>,
) {
for (ip in 0 until n - 1) {
for (iq in ip + 1 until n) {
val g = 100.0 * abs(a[ip, iq])
if (g <= epsilon * abs(d[ip]) && g <= epsilon * abs(d[iq])) {
a[ip, iq] = 0.0
continue
}
var h = d[iq] - d[ip]
val t = when {
g <= epsilon * abs(h) -> (a[ip, iq]) / h
else -> {
val theta = 0.5 * h / (a[ip, iq])
val denominator = abs(theta) + sqrt(1.0 + theta * theta)
if (theta < 0.0) -1.0 / denominator else 1.0 / denominator
}
}
val c = 1.0 / sqrt(1 + t * t)
val s = t * c
val tau = s / (1.0 + c)
h = t * a[ip, iq]
z[ip] -= h
z[iq] += h
d[ip] -= h
d[iq] += h
a[ip, iq] = 0.0
for (j in 0 until ip) {
rotate(a, s, tau, j, ip, j, iq)
}
for (j in (ip + 1) until iq) {
rotate(a, s, tau, ip, j, j, iq)
}
for (j in (iq + 1) until n) {
rotate(a, s, tau, ip, j, iq, j)
}
for (j in 0 until n) {
rotate(v, s, tau, j, ip, j, iq)
}
}
}
}
fun updateDiagonal(
d: MutableStructure1D<Double>,
z: MutableStructure1D<Double>,
b: MutableStructure1D<Double>,
) {
for (ip in 0 until d.size) {
b[ip] += z[ip]
d[ip] = b[ip]
z[ip] = 0.0
}
}
var sm = maxOffDiagonal(A_)
for (iteration in 0 until maxIteration) {
if (sm < epsilon) {
break
}
jacobiIteration(A_, V, D, Z)
updateDiagonal(D, Z, B)
sm = maxOffDiagonal(A_)
}
// TODO sort eigenvalues
return D to V.as2D()
}
/**
* Computes the determinant of a square matrix input, or of each square matrix in a batched input
* using LU factorization algorithm.
@ -997,5 +1139,6 @@ public open class DoubleTensorAlgebra :
}
public val Double.Companion.tensorAlgebra: DoubleTensorAlgebra.Companion get() = DoubleTensorAlgebra
public val DoubleField.tensorAlgebra: DoubleTensorAlgebra.Companion get() = DoubleTensorAlgebra

2059
kotlin-js-store/yarn.lock
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