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Feature/tensors performance #497

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margarita0303 wants to merge 91 commits from feature/tensors-performance into feature/tensors-performance
pull from: feature/tensors-performance
merge into: kscience:feature/tensors-performance
Conversation 1 Commits 91 Files Changed 96 +87 -18
10 changed files with 87 additions and 18 deletions
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7
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/DotBenchmark.kt
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@ -15,8 +15,10 @@ import space.kscience.kmath.linear.invoke
import space.kscience.kmath.linear.linearSpace
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
@ -90,4 +92,9 @@ internal class DotBenchmark {
fun doubleDot(blackhole: Blackhole) = with(DoubleField.linearSpace) {
blackhole.consume(matrix1 dot matrix2)
}
@Benchmark
fun doubleTensorDot(blackhole: Blackhole) = DoubleTensorAlgebra.invoke {
blackhole.consume(matrix1 dot matrix2)
}
}

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)

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)

16
kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/SplineInterpolator.kt
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@ -63,8 +63,8 @@ public class SplineInterpolator<T : Comparable<T>>(
//Shift coefficients to represent absolute polynomial instead of one with an offset
val polynomial = Polynomial(
a - b * x0 + c * x02 - d * x03,
b - 2*c*x0 + 3*d*x02,
c - 3*d*x0,
b - 2 * c * x0 + 3 * d * x02,
c - 3 * d * x0,
d
)
cOld = c
@ -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)
}
}
}

2
kmath-tensorflow/src/test/kotlin/space/kscience/kmath/tensorflow/DoubleTensorFlowOps.kt
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@ -6,7 +6,6 @@ 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.random.Random
import kotlin.test.assertEquals
class DoubleTensorFlowOps {
@ -23,7 +22,6 @@ class DoubleTensorFlowOps {
@Test
fun dot(){
val random = Random(12224)
val dim = 1000
val tensor1 = DoubleTensorAlgebra.randomNormal(shape = intArrayOf(dim, dim), 12224)