[WIP] TensorFlow
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kmath-tensorflow/build.gradle.kts
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14
kmath-tensorflow/build.gradle.kts
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plugins {
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id("ru.mipt.npm.gradle.jvm")
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}
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description = "Google tensorflow connector"
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dependencies {
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api(project(":kmath-tensors"))
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api("org.tensorflow:tensorflow-core-api:0.3.3")
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}
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readme {
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maturity = ru.mipt.npm.gradle.Maturity.PROTOTYPE
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}
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package space.kscience.kmath.tensorflow
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import org.tensorflow.Graph
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import org.tensorflow.Operand
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import org.tensorflow.Output
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import org.tensorflow.Session
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import org.tensorflow.ndarray.NdArray
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import org.tensorflow.op.Ops
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import org.tensorflow.op.core.Constant
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import org.tensorflow.types.family.TType
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import space.kscience.kmath.misc.PerformancePitfall
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import space.kscience.kmath.nd.Shape
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import space.kscience.kmath.tensors.api.Tensor
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import space.kscience.kmath.tensors.api.TensorAlgebra
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private fun IntArray.toLongArray() = LongArray(size) { get(it).toLong() }
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private fun LongArray.toIntArray() = IntArray(size) { get(it).toInt() }
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private val <T> NdArray<T>.scalar: T
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get() = getObject()
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public sealed interface TensorFlowTensor<T> : Tensor<T>
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@JvmInline
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public value class TensorFlowArray<T>(public val tensor: NdArray<T>) : Tensor<T> {
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override val shape: Shape get() = tensor.shape().asArray().toIntArray()
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override fun get(index: IntArray): T = tensor.getObject(*index.toLongArray())
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@PerformancePitfall
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override fun elements(): Sequence<Pair<IntArray, T>> = sequence {
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tensor.scalars().forEachIndexed { index: LongArray, ndArray: NdArray<T> ->
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//yield(index.toIntArray() to ndArray.scalar)
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TODO()
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}
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}
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override fun set(index: IntArray, value: T) {
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tensor.setObject(value, *index.toLongArray())
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}
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}
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public abstract class TensorFlowOutput<T, TT : TType>(
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private val graph: Graph,
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output: Output<TT>
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) : TensorFlowTensor<T> {
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public var output: Output<TT> = output
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internal set
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override val shape: Shape get() = output.shape().asArray().toIntArray()
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protected abstract fun org.tensorflow.Tensor.actualizeTensor(): NdArray<T>
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private val actualTensor by lazy {
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val session = Session(graph)
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TensorFlowArray(session.runner().fetch(output).run().first().actualizeTensor())
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}
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override fun get(index: IntArray): T = actualTensor[index]
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@PerformancePitfall
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override fun elements(): Sequence<Pair<IntArray, T>> = actualTensor.elements()
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override fun set(index: IntArray, value: T) {
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actualTensor[index] = value
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}
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}
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public abstract class TensorFlowAlgebra<T, TT : TType> internal constructor(
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private val graph: Graph
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) : TensorAlgebra<T> {
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private val ops by lazy { Ops.create(graph) }
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protected fun Tensor<T>.asTensorFlow(): TensorFlowOutput<T, TT> = if (this is TensorFlowOutput<T, TT>) this else {
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TODO()
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}
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protected abstract fun Output<TT>.wrap(): TensorFlowOutput<T, TT>
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protected abstract fun const(value: T): Constant<TT>
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override fun Tensor<T>.valueOrNull(): T? = if (shape contentEquals intArrayOf(1))
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get(Shape(0)) else null
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private inline fun Tensor<T>.biOp(
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other: Tensor<T>,
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operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>
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): TensorFlowOutput<T, TT> {
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val left = asTensorFlow().output
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val right = other.asTensorFlow().output
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return operation(left, right).asOutput().wrap()
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}
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private inline fun T.biOp(
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other: Tensor<T>,
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operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>
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): TensorFlowOutput<T, TT> {
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val left = const(this)
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val right = other.asTensorFlow().output
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return operation(left, right).asOutput().wrap()
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}
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private inline fun Tensor<T>.biOp(
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value: T,
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operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>
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): TensorFlowOutput<T, TT> {
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val left = asTensorFlow().output
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val right = const(value)
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return operation(left, right).asOutput().wrap()
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}
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private inline fun Tensor<T>.inPlaceOp(
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other: Tensor<T>,
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operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>
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): Unit {
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val origin = asTensorFlow()
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val left = origin.output
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val right = other.asTensorFlow().output
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origin.output = operation(left, right).asOutput()
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}
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private inline fun Tensor<T>.inPlaceOp(
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value: T,
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operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>
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): Unit {
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val origin = asTensorFlow()
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val left = origin.output
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val right = const(value)
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origin.output = operation(left, right).asOutput()
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}
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private inline fun unOp(value: Tensor<T>, operation: (Operand<TT>) -> Operand<TT>): TensorFlowOutput<T, TT> =
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operation(value.asTensorFlow().output).asOutput().wrap()
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override fun T.plus(other: Tensor<T>) = biOp(other, ops.math::add)
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override fun Tensor<T>.plus(value: T) = biOp(value, ops.math::add)
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override fun Tensor<T>.plus(other: Tensor<T>) = biOp(other, ops.math::add)
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override fun Tensor<T>.plusAssign(value: T): Unit = inPlaceOp(value, ops.math::add)
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override fun Tensor<T>.plusAssign(other: Tensor<T>): Unit = inPlaceOp(other, ops.math::add)
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override fun Tensor<T>.minus(value: T) = biOp(value, ops.math::sub)
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override fun Tensor<T>.minus(other: Tensor<T>) = biOp(other, ops.math::sub)
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override fun Tensor<T>.minusAssign(value: T): Unit = inPlaceOp(value, ops.math::sub)
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override fun Tensor<T>.minusAssign(other: Tensor<T>): Unit = inPlaceOp(other, ops.math::sub)
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override fun T.times(other: Tensor<T>) = biOp(other, ops.math::mul)
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override fun Tensor<T>.times(value: T) = biOp(value, ops.math::mul)
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override fun Tensor<T>.times(other: Tensor<T>): TensorFlowOutput<T, TT> = biOp(other, ops.math::mul)
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override fun Tensor<T>.timesAssign(value: T): Unit = inPlaceOp(value, ops.math::mul)
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override fun Tensor<T>.timesAssign(other: Tensor<T>): Unit = inPlaceOp(other, ops.math::mul)
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override fun Tensor<T>.unaryMinus() = unOp(this, ops.math::neg)
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override fun Tensor<T>.get(i: Int): Tensor<T>{
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ops.
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}
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override fun Tensor<T>.transpose(i: Int, j: Int): Tensor<T> {
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TODO("Not yet implemented")
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}
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override fun Tensor<T>.view(shape: IntArray): Tensor<T> {
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TODO("Not yet implemented")
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}
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override fun Tensor<T>.viewAs(other: Tensor<T>): Tensor<T> {
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TODO("Not yet implemented")
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}
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override fun Tensor<T>.dot(other: Tensor<T>) = biOp(other, ops.math.)
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override fun diagonalEmbedding(diagonalEntries: Tensor<T>, offset: Int, dim1: Int, dim2: Int): Tensor<T> = ops.run {
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TODO("Not yet implemented")
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}
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override fun Tensor<T>.sum(): T {
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TODO("Not yet implemented")
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}
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override fun Tensor<T>.sum(dim: Int, keepDim: Boolean): Tensor<T> {
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TODO("Not yet implemented")
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}
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override fun Tensor<T>.min(): T {
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TODO("Not yet implemented")
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}
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override fun Tensor<T>.min(dim: Int, keepDim: Boolean): Tensor<T> {
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TODO("Not yet implemented")
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}
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override fun Tensor<T>.max(): T {
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TODO("Not yet implemented")
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}
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override fun Tensor<T>.max(dim: Int, keepDim: Boolean): Tensor<T> {
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TODO("Not yet implemented")
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}
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override fun Tensor<T>.argMax(dim: Int, keepDim: Boolean): Tensor<T> {
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TODO("Not yet implemented")
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}
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}
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@ -33,6 +33,7 @@ include(
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":kmath-commons",
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":kmath-viktor",
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":kmath-multik",
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":kmath-tensorflow",
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":kmath-optimization",
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":kmath-stat",
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":kmath-nd4j",
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