First tensorflow test.

kmath-tensorflow/build.gradle.kts

@@ -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 {

kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/DoubleTensorFlowAlgebra.kt

@@ -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) }
 }

kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/IntTensorFlowAlgebra.kt

@@ -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
+}

kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/TensorFlowAlgebra.kt

@@ -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,
+    protected val graph: Graph,
-    output: Output<TT>
+    output: Output<TT>,
 ) : TensorFlowTensor<T> {
 
     public var output: Output<TT> = output
@@ -49,9 +54,10 @@ public abstract class TensorFlowOutput<T, TT : TType>(
 
     protected abstract fun org.tensorflow.Tensor.actualizeTensor(): NdArray<T>
 
-    private val actualTensor by lazy {
+    internal val actualTensor by lazy {
-        val session = Session(graph)
+        Session(graph).use { session ->
-        TensorFlowArray(session.runner().fetch(output).run().first().actualizeTensor())
+            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(
+public abstract class TensorFlowAlgebra<T, TT : TType, A : Ring<T>> internal constructor(
-    protected val graph: Graph
+    protected val graph: Graph,
-) : TensorAlgebra<T,A> {
+) : 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> =
+    private inline fun StructureND<T>.unOp(operation: (Operand<TT>) -> Operand<TT>): TensorFlowOutput<T, TT> =
-        operation(value.asTensorFlow().output).asOutput().wrap()
+        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> {
+    override fun Tensor<T>.view(shape: IntArray): Tensor<T> = unOp {
-        TODO("Not yet implemented")
+        ops.reshape(it, ops.constant(shape))
     }
 
-    override fun Tensor<T>.viewAs(other: StructureND<T>): Tensor<T> {
+    override fun Tensor<T>.viewAs(other: StructureND<T>): Tensor<T> = biOp(other) { l, r ->
-        TODO("Not yet implemented")
+        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 {
+    override fun diagonalEmbedding(
-        TODO("Not yet implemented")
+        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> {
+    override fun StructureND<T>.sum(dim: Int, keepDim: Boolean): TensorFlowOutput<T, TT> = unOp {
-        TODO("Not yet implemented")
+        ops.sum(it, ops.constant(dim), Sum.keepDims(keepDim))
     }
 
-    override fun StructureND<T>.min(): T {
+    override fun StructureND<T>.min(): T = unOp {
-        TODO("Not yet implemented")
+        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> {
+    override fun StructureND<T>.max(): T = unOp {
-        TODO("Not yet implemented")
+        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 {
+    override fun StructureND<T>.argMax(dim: Int, keepDim: Boolean): Tensor<Int> = IntTensorFlowOutput(
-        TODO("Not yet implemented")
+        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> {
+    @OptIn(UnstableKMathAPI::class)
-        TODO("Not yet implemented")
+    override fun export(arg: StructureND<T>): StructureND<T> =
-    }
+        if (arg is TensorFlowOutput<T, *>) arg.actualTensor else arg
-
-    override fun StructureND<T>.argMax(dim: Int, keepDim: Boolean): Tensor<T> {
-        TODO("Not yet implemented")
-    }
 }

kmath-tensorflow/src/test/kotlin/space/kscience/kmath/tensorflow/DoubleTensorFlowOps.kt

@@ -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))
+    }
+
+}

kmath-viktor/src/main/kotlin/space/kscience/kmath/viktor/ViktorFieldOpsND.kt

@@ -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