forked from kscience/kmath
67 lines
2.2 KiB
Markdown
67 lines
2.2 KiB
Markdown
# LibTorch extension (`kmath-torch`)
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This is a `Kotlin/Native` & `JVM` module, with only `linuxX64` supported so far. The library wraps some of
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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 and `jtorch` a JNI 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.
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## Usage
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Tensors are implemented over the `MutableNDStructure`. They can only be created through provided factory methods
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and require scoping within a `TensorAlgebra` instance:
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```kotlin
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TorchTensorRealAlgebra {
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val realTensor: TorchTensorReal = copyFromArray(
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array = (1..10).map { it + 50.0 }.toList().toDoubleArray(),
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shape = intArrayOf(2, 5)
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)
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println(realTensor)
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val gpuRealTensor: TorchTensorReal = copyFromArray(
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array = (1..8).map { it * 2.5 }.toList().toDoubleArray(),
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shape = intArrayOf(2, 2, 2),
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device = Device.CUDA(0)
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)
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println(gpuRealTensor)
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}
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```
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High performance automatic differentiation engine is available:
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```kotlin
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TorchTensorRealAlgebra {
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val dim = 10
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val device = Device.CPU //or Device.CUDA(0)
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val tensorX = randNormal(shape = intArrayOf(dim), device = device)
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val randFeatures = randNormal(shape = intArrayOf(dim, dim), device = device)
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val tensorSigma = randFeatures + randFeatures.transpose(0, 1)
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val tensorMu = randNormal(shape = intArrayOf(dim), device = device)
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// expression to differentiate w.r.t. x evaluated at x = tensorX
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val expressionAtX = withGradAt(tensorX, { x ->
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0.5 * (x dot (tensorSigma dot x)) + (tensorMu dot x) + 25.9
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})
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// value of the gradient at x = tensorX
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val gradientAtX = expressionAtX.grad(tensorX, retainGraph = true)
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// value of the hessian at x = tensorX
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val hessianAtX = expressionAtX hess tensorX
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}
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```
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Contributed by [Roland Grinis](https://github.com/rgrit91)
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