Autograd support drafting

kmath-torch/README.md

@@ -76,7 +76,7 @@ pluginManagement {
 
 ## Usage
 
-Tensors implement the buffer protocol over `MutableNDStructure`. They can only be instantiated through provided factory methods and require scoping:
+Tensors are implemented over the `MutableNDStructure`. They can only be instantiated through provided factory methods and require scoping:
 ```kotlin
 TorchTensorRealAlgebra {
 
@@ -94,4 +94,24 @@ TorchTensorRealAlgebra {
     println(gpuRealTensor)
 }
 ```
+Enjoy a high performance automatic differentiation engine:
+```kotlin
+TorchTensorRealAlgebra {
+    val dim = 10
+    val device = TorchDevice.TorchCPU //or TorchDevice.TorchCUDA(0)
+    val x = randNormal(shape = intArrayOf(dim), device = device)
+    // x is the variable
+    x.requiresGrad = true
+    
+    val X = randNormal(shape = intArrayOf(dim,dim), device = device)
+    val Q = X + X.transpose(0,1)
+    val mu = randNormal(shape = intArrayOf(dim), device = device)
+    val c = randNormal(shape = IntArray(0), device = device)
+    
+    // expression to differentiate w.r.t. x
+    val f = 0.5 * (x dot (Q dot x)) + (mu dot x) + c
+    // value of the gradient at x
+    val gradf = f grad x
+}
+```
 

kmath-torch/ctorch/include/ctorch.h

@@ -40,7 +40,6 @@ extern "C"
     void dispose_char(char *ptr);
     void dispose_tensor(TorchTensorHandle tensor_handle);
 
- 
     double get_double(TorchTensorHandle tensor_handle, int *index);
     float get_float(TorchTensorHandle tensor_handle, int *index);
     long get_long(TorchTensorHandle tensor_handle, int *index);
@@ -50,14 +49,42 @@ extern "C"
     void set_long(TorchTensorHandle tensor_handle, int *index, long value);
     void set_int(TorchTensorHandle tensor_handle, int *index, int value);
 
-
     TorchTensorHandle randn_double(int *shape, int shape_size, int device);
     TorchTensorHandle rand_double(int *shape, int shape_size, int device);
     TorchTensorHandle randn_float(int *shape, int shape_size, int device);
     TorchTensorHandle rand_float(int *shape, int shape_size, int device);
 
+    TorchTensorHandle full_double(double value, int *shape, int shape_size, int device);
+    TorchTensorHandle full_float(float value, int *shape, int shape_size, int device);
+    TorchTensorHandle full_long(long value, int *shape, int shape_size, int device);
+    TorchTensorHandle full_int(int value, int *shape, int shape_size, int device);
+
     TorchTensorHandle matmul(TorchTensorHandle lhs, TorchTensorHandle rhs);
     void matmul_assign(TorchTensorHandle lhs, TorchTensorHandle rhs);
+    void matmul_right_assign(TorchTensorHandle lhs, TorchTensorHandle rhs);
+
+    TorchTensorHandle times_double(double value, TorchTensorHandle other);
+    TorchTensorHandle times_float(float value, TorchTensorHandle other);
+    TorchTensorHandle times_long(long value, TorchTensorHandle other);
+    TorchTensorHandle times_int(int value, TorchTensorHandle other);
+
+    void times_assign_double(double value, TorchTensorHandle other);
+    void times_assign_float(float value, TorchTensorHandle other);
+    void times_assign_long(long value, TorchTensorHandle other);
+    void times_assign_int(int value, TorchTensorHandle other);
+
+    TorchTensorHandle plus_tensor(TorchTensorHandle lhs, TorchTensorHandle rhs);
+    void plus_tensor_assign(TorchTensorHandle lhs, TorchTensorHandle rhs);
+    TorchTensorHandle minus_tensor(TorchTensorHandle lhs, TorchTensorHandle rhs);
+    void minus_tensor_assign(TorchTensorHandle lhs, TorchTensorHandle rhs);
+    TorchTensorHandle abs_tensor(TorchTensorHandle tensor_handle);
+    TorchTensorHandle sum_tensor(TorchTensorHandle tensor_handle);
+    TorchTensorHandle transpose_tensor(TorchTensorHandle tensor_handle, int i, int j);
+
+    bool requires_grad(TorchTensorHandle tensor_handle);
+    void requires_grad_(TorchTensorHandle tensor_handle, bool status);
+    void detach_from_graph(TorchTensorHandle tensor_handle);
+    TorchTensorHandle autograd_tensor(TorchTensorHandle value, TorchTensorHandle variable);
 
 #ifdef __cplusplus
 }

kmath-torch/ctorch/include/utils.hh

@@ -92,4 +92,10 @@ namespace ctorch
         return torch::rand(shape, torch::TensorOptions().dtype(dtype<Dtype>()).layout(torch::kStrided).device(device));
     }
 
+    template <typename Dtype>
+    inline torch::Tensor full(Dtype value, std::vector<int64_t> shape, torch::Device device)
+    {
+        return torch::full(shape, value, torch::TensorOptions().dtype(dtype<Dtype>()).layout(torch::kStrided).device(device));
+    }
+
 } // namespace ctorch

kmath-torch/ctorch/src/ctorch.cc

@@ -156,12 +156,111 @@ TorchTensorHandle rand_float(int *shape, int shape_size, int device)
   return new torch::Tensor(ctorch::rand<float>(ctorch::to_vec_int(shape, shape_size), ctorch::int_to_device(device)));
 }
 
+TorchTensorHandle full_double(double value, int *shape, int shape_size, int device)
+{
+  return new torch::Tensor(ctorch::full<double>(value, ctorch::to_vec_int(shape, shape_size), ctorch::int_to_device(device)));
+}
+TorchTensorHandle full_float(float value, int *shape, int shape_size, int device)
+{
+  return new torch::Tensor(ctorch::full<float>(value, ctorch::to_vec_int(shape, shape_size), ctorch::int_to_device(device)));
+}
+TorchTensorHandle full_long(long value, int *shape, int shape_size, int device)
+{
+  return new torch::Tensor(ctorch::full<long>(value, ctorch::to_vec_int(shape, shape_size), ctorch::int_to_device(device)));
+}
+TorchTensorHandle full_int(int value, int *shape, int shape_size, int device)
+{
+  return new torch::Tensor(ctorch::full<int>(value, ctorch::to_vec_int(shape, shape_size), ctorch::int_to_device(device)));
+}
+
 TorchTensorHandle matmul(TorchTensorHandle lhs, TorchTensorHandle rhs)
 {
   return new torch::Tensor(torch::matmul(ctorch::cast(lhs), ctorch::cast(rhs)));
 }
-
 void matmul_assign(TorchTensorHandle lhs, TorchTensorHandle rhs)
 {
   ctorch::cast(lhs) = ctorch::cast(lhs).matmul(ctorch::cast(rhs));
 }
+void matmul_right_assign(TorchTensorHandle lhs, TorchTensorHandle rhs)
+{
+  ctorch::cast(rhs) = ctorch::cast(lhs).matmul(ctorch::cast(rhs));
+}
+
+TorchTensorHandle times_double(double value, TorchTensorHandle other)
+{
+  return new torch::Tensor(value * ctorch::cast(other));
+}
+TorchTensorHandle times_float(float value, TorchTensorHandle other)
+{
+  return new torch::Tensor(value * ctorch::cast(other));
+}
+TorchTensorHandle times_long(long value, TorchTensorHandle other)
+{
+  return new torch::Tensor(value * ctorch::cast(other));
+}
+TorchTensorHandle times_int(int value, TorchTensorHandle other)
+{
+  return new torch::Tensor(value * ctorch::cast(other));
+}
+void times_assign_double(double value, TorchTensorHandle other)
+{
+  ctorch::cast(other) *= value;
+}
+void times_assign_float(float value, TorchTensorHandle other)
+{
+  ctorch::cast(other) *= value;
+}
+void times_assign_long(long value, TorchTensorHandle other)
+{
+  ctorch::cast(other) *= value;
+}
+void times_assign_int(int value, TorchTensorHandle other)
+{
+  ctorch::cast(other) *= value;
+}
+
+TorchTensorHandle plus_tensor(TorchTensorHandle lhs, TorchTensorHandle rhs)
+{
+  return new torch::Tensor(ctorch::cast(lhs) + ctorch::cast(rhs));
+}
+void plus_tensor_assign(TorchTensorHandle lhs, TorchTensorHandle rhs)
+{
+  ctorch::cast(lhs) += ctorch::cast(rhs);
+}
+TorchTensorHandle minus_tensor(TorchTensorHandle lhs, TorchTensorHandle rhs)
+{
+  return new torch::Tensor(ctorch::cast(lhs) - ctorch::cast(rhs));
+}
+void minus_tensor_assign(TorchTensorHandle lhs, TorchTensorHandle rhs)
+{
+  ctorch::cast(lhs) -= ctorch::cast(rhs);
+}
+TorchTensorHandle abs_tensor(TorchTensorHandle tensor_handle)
+{
+  return new torch::Tensor(ctorch::cast(tensor_handle).abs());
+}
+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)
+{
+  return new torch::Tensor(ctorch::cast(tensor_handle).transpose(i,j));
+}
+
+bool requires_grad(TorchTensorHandle tensor_handle)
+{
+  return ctorch::cast(tensor_handle).requires_grad();
+}
+void requires_grad_(TorchTensorHandle tensor_handle, bool status)
+{
+  ctorch::cast(tensor_handle).requires_grad_(status);
+}
+void detach_from_graph(TorchTensorHandle tensor_handle)
+{
+  ctorch::cast(tensor_handle).detach();
+}
+TorchTensorHandle autograd_tensor(TorchTensorHandle value, TorchTensorHandle variable)
+{
+  return new torch::Tensor(torch::autograd::grad({ctorch::cast(value)}, {ctorch::cast(variable)})[0]);
+}

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

@@ -6,8 +6,17 @@ import kotlin.test.*
 class TestTorchTensorAlgebraGPU {
 
     @Test
-    fun testScalarProduct() = testingScalarProduct(device = TorchDevice.TorchCUDA(0))
+    fun testScalarProduct() =
+        testingScalarProduct(device = TorchDevice.TorchCUDA(0))
 
     @Test
-    fun testMatrixMultiplication() = testingMatrixMultiplication(device = TorchDevice.TorchCUDA(0))
+    fun testMatrixMultiplication() =
+        testingMatrixMultiplication(device = TorchDevice.TorchCUDA(0))
+
+    @Test
+    fun testLinearStructure() =
+        testingLinearStructure(device = TorchDevice.TorchCUDA(0))
+
+    @Test
+    fun testAutoGrad() = testingAutoGrad(dim = 3, device = TorchDevice.TorchCUDA(0))
 }

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

@@ -13,21 +13,4 @@ internal class TestUtilsGPU {
     @Test
     fun testSetSeed() = testingSetSeed(TorchDevice.TorchCUDA(0))
 
-    @Test
-    fun testReadmeFactory() = TorchTensorRealAlgebra {
-
-        val realTensor: TorchTensorReal = copyFromArray(
-            array = (1..10).map { it + 50.0 }.toList().toDoubleArray(),
-            shape = intArrayOf(2,5)
-        )
-        println(realTensor)
-
-        val gpuRealTensor: TorchTensorReal = copyFromArray(
-            array = (1..8).map { it * 2.5 }.toList().toDoubleArray(),
-            shape = intArrayOf(2, 2, 2),
-            device = TorchDevice.TorchCUDA(0)
-        )
-        println(gpuRealTensor)
-    }
-
 }

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

@@ -62,6 +62,13 @@ public sealed class TorchTensor<T> constructor(
             outTensorHandle = copy_to_device(tensorHandle, device.toInt())!!
         )
 
+    public var requiresGrad: Boolean
+        get() = requires_grad(tensorHandle)
+        set(value) = requires_grad_(tensorHandle, value)
+
+    public fun detachFromGraph(): Unit {
+        detach_from_graph(tensorHandle)
+    }
 }
 
 public class TorchTensorReal internal constructor(

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

@@ -14,6 +14,11 @@ public sealed class TorchTensorAlgebra<T, PrimitiveArrayType> constructor(
     ): TorchTensor<T>
 
     public abstract fun TorchTensor<T>.copyToArray(): PrimitiveArrayType
+    public abstract fun full(value: T, shape: IntArray, device: TorchDevice): TorchTensor<T>
+
+    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 infix fun TorchTensor<T>.dot(other: TorchTensor<T>): TorchTensor<T> =
         wrap(matmul(this.tensorHandle, other.tensorHandle)!!)
@@ -21,6 +26,34 @@ public sealed class TorchTensorAlgebra<T, PrimitiveArrayType> constructor(
     public infix fun TorchTensor<T>.dotAssign(other: TorchTensor<T>): Unit {
         matmul_assign(this.tensorHandle, other.tensorHandle)
     }
+
+    public infix fun TorchTensor<T>.dotRightAssign(other: TorchTensor<T>): Unit {
+        matmul_right_assign(this.tensorHandle, other.tensorHandle)
+    }
+
+    public operator fun TorchTensor<T>.plus(other: TorchTensor<T>): TorchTensor<T> =
+        wrap(plus_tensor(this.tensorHandle, other.tensorHandle)!!)
+
+    public operator fun TorchTensor<T>.plusAssign(other: TorchTensor<T>): Unit {
+        plus_tensor_assign(this.tensorHandle, other.tensorHandle)
+    }
+
+    public operator fun TorchTensor<T>.minus(other: TorchTensor<T>): TorchTensor<T> =
+        wrap(minus_tensor(this.tensorHandle, other.tensorHandle)!!)
+
+    public operator fun TorchTensor<T>.minusAssign(other: TorchTensor<T>): Unit {
+        minus_tensor_assign(this.tensorHandle, other.tensorHandle)
+    }
+
+    public fun TorchTensor<T>.abs(): TorchTensor<T> = wrap(abs_tensor(tensorHandle)!!)
+
+    public fun TorchTensor<T>.sum(): TorchTensor<T> = wrap(sum_tensor(tensorHandle)!!)
+
+    public fun TorchTensor<T>.transpose(i: Int, j: Int): TorchTensor<T> =
+        wrap(transpose_tensor(tensorHandle, i , j)!!)
+
+    public infix fun TorchTensor<T>.grad(variable: TorchTensor<T>): TorchTensor<T> =
+        wrap(autograd_tensor(this.tensorHandle, variable.tensorHandle)!!)
 }
 
 public sealed class TorchTensorFieldAlgebra<T, PrimitiveArrayType>(scope: DeferScope) :
@@ -60,6 +93,27 @@ public class TorchTensorRealAlgebra(scope: DeferScope) : TorchTensorFieldAlgebra
         scope = scope,
         tensorHandle = rand_double(shape.toCValues(), shape.size, device.toInt())!!
     )
+
+    override operator fun Double.times(other: TorchTensor<Double>): TorchTensorReal = TorchTensorReal(
+        scope = scope,
+        tensorHandle = times_double(this, other.tensorHandle)!!
+    )
+
+    override fun TorchTensor<Double>.times(value: Double): TorchTensorReal = TorchTensorReal(
+        scope = scope,
+        tensorHandle = times_double(value, this.tensorHandle)!!
+    )
+
+    override fun TorchTensor<Double>.timesAssign(value: Double): Unit {
+        times_assign_double(value, this.tensorHandle)
+    }
+
+
+    override fun full(value: Double, shape: IntArray, device: TorchDevice):  TorchTensorReal = TorchTensorReal(
+        scope = scope,
+        tensorHandle = full_double(value, shape.toCValues(), shape.size, device.toInt())!!
+    )
+
 }
 
 public fun <R> TorchTensorRealAlgebra(block: TorchTensorRealAlgebra.() -> R): R =

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

@@ -19,4 +19,14 @@ class TestTorchTensor {
 
     @Test
     fun testCopyFromArray() = testingCopyFromArray()
+
+    @Test
+    fun testRequiresGrad() =  TorchTensorRealAlgebra {
+        val tensor = randNormal(intArrayOf(3))
+        assertTrue(!tensor.requiresGrad)
+        tensor.requiresGrad = true
+        assertTrue(tensor.requiresGrad)
+        tensor.requiresGrad = false
+        assertTrue(!tensor.requiresGrad)
+    }
 }

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

@@ -4,12 +4,13 @@ import kscience.kmath.linear.RealMatrixContext
 import kscience.kmath.operations.invoke
 import kscience.kmath.structures.Matrix
 import kotlin.math.abs
+import kotlin.math.exp
 import kotlin.test.*
 
 internal fun testingScalarProduct(device: TorchDevice = TorchDevice.TorchCPU): Unit {
     TorchTensorRealAlgebra {
-        val lhs = randUniform(shape = intArrayOf(10), device = device)
+        val lhs = randUniform(shape = intArrayOf(3), device = device)
-        val rhs = randUniform(shape = intArrayOf(10), device = device)
+        val rhs = randUniform(shape = intArrayOf(3), device = device)
         val product = lhs dot rhs
         var expected = 0.0
         lhs.elements().forEach {
@@ -23,27 +24,76 @@ internal fun testingMatrixMultiplication(device: TorchDevice = TorchDevice.Torch
     TorchTensorRealAlgebra {
         setSeed(SEED)
 
-        val lhsTensor = randNormal(shape = intArrayOf(20, 20), device = device)
+        val lhsTensor = randNormal(shape = intArrayOf(3, 3), device = device)
-        val rhsTensor = randNormal(shape = intArrayOf(20, 20), device = device)
+        val rhsTensor = randNormal(shape = intArrayOf(3, 3), device = device)
         val product = lhsTensor dot rhsTensor
 
         val expected: Matrix<Double> = RealMatrixContext {
-            val lhs = produce(20, 20) { i, j -> lhsTensor[intArrayOf(i, j)] }
+            val lhs = produce(3, 3) { i, j -> lhsTensor[intArrayOf(i, j)] }
-            val rhs = produce(20, 20) { i, j -> rhsTensor[intArrayOf(i, j)] }
+            val rhs = produce(3, 3) { i, j -> rhsTensor[intArrayOf(i, j)] }
             lhs dot rhs
         }
 
-        lhsTensor dotAssign rhsTensor
+        val lhsTensorCopy = lhsTensor.copy()
+        val rhsTensorCopy = rhsTensor.copy()
+
+        lhsTensorCopy dotAssign rhsTensor
+        lhsTensor dotRightAssign rhsTensorCopy
 
         var error: Double = 0.0
         product.elements().forEach {
             error += abs(expected[it.first] - it.second) +
-                    abs(expected[it.first] - lhsTensor[it.first])
+                    abs(expected[it.first] - lhsTensorCopy[it.first]) +
+                    abs(expected[it.first] - rhsTensorCopy[it.first])
         }
         assertTrue(error < TOLERANCE)
     }
 }
 
+internal fun testingLinearStructure(device: TorchDevice = TorchDevice.TorchCPU): Unit {
+    TorchTensorRealAlgebra {
+        val shape = intArrayOf(3)
+        val tensorA = full(value = -4.5, shape = shape, device = device)
+        val tensorB = full(value = 10.9, shape = shape, device = device)
+        val tensorC = full(value = 789.3, shape = shape, device = device)
+        val result = 15.8 * tensorA - 1.5 * tensorB + 0.02 * tensorC
+        val expected = copyFromArray(
+            array = (1..3).map { 15.8 * (-4.5) - 1.5 * 10.9 + 0.02 * 789.3 }.toDoubleArray(),
+            shape = shape,
+            device = device
+        )
+
+        val assignResult = full(value = 0.0, shape = shape, device = device)
+        tensorA *= 15.8
+        tensorB *= 1.5
+        tensorC *= 0.02
+        assignResult += tensorA
+        assignResult -= tensorB
+        assignResult += tensorC
+
+        val error = (expected - result).abs().sum().value() +
+                (expected - assignResult).abs().sum().value()
+        assertTrue(error < TOLERANCE)
+    }
+}
+
+internal fun testingAutoGrad(dim: Int, device: TorchDevice = TorchDevice.TorchCPU): Unit {
+    TorchTensorRealAlgebra {
+        setSeed(SEED)
+        val x = randNormal(shape = intArrayOf(dim), device = device)
+        x.requiresGrad = true
+        val X = randNormal(shape = intArrayOf(dim,dim), device = device)
+        val Q = X + X.transpose(0,1)
+        val mu = randNormal(shape = intArrayOf(dim), device = device)
+        val c = randNormal(shape = IntArray(0), device = device)
+        val f = 0.5 * (x dot (Q dot x)) + (mu dot x) + c
+        val gradf = f grad x
+        val error = (gradf - ((Q dot x) + mu)).abs().sum().value()
+        assertTrue(error < TOLERANCE)
+    }
+}
+
+
 internal class TestTorchTensorAlgebra {
 
     @Test
@@ -52,4 +102,10 @@ internal class TestTorchTensorAlgebra {
     @Test
     fun testMatrixMultiplication() = testingMatrixMultiplication()
 
+    @Test
+    fun testLinearStructure() = testingLinearStructure()
+
+    @Test
+    fun testAutoGrad() = testingAutoGrad(dim = 100)
+
 }