forked from kscience/kmath
134 lines
4.0 KiB
Plaintext
134 lines
4.0 KiB
Plaintext
/*
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* Copyright 2018-2021 KMath contributors.
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* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
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*/
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import de.undercouch.gradle.tasks.download.Download
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plugins {
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kotlin("jvm")
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id("ru.mipt.npm.gradle.common")
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id("de.undercouch.download")
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}
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description = "Wrapper for the Bayesian Computation library NOA on top of LibTorch"
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dependencies {
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implementation(project(":kmath-tensors"))
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}
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val home: String = System.getProperty("user.home")
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val javaHome: String = System.getProperty("java.home")
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val thirdPartyDir = "$home/.konan/third-party/kmath-noa-${project.property("version")}"
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val cppBuildDir = "$thirdPartyDir/cpp-build"
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val cppSources = projectDir.resolve("src/main/cpp")
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val cudaHome: String? = System.getenv("CUDA_HOME")
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val cudaDefault = file("/usr/local/cuda").exists()
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val cudaFound = cudaHome?.isNotEmpty() ?: false or cudaDefault
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val cmakeArchive = "cmake-3.20.5-linux-x86_64"
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val torchArchive = "libtorch"
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val cmakeCmd = "$thirdPartyDir/cmake/$cmakeArchive/bin/cmake"
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val ninjaCmd = "$thirdPartyDir/ninja/ninja"
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val generateJNIHeader by tasks.registering {
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println(cmakeCmd)
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doLast {
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exec {
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workingDir(projectDir.resolve("src/main/java/space/kscience/kmath/noa"))
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commandLine("$javaHome/bin/javac", "-h", cppSources , "JNoa.java")
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}
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}
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}
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val downloadCMake by tasks.registering(Download::class) {
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val tarFile = "$cmakeArchive.tar.gz"
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src("https://github.com/Kitware/CMake/releases/download/v3.20.5/$tarFile")
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dest(File("$thirdPartyDir/cmake", tarFile))
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overwrite(false)
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}
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val downloadNinja by tasks.registering(Download::class) {
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src("https://github.com/ninja-build/ninja/releases/download/v1.10.2/ninja-linux.zip")
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dest(File("$thirdPartyDir/ninja", "ninja-linux.zip"))
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overwrite(false)
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}
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val downloadTorch by tasks.registering(Download::class) {
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val torchVersion = "$torchArchive-shared-with-deps-1.9.0%2B"
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val cudaUrl = "https://download.pytorch.org/libtorch/cu111/${torchVersion}cu111.zip"
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val cpuUrl = "https://download.pytorch.org/libtorch/cpu/${torchVersion}cpu.zip"
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val url = if (cudaFound) cudaUrl else cpuUrl
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src(url)
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dest(File("$thirdPartyDir/torch", "$torchArchive.zip"))
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overwrite(false)
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}
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val extractCMake by tasks.registering(Copy::class) {
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dependsOn(downloadCMake)
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from(tarTree(resources.gzip(downloadCMake.get().dest)))
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into("$thirdPartyDir/cmake")
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}
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val extractNinja by tasks.registering(Copy::class) {
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dependsOn(downloadNinja)
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from(zipTree(downloadNinja.get().dest))
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into("$thirdPartyDir/ninja")
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}
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val extractTorch by tasks.registering(Copy::class) {
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dependsOn(downloadTorch)
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from(zipTree(downloadTorch.get().dest))
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into("$thirdPartyDir/torch")
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}
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val configureCpp by tasks.registering {
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dependsOn(extractCMake)
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dependsOn(extractNinja)
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dependsOn(extractTorch)
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onlyIf { !file(cppBuildDir).exists() }
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doLast {
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exec {
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workingDir(thirdPartyDir)
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commandLine("mkdir", "-p", cppBuildDir)
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}
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exec {
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workingDir(cppBuildDir)
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commandLine(
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cmakeCmd,
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cppSources,
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"-GNinja",
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"-DCMAKE_MAKE_PROGRAM=$ninjaCmd",
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"-DCMAKE_PREFIX_PATH=$thirdPartyDir/torch/$torchArchive",
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"-DJAVA_HOME=$javaHome",
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"-DCMAKE_BUILD_TYPE=Release",
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"-DBUILD_NOA_CUDA=${if(!cudaFound) "ON" else "OFF"}"
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)
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}
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}
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}
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val cleanCppBuild by tasks.registering {
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onlyIf { file(cppBuildDir).exists() }
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doLast {
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exec {
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workingDir(thirdPartyDir)
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commandLine("rm", "-rf", cppBuildDir)
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}
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}
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}
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val buildCpp by tasks.registering {
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dependsOn(configureCpp)
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doLast {
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exec {
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workingDir(cppBuildDir)
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commandLine(cmakeCmd, "--build", ".", "--config", "Release")
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}
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}
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}
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tasks["compileJava"].dependsOn(buildCpp)
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