Float, Long and Int support

kmath-torch/ctorch/include/ctorch.h

@@ -20,22 +20,21 @@ extern "C"
 
     TorchTensorHandle empty_tensor();
 
-    double *get_data_double(TorchTensorHandle tensor_handle);
-    float *get_data_float(TorchTensorHandle tensor_handle);
-    long *get_data_long(TorchTensorHandle tensor_handle);
-    int *get_data_int(TorchTensorHandle tensor_handle);
-
     TorchTensorHandle from_blob_double(double *data, int *shape, int dim, int device, bool copy);
     TorchTensorHandle from_blob_float(float *data, int *shape, int dim, int device, bool copy);
     TorchTensorHandle from_blob_long(long *data, int *shape, int dim, int device, bool copy);
     TorchTensorHandle from_blob_int(int *data, int *shape, int dim, int device, bool copy);
     TorchTensorHandle copy_tensor(TorchTensorHandle tensor_handle);
     TorchTensorHandle copy_to_device(TorchTensorHandle tensor_handle, int device);
+    TorchTensorHandle copy_to_double(TorchTensorHandle tensor_handle);
+    TorchTensorHandle copy_to_float(TorchTensorHandle tensor_handle);
+    TorchTensorHandle copy_to_long(TorchTensorHandle tensor_handle);
+    TorchTensorHandle copy_to_int(TorchTensorHandle tensor_handle);
+    void swap_tensors(TorchTensorHandle lhs_handle, TorchTensorHandle rhs_handle);
 
-    double get_item_double(TorchTensorHandle tensor_handle);
+    char *tensor_to_string(TorchTensorHandle tensor_handle);
-    float get_item_float(TorchTensorHandle tensor_handle);
+    void dispose_char(char *ptr);
-    long get_item_long(TorchTensorHandle tensor_handle);
+    void dispose_tensor(TorchTensorHandle tensor_handle);
-    int get_item_int(TorchTensorHandle tensor_handle);
 
     int get_dim(TorchTensorHandle tensor_handle);
     int get_numel(TorchTensorHandle tensor_handle);
@@ -43,9 +42,15 @@ extern "C"
     int get_stride_at(TorchTensorHandle tensor_handle, int d);
     int get_device(TorchTensorHandle tensor_handle);
 
-    char *tensor_to_string(TorchTensorHandle tensor_handle);
+    double *get_data_double(TorchTensorHandle tensor_handle);
-    void dispose_char(char *ptr);
+    float *get_data_float(TorchTensorHandle tensor_handle);
-    void dispose_tensor(TorchTensorHandle tensor_handle);
+    long *get_data_long(TorchTensorHandle tensor_handle);
+    int *get_data_int(TorchTensorHandle tensor_handle);
+
+    double get_item_double(TorchTensorHandle tensor_handle);
+    float get_item_float(TorchTensorHandle tensor_handle);
+    long get_item_long(TorchTensorHandle tensor_handle);
+    int get_item_int(TorchTensorHandle tensor_handle);
 
     double get_double(TorchTensorHandle tensor_handle, int *index);
     float get_float(TorchTensorHandle tensor_handle, int *index);
@@ -61,15 +66,14 @@ extern "C"
     TorchTensorHandle randn_float(int *shape, int shape_size, int device);
     TorchTensorHandle rand_float(int *shape, int shape_size, int device);
 
+    TorchTensorHandle randint_long(long low, long high, int *shape, int shape_size, int device);
+    TorchTensorHandle randint_int(int low, int high, 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);
@@ -115,10 +119,9 @@ extern "C"
     TorchTensorHandle sum_tensor(TorchTensorHandle tensor_handle);
     void sum_tensor_assign(TorchTensorHandle tensor_handle);
 
-    bool requires_grad(TorchTensorHandle tensor_handle);
+    TorchTensorHandle matmul(TorchTensorHandle lhs, TorchTensorHandle rhs);
-    void requires_grad_(TorchTensorHandle tensor_handle, bool status);
+    void matmul_assign(TorchTensorHandle lhs, TorchTensorHandle rhs);
-    TorchTensorHandle detach_from_graph(TorchTensorHandle tensor_handle);
+    void matmul_right_assign(TorchTensorHandle lhs, TorchTensorHandle rhs);
-    TorchTensorHandle autograd_tensor(TorchTensorHandle value, TorchTensorHandle variable);
 
     TorchTensorHandle diag_embed(TorchTensorHandle diags_handle, int offset, int dim1, int dim2);
 
@@ -132,6 +135,11 @@ extern "C"
                        TorchTensorHandle V_handle,
                        bool eigenvectors);
 
+    bool requires_grad(TorchTensorHandle tensor_handle);
+    void requires_grad_(TorchTensorHandle tensor_handle, bool status);
+    TorchTensorHandle detach_from_graph(TorchTensorHandle tensor_handle);
+    TorchTensorHandle autograd_tensor(TorchTensorHandle value, TorchTensorHandle variable);
+
 #ifdef __cplusplus
 }
 #endif

kmath-torch/ctorch/include/utils.hh

@@ -92,6 +92,12 @@ namespace ctorch
         return torch::rand(shape, torch::TensorOptions().dtype(dtype<Dtype>()).layout(torch::kStrided).device(device));
     }
 
+    template <typename Dtype>
+    inline torch::Tensor randint(Dtype low, Dtype high, std::vector<int64_t> shape, torch::Device device)
+    {
+        return torch::randint(low, high, 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)
     {

kmath-torch/ctorch/src/ctorch.cc

@@ -30,44 +30,6 @@ TorchTensorHandle empty_tensor()
   return new torch::Tensor;
 }
 
-double *get_data_double(TorchTensorHandle tensor_handle)
-{
-  return ctorch::cast(tensor_handle).data_ptr<double>();
-}
-float *get_data_float(TorchTensorHandle tensor_handle)
-{
-  return ctorch::cast(tensor_handle).data_ptr<float>();
-}
-long *get_data_long(TorchTensorHandle tensor_handle)
-{
-  return ctorch::cast(tensor_handle).data_ptr<long>();
-}
-int *get_data_int(TorchTensorHandle tensor_handle)
-{
-  return ctorch::cast(tensor_handle).data_ptr<int>();
-}
-
-int get_dim(TorchTensorHandle tensor_handle)
-{
-  return ctorch::cast(tensor_handle).dim();
-}
-int get_numel(TorchTensorHandle tensor_handle)
-{
-  return ctorch::cast(tensor_handle).numel();
-}
-int get_shape_at(TorchTensorHandle tensor_handle, int d)
-{
-  return ctorch::cast(tensor_handle).size(d);
-}
-int get_stride_at(TorchTensorHandle tensor_handle, int d)
-{
-  return ctorch::cast(tensor_handle).stride(d);
-}
-int get_device(TorchTensorHandle tensor_handle)
-{
-  return ctorch::device_to_int(ctorch::cast(tensor_handle));
-}
-
 TorchTensorHandle from_blob_double(double *data, int *shape, int dim, int device, bool copy)
 {
   return new torch::Tensor(ctorch::from_blob<double>(data, ctorch::to_vec_int(shape, dim), ctorch::int_to_device(device), copy));
@@ -92,6 +54,26 @@ TorchTensorHandle copy_to_device(TorchTensorHandle tensor_handle, int device)
 {
   return new torch::Tensor(ctorch::cast(tensor_handle).to(ctorch::int_to_device(device), false, true));
 }
+TorchTensorHandle copy_to_double(TorchTensorHandle tensor_handle)
+{
+  return new torch::Tensor(ctorch::cast(tensor_handle).to(ctorch::dtype<double>(), false, true));
+}
+TorchTensorHandle copy_to_float(TorchTensorHandle tensor_handle)
+{
+  return new torch::Tensor(ctorch::cast(tensor_handle).to(ctorch::dtype<float>(), false, true));
+}
+TorchTensorHandle copy_to_long(TorchTensorHandle tensor_handle)
+{
+  return new torch::Tensor(ctorch::cast(tensor_handle).to(ctorch::dtype<long>(), false, true));
+}
+TorchTensorHandle copy_to_int(TorchTensorHandle tensor_handle)
+{
+  return new torch::Tensor(ctorch::cast(tensor_handle).to(ctorch::dtype<int>(), false, true));
+}
+void swap_tensors(TorchTensorHandle lhs_handle, TorchTensorHandle rhs_handle)
+{
+  std::swap(ctorch::cast(lhs_handle), ctorch::cast(rhs_handle));
+}
 
 char *tensor_to_string(TorchTensorHandle tensor_handle)
 {
@@ -111,6 +93,61 @@ void dispose_tensor(TorchTensorHandle tensor_handle)
   delete static_cast<torch::Tensor *>(tensor_handle);
 }
 
+int get_dim(TorchTensorHandle tensor_handle)
+{
+  return ctorch::cast(tensor_handle).dim();
+}
+int get_numel(TorchTensorHandle tensor_handle)
+{
+  return ctorch::cast(tensor_handle).numel();
+}
+int get_shape_at(TorchTensorHandle tensor_handle, int d)
+{
+  return ctorch::cast(tensor_handle).size(d);
+}
+int get_stride_at(TorchTensorHandle tensor_handle, int d)
+{
+  return ctorch::cast(tensor_handle).stride(d);
+}
+int get_device(TorchTensorHandle tensor_handle)
+{
+  return ctorch::device_to_int(ctorch::cast(tensor_handle));
+}
+
+double *get_data_double(TorchTensorHandle tensor_handle)
+{
+  return ctorch::cast(tensor_handle).data_ptr<double>();
+}
+float *get_data_float(TorchTensorHandle tensor_handle)
+{
+  return ctorch::cast(tensor_handle).data_ptr<float>();
+}
+long *get_data_long(TorchTensorHandle tensor_handle)
+{
+  return ctorch::cast(tensor_handle).data_ptr<long>();
+}
+int *get_data_int(TorchTensorHandle tensor_handle)
+{
+  return ctorch::cast(tensor_handle).data_ptr<int>();
+}
+
+double get_item_double(TorchTensorHandle tensor_handle)
+{
+  return ctorch::cast(tensor_handle).item<double>();
+}
+float get_item_float(TorchTensorHandle tensor_handle)
+{
+  return ctorch::cast(tensor_handle).item<float>();
+}
+long get_item_long(TorchTensorHandle tensor_handle)
+{
+  return ctorch::cast(tensor_handle).item<long>();
+}
+int get_item_int(TorchTensorHandle tensor_handle)
+{
+  return ctorch::cast(tensor_handle).item<int>();
+}
+
 double get_double(TorchTensorHandle tensor_handle, int *index)
 {
   return ctorch::get<double>(tensor_handle, index);
@@ -144,23 +181,6 @@ void set_int(TorchTensorHandle tensor_handle, int *index, int value)
   ctorch::set<int>(tensor_handle, index, value);
 }
 
-double get_item_double(TorchTensorHandle tensor_handle)
-{
-  return ctorch::cast(tensor_handle).item<double>();
-}
-float get_item_float(TorchTensorHandle tensor_handle)
-{
-  return ctorch::cast(tensor_handle).item<float>();
-}
-long get_item_long(TorchTensorHandle tensor_handle)
-{
-  return ctorch::cast(tensor_handle).item<long>();
-}
-int get_item_int(TorchTensorHandle tensor_handle)
-{
-  return ctorch::cast(tensor_handle).item<int>();
-}
-
 TorchTensorHandle randn_double(int *shape, int shape_size, int device)
 {
   return new torch::Tensor(ctorch::randn<double>(ctorch::to_vec_int(shape, shape_size), ctorch::int_to_device(device)));
@@ -178,6 +198,15 @@ 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 randint_long(long low, long high, int *shape, int shape_size, int device)
+{
+  return new torch::Tensor(ctorch::randint<long>(low, high, ctorch::to_vec_int(shape, shape_size), ctorch::int_to_device(device)));
+}
+TorchTensorHandle randint_int(int low, int high, int *shape, int shape_size, int device)
+{
+  return new torch::Tensor(ctorch::randint<int>(low, high, 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)));
@@ -195,19 +224,6 @@ 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 plus_double(double value, TorchTensorHandle other)
 {
   return new torch::Tensor(ctorch::cast(other) + value);
@@ -356,21 +372,17 @@ void sum_tensor_assign(TorchTensorHandle tensor_handle)
   ctorch::cast(tensor_handle) = ctorch::cast(tensor_handle).sum();
 }
 
-bool requires_grad(TorchTensorHandle tensor_handle)
+TorchTensorHandle matmul(TorchTensorHandle lhs, TorchTensorHandle rhs)
 {
-  return ctorch::cast(tensor_handle).requires_grad();
+  return new torch::Tensor(torch::matmul(ctorch::cast(lhs), ctorch::cast(rhs)));
 }
-void requires_grad_(TorchTensorHandle tensor_handle, bool status)
+void matmul_assign(TorchTensorHandle lhs, TorchTensorHandle rhs)
 {
-  ctorch::cast(tensor_handle).requires_grad_(status);
+  ctorch::cast(lhs) = ctorch::cast(lhs).matmul(ctorch::cast(rhs));
 }
-TorchTensorHandle detach_from_graph(TorchTensorHandle tensor_handle)
+void matmul_right_assign(TorchTensorHandle lhs, TorchTensorHandle rhs)
 {
-  return new torch::Tensor(ctorch::cast(tensor_handle).detach());
+  ctorch::cast(rhs) = ctorch::cast(lhs).matmul(ctorch::cast(rhs));
-}
-TorchTensorHandle autograd_tensor(TorchTensorHandle value, TorchTensorHandle variable)
-{
-  return new torch::Tensor(torch::autograd::grad({ctorch::cast(value)}, {ctorch::cast(variable)})[0]);
 }
 
 TorchTensorHandle diag_embed(TorchTensorHandle diags_handle, int offset, int dim1, int dim2)
@@ -398,3 +410,20 @@ void symeig_tensor(TorchTensorHandle tensor_handle,
   ctorch::cast(S_handle) = S;
   ctorch::cast(V_handle) = V;
 }
+
+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);
+}
+TorchTensorHandle detach_from_graph(TorchTensorHandle tensor_handle)
+{
+  return new torch::Tensor(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/BenchmarkMatMultFloatGPU.kt

@@ -0,0 +1,20 @@
+package kscience.kmath.torch
+
+import kotlin.test.Test
+
+class BenchmarkMatMultFloatGPU {
+    @Test
+    fun benchmarkMatMult20() =
+        benchmarkingMatMultFloat(20, 10, 100000,
+            device = TorchDevice.TorchCUDA(0))
+
+    @Test
+    fun benchmarkMatMult200() =
+        benchmarkingMatMultFloat(200, 10, 10000,
+            device = TorchDevice.TorchCUDA(0))
+
+    @Test
+    fun benchmarkMatMult2000() =
+        benchmarkingMatMultFloat(2000, 10, 1000,
+            device = TorchDevice.TorchCUDA(0))
+}

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

@@ -29,5 +29,4 @@ class TestTorchTensorAlgebraGPU {
     fun testBatchedSymEig() =
         testingBatchedSymEig(device = TorchDevice.TorchCUDA(0))
 
-
 }

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

@@ -2,7 +2,11 @@ package kscience.kmath.torch
 
 
 public sealed class TorchDevice {
-    public object TorchCPU: TorchDevice()
+    public object TorchCPU: TorchDevice() {
+        override fun toString(): String {
+            return "TorchCPU"
+        }
+    }
     public data class TorchCUDA(val index: Int): TorchDevice()
     public fun toInt(): Int {
         when(this) {

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

@@ -53,6 +53,23 @@ public sealed class TorchTensor<T> constructor(
         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)!!
+    )
+
 }
 
 public class TorchTensorReal internal constructor(
@@ -66,6 +83,39 @@ public class TorchTensorReal internal constructor(
     }
 }
 
+public class TorchTensorFloat internal constructor(
+    scope: DeferScope,
+    tensorHandle: COpaquePointer
+) : TorchTensor<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) {
+        set_float(tensorHandle, index.toCValues(), value)
+    }
+}
+
+public class TorchTensorLong internal constructor(
+    scope: DeferScope,
+    tensorHandle: COpaquePointer
+) : TorchTensor<Long>(scope, tensorHandle) {
+    override fun item(): Long = get_item_long(tensorHandle)
+    override fun get(index: IntArray): Long = get_long(tensorHandle, index.toCValues())
+    override fun set(index: IntArray, value: Long) {
+        set_long(tensorHandle, index.toCValues(), value)
+    }
+}
+
+public class TorchTensorInt internal constructor(
+    scope: DeferScope,
+    tensorHandle: COpaquePointer
+) : TorchTensor<Int>(scope, tensorHandle) {
+    override fun item(): Int = get_item_int(tensorHandle)
+    override fun get(index: IntArray): Int = get_int(tensorHandle, index.toCValues())
+    override fun set(index: IntArray, value: Int) {
+        set_int(tensorHandle, index.toCValues(), value)
+    }
+}
+
 
 private inline fun indexFromOffset(offset: Int, strides: IntArray, nDim: Int): IntArray {
     val res = IntArray(nDim)

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

@@ -42,13 +42,6 @@ public sealed class TorchTensorAlgebra<
         times_tensor_assign(this.tensorHandle, other.tensorHandle)
     }
 
-    public operator fun TorchTensorType.div(other: TorchTensorType): TorchTensorType =
-        wrap(div_tensor(this.tensorHandle, other.tensorHandle)!!)
-
-    public operator fun TorchTensorType.divAssign(other: TorchTensorType): Unit {
-        div_tensor_assign(this.tensorHandle, other.tensorHandle)
-    }
-
     public infix fun TorchTensorType.dot(other: TorchTensorType): TorchTensorType =
         wrap(matmul(this.tensorHandle, other.tensorHandle)!!)
 
@@ -94,11 +87,48 @@ public sealed class TorchTensorAlgebra<
         sum_tensor_assign(tensorHandle)
     }
 
-    public fun diagEmbed(diagonalEntries: TorchTensorType,
+    public fun diagEmbed(
-                         offset: Int = 0, dim1: Int = -2, dim2: Int = -1): TorchTensorType =
+        diagonalEntries: TorchTensorType,
+        offset: Int = 0, dim1: Int = -2, dim2: Int = -1
+    ): TorchTensorType =
         wrap(diag_embed(diagonalEntries.tensorHandle, offset, dim1, dim2)!!)
 
-    public fun TorchTensorType.svd(): Triple<TorchTensorType,TorchTensorType,TorchTensorType> {
+    public fun TorchTensorType.copy(): TorchTensorType =
+        wrap(copy_tensor(this.tensorHandle)!!)
+
+    public fun TorchTensorType.copyToDevice(device: TorchDevice): TorchTensorType =
+        wrap(copy_to_device(this.tensorHandle, device.toInt())!!)
+
+    public infix fun TorchTensorType.swap(otherTensor: TorchTensorType): Unit {
+        swap_tensors(this.tensorHandle, otherTensor.tensorHandle)
+    }
+}
+
+public sealed class TorchTensorFieldAlgebra<T, TVar : CPrimitiveVar,
+        PrimitiveArrayType, TorchTensorType : TorchTensor<T>>(scope: DeferScope) :
+    TorchTensorAlgebra<T, TVar, PrimitiveArrayType, TorchTensorType>(scope) {
+
+    public abstract fun randNormal(shape: IntArray, device: TorchDevice = TorchDevice.TorchCPU): TorchTensorType
+    public abstract fun randUniform(shape: IntArray, device: TorchDevice = TorchDevice.TorchCPU): TorchTensorType
+
+    public operator fun TorchTensorType.div(other: TorchTensorType): TorchTensorType =
+        wrap(div_tensor(this.tensorHandle, other.tensorHandle)!!)
+
+    public operator fun TorchTensorType.divAssign(other: TorchTensorType): Unit {
+        div_tensor_assign(this.tensorHandle, other.tensorHandle)
+    }
+
+    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 fun TorchTensorType.svd(): Triple<TorchTensorType, TorchTensorType, TorchTensorType> {
         val U = empty_tensor()!!
         val V = empty_tensor()!!
         val S = empty_tensor()!!
@@ -116,33 +146,17 @@ public sealed class TorchTensorAlgebra<
     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 fun TorchTensorType.detachFromGraph(): TorchTensorType =
         wrap(tensorHandle = detach_from_graph(this.tensorHandle)!!)
-
 }
 
-public sealed class TorchTensorFieldAlgebra<T, TVar : CPrimitiveVar,
+public sealed class TorchTensorRingAlgebra<T, TVar : CPrimitiveVar,
         PrimitiveArrayType, TorchTensorType : TorchTensor<T>>(scope: DeferScope) :
     TorchTensorAlgebra<T, TVar, PrimitiveArrayType, TorchTensorType>(scope) {
-    public abstract fun randNormal(shape: IntArray, device: TorchDevice = TorchDevice.TorchCPU): TorchTensorType
+    public abstract fun randIntegral(
-    public abstract fun randUniform(shape: IntArray, device: TorchDevice = TorchDevice.TorchCPU): TorchTensorType
+        low: T, high: T, shape: IntArray,
-
+        device: TorchDevice = TorchDevice.TorchCPU
-    public fun TorchTensorType.exp(): TorchTensorType = wrap(exp_tensor(tensorHandle)!!)
+    ): TorchTensorType
-    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) :
@@ -153,33 +167,11 @@ public class TorchTensorRealAlgebra(scope: DeferScope) :
     override fun TorchTensorReal.copyToArray(): DoubleArray =
         this.elements().map { it.second }.toList().toDoubleArray()
 
-    override fun copyFromArray(
+    override fun copyFromArray(array: DoubleArray, shape: IntArray, device: TorchDevice): TorchTensorReal =
-        array: DoubleArray,
+        wrap(from_blob_double(array.toCValues(), shape.toCValues(), shape.size, device.toInt(), true)!!)
-        shape: IntArray,
-        device: TorchDevice
-    ): TorchTensorReal =
-        TorchTensorReal(
-            scope = scope,
-            tensorHandle = from_blob_double(
-                array.toCValues(),
-                shape.toCValues(),
-                shape.size,
-                device.toInt(),
-                true
-            )!!
-        )
 
     override fun fromBlob(arrayBlob: CPointer<DoubleVar>, shape: IntArray): TorchTensorReal =
-        TorchTensorReal(
+        wrap(from_blob_double(arrayBlob, shape.toCValues(), shape.size, TorchDevice.TorchCPU.toInt(), false)!!)
-            scope = scope,
-            tensorHandle = from_blob_double(
-                arrayBlob,
-                shape.toCValues(),
-                shape.size,
-                TorchDevice.TorchCPU.toInt(),
-                false
-            )!!
-        )
 
     override fun TorchTensorReal.getData(): CPointer<DoubleVar> {
         require(this.device is TorchDevice.TorchCPU) {
@@ -188,65 +180,231 @@ public class TorchTensorRealAlgebra(scope: DeferScope) :
         return get_data_double(this.tensorHandle)!!
     }
 
-    override fun randNormal(shape: IntArray, device: TorchDevice): TorchTensorReal = TorchTensorReal(
+    override fun randNormal(shape: IntArray, device: TorchDevice): TorchTensorReal =
-        scope = scope,
+        wrap(randn_double(shape.toCValues(), shape.size, device.toInt())!!)
-        tensorHandle = randn_double(shape.toCValues(), shape.size, device.toInt())!!
-    )
 
-    override fun randUniform(shape: IntArray, device: TorchDevice): TorchTensorReal = TorchTensorReal(
+    override fun randUniform(shape: IntArray, device: TorchDevice): TorchTensorReal =
-        scope = scope,
+        wrap(rand_double(shape.toCValues(), shape.size, device.toInt())!!)
-        tensorHandle = rand_double(shape.toCValues(), shape.size, device.toInt())!!
-    )
 
-    override operator fun Double.plus(other: TorchTensorReal): TorchTensorReal = TorchTensorReal(
+    override operator fun Double.plus(other: TorchTensorReal): TorchTensorReal =
-        scope = scope,
+        wrap(plus_double(this, other.tensorHandle)!!)
-        tensorHandle = plus_double(this, other.tensorHandle)!!
-    )
 
-    override fun TorchTensorReal.plus(value: Double): TorchTensorReal = TorchTensorReal(
+    override fun TorchTensorReal.plus(value: Double): TorchTensorReal =
-        scope = scope,
+        wrap(plus_double(value, this.tensorHandle)!!)
-        tensorHandle = plus_double(value, this.tensorHandle)!!
-    )
 
     override fun TorchTensorReal.plusAssign(value: Double): Unit {
         plus_double_assign(value, this.tensorHandle)
     }
 
-    override operator fun Double.minus(other: TorchTensorReal): TorchTensorReal = TorchTensorReal(
+    override operator fun Double.minus(other: TorchTensorReal): TorchTensorReal =
-        scope = scope,
+        wrap(plus_double(-this, other.tensorHandle)!!)
-        tensorHandle = plus_double(-this, other.tensorHandle)!!
-    )
 
-    override fun TorchTensorReal.minus(value: Double): TorchTensorReal = TorchTensorReal(
+    override fun TorchTensorReal.minus(value: Double): TorchTensorReal =
-        scope = scope,
+        wrap(plus_double(-value, this.tensorHandle)!!)
-        tensorHandle = plus_double(-value, this.tensorHandle)!!
-    )
 
     override fun TorchTensorReal.minusAssign(value: Double): Unit {
         plus_double_assign(-value, this.tensorHandle)
     }
 
-    override operator fun Double.times(other: TorchTensorReal): TorchTensorReal = TorchTensorReal(
+    override operator fun Double.times(other: TorchTensorReal): TorchTensorReal =
-        scope = scope,
+        wrap(times_double(this, other.tensorHandle)!!)
-        tensorHandle = times_double(this, other.tensorHandle)!!
-    )
 
-    override fun TorchTensorReal.times(value: Double): TorchTensorReal = TorchTensorReal(
+    override fun TorchTensorReal.times(value: Double): TorchTensorReal =
-        scope = scope,
+        wrap(times_double(value, this.tensorHandle)!!)
-        tensorHandle = times_double(value, this.tensorHandle)!!
-    )
 
     override fun TorchTensorReal.timesAssign(value: Double): Unit {
         times_double_assign(value, this.tensorHandle)
     }
 
+    override fun full(value: Double, shape: IntArray, device: TorchDevice): TorchTensorReal =
+        wrap(full_double(value, shape.toCValues(), shape.size, device.toInt())!!)
+}
 
-    override fun full(value: Double, shape: IntArray, device: TorchDevice): TorchTensorReal = TorchTensorReal(
+public class TorchTensorFloatAlgebra(scope: DeferScope) :
-        scope = scope,
+    TorchTensorFieldAlgebra<Float, FloatVar, FloatArray, TorchTensorFloat>(scope) {
-        tensorHandle = full_double(value, shape.toCValues(), shape.size, device.toInt())!!
+    override fun wrap(tensorHandle: COpaquePointer): TorchTensorFloat =
-    )
+        TorchTensorFloat(scope = scope, tensorHandle = tensorHandle)
 
+    override fun TorchTensorFloat.copyToArray(): FloatArray =
+        this.elements().map { it.second }.toList().toFloatArray()
+
+    override fun copyFromArray(array: FloatArray, shape: IntArray, device: TorchDevice): TorchTensorFloat =
+        wrap(from_blob_float(array.toCValues(), shape.toCValues(), shape.size, device.toInt(), true)!!)
+
+    override fun fromBlob(arrayBlob: CPointer<FloatVar>, shape: IntArray): TorchTensorFloat =
+        wrap(from_blob_float(arrayBlob, shape.toCValues(), shape.size, TorchDevice.TorchCPU.toInt(), false)!!)
+
+    override fun TorchTensorFloat.getData(): CPointer<FloatVar> {
+        require(this.device is TorchDevice.TorchCPU) {
+            "This tensor is not on available on CPU"
+        }
+        return get_data_float(this.tensorHandle)!!
+    }
+
+    override fun randNormal(shape: IntArray, device: TorchDevice): TorchTensorFloat =
+        wrap(randn_float(shape.toCValues(), shape.size, device.toInt())!!)
+
+    override fun randUniform(shape: IntArray, device: TorchDevice): TorchTensorFloat =
+        wrap(rand_float(shape.toCValues(), shape.size, device.toInt())!!)
+
+    override operator fun Float.plus(other: TorchTensorFloat): TorchTensorFloat =
+        wrap(plus_float(this, other.tensorHandle)!!)
+
+    override fun TorchTensorFloat.plus(value: Float): TorchTensorFloat =
+        wrap(plus_float(value, this.tensorHandle)!!)
+
+    override fun TorchTensorFloat.plusAssign(value: Float): Unit {
+        plus_float_assign(value, this.tensorHandle)
+    }
+
+    override operator fun Float.minus(other: TorchTensorFloat): TorchTensorFloat =
+        wrap(plus_float(-this, other.tensorHandle)!!)
+
+    override fun TorchTensorFloat.minus(value: Float): TorchTensorFloat =
+        wrap(plus_float(-value, this.tensorHandle)!!)
+
+    override fun TorchTensorFloat.minusAssign(value: Float): Unit {
+        plus_float_assign(-value, this.tensorHandle)
+    }
+
+    override operator fun Float.times(other: TorchTensorFloat): TorchTensorFloat =
+        wrap(times_float(this, other.tensorHandle)!!)
+
+    override fun TorchTensorFloat.times(value: Float): TorchTensorFloat =
+        wrap(times_float(value, this.tensorHandle)!!)
+
+    override fun TorchTensorFloat.timesAssign(value: Float): Unit {
+        times_float_assign(value, this.tensorHandle)
+    }
+
+    override fun full(value: Float, shape: IntArray, device: TorchDevice): TorchTensorFloat =
+        wrap(full_float(value, shape.toCValues(), shape.size, device.toInt())!!)
+}
+
+public class TorchTensorLongAlgebra(scope: DeferScope) :
+    TorchTensorRingAlgebra<Long, LongVar, LongArray, TorchTensorLong>(scope) {
+    override fun wrap(tensorHandle: COpaquePointer): TorchTensorLong =
+        TorchTensorLong(scope = scope, tensorHandle = tensorHandle)
+
+    override fun TorchTensorLong.copyToArray(): LongArray =
+        this.elements().map { it.second }.toList().toLongArray()
+
+    override fun copyFromArray(array: LongArray, shape: IntArray, device: TorchDevice): TorchTensorLong =
+        wrap(from_blob_long(array.toCValues(), shape.toCValues(), shape.size, device.toInt(), true)!!)
+
+    override fun fromBlob(arrayBlob: CPointer<LongVar>, shape: IntArray): TorchTensorLong =
+        wrap(from_blob_long(arrayBlob, shape.toCValues(), shape.size, TorchDevice.TorchCPU.toInt(), false)!!)
+
+    override fun TorchTensorLong.getData(): CPointer<LongVar> {
+        require(this.device is TorchDevice.TorchCPU) {
+            "This tensor is not on available on CPU"
+        }
+        return get_data_long(this.tensorHandle)!!
+    }
+
+    override fun randIntegral(low: Long, high: Long, shape: IntArray, device: TorchDevice): TorchTensorLong =
+        wrap(randint_long(low, high, shape.toCValues(), shape.size, device.toInt())!!)
+
+    override operator fun Long.plus(other: TorchTensorLong): TorchTensorLong =
+        wrap(plus_long(this, other.tensorHandle)!!)
+
+    override fun TorchTensorLong.plus(value: Long): TorchTensorLong =
+        wrap(plus_long(value, this.tensorHandle)!!)
+
+    override fun TorchTensorLong.plusAssign(value: Long): Unit {
+        plus_long_assign(value, this.tensorHandle)
+    }
+
+    override operator fun Long.minus(other: TorchTensorLong): TorchTensorLong =
+        wrap(plus_long(-this, other.tensorHandle)!!)
+
+    override fun TorchTensorLong.minus(value: Long): TorchTensorLong =
+        wrap(plus_long(-value, this.tensorHandle)!!)
+
+    override fun TorchTensorLong.minusAssign(value: Long): Unit {
+        plus_long_assign(-value, this.tensorHandle)
+    }
+
+    override operator fun Long.times(other: TorchTensorLong): TorchTensorLong =
+        wrap(times_long(this, other.tensorHandle)!!)
+
+    override fun TorchTensorLong.times(value: Long): TorchTensorLong =
+        wrap(times_long(value, this.tensorHandle)!!)
+
+    override fun TorchTensorLong.timesAssign(value: Long): Unit {
+        times_long_assign(value, this.tensorHandle)
+    }
+
+    override fun full(value: Long, shape: IntArray, device: TorchDevice): TorchTensorLong =
+        wrap(full_long(value, shape.toCValues(), shape.size, device.toInt())!!)
+}
+
+public class TorchTensorIntAlgebra(scope: DeferScope) :
+    TorchTensorRingAlgebra<Int, IntVar, IntArray, TorchTensorInt>(scope) {
+    override fun wrap(tensorHandle: COpaquePointer): TorchTensorInt =
+        TorchTensorInt(scope = scope, tensorHandle = tensorHandle)
+
+    override fun TorchTensorInt.copyToArray(): IntArray =
+        this.elements().map { it.second }.toList().toIntArray()
+
+    override fun copyFromArray(array: IntArray, shape: IntArray, device: TorchDevice): TorchTensorInt =
+        wrap(from_blob_int(array.toCValues(), shape.toCValues(), shape.size, device.toInt(), true)!!)
+
+    override fun fromBlob(arrayBlob: CPointer<IntVar>, shape: IntArray): TorchTensorInt =
+        wrap(from_blob_int(arrayBlob, shape.toCValues(), shape.size, TorchDevice.TorchCPU.toInt(), false)!!)
+
+    override fun TorchTensorInt.getData(): CPointer<IntVar> {
+        require(this.device is TorchDevice.TorchCPU) {
+            "This tensor is not on available on CPU"
+        }
+        return get_data_int(this.tensorHandle)!!
+    }
+
+    override fun randIntegral(low: Int, high: Int, shape: IntArray, device: TorchDevice): TorchTensorInt =
+        wrap(randint_int(low, high, shape.toCValues(), shape.size, device.toInt())!!)
+
+    override operator fun Int.plus(other: TorchTensorInt): TorchTensorInt =
+        wrap(plus_int(this, other.tensorHandle)!!)
+
+    override fun TorchTensorInt.plus(value: Int): TorchTensorInt =
+        wrap(plus_int(value, this.tensorHandle)!!)
+
+    override fun TorchTensorInt.plusAssign(value: Int): Unit {
+        plus_int_assign(value, this.tensorHandle)
+    }
+
+    override operator fun Int.minus(other: TorchTensorInt): TorchTensorInt =
+        wrap(plus_int(-this, other.tensorHandle)!!)
+
+    override fun TorchTensorInt.minus(value: Int): TorchTensorInt =
+        wrap(plus_int(-value, this.tensorHandle)!!)
+
+    override fun TorchTensorInt.minusAssign(value: Int): Unit {
+        plus_int_assign(-value, this.tensorHandle)
+    }
+
+    override operator fun Int.times(other: TorchTensorInt): TorchTensorInt =
+        wrap(times_int(this, other.tensorHandle)!!)
+
+    override fun TorchTensorInt.times(value: Int): TorchTensorInt =
+        wrap(times_int(value, this.tensorHandle)!!)
+
+    override fun TorchTensorInt.timesAssign(value: Int): Unit {
+        times_int_assign(value, this.tensorHandle)
+    }
+
+    override fun full(value: Int, shape: IntArray, device: TorchDevice): TorchTensorInt =
+        wrap(full_int(value, shape.toCValues(), shape.size, device.toInt())!!)
 }
 
 public inline fun <R> TorchTensorRealAlgebra(block: TorchTensorRealAlgebra.() -> R): R =
     memScoped { TorchTensorRealAlgebra(this).block() }
+
+public inline fun <R> TorchTensorFloatAlgebra(block: TorchTensorFloatAlgebra.() -> R): R =
+    memScoped { TorchTensorFloatAlgebra(this).block() }
+
+public inline fun <R> TorchTensorLongAlgebra(block: TorchTensorLongAlgebra.() -> R): R =
+    memScoped { TorchTensorLongAlgebra(this).block() }
+
+public inline fun <R> TorchTensorIntAlgebra(block: TorchTensorIntAlgebra.() -> R): R =
+    memScoped { TorchTensorIntAlgebra(this).block() }

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

@@ -3,14 +3,14 @@ package kscience.kmath.torch
 import kotlin.test.Test
 import kotlin.time.measureTime
 
-internal fun benchmarkingDoubleMatrixMultiplication(
+internal fun benchmarkingMatMultDouble(
     scale: Int,
     numWarmUp: Int,
     numIter: Int,
     device: TorchDevice = TorchDevice.TorchCPU
 ): Unit {
     TorchTensorRealAlgebra {
-        println("Benchmarking $scale x $scale matrices over Double's: ")
+        println("Benchmarking $scale x $scale matrices over Double's on $device: ")
         setSeed(SEED)
         val lhs = randNormal(shape = intArrayOf(scale, scale), device = device)
         val rhs = randNormal(shape = intArrayOf(scale, scale), device = device)
@@ -20,15 +20,18 @@ internal fun benchmarkingDoubleMatrixMultiplication(
     }
 }
 
-class BenchmarkMatrixMultiplicationDouble {
+internal class BenchmarkMatMultDouble {
 
     @Test
-    fun benchmarkMatrixMultiplication20() = benchmarkingDoubleMatrixMultiplication(20, 10, 100000)
+    fun benchmarkMatMult20() =
+        benchmarkingMatMultDouble(20, 10, 100000)
 
     @Test
-    fun benchmarkMatrixMultiplication200() = benchmarkingDoubleMatrixMultiplication(200, 10, 10000)
+    fun benchmarkMatMult200() =
+        benchmarkingMatMultDouble(200, 10, 10000)
 
     @Test
-    fun benchmarkMatrixMultiplication2000() = benchmarkingDoubleMatrixMultiplication(2000, 3, 20)
+    fun benchmarkMatMult2000() =
+        benchmarkingMatMultDouble(2000, 3, 20)
 
 }

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

@@ -0,0 +1,36 @@
+package kscience.kmath.torch
+
+import kotlin.test.Test
+import kotlin.time.measureTime
+
+internal fun benchmarkingMatMultFloat(
+    scale: Int,
+    numWarmUp: Int,
+    numIter: Int,
+    device: TorchDevice = TorchDevice.TorchCPU
+): Unit {
+    TorchTensorFloatAlgebra {
+        println("Benchmarking $scale x $scale matrices over Float's on $device: ")
+        setSeed(SEED)
+        val lhs = randNormal(shape = intArrayOf(scale, scale), device = device)
+        val rhs = randNormal(shape = intArrayOf(scale, scale), device = device)
+        repeat(numWarmUp) { lhs dotAssign rhs }
+        val measuredTime = measureTime { repeat(numIter) { lhs dotAssign rhs } }
+        println("   ${measuredTime / numIter} p.o. with $numIter iterations")
+    }
+}
+
+internal class BenchmarkMatMultFloat {
+
+    @Test
+    fun benchmarkMatMult20() =
+        benchmarkingMatMultFloat(20, 10, 100000)
+
+    @Test
+    fun benchmarkMatMult200() =
+        benchmarkingMatMultFloat(200, 10, 10000)
+
+    @Test
+    fun benchmarkMatMult2000() =
+        benchmarkingMatMultFloat(2000, 3, 20)
+}

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

@@ -44,7 +44,6 @@ internal fun testingBatchedAutoGrad(bath: IntArray,
 
         val error = (gradientAtX - expectedGradientAtX).abs().sum().value()
         assertTrue(error < TOLERANCE)
-
     }
 }
 

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

@@ -49,4 +49,15 @@ class TestTorchTensor {
         val detachedTensor = tensor.detachFromGraph()
         assertTrue(!detachedTensor.requiresGrad)
     }
+
+    @Test
+    fun testTypeMoving() = TorchTensorFloatAlgebra {
+        val tensorInt = copyFromArray(floatArrayOf(1f,2f,3f), intArrayOf(3)).copyToInt()
+        TorchTensorIntAlgebra {
+            val temporalTensor = copyFromArray(intArrayOf(4,5,6),intArrayOf(3))
+            tensorInt swap temporalTensor
+            assertTrue(temporalTensor.copyToArray() contentEquals intArrayOf(1,2,3))
+        }
+        assertTrue(tensorInt.copyToFloat().copyToArray() contentEquals floatArrayOf(4f,5f,6f))
+    }
 }