kmath/kmath-torch/README.md

LibTorch extension (kmath-torch)

This is a Kotlin/Native module, with only linuxX64 supported so far. This library wraps some of the PyTorch C++ API, focusing on integrating Aten & Autograd with KMath.

Installation

To install the library, you have to build & publish locally kmath-core, kmath-memory with kmath-torch:

./gradlew -q :kmath-core:publishToMavenLocal :kmath-memory:publishToMavenLocal :kmath-torch:publishToMavenLocal

This builds ctorch, a C wrapper for LibTorch placed inside:

~/.konan/third-party/kmath-torch-0.2.0-dev-4/cpp-build

You will have to link against it in your own project. Here is an example of build script for a standalone application:

//build.gradle.kts
plugins {
    id("ru.mipt.npm.mpp")
}

repositories {
    jcenter()
    mavenLocal()
}

val home = System.getProperty("user.home")
val kver = "0.2.0-dev-4"
val cppBuildDir = "$home/.konan/third-party/kmath-torch-$kver/cpp-build"

kotlin {
    explicitApiWarning()

    val nativeTarget = linuxX64("your.app")
    nativeTarget.apply {
        binaries {
            executable {
                entryPoint = "your.app.main"
            }
            all {
                linkerOpts(
                    "-L$cppBuildDir",
                    "-Wl,-rpath=$cppBuildDir",
                    "-lctorch"
                )
            }
        }
    }

    val main by nativeTarget.compilations.getting

    sourceSets {
        val nativeMain by creating {
            dependencies {
                implementation("kscience.kmath:kmath-torch:$kver")
            }
        }
        main.defaultSourceSet.dependsOn(nativeMain)
    }
}

//settings.gradle.kts
pluginManagement {
    repositories {
        gradlePluginPortal()
        jcenter()
        maven("https://dl.bintray.com/mipt-npm/dev")
    }
    plugins {
        id("ru.mipt.npm.mpp") version "0.7.1"
        kotlin("jvm") version "1.4.21"
    }
}

Usage

Tensors are implemented over the MutableNDStructure. They can only be instantiated through provided factory methods and require scoping:

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)
}

Enjoy a high performance automatic differentiation engine:

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
}