Tensor transformations first examples

kmath-torch/ctorch/include/ctorch.h

@@ -73,20 +73,20 @@ extern "C"
     TorchTensorHandle times_long(long value, TorchTensorHandle other);
     TorchTensorHandle times_int(int value, TorchTensorHandle other);
 
-    void times_assign_double(double value, TorchTensorHandle other);
+    void times_double_assign(double value, TorchTensorHandle other);
-    void times_assign_float(float value, TorchTensorHandle other);
+    void times_float_assign(float value, TorchTensorHandle other);
-    void times_assign_long(long value, TorchTensorHandle other);
+    void times_long_assign(long value, TorchTensorHandle other);
-    void times_assign_int(int value, TorchTensorHandle other);
+    void times_int_assign(int value, TorchTensorHandle other);
 
     TorchTensorHandle plus_double(double value, TorchTensorHandle other);
     TorchTensorHandle plus_float(float value, TorchTensorHandle other);
     TorchTensorHandle plus_long(long value, TorchTensorHandle other);
     TorchTensorHandle plus_int(int value, TorchTensorHandle other);
 
-    void plus_assign_double(double value, TorchTensorHandle other);
+    void plus_double_assign(double value, TorchTensorHandle other);
-    void plus_assign_float(float value, TorchTensorHandle other);
+    void plus_float_assign(float value, TorchTensorHandle other);
-    void plus_assign_long(long value, TorchTensorHandle other);
+    void plus_long_assign(long value, TorchTensorHandle other);
-    void plus_assign_int(int value, TorchTensorHandle other);
+    void plus_int_assign(int value, TorchTensorHandle other);
 
     TorchTensorHandle plus_tensor(TorchTensorHandle lhs, TorchTensorHandle rhs);
     void plus_tensor_assign(TorchTensorHandle lhs, TorchTensorHandle rhs);
@@ -96,10 +96,22 @@ extern "C"
     void times_tensor_assign(TorchTensorHandle lhs, TorchTensorHandle rhs);
     TorchTensorHandle div_tensor(TorchTensorHandle lhs, TorchTensorHandle rhs);
     void div_tensor_assign(TorchTensorHandle lhs, TorchTensorHandle rhs);
-    TorchTensorHandle unary_minus(TorchTensorHandle tensor);
+    TorchTensorHandle unary_minus(TorchTensorHandle tensor_handle);
+
     TorchTensorHandle abs_tensor(TorchTensorHandle tensor_handle);
-    TorchTensorHandle sum_tensor(TorchTensorHandle tensor_handle);
+    void abs_tensor_assign(TorchTensorHandle tensor_handle);
+
     TorchTensorHandle transpose_tensor(TorchTensorHandle tensor_handle, int i, int j);
+    void transpose_tensor_assign(TorchTensorHandle tensor_handle, int i, int j);
+
+    TorchTensorHandle exp_tensor(TorchTensorHandle tensor_handle);
+    void exp_tensor_assign(TorchTensorHandle tensor_handle);
+
+    TorchTensorHandle log_tensor(TorchTensorHandle tensor_handle);
+    void log_tensor_assign(TorchTensorHandle tensor_handle);
+
+    TorchTensorHandle sum_tensor(TorchTensorHandle tensor_handle);
+    void sum_tensor_assign(TorchTensorHandle tensor_handle);
 
     bool requires_grad(TorchTensorHandle tensor_handle);
     void requires_grad_(TorchTensorHandle tensor_handle, bool status);

kmath-torch/ctorch/src/ctorch.cc

@@ -219,19 +219,19 @@ TorchTensorHandle plus_int(int value, TorchTensorHandle other)
 {
   return new torch::Tensor(ctorch::cast(other) + value);
 }
-void plus_assign_double(double value, TorchTensorHandle other)
+void plus_double_assign(double value, TorchTensorHandle other)
 {
   ctorch::cast(other) += value;
 }
-void plus_assign_float(float value, TorchTensorHandle other)
+void plus_float_assign(float value, TorchTensorHandle other)
 {
   ctorch::cast(other) += value;
 }
-void plus_assign_long(long value, TorchTensorHandle other)
+void plus_long_assign(long value, TorchTensorHandle other)
 {
   ctorch::cast(other) += value;
 }
-void plus_assign_int(int value, TorchTensorHandle other)
+void plus_int_assign(int value, TorchTensorHandle other)
 {
   ctorch::cast(other) += value;
 }
@@ -252,19 +252,19 @@ TorchTensorHandle times_int(int value, TorchTensorHandle other)
 {
   return new torch::Tensor(value * ctorch::cast(other));
 }
-void times_assign_double(double value, TorchTensorHandle other)
+void times_double_assign(double value, TorchTensorHandle other)
 {
   ctorch::cast(other) *= value;
 }
-void times_assign_float(float value, TorchTensorHandle other)
+void times_float_assign(float value, TorchTensorHandle other)
 {
   ctorch::cast(other) *= value;
 }
-void times_assign_long(long value, TorchTensorHandle other)
+void times_long_assign(long value, TorchTensorHandle other)
 {
   ctorch::cast(other) *= value;
 }
-void times_assign_int(int value, TorchTensorHandle other)
+void times_int_assign(int value, TorchTensorHandle other)
 {
   ctorch::cast(other) *= value;
 }
@@ -301,21 +301,54 @@ void div_tensor_assign(TorchTensorHandle lhs, TorchTensorHandle rhs)
 {
   ctorch::cast(lhs) /= ctorch::cast(rhs);
 }
-TorchTensorHandle unary_minus(TorchTensorHandle tensor)
+TorchTensorHandle unary_minus(TorchTensorHandle tensor_handle)
 {
-  return new torch::Tensor(-ctorch::cast(tensor));
+  return new torch::Tensor(-ctorch::cast(tensor_handle));
 }
+
 TorchTensorHandle abs_tensor(TorchTensorHandle tensor_handle)
 {
   return new torch::Tensor(ctorch::cast(tensor_handle).abs());
 }
+void abs_tensor_assign(TorchTensorHandle tensor_handle)
+{
+  ctorch::cast(tensor_handle) = ctorch::cast(tensor_handle).abs();
+}
+
+TorchTensorHandle transpose_tensor(TorchTensorHandle tensor_handle, int i, int j)
+{
+  return new torch::Tensor(ctorch::cast(tensor_handle).transpose(i, j));
+}
+void transpose_tensor_assign(TorchTensorHandle tensor_handle, int i, int j)
+{
+  ctorch::cast(tensor_handle) = ctorch::cast(tensor_handle).transpose(i, j);
+}
+
+TorchTensorHandle exp_tensor(TorchTensorHandle tensor_handle)
+{
+  return new torch::Tensor(ctorch::cast(tensor_handle).exp());
+}
+void exp_tensor_assign(TorchTensorHandle tensor_handle)
+{
+  ctorch::cast(tensor_handle) = ctorch::cast(tensor_handle).exp();
+}
+
+TorchTensorHandle log_tensor(TorchTensorHandle tensor_handle)
+{
+  return new torch::Tensor(ctorch::cast(tensor_handle).log());
+}
+void log_tensor_assign(TorchTensorHandle tensor_handle)
+{
+  ctorch::cast(tensor_handle) = ctorch::cast(tensor_handle).log();
+}
+
 TorchTensorHandle sum_tensor(TorchTensorHandle tensor_handle)
 {
   return new torch::Tensor(ctorch::cast(tensor_handle).sum());
 }
-TorchTensorHandle transpose_tensor(TorchTensorHandle tensor_handle, int i, int j)
+void sum_tensor_assign(TorchTensorHandle tensor_handle)
 {
-  return new torch::Tensor(ctorch::cast(tensor_handle).transpose(i, j));
+  ctorch::cast(tensor_handle) = ctorch::cast(tensor_handle).sum();
 }
 
 bool requires_grad(TorchTensorHandle tensor_handle)

kmath-torch/src/nativeGPUTest/kotlin/kscience/kmath/torch/TestAutogradGPU.kt

@@ -0,0 +1,9 @@
+package kscience.kmath.torch
+
+import kotlin.test.*
+
+
+internal class TestAutogradGPU {
+    @Test
+    fun testAutoGrad() = testingAutoGrad(dim = 3, device = TorchDevice.TorchCUDA(0))
+}

kmath-torch/src/nativeGPUTest/kotlin/kscience/kmath/torch/TestTorchTensorAlgebraGPU.kt

@@ -18,5 +18,7 @@ class TestTorchTensorAlgebraGPU {
         testingLinearStructure(device = TorchDevice.TorchCUDA(0))
 
     @Test
-    fun testAutoGrad() = testingAutoGrad(dim = 3, device = TorchDevice.TorchCUDA(0))
+    fun testTensorTransformations() =
+        testingTensorTransformations(device = TorchDevice.TorchCUDA(0))
+
 }

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

@@ -16,7 +16,6 @@ public sealed class TorchTensor<T> constructor(
     private fun close(): Unit = dispose_tensor(tensorHandle)
 
     protected abstract fun item(): T
-    internal abstract fun wrap(outScope: DeferScope, outTensorHandle: COpaquePointer): TorchTensor<T>
 
     override val dimension: Int get() = get_dim(tensorHandle)
     override val shape: IntArray
@@ -50,18 +49,6 @@ public sealed class TorchTensor<T> constructor(
         return item()
     }
 
-    public fun copy(): TorchTensor<T> =
-        wrap(
-            outScope = scope,
-            outTensorHandle = copy_tensor(tensorHandle)!!
-        )
-
-    public fun copyToDevice(device: TorchDevice): TorchTensor<T> =
-        wrap(
-            outScope = scope,
-            outTensorHandle = copy_to_device(tensorHandle, device.toInt())!!
-        )
-
     public var requiresGrad: Boolean
         get() = requires_grad(tensorHandle)
         set(value) = requires_grad_(tensorHandle, value)
@@ -76,9 +63,6 @@ public class TorchTensorReal internal constructor(
     tensorHandle: COpaquePointer
 ) : TorchTensor<Double>(scope, tensorHandle) {
     override fun item(): Double = get_item_double(tensorHandle)
-    override fun wrap(outScope: DeferScope, outTensorHandle: COpaquePointer
-    ): TorchTensorReal = TorchTensorReal(scope = outScope, tensorHandle = outTensorHandle)
-
     override fun get(index: IntArray): Double = get_double(tensorHandle, index.toCValues())
     override fun set(index: IntArray, value: Double) {
         set_double(tensorHandle, index.toCValues(), value)

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

@@ -3,93 +3,129 @@ package kscience.kmath.torch
 import kotlinx.cinterop.*
 import kscience.kmath.ctorch.*
 
-public sealed class TorchTensorAlgebra<T, TVar: CPrimitiveVar, PrimitiveArrayType> constructor(
+public sealed class TorchTensorAlgebra<
+        T,
+        TVar : CPrimitiveVar,
+        PrimitiveArrayType,
+        TorchTensorType : TorchTensor<T>> constructor(
     internal val scope: DeferScope
 ) {
-    internal abstract fun wrap(tensorHandle: COpaquePointer): TorchTensor<T>
+    internal abstract fun wrap(tensorHandle: COpaquePointer): TorchTensorType
 
     public abstract fun copyFromArray(
         array: PrimitiveArrayType,
         shape: IntArray,
         device: TorchDevice = TorchDevice.TorchCPU
-    ): TorchTensor<T>
+    ): TorchTensorType
-    public abstract fun TorchTensor<T>.copyToArray(): PrimitiveArrayType
 
-    public abstract fun fromBlob(arrayBlob: CPointer<TVar>, shape: IntArray): TorchTensor<T>
+    public abstract fun TorchTensorType.copyToArray(): PrimitiveArrayType
-    public abstract fun TorchTensor<T>.getData(): CPointer<TVar>
 
-    public abstract fun full(value: T, shape: IntArray, device: TorchDevice): TorchTensor<T>
+    public abstract fun fromBlob(arrayBlob: CPointer<TVar>, shape: IntArray): TorchTensorType
+    public abstract fun TorchTensorType.getData(): CPointer<TVar>
 
-    public abstract operator fun T.plus(other: TorchTensor<T>): TorchTensor<T>
+    public abstract fun full(value: T, shape: IntArray, device: TorchDevice): TorchTensorType
-    public abstract operator fun TorchTensor<T>.plus(value: T): TorchTensor<T>
-    public abstract operator fun TorchTensor<T>.plusAssign(value: T): Unit
-    public abstract operator fun T.minus(other: TorchTensor<T>): TorchTensor<T>
-    public abstract operator fun TorchTensor<T>.minus(value: T): TorchTensor<T>
-    public abstract operator fun TorchTensor<T>.minusAssign(value: T): Unit
-    public abstract operator fun T.times(other: TorchTensor<T>): TorchTensor<T>
-    public abstract operator fun TorchTensor<T>.times(value: T): TorchTensor<T>
-    public abstract operator fun TorchTensor<T>.timesAssign(value: T): Unit
 
-    public operator fun TorchTensor<T>.times(other: TorchTensor<T>): TorchTensor<T> =
+    public abstract operator fun T.plus(other: TorchTensorType): TorchTensorType
+    public abstract operator fun TorchTensorType.plus(value: T): TorchTensorType
+    public abstract operator fun TorchTensorType.plusAssign(value: T): Unit
+    public abstract operator fun T.minus(other: TorchTensorType): TorchTensorType
+    public abstract operator fun TorchTensorType.minus(value: T): TorchTensorType
+    public abstract operator fun TorchTensorType.minusAssign(value: T): Unit
+    public abstract operator fun T.times(other: TorchTensorType): TorchTensorType
+    public abstract operator fun TorchTensorType.times(value: T): TorchTensorType
+    public abstract operator fun TorchTensorType.timesAssign(value: T): Unit
+
+    public operator fun TorchTensorType.times(other: TorchTensorType): TorchTensorType =
         wrap(times_tensor(this.tensorHandle, other.tensorHandle)!!)
-    public operator fun TorchTensor<T>.timesAssign(other: TorchTensor<T>): Unit {
+
+    public operator fun TorchTensorType.timesAssign(other: TorchTensorType): Unit {
         times_tensor_assign(this.tensorHandle, other.tensorHandle)
     }
-    public operator fun TorchTensor<T>.div(other: TorchTensor<T>): TorchTensor<T> =
+
+    public operator fun TorchTensorType.div(other: TorchTensorType): TorchTensorType =
         wrap(div_tensor(this.tensorHandle, other.tensorHandle)!!)
-    public operator fun TorchTensor<T>.divAssign(other: TorchTensor<T>): Unit {
+
+    public operator fun TorchTensorType.divAssign(other: TorchTensorType): Unit {
         div_tensor_assign(this.tensorHandle, other.tensorHandle)
     }
 
-    public infix fun TorchTensor<T>.dot(other: TorchTensor<T>): TorchTensor<T> =
+    public infix fun TorchTensorType.dot(other: TorchTensorType): TorchTensorType =
         wrap(matmul(this.tensorHandle, other.tensorHandle)!!)
 
-    public infix fun TorchTensor<T>.dotAssign(other: TorchTensor<T>): Unit {
+    public infix fun TorchTensorType.dotAssign(other: TorchTensorType): Unit {
         matmul_assign(this.tensorHandle, other.tensorHandle)
     }
 
-    public infix fun TorchTensor<T>.dotRightAssign(other: TorchTensor<T>): Unit {
+    public infix fun TorchTensorType.dotRightAssign(other: TorchTensorType): Unit {
         matmul_right_assign(this.tensorHandle, other.tensorHandle)
     }
 
-    public operator fun TorchTensor<T>.plus(other: TorchTensor<T>): TorchTensor<T> =
+    public operator fun TorchTensorType.plus(other: TorchTensorType): TorchTensorType =
         wrap(plus_tensor(this.tensorHandle, other.tensorHandle)!!)
 
-    public operator fun TorchTensor<T>.plusAssign(other: TorchTensor<T>): Unit {
+    public operator fun TorchTensorType.plusAssign(other: TorchTensorType): Unit {
         plus_tensor_assign(this.tensorHandle, other.tensorHandle)
     }
 
-    public operator fun TorchTensor<T>.minus(other: TorchTensor<T>): TorchTensor<T> =
+    public operator fun TorchTensorType.minus(other: TorchTensorType): TorchTensorType =
         wrap(minus_tensor(this.tensorHandle, other.tensorHandle)!!)
 
-    public operator fun TorchTensor<T>.minusAssign(other: TorchTensor<T>): Unit {
+    public operator fun TorchTensorType.minusAssign(other: TorchTensorType): Unit {
         minus_tensor_assign(this.tensorHandle, other.tensorHandle)
     }
-    public operator fun TorchTensor<T>.unaryMinus(): TorchTensor<T> =
+
+    public operator fun TorchTensorType.unaryMinus(): TorchTensorType =
         wrap(unary_minus(this.tensorHandle)!!)
 
-    public fun TorchTensor<T>.abs(): TorchTensor<T> = wrap(abs_tensor(tensorHandle)!!)
+    public fun TorchTensorType.abs(): TorchTensorType = wrap(abs_tensor(tensorHandle)!!)
+    public fun TorchTensorType.absAssign(): Unit {
+        abs_tensor_assign(tensorHandle)
+    }
 
-    public fun TorchTensor<T>.sum(): TorchTensor<T> = wrap(sum_tensor(tensorHandle)!!)
+    public fun TorchTensorType.transpose(i: Int, j: Int): TorchTensorType =
+        wrap(transpose_tensor(tensorHandle, i, j)!!)
+    public fun TorchTensorType.transposeAssign(i: Int, j: Int): Unit {
+        transpose_tensor_assign(tensorHandle, i, j)
+    }
 
-    public fun TorchTensor<T>.transpose(i: Int, j: Int): TorchTensor<T> =
+    public fun TorchTensorType.sum(): TorchTensorType = wrap(sum_tensor(tensorHandle)!!)
-        wrap(transpose_tensor(tensorHandle, i , j)!!)
+    public fun TorchTensorType.sumAssign(): Unit {
+        sum_tensor_assign(tensorHandle)
+    }
 
-    public infix fun TorchTensor<T>.grad(variable: TorchTensor<T>): TorchTensor<T> =
+    public infix fun TorchTensorType.grad(variable: TorchTensorType): TorchTensorType =
         wrap(autograd_tensor(this.tensorHandle, variable.tensorHandle)!!)
+
+    public fun TorchTensorType.copy(): TorchTensorType =
+        wrap(tensorHandle = copy_tensor(this.tensorHandle)!!)
+    public fun TorchTensorType.copyToDevice(device: TorchDevice): TorchTensorType =
+        wrap(tensorHandle = copy_to_device(this.tensorHandle, device.toInt())!!)
 }
 
-public sealed class TorchTensorFieldAlgebra<T, TVar: CPrimitiveVar, PrimitiveArrayType>(scope: DeferScope) :
+public sealed class TorchTensorFieldAlgebra<T, TVar : CPrimitiveVar,
-    TorchTensorAlgebra<T, TVar, PrimitiveArrayType>(scope) {
+        PrimitiveArrayType, TorchTensorType : TorchTensor<T>>(scope: DeferScope) :
-    public abstract fun randNormal(shape: IntArray, device: TorchDevice = TorchDevice.TorchCPU): TorchTensor<T>
+    TorchTensorAlgebra<T, TVar, PrimitiveArrayType, TorchTensorType>(scope) {
-    public abstract fun randUniform(shape: IntArray, device: TorchDevice = TorchDevice.TorchCPU): TorchTensor<T>
+    public abstract fun randNormal(shape: IntArray, device: TorchDevice = TorchDevice.TorchCPU): TorchTensorType
+    public abstract fun randUniform(shape: IntArray, device: TorchDevice = TorchDevice.TorchCPU): TorchTensorType
+
+    public fun TorchTensorType.exp(): TorchTensorType = wrap(exp_tensor(tensorHandle)!!)
+    public fun TorchTensorType.expAssign(): Unit {
+        exp_tensor_assign(tensorHandle)
+    }
+
+    public fun TorchTensorType.log(): TorchTensorType = wrap(log_tensor(tensorHandle)!!)
+    public fun TorchTensorType.logAssign(): Unit {
+        log_tensor_assign(tensorHandle)
+    }
+
 }
 
-public class TorchTensorRealAlgebra(scope: DeferScope) : TorchTensorFieldAlgebra<Double, DoubleVar, DoubleArray>(scope) {
+public class TorchTensorRealAlgebra(scope: DeferScope) :
+    TorchTensorFieldAlgebra<Double, DoubleVar, DoubleArray, TorchTensorReal>(scope) {
     override fun wrap(tensorHandle: COpaquePointer): TorchTensorReal =
         TorchTensorReal(scope = scope, tensorHandle = tensorHandle)
 
-    override fun TorchTensor<Double>.copyToArray(): DoubleArray =
+    override fun TorchTensorReal.copyToArray(): DoubleArray =
         this.elements().map { it.second }.toList().toDoubleArray()
 
     override fun copyFromArray(
@@ -120,8 +156,8 @@ public class TorchTensorRealAlgebra(scope: DeferScope) : TorchTensorFieldAlgebra
             )!!
         )
 
-    override fun TorchTensor<Double>.getData(): CPointer<DoubleVar> {
+    override fun TorchTensorReal.getData(): CPointer<DoubleVar> {
-        require(this.device is TorchDevice.TorchCPU){
+        require(this.device is TorchDevice.TorchCPU) {
             "This tensor is not on available on CPU"
         }
         return get_data_double(this.tensorHandle)!!
@@ -137,50 +173,50 @@ public class TorchTensorRealAlgebra(scope: DeferScope) : TorchTensorFieldAlgebra
         tensorHandle = rand_double(shape.toCValues(), shape.size, device.toInt())!!
     )
 
-    override operator fun Double.plus(other: TorchTensor<Double>): TorchTensorReal = TorchTensorReal(
+    override operator fun Double.plus(other: TorchTensorReal): TorchTensorReal = TorchTensorReal(
         scope = scope,
         tensorHandle = plus_double(this, other.tensorHandle)!!
     )
 
-    override fun TorchTensor<Double>.plus(value: Double): TorchTensorReal = TorchTensorReal(
+    override fun TorchTensorReal.plus(value: Double): TorchTensorReal = TorchTensorReal(
         scope = scope,
         tensorHandle = plus_double(value, this.tensorHandle)!!
     )
 
-    override fun TorchTensor<Double>.plusAssign(value: Double): Unit {
+    override fun TorchTensorReal.plusAssign(value: Double): Unit {
-        plus_assign_double(value, this.tensorHandle)
+        plus_double_assign(value, this.tensorHandle)
     }
 
-    override operator fun Double.minus(other: TorchTensor<Double>): TorchTensorReal = TorchTensorReal(
+    override operator fun Double.minus(other: TorchTensorReal): TorchTensorReal = TorchTensorReal(
         scope = scope,
         tensorHandle = plus_double(-this, other.tensorHandle)!!
     )
 
-    override fun TorchTensor<Double>.minus(value: Double): TorchTensorReal = TorchTensorReal(
+    override fun TorchTensorReal.minus(value: Double): TorchTensorReal = TorchTensorReal(
         scope = scope,
         tensorHandle = plus_double(-value, this.tensorHandle)!!
     )
 
-    override fun TorchTensor<Double>.minusAssign(value: Double): Unit {
+    override fun TorchTensorReal.minusAssign(value: Double): Unit {
-        plus_assign_double(-value, this.tensorHandle)
+        plus_double_assign(-value, this.tensorHandle)
     }
 
-    override operator fun Double.times(other: TorchTensor<Double>): TorchTensorReal = TorchTensorReal(
+    override operator fun Double.times(other: TorchTensorReal): TorchTensorReal = TorchTensorReal(
         scope = scope,
         tensorHandle = times_double(this, other.tensorHandle)!!
     )
 
-    override fun TorchTensor<Double>.times(value: Double): TorchTensorReal = TorchTensorReal(
+    override fun TorchTensorReal.times(value: Double): TorchTensorReal = TorchTensorReal(
         scope = scope,
         tensorHandle = times_double(value, this.tensorHandle)!!
     )
 
-    override fun TorchTensor<Double>.timesAssign(value: Double): Unit {
+    override fun TorchTensorReal.timesAssign(value: Double): Unit {
-        times_assign_double(value, this.tensorHandle)
+        times_double_assign(value, this.tensorHandle)
     }
 
 
-    override fun full(value: Double, shape: IntArray, device: TorchDevice):  TorchTensorReal = TorchTensorReal(
+    override fun full(value: Double, shape: IntArray, device: TorchDevice): TorchTensorReal = TorchTensorReal(
         scope = scope,
         tensorHandle = full_double(value, shape.toCValues(), shape.size, device.toInt())!!
     )

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

@@ -0,0 +1,28 @@
+package kscience.kmath.torch
+
+import kotlin.test.*
+
+internal fun testingAutoGrad(dim: Int, device: TorchDevice = TorchDevice.TorchCPU): 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 =
+            0.5 * (tensorX dot (tensorSigma dot tensorX)) + (tensorMu dot tensorX) + 25.9
+
+        val gradientAtX = expressionAtX grad tensorX
+        val expectedGradientAtX = (tensorSigma dot tensorX) + tensorMu
+
+        val error = (gradientAtX - expectedGradientAtX).abs().sum().value()
+        assertTrue(error < TOLERANCE)
+    }
+}
+
+internal class TestAutograd {
+    @Test
+    fun testAutoGrad() = testingAutoGrad(dim = 100)
+}

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

@@ -3,8 +3,7 @@ package kscience.kmath.torch
 import kscience.kmath.linear.RealMatrixContext
 import kscience.kmath.operations.invoke
 import kscience.kmath.structures.Matrix
-import kotlin.math.abs
+import kotlin.math.*
-import kotlin.math.exp
 import kotlin.test.*
 
 internal fun testingScalarProduct(device: TorchDevice = TorchDevice.TorchCPU): Unit {
@@ -40,7 +39,7 @@ internal fun testingMatrixMultiplication(device: TorchDevice = TorchDevice.Torch
         lhsTensorCopy dotAssign rhsTensor
         lhsTensor dotRightAssign rhsTensorCopy
 
-        var error: Double = 0.0
+        var error = 0.0
         product.elements().forEach {
             error += abs(expected[it.first] - it.second) +
                     abs(expected[it.first] - lhsTensorCopy[it.first]) +
@@ -85,27 +84,24 @@ internal fun testingLinearStructure(device: TorchDevice = TorchDevice.TorchCPU):
     }
 }
 
-internal fun testingAutoGrad(dim: Int, device: TorchDevice = TorchDevice.TorchCPU): Unit {
+internal fun testingTensorTransformations(device: TorchDevice = TorchDevice.TorchCPU): Unit {
     TorchTensorRealAlgebra {
         setSeed(SEED)
-        val tensorX = randNormal(shape = intArrayOf(dim), device = device)
+        val tensor = randNormal(shape = intArrayOf(3, 3), device = device)
-        tensorX.requiresGrad = true
+        val result = tensor.exp().log()
-        val randFeatures = randNormal(shape = intArrayOf(dim, dim), device = device)
+        val assignResult = tensor.copy()
-        val tensorSigma = randFeatures + randFeatures.transpose(0,1)
+        assignResult.transposeAssign(0,1)
-        val tensorMu = randNormal(shape = intArrayOf(dim), device = device)
+        assignResult.expAssign()
-
+        assignResult.logAssign()
-        val expressionAtX =
+        assignResult.transposeAssign(0,1)
-            0.5 * (tensorX dot (tensorSigma dot tensorX)) + (tensorMu dot tensorX) + 25.9
+        val error = tensor - result
-
+        error.absAssign()
-        val gradientAtX = expressionAtX grad tensorX
+        error.sumAssign()
-        val expectedGradientAtX = (tensorSigma dot tensorX) + tensorMu
+        error += (tensor - assignResult).abs().sum()
-
+        assertTrue (error.value()< TOLERANCE)
-        val error = (gradientAtX - expectedGradientAtX).abs().sum().value()
-        assertTrue(error < TOLERANCE)
     }
 }
 
-
 internal class TestTorchTensorAlgebra {
 
     @Test
@@ -118,6 +114,6 @@ internal class TestTorchTensorAlgebra {
     fun testLinearStructure() = testingLinearStructure()
 
     @Test
-    fun testAutoGrad() = testingAutoGrad(dim = 100)
+    fun testTensorTransformations() = testingTensorTransformations()
 
 }