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[WIP] TensorFlow refactoring

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This commit is contained in:
Alexander Nozik 2021-10-28 10:52:40 +03:00
parent 7bdc54c818
commit 64629561af
2 changed files with 58 additions and 46 deletions
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26
kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/DoubleTensorFlowAlgebra.kt
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@ -3,11 +3,13 @@ package space.kscience.kmath.tensorflow
import org.tensorflow.Graph
import org.tensorflow.Output
import org.tensorflow.ndarray.NdArray
import org.tensorflow.ndarray.Shape
import org.tensorflow.op.core.Constant
import org.tensorflow.types.TFloat64
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.tensors.api.Tensor
import space.kscience.kmath.nd.DefaultStrides
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.operations.DoubleField
public class DoubleTensorFlowOutput(
graph: Graph,
@ -18,14 +20,28 @@ public class DoubleTensorFlowOutput(
public class DoubleTensorFlowAlgebra internal constructor(
graph: Graph
) : TensorFlowAlgebra<Double, TFloat64>(graph) {
) : TensorFlowAlgebra<Double, TFloat64, DoubleField>(graph) {
override fun Tensor<Double>.asTensorFlow(): TensorFlowOutput<Double, TFloat64> =
override val elementAlgebra: DoubleField get() = DoubleField
override fun structureND(
shape: Shape,
initializer: DoubleField.(IntArray) -> Double
): StructureND<Double> {
val res = TFloat64.tensorOf(org.tensorflow.ndarray.Shape.of(*shape.toLongArray())) { array ->
DefaultStrides(shape).forEach { index ->
array.setDouble(elementAlgebra.initializer(index), *index.toLongArray())
}
}
return DoubleTensorFlowOutput(graph, ops.constant(res).asOutput())
}
override fun StructureND<Double>.asTensorFlow(): TensorFlowOutput<Double, TFloat64> =
if (this is TensorFlowOutput<Double, *> && output.type() == TFloat64::class.java) {
@Suppress("UNCHECKED_CAST")
this as TensorFlowOutput<Double, TFloat64>
} else {
val res = TFloat64.tensorOf(Shape.of(*shape.toLongArray())) { array ->
val res = TFloat64.tensorOf(org.tensorflow.ndarray.Shape.of(*shape.toLongArray())) { array ->
@OptIn(PerformancePitfall::class)
elements().forEach { (index, value) ->
array.setDouble(value, *index.toLongArray())

78
kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/TensorFlowAlgebra.kt
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@ -11,6 +11,8 @@ import org.tensorflow.op.core.Constant
import org.tensorflow.types.family.TType
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.operations.Ring
import space.kscience.kmath.tensors.api.Tensor
import space.kscience.kmath.tensors.api.TensorAlgebra
@ -28,13 +30,7 @@ public value class TensorFlowArray<T>(public val tensor: NdArray<T>) : Tensor<T>
override fun get(index: IntArray): T = tensor.getObject(*index.toLongArray())
@PerformancePitfall
override fun elements(): Sequence<Pair<IntArray, T>> = sequence {
tensor.scalars().forEachIndexed { index: LongArray, ndArray: NdArray<T> ->
//yield(index.toIntArray() to ndArray.scalar)
TODO()
}
}
//TODO implement native element sequence
override fun set(index: IntArray, value: T) {
tensor.setObject(value, *index.toLongArray())
@ -70,23 +66,23 @@ public abstract class TensorFlowOutput<T, TT : TType>(
}
public abstract class TensorFlowAlgebra<T, TT : TType> internal constructor(
public abstract class TensorFlowAlgebra<T, TT : TType, A: Ring<T>> internal constructor(
protected val graph: Graph
) : TensorAlgebra<T> {
) : TensorAlgebra<T,A> {
protected val ops: Ops by lazy { Ops.create(graph) }
protected abstract fun Tensor<T>.asTensorFlow(): TensorFlowOutput<T, TT>
protected abstract fun StructureND<T>.asTensorFlow(): TensorFlowOutput<T, TT>
protected abstract fun Output<TT>.wrap(): TensorFlowOutput<T, TT>
protected abstract fun const(value: T): Constant<TT>
override fun Tensor<T>.valueOrNull(): T? = if (shape contentEquals intArrayOf(1))
override fun StructureND<T>.valueOrNull(): T? = if (shape contentEquals intArrayOf(1))
get(Shape(0)) else null
private inline fun Tensor<T>.biOp(
other: Tensor<T>,
private inline fun StructureND<T>.biOp(
other: StructureND<T>,
operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>
): TensorFlowOutput<T, TT> {
val left = asTensorFlow().output
@ -95,7 +91,7 @@ public abstract class TensorFlowAlgebra<T, TT : TType> internal constructor(
}
private inline fun T.biOp(
other: Tensor<T>,
other: StructureND<T>,
operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>
): TensorFlowOutput<T, TT> {
val left = const(this)
@ -103,7 +99,7 @@ public abstract class TensorFlowAlgebra<T, TT : TType> internal constructor(
return operation(left, right).asOutput().wrap()
}
private inline fun Tensor<T>.biOp(
private inline fun StructureND<T>.biOp(
value: T,
operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>
): TensorFlowOutput<T, TT> {
@ -113,7 +109,7 @@ public abstract class TensorFlowAlgebra<T, TT : TType> internal constructor(
}
private inline fun Tensor<T>.inPlaceOp(
other: Tensor<T>,
other: StructureND<T>,
operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>
): Unit {
val origin = asTensorFlow()
@ -132,46 +128,46 @@ public abstract class TensorFlowAlgebra<T, TT : TType> internal constructor(
origin.output = operation(left, right).asOutput()
}
private inline fun unOp(value: Tensor<T>, operation: (Operand<TT>) -> Operand<TT>): TensorFlowOutput<T, TT> =
private inline fun unOp(value: StructureND<T>, operation: (Operand<TT>) -> Operand<TT>): TensorFlowOutput<T, TT> =
operation(value.asTensorFlow().output).asOutput().wrap()
override fun T.plus(other: Tensor<T>): TensorFlowOutput<T, TT> = biOp(other, ops.math::add)
override fun T.plus(arg: StructureND<T>): TensorFlowOutput<T, TT> = biOp(arg, ops.math::add)
override fun Tensor<T>.plus(value: T): TensorFlowOutput<T, TT> = biOp(value, ops.math::add)
override fun StructureND<T>.plus(arg: T): TensorFlowOutput<T, TT> = biOp(arg, ops.math::add)
override fun Tensor<T>.plus(other: Tensor<T>): TensorFlowOutput<T, TT> = biOp(other, ops.math::add)
override fun StructureND<T>.plus(arg: StructureND<T>): TensorFlowOutput<T, TT> = biOp(arg, ops.math::add)
override fun Tensor<T>.plusAssign(value: T): Unit = inPlaceOp(value, ops.math::add)
override fun Tensor<T>.plusAssign(other: Tensor<T>): Unit = inPlaceOp(other, ops.math::add)
override fun Tensor<T>.plusAssign(arg: StructureND<T>): Unit = inPlaceOp(arg, ops.math::add)
override fun Tensor<T>.minus(value: T): TensorFlowOutput<T, TT> = biOp(value, ops.math::sub)
override fun StructureND<T>.minus(arg: T): TensorFlowOutput<T, TT> = biOp(arg, ops.math::sub)
override fun Tensor<T>.minus(other: Tensor<T>): TensorFlowOutput<T, TT> = biOp(other, ops.math::sub)
override fun StructureND<T>.minus(arg: StructureND<T>): TensorFlowOutput<T, TT> = biOp(arg, ops.math::sub)
override fun T.minus(other: Tensor<T>): Tensor<T> = biOp(other, ops.math::sub)
override fun T.minus(arg: StructureND<T>): Tensor<T> = biOp(arg, ops.math::sub)
override fun Tensor<T>.minusAssign(value: T): Unit = inPlaceOp(value, ops.math::sub)
override fun Tensor<T>.minusAssign(other: Tensor<T>): Unit = inPlaceOp(other, ops.math::sub)
override fun Tensor<T>.minusAssign(other: StructureND<T>): Unit = inPlaceOp(other, ops.math::sub)
override fun T.times(other: Tensor<T>): TensorFlowOutput<T, TT> = biOp(other, ops.math::mul)
override fun T.times(arg: StructureND<T>): TensorFlowOutput<T, TT> = biOp(arg, ops.math::mul)
override fun Tensor<T>.times(value: T): TensorFlowOutput<T, TT> = biOp(value, ops.math::mul)
override fun StructureND<T>.times(arg: T): TensorFlowOutput<T, TT> = biOp(arg, ops.math::mul)
override fun Tensor<T>.times(other: Tensor<T>): TensorFlowOutput<T, TT> = biOp(other, ops.math::mul)
override fun StructureND<T>.times(other: StructureND<T>): TensorFlowOutput<T, TT> = biOp(other, ops.math::mul)
override fun Tensor<T>.timesAssign(value: T): Unit = inPlaceOp(value, ops.math::mul)
override fun Tensor<T>.timesAssign(other: Tensor<T>): Unit = inPlaceOp(other, ops.math::mul)
override fun Tensor<T>.timesAssign(arg: StructureND<T>): Unit = inPlaceOp(arg, ops.math::mul)
override fun Tensor<T>.unaryMinus(): TensorFlowOutput<T, TT> = unOp(this, ops.math::neg)
override fun StructureND<T>.unaryMinus(): TensorFlowOutput<T, TT> = unOp(this, ops.math::neg)
override fun Tensor<T>.get(i: Int): Tensor<T> {
override fun StructureND<T>.get(i: Int): Tensor<T> {
TODO("Not yet implemented")
}
override fun Tensor<T>.transpose(i: Int, j: Int): Tensor<T> = unOp(this) {
override fun StructureND<T>.transpose(i: Int, j: Int): Tensor<T> = unOp(this) {
ops.linalg.transpose(it, ops.constant(intArrayOf(i, j)))
}
@ -179,11 +175,11 @@ public abstract class TensorFlowAlgebra<T, TT : TType> internal constructor(
TODO("Not yet implemented")
}
override fun Tensor<T>.viewAs(other: Tensor<T>): Tensor<T> {
override fun Tensor<T>.viewAs(other: StructureND<T>): Tensor<T> {
TODO("Not yet implemented")
}
override fun Tensor<T>.dot(other: Tensor<T>): TensorFlowOutput<T, TT> = biOp(other) { l, r ->
override fun StructureND<T>.dot(other: StructureND<T>): TensorFlowOutput<T, TT> = biOp(other) { l, r ->
ops.linalg.matMul(l, r)
}
@ -191,29 +187,29 @@ public abstract class TensorFlowAlgebra<T, TT : TType> internal constructor(
TODO("Not yet implemented")
}
override fun Tensor<T>.sum(): T = TODO("Not yet implemented")
override fun StructureND<T>.sum(): T = TODO("Not yet implemented")
override fun Tensor<T>.sum(dim: Int, keepDim: Boolean): Tensor<T> {
override fun StructureND<T>.sum(dim: Int, keepDim: Boolean): Tensor<T> {
TODO("Not yet implemented")
}
override fun Tensor<T>.min(): T {
override fun StructureND<T>.min(): T {
TODO("Not yet implemented")
}
override fun Tensor<T>.min(dim: Int, keepDim: Boolean): Tensor<T> {
override fun StructureND<T>.min(dim: Int, keepDim: Boolean): Tensor<T> {
TODO("Not yet implemented")
}
override fun Tensor<T>.max(): T {
override fun StructureND<T>.max(): T {
TODO("Not yet implemented")
}
override fun Tensor<T>.max(dim: Int, keepDim: Boolean): Tensor<T> {
override fun StructureND<T>.max(dim: Int, keepDim: Boolean): Tensor<T> {
TODO("Not yet implemented")
}
override fun Tensor<T>.argMax(dim: Int, keepDim: Boolean): Tensor<T> {
override fun StructureND<T>.argMax(dim: Int, keepDim: Boolean): Tensor<T> {
TODO("Not yet implemented")
}
}