# 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](https://pytorch.org/cppdocs), 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: ```kotlin //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) } } ``` ```kotlin //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 implement the buffer protocol over `MutableNDStructure`. They can only be instantiated through provided factory methods and require scoping: ```kotlin 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) } ```