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First tensorflow test.

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This commit is contained in:
Alexander Nozik 2021-11-10 20:04:29 +03:00
parent c583320051
commit 6c2abdaab0
6 changed files with 127 additions and 47 deletions
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1
kmath-tensorflow/build.gradle.kts
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@ -7,6 +7,7 @@ description = "Google tensorflow connector"
dependencies {
api(project(":kmath-tensors"))
api("org.tensorflow:tensorflow-core-api:0.3.3")
testImplementation("org.tensorflow:tensorflow-core-platform:0.3.3")
}
readme {

27
kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/DoubleTensorFlowAlgebra.kt
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@ -5,6 +5,7 @@ import org.tensorflow.Output
import org.tensorflow.ndarray.NdArray
import org.tensorflow.op.core.Constant
import org.tensorflow.types.TFloat64
import space.kscience.kmath.expressions.Symbol
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.nd.DefaultStrides
import space.kscience.kmath.nd.Shape
@ -13,20 +14,22 @@ import space.kscience.kmath.operations.DoubleField
public class DoubleTensorFlowOutput(
graph: Graph,
output: Output<TFloat64>
output: Output<TFloat64>,
) : TensorFlowOutput<Double, TFloat64>(graph, output) {
override fun org.tensorflow.Tensor.actualizeTensor(): NdArray<Double> = output.asTensor()
override fun org.tensorflow.Tensor.actualizeTensor(): NdArray<Double> = this as TFloat64
}
public class DoubleTensorFlowAlgebra internal constructor(
graph: Graph
graph: Graph,
) : TensorFlowAlgebra<Double, TFloat64, DoubleField>(graph) {
override val elementAlgebra: DoubleField get() = DoubleField
override fun structureND(
shape: Shape,
initializer: DoubleField.(IntArray) -> Double
initializer: DoubleField.(IntArray) -> Double,
): StructureND<Double> {
val res = TFloat64.tensorOf(org.tensorflow.ndarray.Shape.of(*shape.toLongArray())) { array ->
DefaultStrides(shape).forEach { index ->
@ -53,4 +56,20 @@ public class DoubleTensorFlowAlgebra internal constructor(
override fun Output<TFloat64>.wrap(): TensorFlowOutput<Double, TFloat64> = DoubleTensorFlowOutput(graph, this)
override fun const(value: Double): Constant<TFloat64> = ops.constant(value)
}
public fun DoubleField.produceWithTF(
block: DoubleTensorFlowAlgebra.() -> StructureND<Double>,
): StructureND<Double> = Graph().use { graph ->
val scope = DoubleTensorFlowAlgebra(graph)
scope.export(scope.block())
}
public fun DoubleField.produceMapWithTF(
block: DoubleTensorFlowAlgebra.() -> Map<Symbol, StructureND<Double>>,
): Map<Symbol, StructureND<Double>> = Graph().use { graph ->
val scope = DoubleTensorFlowAlgebra(graph)
scope.block().mapValues { scope.export(it.value) }
}

21
kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/IntTensorFlowAlgebra.kt Normal file
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@ -0,0 +1,21 @@
package space.kscience.kmath.tensorflow
import org.tensorflow.Graph
import org.tensorflow.Output
import org.tensorflow.ndarray.NdArray
import org.tensorflow.types.TInt32
import org.tensorflow.types.TInt64
public class IntTensorFlowOutput(
graph: Graph,
output: Output<TInt32>,
) : TensorFlowOutput<Int, TInt32>(graph, output) {
override fun org.tensorflow.Tensor.actualizeTensor(): NdArray<Int> = this as TInt32
}
public class LongTensorFlowOutput(
graph: Graph,
output: Output<TInt64>,
) : TensorFlowOutput<Long, TInt64>(graph, output) {
override fun org.tensorflow.Tensor.actualizeTensor(): NdArray<Long> = this as TInt64
}

102
kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/TensorFlowAlgebra.kt
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@ -8,8 +8,13 @@ import org.tensorflow.Session
import org.tensorflow.ndarray.NdArray
import org.tensorflow.op.Ops
import org.tensorflow.op.core.Constant
import org.tensorflow.op.core.Max
import org.tensorflow.op.core.Min
import org.tensorflow.op.core.Sum
import org.tensorflow.types.TInt32
import org.tensorflow.types.family.TType
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.operations.Ring
@ -38,8 +43,8 @@ public value class TensorFlowArray<T>(public val tensor: NdArray<T>) : Tensor<T>
}
public abstract class TensorFlowOutput<T, TT : TType>(
private val graph: Graph,
output: Output<TT>
protected val graph: Graph,
output: Output<TT>,
) : TensorFlowTensor<T> {
public var output: Output<TT> = output
@ -49,10 +54,11 @@ public abstract class TensorFlowOutput<T, TT : TType>(
protected abstract fun org.tensorflow.Tensor.actualizeTensor(): NdArray<T>
private val actualTensor by lazy {
val session = Session(graph)
internal val actualTensor by lazy {
Session(graph).use { session ->
TensorFlowArray(session.runner().fetch(output).run().first().actualizeTensor())
}
}
override fun get(index: IntArray): T = actualTensor[index]
@ -66,9 +72,9 @@ public abstract class TensorFlowOutput<T, TT : TType>(
}
public abstract class TensorFlowAlgebra<T, TT : TType, A: Ring<T>> internal constructor(
protected val graph: Graph
) : TensorAlgebra<T,A> {
public abstract class TensorFlowAlgebra<T, TT : TType, A : Ring<T>> internal constructor(
protected val graph: Graph,
) : TensorAlgebra<T, A> {
protected val ops: Ops by lazy { Ops.create(graph) }
@ -83,7 +89,7 @@ public abstract class TensorFlowAlgebra<T, TT : TType, A: Ring<T>> internal cons
private inline fun StructureND<T>.biOp(
other: StructureND<T>,
operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>
operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>,
): TensorFlowOutput<T, TT> {
val left = asTensorFlow().output
val right = other.asTensorFlow().output
@ -92,7 +98,7 @@ public abstract class TensorFlowAlgebra<T, TT : TType, A: Ring<T>> internal cons
private inline fun T.biOp(
other: StructureND<T>,
operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>
operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>,
): TensorFlowOutput<T, TT> {
val left = const(this)
val right = other.asTensorFlow().output
@ -101,7 +107,7 @@ public abstract class TensorFlowAlgebra<T, TT : TType, A: Ring<T>> internal cons
private inline fun StructureND<T>.biOp(
value: T,
operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>
operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>,
): TensorFlowOutput<T, TT> {
val left = asTensorFlow().output
val right = const(value)
@ -110,7 +116,7 @@ public abstract class TensorFlowAlgebra<T, TT : TType, A: Ring<T>> internal cons
private inline fun Tensor<T>.inPlaceOp(
other: StructureND<T>,
operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>
operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>,
): Unit {
val origin = asTensorFlow()
val left = origin.output
@ -120,7 +126,7 @@ public abstract class TensorFlowAlgebra<T, TT : TType, A: Ring<T>> internal cons
private inline fun Tensor<T>.inPlaceOp(
value: T,
operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>
operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>,
): Unit {
val origin = asTensorFlow()
val left = origin.output
@ -128,8 +134,8 @@ public abstract class TensorFlowAlgebra<T, TT : TType, A: Ring<T>> internal cons
origin.output = operation(left, right).asOutput()
}
private inline fun unOp(value: StructureND<T>, operation: (Operand<TT>) -> Operand<TT>): TensorFlowOutput<T, TT> =
operation(value.asTensorFlow().output).asOutput().wrap()
private inline fun StructureND<T>.unOp(operation: (Operand<TT>) -> Operand<TT>): TensorFlowOutput<T, TT> =
operation(asTensorFlow().output).asOutput().wrap()
override fun T.plus(arg: StructureND<T>): TensorFlowOutput<T, TT> = biOp(arg, ops.math::add)
@ -149,67 +155,79 @@ public abstract class TensorFlowAlgebra<T, TT : TType, A: Ring<T>> internal cons
override fun Tensor<T>.minusAssign(value: T): Unit = inPlaceOp(value, ops.math::sub)
override fun Tensor<T>.minusAssign(other: StructureND<T>): Unit = inPlaceOp(other, ops.math::sub)
override fun Tensor<T>.minusAssign(arg: StructureND<T>): Unit = inPlaceOp(arg, ops.math::sub)
override fun T.times(arg: StructureND<T>): TensorFlowOutput<T, TT> = biOp(arg, ops.math::mul)
override fun StructureND<T>.times(arg: T): TensorFlowOutput<T, TT> = biOp(arg, ops.math::mul)
override fun StructureND<T>.times(other: StructureND<T>): TensorFlowOutput<T, TT> = biOp(other, ops.math::mul)
override fun StructureND<T>.times(arg: StructureND<T>): TensorFlowOutput<T, TT> = biOp(arg, ops.math::mul)
override fun Tensor<T>.timesAssign(value: T): Unit = inPlaceOp(value, ops.math::mul)
override fun Tensor<T>.timesAssign(arg: StructureND<T>): Unit = inPlaceOp(arg, ops.math::mul)
override fun StructureND<T>.unaryMinus(): TensorFlowOutput<T, TT> = unOp(this, ops.math::neg)
override fun StructureND<T>.unaryMinus(): TensorFlowOutput<T, TT> = unOp(ops.math::neg)
override fun StructureND<T>.get(i: Int): Tensor<T> {
override fun Tensor<T>.get(i: Int): Tensor<T> = unOp {
TODO("Not yet implemented")
}
override fun StructureND<T>.transpose(i: Int, j: Int): Tensor<T> = unOp(this) {
override fun Tensor<T>.transpose(i: Int, j: Int): Tensor<T> = unOp {
ops.linalg.transpose(it, ops.constant(intArrayOf(i, j)))
}
override fun Tensor<T>.view(shape: IntArray): Tensor<T> {
TODO("Not yet implemented")
override fun Tensor<T>.view(shape: IntArray): Tensor<T> = unOp {
ops.reshape(it, ops.constant(shape))
}
override fun Tensor<T>.viewAs(other: StructureND<T>): Tensor<T> {
TODO("Not yet implemented")
override fun Tensor<T>.viewAs(other: StructureND<T>): Tensor<T> = biOp(other) { l, r ->
ops.reshape(l, ops.shape(r))
}
override fun StructureND<T>.dot(other: StructureND<T>): TensorFlowOutput<T, TT> = biOp(other) { l, r ->
ops.linalg.matMul(l, r)
}
override fun diagonalEmbedding(diagonalEntries: Tensor<T>, offset: Int, dim1: Int, dim2: Int): Tensor<T> = ops.run {
TODO("Not yet implemented")
override fun diagonalEmbedding(
diagonalEntries: Tensor<T>,
offset: Int,
dim1: Int,
dim2: Int,
): TensorFlowOutput<T, TT> = diagonalEntries.unOp {
TODO()
}
override fun StructureND<T>.sum(): T = TODO("Not yet implemented")
override fun StructureND<T>.sum(): T = unOp {
ops.sum(it, ops.constant(intArrayOf()))
}.value()
override fun StructureND<T>.sum(dim: Int, keepDim: Boolean): Tensor<T> {
TODO("Not yet implemented")
override fun StructureND<T>.sum(dim: Int, keepDim: Boolean): TensorFlowOutput<T, TT> = unOp {
ops.sum(it, ops.constant(dim), Sum.keepDims(keepDim))
}
override fun StructureND<T>.min(): T {
TODO("Not yet implemented")
override fun StructureND<T>.min(): T = unOp {
ops.min(it, ops.constant(intArrayOf()))
}.value()
override fun StructureND<T>.min(dim: Int, keepDim: Boolean): Tensor<T> = unOp {
ops.min(it, ops.constant(dim), Min.keepDims(keepDim))
}
override fun StructureND<T>.min(dim: Int, keepDim: Boolean): Tensor<T> {
TODO("Not yet implemented")
override fun StructureND<T>.max(): T = unOp {
ops.max(it, ops.constant(intArrayOf()))
}.value()
override fun StructureND<T>.max(dim: Int, keepDim: Boolean): Tensor<T> = unOp {
ops.max(it, ops.constant(dim), Max.keepDims(keepDim))
}
override fun StructureND<T>.max(): T {
TODO("Not yet implemented")
}
override fun StructureND<T>.argMax(dim: Int, keepDim: Boolean): Tensor<Int> = IntTensorFlowOutput(
graph,
ops.math.argMax(asTensorFlow().output, ops.constant(dim), TInt32::class.java).output()
).actualTensor
override fun StructureND<T>.max(dim: Int, keepDim: Boolean): Tensor<T> {
TODO("Not yet implemented")
}
override fun StructureND<T>.argMax(dim: Int, keepDim: Boolean): Tensor<T> {
TODO("Not yet implemented")
}
@OptIn(UnstableKMathAPI::class)
override fun export(arg: StructureND<T>): StructureND<T> =
if (arg is TensorFlowOutput<T, *>) arg.actualTensor else arg
}

19
kmath-tensorflow/src/test/kotlin/space/kscience/kmath/tensorflow/DoubleTensorFlowOps.kt Normal file
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@ -0,0 +1,19 @@
package space.kscience.kmath.tensorflow
import org.junit.jupiter.api.Test
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.structureND
import space.kscience.kmath.operations.DoubleField
class DoubleTensorFlowOps {
@Test
fun basicOps() {
val res = DoubleField.produceWithTF {
val initial = structureND(2, 2) { 1.0 }
initial + (initial * 2.0)
}
println(StructureND.toString(res))
}
}

2
kmath-viktor/src/main/kotlin/space/kscience/kmath/viktor/ViktorFieldOpsND.kt
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@ -3,6 +3,8 @@
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
@file:OptIn(PerformancePitfall::class)
package space.kscience.kmath.viktor
import org.jetbrains.bio.viktor.F64Array