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michalharakalmichalharakal
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Fix package names and add nn package with basic layer and functions
1 parent 1519949 commit 565c2c2

32 files changed

Lines changed: 873 additions & 44 deletions

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‎settings.gradle.kts‎

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@@ -18,3 +18,5 @@ rootProject.name = "skainet"
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include("skainet-core:skainet-tensors-api")
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include("skainet-core:skainet-tensors")
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include("skainet-core:skainet-performance")
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include("skainet-nn:skainet-nn-api")
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include("skainet-nn:skainet-nn-relection")

‎skainet-core/skainet-tensors-api/src/commonMain/kotlin/sk/ainet/core/tensor/DType.kt‎

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package sk.ai.net.core.tensor
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package sk.ainet.core.tensor
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// Base marker interface for all dtypes
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public sealed interface DType {

‎skainet-core/skainet-tensors-api/src/commonMain/kotlin/sk/ainet/core/tensor/Shape.kt‎

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package sk.ai.net.core.tensor
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package sk.ainet.core.tensor
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/**
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* Data class representing the shape of a multi-dimensional array (tensor).

‎skainet-core/skainet-tensors-api/src/commonMain/kotlin/sk/ainet/core/tensor/Tensor.kt‎

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package sk.ai.net.core.tensor
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package sk.ainet.core.tensor
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/**
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* Interface representing a multi-dimensional array of numeric values.

‎skainet-core/skainet-tensors-api/src/commonMain/kotlin/sk/ainet/core/tensor/TensorData.kt‎

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package sk.ai.net.core.tensor
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package sk.ainet.core.tensor
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public interface TensorData<T:DType, V> {
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/**

‎skainet-core/skainet-tensors-api/src/commonMain/kotlin/sk/ainet/core/tensor/TensorOps.kt‎

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@@ -1,12 +1,12 @@
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package sk.ai.net.core.tensor
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package sk.ainet.core.tensor
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/**
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* Interface representing mathematical operations on tensors.
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*
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* Keeps math separate from storage, so the same math API can work
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* with multiple backends (dense, sparse, GPU, etc.).
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*/
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public interface TensorOps<T: Tensor<*, *>> {
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public interface TensorOps<T : Tensor<*, *>> {
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/**
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* Performs matrix multiplication of two tensors.
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*
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public operator fun Float.minus(t: T): T = this.toDouble() - t
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public operator fun Float.times(t: T): T = this.toDouble() * t
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public operator fun Float.div(t: T): T = this.toDouble() / t
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}
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public fun T.t(): T // transpose
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public fun T.relu(): T
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/**
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* Applies the softmax function along the specified dimension of the tensor.
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*/
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public fun T.softmax(dimension: Int): T
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/**
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* Applies the sigmoid function element-wise to the tensor.
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*
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* @return A new tensor with the sigmoid function applied to each element.
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*/
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public fun T.sigmoid(): T
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/**
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* Applies the hyperbolic tangent (tanh) function element-wise to the tensor.
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*
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* @return A new tensor with the tanh function applied to each element.
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*/
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public fun T.tanh(): T
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/**
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* Flattens the tensor into a 1D tensor.
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*
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* @param startDim The first dimension to flatten (inclusive).
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* @param endDim The last dimension to flatten (inclusive).
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* @return A new flattened tensor.
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*/
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public fun T.flatten(startDim: Int = 1, endDim: Int = -1): T
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}
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skainet-core/skainet-tensors-api/src/commonMain/kotlin/sk/ainet/core/tensor/ComputeBackend.kt renamed to skainet-core/skainet-tensors-api/src/commonMain/kotlin/sk/ainet/core/tensor/backend/ComputeBackend.kt

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package sk.ai.net.core.tensor.backend
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package sk.ainet.core.tensor.backend
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import sk.ainet.core.tensor.DType
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import sk.ainet.core.tensor.Tensor
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import sk.ainet.core.tensor.TensorOps
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import sk.ai.net.core.tensor.DType
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import sk.ai.net.core.tensor.Tensor
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import sk.ai.net.core.tensor.TensorOps
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/**
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* Interface representing a computation backend for tensor operations.

‎skainet-core/skainet-tensors/src/commonMain/kotlin/sk/ainet/core/tensor/backend/CpuBackend.kt‎

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package sk.ainet.core.tensor.backend
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import sk.ai.net.core.tensor.*
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import sk.ai.net.core.tensor.backend.ComputeBackend
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import sk.ainet.core.tensor.*
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import sk.ainet.core.tensor.backend.ComputeBackend
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import kotlin.math.*
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/**
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* Convenient type alias for FP32 tensors with Float values.
@@ -149,6 +150,14 @@ public class CpuTensorFP32(
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override fun Double.times(t: Tensor<FP32, Float>): Tensor<FP32, Float> = with(backend) { this@times.times(t) }
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override fun Double.div(t: Tensor<FP32, Float>): Tensor<FP32, Float> = with(backend) { this@div.div(t) }
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// Advanced tensor operations - delegate to backend
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override fun Tensor<FP32, Float>.t(): Tensor<FP32, Float> = with(backend) { this@t.t() }
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override fun Tensor<FP32, Float>.relu(): Tensor<FP32, Float> = with(backend) { this@relu.relu() }
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override fun Tensor<FP32, Float>.sigmoid(): Tensor<FP32, Float> = with(backend) { this@sigmoid.sigmoid() }
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override fun Tensor<FP32, Float>.tanh(): Tensor<FP32, Float> = with(backend) { this@tanh.tanh() }
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override fun Tensor<FP32, Float>.softmax(dimension: Int): Tensor<FP32, Float> = with(backend) { this@softmax.softmax(dimension) }
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override fun Tensor<FP32, Float>.flatten(startDim: Int, endDim: Int): Tensor<FP32, Float> = with(backend) { this@flatten.flatten(startDim, endDim) }
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public companion object {
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/**
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* Creates a tensor from an array with the given shape.
@@ -369,4 +378,127 @@ public class CpuBackend : ComputeBackend<FP32, Float> {
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val result = t.data.map { this.toFloat() / it }.toFloatArray()
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return CpuTensorFP32(t.shape, result)
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}
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// Advanced tensor operations
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override fun Tensor<FP32, Float>.t(): Tensor<FP32, Float> {
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require(this is CpuTensorFP32) { "Tensor must be CpuTensorFP32" }
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require(this.shape.rank == 2) { "Transpose only supported for 2D tensors (matrices)" }
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val rows = this.shape[0]
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val cols = this.shape[1]
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val result = FloatArray(rows * cols)
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for (i in 0 until rows) {
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for (j in 0 until cols) {
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result[j * rows + i] = this.data[i * cols + j]
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}
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}
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return CpuTensorFP32(Shape(cols, rows), result)
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}
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override fun Tensor<FP32, Float>.relu(): Tensor<FP32, Float> {
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require(this is CpuTensorFP32) { "Tensor must be CpuTensorFP32" }
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val result = this.data.map { maxOf(0f, it) }.toFloatArray()
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return CpuTensorFP32(this.shape, result)
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}
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override fun Tensor<FP32, Float>.sigmoid(): Tensor<FP32, Float> {
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require(this is CpuTensorFP32) { "Tensor must be CpuTensorFP32" }
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val result = this.data.map { 1f / (1f + exp(-it)) }.toFloatArray()
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return CpuTensorFP32(this.shape, result)
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}
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override fun Tensor<FP32, Float>.tanh(): Tensor<FP32, Float> {
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require(this is CpuTensorFP32) { "Tensor must be CpuTensorFP32" }
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val result = this.data.map { tanh(it) }.toFloatArray()
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return CpuTensorFP32(this.shape, result)
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}
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override fun Tensor<FP32, Float>.softmax(dimension: Int): Tensor<FP32, Float> {
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require(this is CpuTensorFP32) { "Tensor must be CpuTensorFP32" }
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require(dimension in 0 until this.shape.rank) { "Dimension $dimension is out of bounds for tensor with rank ${this.shape.rank}" }
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when (this.shape.rank) {
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1 -> {
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// For 1D tensor, apply softmax across the single dimension
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val maxVal = this.data.maxOrNull() ?: 0f
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val expValues = this.data.map { exp(it - maxVal) }
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val sum = expValues.sum()
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val result = expValues.map { it / sum }.toFloatArray()
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return CpuTensorFP32(this.shape, result)
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}
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2 -> {
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// For 2D tensor (matrix), apply softmax along specified dimension
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val rows = this.shape[0]
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val cols = this.shape[1]
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val result = FloatArray(this.data.size)
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if (dimension == 0) {
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// Apply softmax along rows (for each column)
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for (j in 0 until cols) {
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val columnValues = FloatArray(rows) { i -> this.data[i * cols + j] }
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val maxVal = columnValues.maxOrNull() ?: 0f
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val expValues = columnValues.map { exp(it - maxVal) }
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val sum = expValues.sum()
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for (i in 0 until rows) {
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result[i * cols + j] = expValues[i] / sum
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}
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}
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} else {
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// Apply softmax along columns (for each row)
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for (i in 0 until rows) {
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val rowStart = i * cols
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val rowValues = this.data.sliceArray(rowStart until rowStart + cols)
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val maxVal = rowValues.maxOrNull() ?: 0f
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val expValues = rowValues.map { exp(it - maxVal) }
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val sum = expValues.sum()
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for (j in 0 until cols) {
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result[rowStart + j] = expValues[j] / sum
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}
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}
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}
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return CpuTensorFP32(this.shape, result)
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}
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else -> {
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throw UnsupportedOperationException("Softmax not implemented for tensors with rank > 2")
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}
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}
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}
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override fun Tensor<FP32, Float>.flatten(startDim: Int, endDim: Int): Tensor<FP32, Float> {
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require(this is CpuTensorFP32) { "Tensor must be CpuTensorFP32" }
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val actualEndDim = if (endDim == -1) this.shape.rank - 1 else endDim
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require(startDim >= 0 && startDim < this.shape.rank) { "startDim $startDim is out of bounds" }
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require(actualEndDim >= startDim && actualEndDim < this.shape.rank) { "endDim $actualEndDim is out of bounds or less than startDim" }
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if (startDim == actualEndDim) {
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// No flattening needed
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return CpuTensorFP32(this.shape, this.data.copyOf())
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}
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// Calculate new shape
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val newDimensions = mutableListOf<Int>()
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// Add dimensions before startDim
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for (i in 0 until startDim) {
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newDimensions.add(this.shape[i])
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}
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// Calculate flattened dimension size
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var flattenedSize = 1
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for (i in startDim..actualEndDim) {
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flattenedSize *= this.shape[i]
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}
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newDimensions.add(flattenedSize)
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// Add dimensions after endDim
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for (i in (actualEndDim + 1) until this.shape.rank) {
498+
newDimensions.add(this.shape[i])
499+
}
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val newShape = Shape(*newDimensions.toIntArray())
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return CpuTensorFP32(newShape, this.data.copyOf())
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}
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}

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