96 lines
2.5 KiB
Markdown
96 lines
2.5 KiB
Markdown
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# LibTorch extension (`kmath-torch`)
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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`.
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## Installation
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To install the library, you have to build & publish locally `kmath-core`, `kmath-memory` with `kmath-torch`:
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```
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./gradlew -q :kmath-core:publishToMavenLocal :kmath-memory:publishToMavenLocal :kmath-torch:publishToMavenLocal
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```
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This builds `ctorch`, a C wrapper for `LibTorch` placed inside:
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`~/.konan/third-party/kmath-torch-0.2.0-dev-4/cpp-build`
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You will have to link against it in your own project. Here is an example of build script for a standalone application:
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```kotlin
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//build.gradle.kts
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plugins {
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id("ru.mipt.npm.mpp")
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}
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repositories {
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jcenter()
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mavenLocal()
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}
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val home = System.getProperty("user.home")
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val kver = "0.2.0-dev-4"
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val cppBuildDir = "$home/.konan/third-party/kmath-torch-$kver/cpp-build"
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kotlin {
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explicitApiWarning()
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val nativeTarget = linuxX64("your.app")
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nativeTarget.apply {
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binaries {
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executable {
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entryPoint = "your.app.main"
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}
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all {
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linkerOpts(
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"-L$cppBuildDir",
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"-Wl,-rpath=$cppBuildDir",
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"-lctorch"
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)
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}
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}
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}
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val main by nativeTarget.compilations.getting
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sourceSets {
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val nativeMain by creating {
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dependencies {
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implementation("kscience.kmath:kmath-torch:$kver")
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}
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}
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main.defaultSourceSet.dependsOn(nativeMain)
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}
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}
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```
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```kotlin
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//settings.gradle.kts
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pluginManagement {
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repositories {
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gradlePluginPortal()
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jcenter()
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maven("https://dl.bintray.com/mipt-npm/dev")
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}
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plugins {
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id("ru.mipt.npm.mpp") version "0.7.1"
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kotlin("jvm") version "1.4.21"
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}
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}
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```
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## Usage
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Tensors implement the buffer protocol over `MutableNDStructure`. They can only be instantiated through provided factory methods and require scoping:
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```kotlin
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memScoped {
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val intTensor: TorchTensorInt = TorchTensor.copyFromIntArray(
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scope = this,
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array = intArrayOf(7,8,9,2,6,5),
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shape = intArrayOf(3,2))
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println(intTensor)
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val floatTensor: TorchTensorFloat = TorchTensor.copyFromFloatArray(
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scope = this,
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array = floatArrayOf(7f,8.9f,2.6f,5.6f),
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shape = intArrayOf(4))
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println(intTensor)
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}
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```
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