TensorAlgebra first syncing

kmath-torch/src/nativeMain/kotlin/kscience.kmath.torch/TensorAlgebra.kt

@@ -1,50 +0,0 @@
-package kscience.kmath.torch
-
-import kscience.kmath.operations.Field
-import kscience.kmath.operations.Ring
-
-
-public interface TensorAlgebra<T, TorchTensorType : TensorStructure<T>> : Ring<TorchTensorType> {
-
-    public operator fun T.plus(other: TorchTensorType): TorchTensorType
-    public operator fun TorchTensorType.plus(value: T): TorchTensorType
-    public operator fun TorchTensorType.plusAssign(value: T): Unit
-    public operator fun TorchTensorType.plusAssign(b: TorchTensorType): Unit
-
-    public operator fun T.minus(other: TorchTensorType): TorchTensorType
-    public operator fun TorchTensorType.minus(value: T): TorchTensorType
-    public operator fun TorchTensorType.minusAssign(value: T): Unit
-    public operator fun TorchTensorType.minusAssign(b: TorchTensorType): Unit
-
-    public operator fun T.times(other: TorchTensorType): TorchTensorType
-    public operator fun TorchTensorType.times(value: T): TorchTensorType
-    public operator fun TorchTensorType.timesAssign(value: T): Unit
-    public operator fun TorchTensorType.timesAssign(b: TorchTensorType): Unit
-
-    public infix fun TorchTensorType.dot(b: TorchTensorType): TorchTensorType
-
-    public fun diagonalEmbedding(
-        diagonalEntries: TorchTensorType,
-        offset: Int = 0, dim1: Int = -2, dim2: Int = -1
-    ): TorchTensorType
-
-    public fun TorchTensorType.transpose(i: Int, j: Int): TorchTensorType
-    public fun TorchTensorType.view(shape: IntArray): TorchTensorType
-
-    public fun TorchTensorType.abs(): TorchTensorType
-    public fun TorchTensorType.sum(): TorchTensorType
-
-}
-
-public interface TensorFieldAlgebra<T, TorchTensorType : TensorStructure<T>> :
-    TensorAlgebra<T, TorchTensorType>, Field<TorchTensorType> {
-
-    public operator fun TorchTensorType.divAssign(b: TorchTensorType)
-
-    public fun TorchTensorType.exp(): TorchTensorType
-    public fun TorchTensorType.log(): TorchTensorType
-
-    public fun TorchTensorType.svd(): Triple<TorchTensorType, TorchTensorType, TorchTensorType>
-    public fun TorchTensorType.symEig(eigenvectors: Boolean = true): Pair<TorchTensorType, TorchTensorType>
-
-}

kmath-torch/src/nativeMain/kotlin/kscience.kmath.torch/TensorStructure.kt

@@ -1,13 +0,0 @@
-package kscience.kmath.torch
-
-import kscience.kmath.structures.MutableNDStructure
-
-public abstract class TensorStructure<T>: MutableNDStructure<T> {
-
-    // A tensor can have empty shape, in which case it represents just a value
-    public abstract fun value(): T
-
-    // Tensors might hold shared resources
-    override fun equals(other: Any?): Boolean = false
-    override fun hashCode(): Int = 0
-}

kmath-torch/src/nativeMain/kotlin/kscience.kmath.torch/TorchTensor.kt

@@ -1,6 +1,8 @@
 package kscience.kmath.torch
 
 
+import kscience.kmath.structures.TensorStructure
+
 import kotlinx.cinterop.*
 import kscience.kmath.ctorch.*
 
@@ -12,6 +14,7 @@ public sealed class TorchTensor<T> constructor(
     init {
         scope.defer(::close)
     }
+
     private fun close(): Unit = dispose_tensor(tensorHandle)
 
     protected abstract fun item(): T
@@ -44,27 +47,27 @@ public sealed class TorchTensor<T> constructor(
     internal inline fun checkIsValue() = check(isValue()) {
         "This tensor has shape ${shape.toList()}"
     }
+
     override fun value(): T {
         checkIsValue()
         return item()
     }
 
-    public var requiresGrad: Boolean
-        get() = requires_grad(tensorHandle)
-        set(value) = requires_grad_(tensorHandle, value)
-
     public fun copyToDouble(): TorchTensorReal = TorchTensorReal(
         scope = scope,
         tensorHandle = copy_to_double(this.tensorHandle)!!
     )
+
     public fun copyToFloat(): TorchTensorFloat = TorchTensorFloat(
         scope = scope,
         tensorHandle = copy_to_float(this.tensorHandle)!!
     )
+
     public fun copyToLong(): TorchTensorLong = TorchTensorLong(
         scope = scope,
         tensorHandle = copy_to_long(this.tensorHandle)!!
     )
+
     public fun copyToInt(): TorchTensorInt = TorchTensorInt(
         scope = scope,
         tensorHandle = copy_to_int(this.tensorHandle)!!
@@ -72,10 +75,20 @@ public sealed class TorchTensor<T> constructor(
 
 }
 
+public sealed class TorchTensorOverField<T> constructor(
+    scope: DeferScope,
+    tensorHandle: COpaquePointer
+) : TorchTensor<T>(scope, tensorHandle) {
+    internal var requiresGrad: Boolean
+        get() = requires_grad(tensorHandle)
+        set(value) = requires_grad_(tensorHandle, value)
+}
+
+
 public class TorchTensorReal internal constructor(
     scope: DeferScope,
     tensorHandle: COpaquePointer
-) : TorchTensor<Double>(scope, tensorHandle) {
+) : TorchTensorOverField<Double>(scope, tensorHandle) {
     override fun item(): Double = get_item_double(tensorHandle)
     override fun get(index: IntArray): Double = get_double(tensorHandle, index.toCValues())
     override fun set(index: IntArray, value: Double) {
@@ -86,7 +99,7 @@ public class TorchTensorReal internal constructor(
 public class TorchTensorFloat internal constructor(
     scope: DeferScope,
     tensorHandle: COpaquePointer
-) : TorchTensor<Float>(scope, tensorHandle) {
+) : TorchTensorOverField<Float>(scope, tensorHandle) {
     override fun item(): Float = get_item_float(tensorHandle)
     override fun get(index: IntArray): Float = get_float(tensorHandle, index.toCValues())
     override fun set(index: IntArray, value: Float) {

kmath-torch/src/nativeMain/kotlin/kscience.kmath.torch/TorchTensorAlgebra.kt

@@ -1,5 +1,8 @@
 package kscience.kmath.torch
 
+
+import kscience.kmath.structures.*
+
 import kotlinx.cinterop.*
 import kscience.kmath.ctorch.*
 
@@ -40,12 +43,12 @@ public sealed class TorchTensorAlgebra<
     override val one: TorchTensorType
         get() = number(1)
 
-    protected inline fun checkDeviceCompatible(a: TorchTensorType, b: TorchTensorType) =
+    protected inline fun checkDeviceCompatible(a: TorchTensorType, b: TorchTensorType): Unit =
         check(a.device == b.device) {
             "Tensors must be on the same device"
         }
 
-    protected inline fun checkShapeCompatible(a: TorchTensorType, b: TorchTensorType) =
+    protected inline fun checkShapeCompatible(a: TorchTensorType, b: TorchTensorType): Unit =
         check(a.shape contentEquals b.shape) {
             "Tensors must be of identical shape"
         }
@@ -214,7 +217,7 @@ public sealed class TorchTensorAlgebra<
 }
 
 public sealed class TorchTensorFieldAlgebra<T, TVar : CPrimitiveVar,
-        PrimitiveArrayType, TorchTensorType : TorchTensor<T>>(scope: DeferScope) :
+        PrimitiveArrayType, TorchTensorType : TorchTensorOverField<T>>(scope: DeferScope) :
     TorchTensorAlgebra<T, TVar, PrimitiveArrayType, TorchTensorType>(scope),
     TensorFieldAlgebra<T, TorchTensorType> {
 
@@ -598,3 +601,13 @@ public inline fun <R> TorchTensorLongAlgebra(block: TorchTensorLongAlgebra.() ->
 
 public inline fun <R> TorchTensorIntAlgebra(block: TorchTensorIntAlgebra.() -> R): R =
     memScoped { TorchTensorIntAlgebra(this).block() }
+
+public fun TorchTensorReal.withGrad(block: TorchTensorRealAlgebra.() -> TorchTensorReal): TorchTensorReal {
+    this.requiresGrad = true
+    return TorchTensorRealAlgebra(this.scope).block()
+}
+
+public fun TorchTensorFloat.withGrad(block: TorchTensorFloatAlgebra.() -> TorchTensorFloat): TorchTensorFloat {
+    this.requiresGrad = true
+    return TorchTensorFloatAlgebra(this.scope).block()
+}

kmath-torch/src/nativeTest/kotlin/kscience/kmath/torch/TestAutograd.kt

@@ -5,14 +5,15 @@ import kotlin.test.*
 internal fun testingAutoGrad(dim: Int, device: Device = Device.CPU): Unit {
     TorchTensorRealAlgebra {
         setSeed(SEED)
+
         val tensorX = randNormal(shape = intArrayOf(dim), device = device)
-        tensorX.requiresGrad = true
         val randFeatures = randNormal(shape = intArrayOf(dim, dim), device = device)
         val tensorSigma = randFeatures + randFeatures.transpose(0,1)
         val tensorMu = randNormal(shape = intArrayOf(dim), device = device)
 
-        val expressionAtX =
+        val expressionAtX = tensorX.withGrad {
             0.5 * (tensorX dot (tensorSigma dot tensorX)) + (tensorMu dot tensorX) + 25.9
+        }
 
         val gradientAtX = expressionAtX.grad(tensorX, retainGraph = true)
         val hessianAtX = expressionAtX hess tensorX
@@ -31,14 +32,14 @@ internal fun testingBatchedAutoGrad(bath: IntArray,
         setSeed(SEED)
 
         val tensorX = randNormal(shape = bath+intArrayOf(1,dim), device = device)
-        tensorX.requiresGrad = true
-        val tensorXt = tensorX.transpose(-1,-2)
         val randFeatures = randNormal(shape = bath+intArrayOf(dim, dim), device = device)
         val tensorSigma = randFeatures + randFeatures.transpose(-2,-1)
         val tensorMu = randNormal(shape = bath+intArrayOf(1,dim), device = device)
 
-        val expressionAtX =
+        val expressionAtX = tensorX.withGrad{
+            val tensorXt = tensorX.transpose(-1,-2)
             0.5 * (tensorX dot (tensorSigma dot tensorXt)) + (tensorMu dot tensorXt) + 58.2
+        }
         expressionAtX.sumAssign()
 
         val gradientAtX = expressionAtX grad tensorX
@@ -52,7 +53,7 @@ internal fun testingBatchedAutoGrad(bath: IntArray,
 
 internal class TestAutograd {
     @Test
-    fun testAutoGrad() = testingAutoGrad(dim = 100)
+    fun testAutoGrad() = testingAutoGrad(dim = 3)
 
     @Test
     fun testBatchedAutoGrad() = testingBatchedAutoGrad(bath = intArrayOf(2,10), dim=30)