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Multik wrapper

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This commit is contained in:
Alexander Nozik 2021-10-17 21:12:14 +03:00
parent 05ae21580b
commit 827f115a92
8 changed files with 204 additions and 29 deletions
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1
benchmarks/build.gradle.kts
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@ -48,6 +48,7 @@ kotlin {
implementation(project(":kmath-nd4j"))
implementation(project(":kmath-kotlingrad"))
implementation(project(":kmath-viktor"))
implementation(projects.kmathMultik)
implementation("org.nd4j:nd4j-native:1.0.0-M1")
// uncomment if your system supports AVX2
// val os = System.getProperty("os.name")

24
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/NDFieldBenchmark.kt
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@ -9,6 +9,7 @@ import kotlinx.benchmark.Benchmark
import kotlinx.benchmark.Blackhole
import kotlinx.benchmark.Scope
import kotlinx.benchmark.State
import space.kscience.kmath.multik.multikND
import space.kscience.kmath.nd.BufferedFieldOpsND
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.ndAlgebra
@ -17,8 +18,9 @@ import space.kscience.kmath.nd4j.nd4j
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.tensors.core.DoubleTensor
import space.kscience.kmath.tensors.core.ones
import space.kscience.kmath.tensors.core.one
import space.kscience.kmath.tensors.core.tensorAlgebra
import space.kscience.kmath.viktor.viktorAlgebra
@State(Scope.Benchmark)
internal class NDFieldBenchmark {
@ -43,16 +45,30 @@ internal class NDFieldBenchmark {
blackhole.consume(res)
}
@Benchmark
fun multikAdd(blackhole: Blackhole) = with(multikField) {
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
@Benchmark
fun viktorAdd(blackhole: Blackhole) = with(viktorField) {
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
@Benchmark
fun tensorAdd(blackhole: Blackhole) = with(Double.tensorAlgebra) {
var res: DoubleTensor = ones(dim, dim)
var res: DoubleTensor = one(shape)
repeat(n) { res = res + 1.0 }
blackhole.consume(res)
}
@Benchmark
fun tensorInPlaceAdd(blackhole: Blackhole) = with(Double.tensorAlgebra) {
val res: DoubleTensor = ones(dim, dim)
val res: DoubleTensor = one(shape)
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
@ -72,5 +88,7 @@ internal class NDFieldBenchmark {
private val specializedField = DoubleField.ndAlgebra
private val genericField = BufferedFieldOpsND(DoubleField, Buffer.Companion::boxing)
private val nd4jField = DoubleField.nd4j
private val multikField = DoubleField.multikND
private val viktorField = DoubleField.viktorAlgebra
}
}

15
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/ShapeIndex.kt
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@ -105,16 +105,15 @@ public class DefaultStrides private constructor(override val shape: IntArray) :
override fun hashCode(): Int = shape.contentHashCode()
@ThreadLocal
public companion object {
//private val defaultStridesCache = HashMap<IntArray, Strides>()
public companion object {
/**
* Cached builder for default strides
*/
public operator fun invoke(shape: IntArray): Strides = DefaultStrides(shape)
//defaultStridesCache.getOrPut(shape) { DefaultStrides(shape) }
//TODO fix cache
public operator fun invoke(shape: IntArray): Strides =
defaultStridesCache.getOrPut(shape) { DefaultStrides(shape) }
}
}
}
@ThreadLocal
private val defaultStridesCache = HashMap<IntArray, Strides>()

135
kmath-multik/src/main/kotlin/space/kscience/kmath/multik/MultikOpsND.kt Normal file
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@ -0,0 +1,135 @@
package space.kscience.kmath.multik
import org.jetbrains.kotlinx.multik.api.mk
import org.jetbrains.kotlinx.multik.api.zeros
import org.jetbrains.kotlinx.multik.ndarray.data.*
import org.jetbrains.kotlinx.multik.ndarray.operations.*
import space.kscience.kmath.nd.FieldOpsND
import space.kscience.kmath.nd.RingOpsND
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.operations.*
/**
* A ring algebra for Multik operations
*/
public open class MultikRingOpsND<T, A : Ring<T>> internal constructor(
public val type: DataType,
override val elementAlgebra: A
) : RingOpsND<T, A> {
protected fun MutableMultiArray<T, DN>.wrap(): MultikTensor<T> = MultikTensor(this)
override fun produce(shape: Shape, initializer: A.(IntArray) -> T): MultikTensor<T> {
val res = mk.zeros<T, DN>(shape, type).asDNArray()
for (index in res.multiIndices) {
res[index] = elementAlgebra.initializer(index)
}
return res.wrap()
}
protected fun StructureND<T>.asMultik(): MultikTensor<T> = if (this is MultikTensor) {
this
} else {
produce(shape) { get(it) }
}
override fun StructureND<T>.map(transform: A.(T) -> T): MultikTensor<T> {
//taken directly from Multik sources
val array = asMultik().array
val data = initMemoryView<T>(array.size, type)
var count = 0
for (el in array) data[count++] = elementAlgebra.transform(el)
return NDArray(data, shape = array.shape, dim = array.dim).wrap()
}
override fun StructureND<T>.mapIndexed(transform: A.(index: IntArray, T) -> T): MultikTensor<T> {
//taken directly from Multik sources
val array = asMultik().array
val data = initMemoryView<T>(array.size, type)
val indexIter = array.multiIndices.iterator()
var index = 0
for (item in array) {
if (indexIter.hasNext()) {
data[index++] = elementAlgebra.transform(indexIter.next(), item)
} else {
throw ArithmeticException("Index overflow has happened.")
}
}
return NDArray(data, shape = array.shape, dim = array.dim).wrap()
}
override fun zip(left: StructureND<T>, right: StructureND<T>, transform: A.(T, T) -> T): MultikTensor<T> {
require(left.shape.contentEquals(right.shape)) { "ND array shape mismatch" } //TODO replace by ShapeMismatchException
val leftArray = left.asMultik().array
val rightArray = right.asMultik().array
val data = initMemoryView<T>(leftArray.size, type)
var counter = 0
val leftIterator = leftArray.iterator()
val rightIterator = rightArray.iterator()
//iterating them together
while (leftIterator.hasNext()) {
data[counter++] = elementAlgebra.transform(leftIterator.next(), rightIterator.next())
}
return NDArray(data, shape = leftArray.shape, dim = leftArray.dim).wrap()
}
override fun StructureND<T>.unaryMinus(): MultikTensor<T> = asMultik().array.unaryMinus().wrap()
override fun add(left: StructureND<T>, right: StructureND<T>): MultikTensor<T> =
(left.asMultik().array + right.asMultik().array).wrap()
override fun StructureND<T>.plus(arg: T): MultikTensor<T> =
asMultik().array.plus(arg).wrap()
override fun StructureND<T>.minus(arg: T): MultikTensor<T> = asMultik().array.minus(arg).wrap()
override fun T.plus(arg: StructureND<T>): MultikTensor<T> = arg + this
override fun T.minus(arg: StructureND<T>): MultikTensor<T> = arg.map { this@minus - it }
override fun multiply(left: StructureND<T>, right: StructureND<T>): MultikTensor<T> =
left.asMultik().array.times(right.asMultik().array).wrap()
override fun StructureND<T>.times(arg: T): MultikTensor<T> =
asMultik().array.times(arg).wrap()
override fun T.times(arg: StructureND<T>): MultikTensor<T> = arg * this
override fun StructureND<T>.unaryPlus(): MultikTensor<T> = asMultik()
override fun StructureND<T>.plus(other: StructureND<T>): MultikTensor<T> =
asMultik().array.plus(other.asMultik().array).wrap()
override fun StructureND<T>.minus(other: StructureND<T>): MultikTensor<T> =
asMultik().array.minus(other.asMultik().array).wrap()
override fun StructureND<T>.times(other: StructureND<T>): MultikTensor<T> =
asMultik().array.times(other.asMultik().array).wrap()
}
/**
* A field algebra for multik operations
*/
public class MultikFieldOpsND<T, A : Field<T>> internal constructor(
type: DataType,
elementAlgebra: A
) : MultikRingOpsND<T, A>(type, elementAlgebra), FieldOpsND<T, A> {
override fun StructureND<T>.div(other: StructureND<T>): StructureND<T> =
asMultik().array.div(other.asMultik().array).wrap()
}
public val DoubleField.multikND: MultikFieldOpsND<Double, DoubleField>
get() = MultikFieldOpsND(DataType.DoubleDataType, DoubleField)
public val FloatField.multikND: MultikFieldOpsND<Float, FloatField>
get() = MultikFieldOpsND(DataType.FloatDataType, FloatField)
public val ShortRing.multikND: MultikRingOpsND<Short, ShortRing>
get() = MultikRingOpsND(DataType.ShortDataType, ShortRing)
public val IntRing.multikND: MultikRingOpsND<Int, IntRing>
get() = MultikRingOpsND(DataType.IntDataType, IntRing)
public val LongRing.multikND: MultikRingOpsND<Long, LongRing>
get() = MultikRingOpsND(DataType.LongDataType, LongRing)

30
kmath-multik/src/main/kotlin/space/kscience/kmath/multik/MultikTensorAlgebra.kt
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@ -5,6 +5,8 @@
package space.kscience.kmath.multik
import org.jetbrains.kotlinx.multik.api.mk
import org.jetbrains.kotlinx.multik.api.zeros
import org.jetbrains.kotlinx.multik.ndarray.data.*
import org.jetbrains.kotlinx.multik.ndarray.operations.*
import space.kscience.kmath.misc.PerformancePitfall
@ -30,6 +32,7 @@ public value class MultikTensor<T>(public val array: MutableMultiArray<T, DN>) :
public abstract class MultikTensorAlgebra<T>(
public val type: DataType,
public val elementAlgebra: Ring<T>,
public val comparator: Comparator<T>
) : TensorAlgebra<T> {
@ -38,15 +41,19 @@ public abstract class MultikTensorAlgebra<T>(
* Convert a tensor to [MultikTensor] if necessary. If tensor is converted, changes on the resulting tensor
* are not reflected back onto the source
*/
public fun Tensor<T>.asMultik(): MultikTensor<T> {
private fun Tensor<T>.asMultik(): MultikTensor<T> {
return if (this is MultikTensor) {
this
} else {
TODO()
val res = mk.zeros<T, DN>(shape, type).asDNArray()
for (index in res.multiIndices) {
res[index] = this[index]
}
res.wrap()
}
}
public fun MutableMultiArray<T, DN>.wrap(): MultikTensor<T> = MultikTensor(this)
private fun MutableMultiArray<T, DN>.wrap(): MultikTensor<T> = MultikTensor(this)
override fun Tensor<T>.valueOrNull(): T? = if (shape contentEquals intArrayOf(1)) {
get(intArrayOf(0))
@ -77,8 +84,7 @@ public abstract class MultikTensorAlgebra<T>(
}
}
//TODO avoid additional copy
override fun T.minus(other: Tensor<T>): MultikTensor<T> = -(other - this)
override fun T.minus(other: Tensor<T>): MultikTensor<T> = (-(other.asMultik().array - this)).wrap()
override fun Tensor<T>.minus(value: T): MultikTensor<T> =
asMultik().array.deepCopy().apply { minusAssign(value) }.wrap()
@ -130,13 +136,9 @@ public abstract class MultikTensorAlgebra<T>(
override fun Tensor<T>.unaryMinus(): MultikTensor<T> =
asMultik().array.unaryMinus().wrap()
override fun Tensor<T>.get(i: Int): MultikTensor<T> {
TODO("Not yet implemented")
}
override fun Tensor<T>.get(i: Int): MultikTensor<T> = asMultik().array.mutableView(i).wrap()
override fun Tensor<T>.transpose(i: Int, j: Int): MultikTensor<T> {
TODO("Not yet implemented")
}
override fun Tensor<T>.transpose(i: Int, j: Int): MultikTensor<T> = asMultik().array.transpose(i, j).wrap()
override fun Tensor<T>.view(shape: IntArray): MultikTensor<T> {
require(shape.all { it > 0 })
@ -158,16 +160,14 @@ public abstract class MultikTensorAlgebra<T>(
}.wrap()
}
override fun Tensor<T>.viewAs(other: Tensor<T>): MultikTensor<T> {
TODO("Not yet implemented")
}
override fun Tensor<T>.viewAs(other: Tensor<T>): MultikTensor<T> = view(other.shape)
override fun Tensor<T>.dot(other: Tensor<T>): MultikTensor<T> {
TODO("Not yet implemented")
}
override fun diagonalEmbedding(diagonalEntries: Tensor<T>, offset: Int, dim1: Int, dim2: Int): MultikTensor<T> {
TODO("Not yet implemented")
TODO("Diagonal embedding not implemented")
}
override fun Tensor<T>.sum(): T = asMultik().array.reduceMultiIndexed { _: IntArray, acc: T, t: T ->

13
kmath-multik/src/test/kotlin/space/kscience/kmath/multik/MultikNDTest.kt Normal file
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@ -0,0 +1,13 @@
package space.kscience.kmath.multik
import org.junit.jupiter.api.Test
import space.kscience.kmath.nd.one
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.invoke
internal class MultikNDTest {
@Test
fun basicAlgebra(): Unit = DoubleField.multikND{
one(2,2) + 1.0
}
}

5
kmath-nd4j/src/main/kotlin/space/kscience/kmath/nd4j/Nd4jArrayAlgebra.kt
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@ -38,6 +38,7 @@ public sealed interface Nd4jArrayAlgebra<T, out C : Algebra<T>> : AlgebraND<T, C
return struct
}
@OptIn(PerformancePitfall::class)
override fun StructureND<T>.map(transform: C.(T) -> T): Nd4jArrayStructure<T> {
val newStruct = ndArray.dup().wrap()
newStruct.elements().forEach { (idx, value) -> newStruct[idx] = elementAlgebra.transform(value) }
@ -117,7 +118,7 @@ public sealed interface Nd4jArrayRingOps<T, out R : Ring<T>> : RingOpsND<T, R>,
* Creates a most suitable implementation of [RingND] using reified class.
*/
@Suppress("UNCHECKED_CAST")
public inline fun <reified T : Number> auto(vararg shape: Int): Nd4jArrayRingOps<T, Ring<T>> = when {
public inline fun <reified T : Number> auto(): Nd4jArrayRingOps<T, Ring<T>> = when {
T::class == Int::class -> IntRing.nd4j as Nd4jArrayRingOps<T, Ring<T>>
else -> throw UnsupportedOperationException("This factory method only supports Long type.")
}
@ -142,7 +143,7 @@ public sealed interface Nd4jArrayField<T, out F : Field<T>> : FieldOpsND<T, F>,
* Creates a most suitable implementation of [FieldND] using reified class.
*/
@Suppress("UNCHECKED_CAST")
public inline fun <reified T : Any> auto(vararg shape: Int): Nd4jArrayField<T, Field<T>> = when {
public inline fun <reified T : Any> auto(): Nd4jArrayField<T, Field<T>> = when {
T::class == Float::class -> FloatField.nd4j as Nd4jArrayField<T, Field<T>>
T::class == Double::class -> DoubleField.nd4j as Nd4jArrayField<T, Field<T>>
else -> throw UnsupportedOperationException("This factory method only supports Float and Double types.")

10
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorAlgebraExtensions.kt
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@ -5,4 +5,12 @@
package space.kscience.kmath.tensors.core
public fun DoubleTensorAlgebra.ones(vararg shape: Int): DoubleTensor = ones(intArrayOf(*shape))
import space.kscience.kmath.nd.Shape
import kotlin.jvm.JvmName
@JvmName("varArgOne")
public fun DoubleTensorAlgebra.one(vararg shape: Int): DoubleTensor = ones(intArrayOf(*shape))
public fun DoubleTensorAlgebra.one(shape: Shape): DoubleTensor = ones(shape)
@JvmName("varArgZero")
public fun DoubleTensorAlgebra.zero(vararg shape: Int): DoubleTensor = zeros(intArrayOf(*shape))
public fun DoubleTensorAlgebra.zero(shape: Shape): DoubleTensor = zeros(shape)