Negative indices
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@ -28,7 +28,7 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
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public operator fun TensorType.get(i: Int): TensorType
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//https://pytorch.org/docs/stable/generated/torch.transpose.html
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public fun TensorType.transpose(i: Int, j: Int): TensorType
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public fun TensorType.transpose(i: Int = -2, j: Int = -1): TensorType
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//https://pytorch.org/docs/stable/tensor_view.html
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public fun TensorType.view(shape: IntArray): TensorType
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@ -40,7 +40,7 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
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//https://pytorch.org/docs/stable/generated/torch.diag_embed.html
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public fun diagonalEmbedding(
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diagonalEntries: TensorType,
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offset: Int = 0, dim1: Int = 0, dim2: Int = 1
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offset: Int = 0, dim1: Int = -2, dim2: Int = -1
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): TensorType
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}
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@ -198,19 +198,21 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
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}
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override fun DoubleTensor.transpose(i: Int, j: Int): DoubleTensor {
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checkTranspose(this.dimension, i, j)
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val ii = minusIndex(i)
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val jj = minusIndex(j)
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checkTranspose(this.dimension, ii, jj)
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val n = this.linearStructure.size
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val resBuffer = DoubleArray(n)
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val resShape = this.shape.copyOf()
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resShape[i] = resShape[j].also { resShape[j] = resShape[i] }
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resShape[ii] = resShape[jj].also { resShape[jj] = resShape[ii] }
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val resTensor = DoubleTensor(resShape, resBuffer)
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for (offset in 0 until n) {
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val oldMultiIndex = this.linearStructure.index(offset)
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val newMultiIndex = oldMultiIndex.copyOf()
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newMultiIndex[i] = newMultiIndex[j].also { newMultiIndex[j] = newMultiIndex[i] }
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newMultiIndex[ii] = newMultiIndex[jj].also { newMultiIndex[jj] = newMultiIndex[ii] }
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val linearIndex = resTensor.linearStructure.offset(newMultiIndex)
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resTensor.buffer.array()[linearIndex] =
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@ -283,16 +285,18 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
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}
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override fun diagonalEmbedding(diagonalEntries: DoubleTensor, offset: Int, dim1: Int, dim2: Int): DoubleTensor {
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val d1 = minusIndexFrom(diagonalEntries.linearStructure.dim + 1, dim1)
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val d2 = minusIndexFrom(diagonalEntries.linearStructure.dim + 1, dim2)
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val n = diagonalEntries.shape.size
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if (dim1 == dim2) {
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throw RuntimeException("Diagonal dimensions cannot be identical $dim1, $dim2")
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if (d1 == d2) {
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throw RuntimeException("Diagonal dimensions cannot be identical $d1, $d2")
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}
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if (dim1 > n || dim2 > n) {
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if (d1 > n || d2 > n) {
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throw RuntimeException("Dimension out of range")
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}
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var lessDim = dim1
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var greaterDim = dim2
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var lessDim = d1
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var greaterDim = d2
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var realOffset = offset
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if (lessDim > greaterDim) {
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realOffset *= -1
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@ -73,6 +73,9 @@ public class TensorLinearStructure(public val shape: IntArray)
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public val size: Int
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get() = shape.reduce(Int::times)
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public val dim: Int
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get() = shape.size
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public fun indices(): Sequence<IntArray> = (0 until size).asSequence().map {
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index(it)
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}
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@ -39,4 +39,14 @@ internal fun Buffer<Double>.array(): DoubleArray = when (this) {
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internal inline fun getRandomNormals(n: Int, seed: Long): DoubleArray {
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val u = Random(seed)
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return (0 until n).map { sqrt(-2.0 * ln(u.nextDouble())) * cos(2.0 * PI * u.nextDouble()) }.toDoubleArray()
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}
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}
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internal inline fun minusIndexFrom(n: Int, i: Int) : Int = if (i >= 0) i else {
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val ii = n + i
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check(ii >= 0) {
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"Out of bound index $i for tensor of dim $n"
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}
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ii
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}
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internal inline fun <T> BufferedTensor<T>.minusIndex(i: Int): Int = minusIndexFrom(this.linearStructure.dim, i)
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@ -130,7 +130,7 @@ class TestDoubleLinearOpsTensorAlgebra {
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fun testBatchedSVD() = DoubleLinearOpsTensorAlgebra {
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val tensor = randNormal(intArrayOf(7, 5, 3), 0)
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val (tensorU, tensorS, tensorV) = tensor.svd()
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val tensorSVD = tensorU dot (diagonalEmbedding(tensorS,0,1,2) dot tensorV)
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val tensorSVD = tensorU dot (diagonalEmbedding(tensorS) dot tensorV)
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assertTrue(tensor.eq(tensorSVD))
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}
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@ -139,7 +139,7 @@ class TestDoubleLinearOpsTensorAlgebra {
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val tensor = randNormal(shape = intArrayOf(5, 2, 2), 0)
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val tensorSigma = tensor + tensor.transpose(1, 2)
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val (tensorS, tensorV) = tensorSigma.symEig()
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val tensorSigmaCalc = tensorV dot (diagonalEmbedding(tensorS, 0,1,2) dot tensorV.transpose(1, 2))
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val tensorSigmaCalc = tensorV dot (diagonalEmbedding(tensorS) dot tensorV.transpose(1, 2))
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assertTrue(tensorSigma.eq(tensorSigmaCalc))
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}
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@ -35,8 +35,8 @@ class TestDoubleTensorAlgebra {
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fun transpose1x2x3() = DoubleTensorAlgebra {
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val tensor = fromArray(intArrayOf(1, 2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
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val res01 = tensor.transpose(0, 1)
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val res02 = tensor.transpose(0, 2)
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val res12 = tensor.transpose(1, 2)
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val res02 = tensor.transpose(-3, 2)
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val res12 = tensor.transpose()
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assertTrue(res01.shape contentEquals intArrayOf(2, 1, 3))
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assertTrue(res02.shape contentEquals intArrayOf(3, 2, 1))
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