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Corrected readme file

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Roland Grinis 2021-01-16 20:29:47 +00:00
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kmath-torch/README.md
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@ -1,18 +1,22 @@
# LibTorch extension (`kmath-torch`) # 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`. 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 ## Installation
To install the library, you have to build & publish locally `kmath-core`, `kmath-memory` with `kmath-torch`: 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 ./gradlew -q :kmath-core:publishToMavenLocal :kmath-memory:publishToMavenLocal :kmath-torch:publishToMavenLocal
``` ```
This builds `ctorch`, a C wrapper for `LibTorch` placed inside: This builds `ctorch`, a C wrapper for `LibTorch` placed inside:
`~/.konan/third-party/kmath-torch-0.2.0-dev-4/cpp-build` `~/.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: You will have to link against it in your own project. Here is an example of build script for a standalone application:
```kotlin ```kotlin
//build.gradle.kts //build.gradle.kts
plugins { plugins {
@ -59,6 +63,7 @@ kotlin {
} }
} }
``` ```
```kotlin ```kotlin
//settings.gradle.kts //settings.gradle.kts
pluginManagement { pluginManagement {
@ -76,13 +81,15 @@ pluginManagement {
## Usage ## Usage
Tensors are implemented over the `MutableNDStructure`. They can only be instantiated through provided factory methods and require scoping: Tensors are implemented over the `MutableNDStructure`. They can only be instantiated through provided factory methods
and require scoping:
```kotlin ```kotlin
TorchTensorRealAlgebra { TorchTensorRealAlgebra {
val realTensor: TorchTensorReal = copyFromArray( val realTensor: TorchTensorReal = copyFromArray(
array = (1..10).map { it + 50.0 }.toList().toDoubleArray(), array = (1..10).map { it + 50.0 }.toList().toDoubleArray(),
shape = intArrayOf(2,5) shape = intArrayOf(2, 5)
) )
println(realTensor) println(realTensor)
@ -94,23 +101,27 @@ TorchTensorRealAlgebra {
println(gpuRealTensor) println(gpuRealTensor)
} }
``` ```
Enjoy a high performance automatic differentiation engine: Enjoy a high performance automatic differentiation engine:
```kotlin ```kotlin
TorchTensorRealAlgebra { TorchTensorRealAlgebra {
val dim = 10 val dim = 10
val device = TorchDevice.TorchCPU //or TorchDevice.TorchCUDA(0) val device = TorchDevice.TorchCPU //or TorchDevice.TorchCUDA(0)
val x = randNormal(shape = intArrayOf(dim), device = device) 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 X = randNormal(shape = intArrayOf(dim,dim), device = device)
val Q = X + X.transpose(0,1)
val mu = randNormal(shape = intArrayOf(dim), device = device) val mu = randNormal(shape = intArrayOf(dim), device = device)
// expression to differentiate w.r.t. x // expression to differentiate w.r.t. x
val f = 0.5 * (x dot (Q dot x)) + (mu dot x) + 25.3 val f = x.withGrad {
0.5 * (x dot (Q dot x)) + (mu dot x) + 25.3
}
// value of the gradient at x // value of the gradient at x
val gradf = f grad x val gradf = f grad x
// value of the hessian at x
val hessf = f hess x
} }
``` ```