A Kotlin multiplatform automatic differentiation library inspired by micrograd by Andrej Karpathy, featuring compile-time optimization through KSP (Kotlin Symbol Processing).

• KSP Code Generation: Compile-time optimization for mathematical expressions
• Multi-Module Architecture: Separate modules for core library, annotations, processor, and samples
• Visualization Support: Graphviz integration for computational graph visualization

• miKrograd: Core automatic differentiation engine
• miKrograd-annotations: KSP annotations for compile-time code generation
• miKrograd-processor: KSP processor for optimized code generation
• samples: Example implementations and demonstrations

import org.mikrograd.diff.BackpropNode
import org.mikrograd.diff.div
import org.mikrograd.diff.plus

typealias Value = BackpropNode

val a = Value(-4.0)
val b = Value(2.0)
var c = a + b
var d = a * b + b.pow(3.0)
c += c + 1
c += 1.0 + c + (-a)
d += d * 2 + (b + a).relu()
d += d * 3.0 + (b - a).relu()
val e = c - d
val f = e.pow(2.0)
var g = f / 2
g += 10.0 / f
println("$g")  // prints 24.7041, the outcome of this forward pass
g.backward()
println("${a.grad}") // prints 138.8338, i.e. the numerical value of dg/da
println("${b.grad}") // prints 645.5773, i.e. the numerical value of dg/db```

// Compile-time optimized inference
@Mikrograd(mode = ComputationMode.INFERENCE)
@Mikrograd
fun optimizedInference() {
    3.0 * 4.0 + (7.0 + 3.0)
}

// Compile-time optimized training with gradients  
@Mikrograd(mode = ComputationMode.TRAINING)
fun optimizedTraining() {
    3.0 * 4.0 + (7.0 + 3.0)
}

The library includes Graphviz integration for visualizing computational graphs:

fun main() {
    val nn = Neuron(2)
    val x = listOf(Value(1.0), Value(-2.0))
    val y = nn(x)
    drawDot(y).toFile("neuron.dot")
}

Add the following to your build.gradle.kts:

dependencies {
    implementation("org.mikrograd:mikrograd:latest.version")
    ksp("org.mikrograd:mikrograd-processor:latest.version")
}

./gradlew build

• Architecture Documentation: See docs/README.md for comprehensive arc42 architecture documentation
• Core Module: See miKrograd/README.md for detailed API documentation
• KSP Processor: See miKrograd-processor/README.md for code generation details
• Examples: Check the samples directory for working examples

miKrograd/
├── miKrograd/              # Core automatic differentiation library
├── miKrograd-annotations/  # KSP annotations
├── miKrograd-processor/    # KSP code generation processor  
├── samples/               # Example implementations
└── docs/                  # Architecture documentation (arc42)

1. Compile-Time Optimization: KSP generates optimized code at compile time
2. Type Safe: Full Kotlin type safety with multiplatform support
3. Extensible: Well-documented architecture for easy extension

MIT