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Multik integration finished (for now)

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This commit is contained in:
Alexander Nozik 2021-11-04 10:58:27 +03:00
parent 726864ed0e
commit 0e1e97a3ff
4 changed files with 65 additions and 165 deletions
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10
kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/DoubleBufferOps.kt
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@ -17,12 +17,15 @@ import kotlin.math.*
/**
* [ExtendedFieldOps] over [DoubleBuffer].
*/
public abstract class DoubleBufferOps :
BufferAlgebra<Double, DoubleField>, ExtendedFieldOps<Buffer<Double>>, Norm<Buffer<Double>, Double> {
public abstract class DoubleBufferOps : BufferAlgebra<Double, DoubleField>, ExtendedFieldOps<Buffer<Double>>,
Norm<Buffer<Double>, Double> {
override val elementAlgebra: DoubleField get() = DoubleField
override val bufferFactory: BufferFactory<Double> get() = ::DoubleBuffer
override fun Buffer<Double>.map(block: DoubleField.(Double) -> Double): DoubleBuffer =
mapInline { DoubleField.block(it) }
@UnstableKMathAPI
override fun unaryOperationFunction(operation: String): (arg: Buffer<Double>) -> Buffer<Double> =
super<ExtendedFieldOps>.unaryOperationFunction(operation)
@ -87,8 +90,7 @@ public abstract class DoubleBufferOps :
val aArray = left.array
val bArray = right.array
DoubleBuffer(DoubleArray(left.size) { aArray[it] * bArray[it] })
} else
DoubleBuffer(DoubleArray(left.size) { left[it] * right[it] })
} else DoubleBuffer(DoubleArray(left.size) { left[it] * right[it] })
}
override fun divide(left: Buffer<Double>, right: Buffer<Double>): DoubleBuffer {

143
kmath-multik/src/main/kotlin/space/kscience/kmath/multik/MultikDoubleAlgebra.kt
View File

@ -1,134 +1,47 @@
package space.kscience.kmath.multik
import org.jetbrains.kotlinx.multik.ndarray.data.DN
import org.jetbrains.kotlinx.multik.ndarray.data.DataType
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.tensors.api.AnalyticTensorAlgebra
import space.kscience.kmath.tensors.api.LinearOpsTensorAlgebra
import space.kscience.kmath.tensors.api.Tensor
import space.kscience.kmath.operations.ExponentialOperations
import space.kscience.kmath.operations.TrigonometricOperations
public object MultikDoubleAlgebra : MultikDivisionTensorAlgebra<Double, DoubleField>(),
AnalyticTensorAlgebra<Double, DoubleField>, LinearOpsTensorAlgebra<Double, DoubleField> {
TrigonometricOperations<StructureND<Double>>, ExponentialOperations<StructureND<Double>> {
override val elementAlgebra: DoubleField get() = DoubleField
override val type: DataType get() = DataType.DoubleDataType
override fun StructureND<Double>.mean(): Double = multikStat.mean(asMultik().array)
override fun sin(arg: StructureND<Double>): MultikTensor<Double> = multikMath.mathEx.sin(arg.asMultik().array).wrap()
override fun StructureND<Double>.mean(dim: Int, keepDim: Boolean): Tensor<Double> =
multikStat.mean<Double,DN, DN>(asMultik().array, dim).wrap()
override fun cos(arg: StructureND<Double>): MultikTensor<Double> = multikMath.mathEx.cos(arg.asMultik().array).wrap()
override fun StructureND<Double>.std(): Double {
TODO("Not yet implemented")
override fun tan(arg: StructureND<Double>): MultikTensor<Double> = sin(arg) / cos(arg)
override fun asin(arg: StructureND<Double>): MultikTensor<Double> = arg.map { asin(it) }
override fun acos(arg: StructureND<Double>): MultikTensor<Double> = arg.map { acos(it) }
override fun atan(arg: StructureND<Double>): MultikTensor<Double> = arg.map { atan(it) }
override fun exp(arg: StructureND<Double>): MultikTensor<Double> = multikMath.mathEx.exp(arg.asMultik().array).wrap()
override fun ln(arg: StructureND<Double>): MultikTensor<Double> = multikMath.mathEx.log(arg.asMultik().array).wrap()
override fun sinh(arg: StructureND<Double>): MultikTensor<Double> = (exp(arg) - exp(-arg)) / 2.0
override fun cosh(arg: StructureND<Double>): MultikTensor<Double> = (exp(arg) + exp(-arg)) / 2.0
override fun tanh(arg: StructureND<Double>): MultikTensor<Double> {
val expPlus = exp(arg)
val expMinus = exp(-arg)
return (expPlus - expMinus) / (expPlus + expMinus)
}
override fun StructureND<Double>.std(dim: Int, keepDim: Boolean): Tensor<Double> {
TODO("Not yet implemented")
}
override fun asinh(arg: StructureND<Double>): MultikTensor<Double> = arg.map { asinh(it) }
override fun StructureND<Double>.variance(): Double {
TODO("Not yet implemented")
}
override fun acosh(arg: StructureND<Double>): MultikTensor<Double> = arg.map { acosh(it) }
override fun StructureND<Double>.variance(dim: Int, keepDim: Boolean): Tensor<Double> {
TODO("Not yet implemented")
}
override fun StructureND<Double>.exp(): Tensor<Double> {
TODO("Not yet implemented")
}
override fun StructureND<Double>.ln(): Tensor<Double> {
TODO("Not yet implemented")
}
override fun StructureND<Double>.sqrt(): Tensor<Double> {
TODO("Not yet implemented")
}
override fun StructureND<Double>.cos(): Tensor<Double> {
TODO("Not yet implemented")
}
override fun StructureND<Double>.acos(): Tensor<Double> {
TODO("Not yet implemented")
}
override fun StructureND<Double>.cosh(): Tensor<Double> {
TODO("Not yet implemented")
}
override fun StructureND<Double>.acosh(): Tensor<Double> {
TODO("Not yet implemented")
}
override fun StructureND<Double>.sin(): Tensor<Double> {
TODO("Not yet implemented")
}
override fun StructureND<Double>.asin(): Tensor<Double> {
TODO("Not yet implemented")
}
override fun StructureND<Double>.sinh(): Tensor<Double> {
TODO("Not yet implemented")
}
override fun StructureND<Double>.asinh(): Tensor<Double> {
TODO("Not yet implemented")
}
override fun StructureND<Double>.tan(): Tensor<Double> {
TODO("Not yet implemented")
}
override fun StructureND<Double>.atan(): Tensor<Double> {
TODO("Not yet implemented")
}
override fun StructureND<Double>.tanh(): Tensor<Double> {
TODO("Not yet implemented")
}
override fun StructureND<Double>.atanh(): Tensor<Double> {
TODO("Not yet implemented")
}
override fun StructureND<Double>.ceil(): Tensor<Double> {
TODO("Not yet implemented")
}
override fun StructureND<Double>.floor(): Tensor<Double> {
TODO("Not yet implemented")
}
override fun StructureND<Double>.det(): Tensor<Double> {
TODO("Not yet implemented")
}
override fun StructureND<Double>.inv(): Tensor<Double> {
TODO("Not yet implemented")
}
override fun StructureND<Double>.cholesky(): Tensor<Double> {
TODO("Not yet implemented")
}
override fun StructureND<Double>.qr(): Pair<Tensor<Double>, Tensor<Double>> {
TODO("Not yet implemented")
}
override fun StructureND<Double>.lu(): Triple<Tensor<Double>, Tensor<Double>, Tensor<Double>> {
TODO("Not yet implemented")
}
override fun StructureND<Double>.svd(): Triple<Tensor<Double>, Tensor<Double>, Tensor<Double>> {
TODO("Not yet implemented")
}
override fun StructureND<Double>.symEig(): Pair<Tensor<Double>, Tensor<Double>> {
TODO("Not yet implemented")
}
override fun atanh(arg: StructureND<Double>): MultikTensor<Double> = arg.map { atanh(it) }
}
public val Double.Companion.multikAlgebra: MultikTensorAlgebra<Double, DoubleField> get() = MultikDoubleAlgebra

75
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
View File

@ -75,9 +75,9 @@ public open class DoubleTensorAlgebra :
override fun zip(
left: StructureND<Double>,
right: StructureND<Double>,
transform: DoubleField.(Double, Double) -> Double
transform: DoubleField.(Double, Double) -> Double,
): DoubleTensor {
require(left.shape.contentEquals(right.shape)){
require(left.shape.contentEquals(right.shape)) {
"The shapes in zip are not equal: left - ${left.shape}, right - ${right.shape}"
}
val leftTensor = left.tensor
@ -422,14 +422,11 @@ public open class DoubleTensorAlgebra :
for ((res, ab) in resTensor.matrixSequence().zip(newThis.matrixSequence().zip(newOther.matrixSequence()))) {
val (a, b) = ab
dotHelper(a.as2D(), b.as2D(), res.as2D(), l, m1, n)
dotTo(a.as2D(), b.as2D(), res.as2D(), l, m1, n)
}
if (penultimateDim) {
return resTensor.view(
resTensor.shape.dropLast(2).toIntArray() +
intArrayOf(resTensor.shape.last())
)
return resTensor.view(resTensor.shape.dropLast(2).toIntArray() + intArrayOf(resTensor.shape.last()))
}
if (lastDim) {
return resTensor.view(resTensor.shape.dropLast(1).toIntArray())
@ -441,7 +438,7 @@ public open class DoubleTensorAlgebra :
diagonalEntries: Tensor<Double>,
offset: Int,
dim1: Int,
dim2: Int
dim2: Int,
): DoubleTensor {
val n = diagonalEntries.shape.size
val d1 = minusIndexFrom(n + 1, dim1)
@ -577,13 +574,13 @@ public open class DoubleTensorAlgebra :
*/
public fun Tensor<Double>.rowsByIndices(indices: IntArray): DoubleTensor = stack(indices.map { this[it] })
internal inline fun StructureND<Double>.fold(foldFunction: (DoubleArray) -> Double): Double =
private inline fun StructureND<Double>.fold(foldFunction: (DoubleArray) -> Double): Double =
foldFunction(tensor.copyArray())
internal inline fun <reified R: Any> StructureND<Double>.foldDim(
foldFunction: (DoubleArray) -> R,
private inline fun <reified R : Any> StructureND<Double>.foldDim(
dim: Int,
keepDim: Boolean,
foldFunction: (DoubleArray) -> R,
): BufferedTensor<R> {
check(dim < dimension) { "Dimension $dim out of range $dimension" }
val resShape = if (keepDim) {
@ -592,7 +589,7 @@ public open class DoubleTensorAlgebra :
shape.take(dim).toIntArray() + shape.takeLast(dimension - dim - 1).toIntArray()
}
val resNumElements = resShape.reduce(Int::times)
val init = foldFunction(DoubleArray(1){0.0})
val init = foldFunction(DoubleArray(1) { 0.0 })
val resTensor = BufferedTensor(resShape,
MutableBuffer.auto(resNumElements) { init }, 0)
for (index in resTensor.indices) {
@ -608,66 +605,59 @@ public open class DoubleTensorAlgebra :
override fun StructureND<Double>.sum(): Double = tensor.fold { it.sum() }
override fun StructureND<Double>.sum(dim: Int, keepDim: Boolean): DoubleTensor =
foldDim({ x -> x.sum() }, dim, keepDim).toDoubleTensor()
foldDim(dim, keepDim) { x -> x.sum() }.toDoubleTensor()
override fun StructureND<Double>.min(): Double = this.fold { it.minOrNull()!! }
override fun StructureND<Double>.min(dim: Int, keepDim: Boolean): DoubleTensor =
foldDim({ x -> x.minOrNull()!! }, dim, keepDim).toDoubleTensor()
foldDim(dim, keepDim) { x -> x.minOrNull()!! }.toDoubleTensor()
override fun StructureND<Double>.max(): Double = this.fold { it.maxOrNull()!! }
override fun StructureND<Double>.max(dim: Int, keepDim: Boolean): DoubleTensor =
foldDim({ x -> x.maxOrNull()!! }, dim, keepDim).toDoubleTensor()
foldDim(dim, keepDim) { x -> x.maxOrNull()!! }.toDoubleTensor()
override fun StructureND<Double>.argMax(dim: Int, keepDim: Boolean): IntTensor =
foldDim({ x ->
foldDim(dim, keepDim) { x ->
x.withIndex().maxByOrNull { it.value }?.index!!
}, dim, keepDim).toIntTensor()
}.toIntTensor()
override fun StructureND<Double>.mean(): Double = this.fold { it.sum() / tensor.numElements }
override fun StructureND<Double>.mean(dim: Int, keepDim: Boolean): DoubleTensor =
foldDim(
{ arr ->
check(dim < dimension) { "Dimension $dim out of range $dimension" }
arr.sum() / shape[dim]
},
dim,
keepDim
).toDoubleTensor()
override fun StructureND<Double>.mean(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(dim, keepDim) { arr ->
check(dim < dimension) { "Dimension $dim out of range $dimension" }
arr.sum() / shape[dim]
}.toDoubleTensor()
override fun StructureND<Double>.std(): Double = this.fold { arr ->
override fun StructureND<Double>.std(): Double = fold { arr ->
val mean = arr.sum() / tensor.numElements
sqrt(arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1))
}
override fun StructureND<Double>.std(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(
{ arr ->
check(dim < dimension) { "Dimension $dim out of range $dimension" }
val mean = arr.sum() / shape[dim]
sqrt(arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1))
},
dim,
keepDim
).toDoubleTensor()
) { arr ->
check(dim < dimension) { "Dimension $dim out of range $dimension" }
val mean = arr.sum() / shape[dim]
sqrt(arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1))
}.toDoubleTensor()
override fun StructureND<Double>.variance(): Double = this.fold { arr ->
override fun StructureND<Double>.variance(): Double = fold { arr ->
val mean = arr.sum() / tensor.numElements
arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1)
}
override fun StructureND<Double>.variance(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(
{ arr ->
check(dim < dimension) { "Dimension $dim out of range $dimension" }
val mean = arr.sum() / shape[dim]
arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1)
},
dim,
keepDim
).toDoubleTensor()
) { arr ->
check(dim < dimension) { "Dimension $dim out of range $dimension" }
val mean = arr.sum() / shape[dim]
arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1)
}.toDoubleTensor()
private fun cov(x: DoubleTensor, y: DoubleTensor): Double {
val n = x.shape[0]
@ -699,19 +689,14 @@ public open class DoubleTensorAlgebra :
return resTensor
}
@OptIn(PerformancePitfall::class)
override fun StructureND<Double>.exp(): DoubleTensor = tensor.map { exp(it) }
@OptIn(PerformancePitfall::class)
override fun StructureND<Double>.ln(): DoubleTensor = tensor.map { ln(it) }
@OptIn(PerformancePitfall::class)
override fun StructureND<Double>.sqrt(): DoubleTensor = tensor.map { sqrt(it) }
@OptIn(PerformancePitfall::class)
override fun StructureND<Double>.cos(): DoubleTensor = tensor.map { cos(it) }
@OptIn(PerformancePitfall::class)
override fun StructureND<Double>.acos(): DoubleTensor = tensor.map { acos(it) }
override fun StructureND<Double>.cosh(): DoubleTensor = tensor.map { cosh(it) }

2
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/linUtils.kt
View File

@ -53,7 +53,7 @@ internal val <T> BufferedTensor<T>.matrices: VirtualBuffer<BufferedTensor<T>>
internal fun <T> BufferedTensor<T>.matrixSequence(): Sequence<BufferedTensor<T>> = matrices.asSequence()
internal fun dotHelper(
internal fun dotTo(
a: MutableStructure2D<Double>,
b: MutableStructure2D<Double>,
res: MutableStructure2D<Double>,