Package io.github.kirstenali.deepj.tensor

Class Tensor

java.lang.Object

io.github.kirstenali.deepj.tensor.TensorStorageAccess

io.github.kirstenali.deepj.tensor.TensorManipulations

io.github.kirstenali.deepj.tensor.Tensor

public class Tensor
extends TensorManipulations

Constructor Summary

Constructors

Constructor	Description
Tensor(int... shape)
Tensor(int rows, int cols)
Tensor(DType dtype, int... shape)
Tensor(Tensor source)

Method Summary

Modifier and Type	Method	Description
static void	adamWUpdate(Tensor w, Tensor g, Tensor mt, Tensor vt, float lr, float beta1, float beta2, float eps, float weightDecay, float bc1, float bc2)
Tensor	add(Tensor other)
Tensor	addBroadcastCols(Tensor colVector)
Tensor	addBroadcastRows(Tensor rowVector)
Tensor	addInPlace(Tensor other)
Tensor	addRowVector(Tensor rowVector)
Tensor	addScalar(float s)
Tensor	addScalarInPlace(float scalar)
static TensorBackend	backend()
void	backward()
void	backward(Tensor gradient)
static Tensor	causalMask(int size)
Tensor	clamp(float min, float max)
Tensor	copy()	Bit-preserving independent storage with an identity autograd edge.
float	crossEntropyLoss(int[] targets)
float[]	data()	Materializes and returns the backing FLOAT32 array of a full contiguous tensor.
Tensor	detach()	Independent detached storage, preserving DeepJ's existing copy-based detach behavior.
Tensor	divide(Tensor other)
Tensor	divideBroadcastCols(Tensor colVector)
Tensor	divideInPlace(Tensor other)
Tensor	divideScalar(float s)
Tensor	divideScalarInPlace(float scalar)
Tensor	exp()
Tensor	expInPlace()
static Tensor	from(float[] values)
static Tensor	from(float[] values, int... shape)
static Tensor	from(float[] values, DType dtype)
static Tensor	from(float[] values, DType dtype, int... shape)
static Tensor	from2D(float[][] data)
Tensor	geluActivation()
Tensor	geluInPlace()
Tensor	getRow(int row)
Tensor	grad()
float	item()
Tensor	log()
Tensor	logInPlace()
void	materialize()
Tensor	matmul(Tensor other)
Tensor	maxAlongRows()
Tensor	meanAlongRows()
Tensor	multiply(Tensor other)
Tensor	multiplyBroadcastCols(Tensor colVector)
Tensor	multiplyBroadcastRows(Tensor rowVector)
Tensor	multiplyInPlace(Tensor other)
Tensor	multiplyScalar(float s)
Tensor	multiplyScalarInPlace(float scalar)
Tensor	neg()
Tensor	negInPlace()
static Tensor	ones(int rows, int cols)
Tensor	pow(float exponent)
void	print(String label)
static Tensor	random(int rows, int cols, Random rand)
Tensor	reluActivation()
Tensor	reluInPlace()
static void	requireSameShape(Tensor a, Tensor b, String op)
Tensor	requiresGrad(boolean required)
static void	requireTargetsMatchRows(Tensor logits, int[] targets)
Tensor	retainDeviceBuffer()
float[]	rowData(int r)
static Tensor	sampleRows(Tensor t, int n, Random rnd)
static void	scatterAddRows(Tensor target, int[] indices, Tensor grad)
static void	setBackend(TensorBackend backend)
void	setRow(int row, Tensor source, int srcRow)
Tensor	sigmoidActivation()
Tensor	sigmoidInPlace()
static Tensor	sliceRows(Tensor t, int[] rowIndices, int cols)
Tensor	softmaxRows()
Tensor	sqrt()
Tensor	sqrtInPlace()
Tensor	subtract(Tensor other)
Tensor	subtractBroadcastCols(Tensor colVector)
Tensor	subtractInPlace(Tensor other)
float	sum()
float	sumAbs()
Tensor	sumAlongCols()
Tensor	sumAlongRows()
Tensor	sumRows()
Tensor	sumTensor()
Tensor	tanhActivation()
Tensor	tanhInPlace()
protected Tensor	tensor()
Tensor	transpose()
Tensor	varianceAlongRows()
void	zeroGrad()
static Tensor	zeros(int rows, int cols)

Methods inherited from class io.github.kirstenali.deepj.tensor.TensorManipulations

contiguous, copyFrom, expand, indexAddInPlace, indexSelect, maxAxes, maximum, mean, meanAxes, ones, permute, random, requiresGrad, reshape, select, slice, slice, softmax, sum, sumAxes, to, transpose, unsqueeze, variance, variance, varianceAxes, view, zeros

Methods inherited from class io.github.kirstenali.deepj.tensor.TensorStorageAccess

coversStorage, dtype, get, get, getGpuTag, gradientState, hasHostStorage, hasSameShape, hasShape, isContiguous, matrixCols, matrixRows, numel, rank, rawData, releaseDeviceBufferRetention, retainsDeviceBuffer, set, setAt, setGpuTag, shape, size, storageBytes, storageOffset, strides, toFloatArray

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

Tensor

public Tensor(int... shape)

Tensor

public Tensor(int rows,
 int cols)

Tensor

public Tensor(DType dtype,
 int... shape)

Tensor

public Tensor(Tensor source)

Method Details

tensor

protected Tensor tensor()

Specified by:

tensor in class TensorManipulations

requiresGrad

public Tensor requiresGrad(boolean required)

grad

public Tensor grad()

zeroGrad

public void zeroGrad()

backward

public void backward()

backward

public void backward(Tensor gradient)

detach

public Tensor detach()

Independent detached storage, preserving DeepJ's existing copy-based detach behavior.

copy

public Tensor copy()

Bit-preserving independent storage with an identity autograd edge.

sumTensor

public Tensor sumTensor()

item

public float item()

retainDeviceBuffer

public Tensor retainDeviceBuffer()

setBackend

public static void setBackend(TensorBackend backend)

backend

public static TensorBackend backend()

data

public float[] data()

Materializes and returns the backing FLOAT32 array of a full contiguous tensor.

rowData

public float[] rowData(int r)

materialize

public void materialize()

Specified by:

materialize in class TensorStorageAccess

matmul

public Tensor matmul(Tensor other)

add

public Tensor add(Tensor other)

subtract

public Tensor subtract(Tensor other)

multiply

public Tensor multiply(Tensor other)

divide

public Tensor divide(Tensor other)

addRowVector

public Tensor addRowVector(Tensor rowVector)

addBroadcastCols

public Tensor addBroadcastCols(Tensor colVector)

divideBroadcastCols

public Tensor divideBroadcastCols(Tensor colVector)

subtractBroadcastCols

public Tensor subtractBroadcastCols(Tensor colVector)

multiplyBroadcastCols

public Tensor multiplyBroadcastCols(Tensor colVector)

addBroadcastRows

public Tensor addBroadcastRows(Tensor rowVector)

multiplyBroadcastRows

public Tensor multiplyBroadcastRows(Tensor rowVector)

multiplyScalar

public Tensor multiplyScalar(float s)

addScalar

public Tensor addScalar(float s)

divideScalar

public Tensor divideScalar(float s)

sumRows

public Tensor sumRows()

sumAlongRows

public Tensor sumAlongRows()

sumAlongCols

public Tensor sumAlongCols()

meanAlongRows

public Tensor meanAlongRows()

varianceAlongRows

public Tensor varianceAlongRows()

transpose

public Tensor transpose()

sqrt

public Tensor sqrt()

neg

public Tensor neg()

exp

public Tensor exp()

log

public Tensor log()

tanhActivation

public Tensor tanhActivation()

sigmoidActivation

public Tensor sigmoidActivation()

reluActivation

public Tensor reluActivation()

geluActivation

public Tensor geluActivation()

addInPlace

public Tensor addInPlace(Tensor other)

subtractInPlace

public Tensor subtractInPlace(Tensor other)

multiplyInPlace

public Tensor multiplyInPlace(Tensor other)

divideInPlace

public Tensor divideInPlace(Tensor other)

multiplyScalarInPlace

public Tensor multiplyScalarInPlace(float scalar)

addScalarInPlace

public Tensor addScalarInPlace(float scalar)

divideScalarInPlace

public Tensor divideScalarInPlace(float scalar)

sqrtInPlace

public Tensor sqrtInPlace()

negInPlace

public Tensor negInPlace()

expInPlace

public Tensor expInPlace()

logInPlace

public Tensor logInPlace()

reluInPlace

public Tensor reluInPlace()

geluInPlace

public Tensor geluInPlace()

tanhInPlace

public Tensor tanhInPlace()

sigmoidInPlace

public Tensor sigmoidInPlace()

softmaxRows

public Tensor softmaxRows()

adamWUpdate

public static void adamWUpdate(Tensor w,
 Tensor g,
 Tensor mt,
 Tensor vt,
 float lr,
 float beta1,
 float beta2,
 float eps,
 float weightDecay,
 float bc1,
 float bc2)

maxAlongRows

public Tensor maxAlongRows()

clamp

public Tensor clamp(float min,
 float max)

pow

public Tensor pow(float exponent)

scatterAddRows

public static void scatterAddRows(Tensor target,
 int[] indices,
 Tensor grad)

from2D

public static Tensor from2D(float[][] data)

from

public static Tensor from(float[] values,
 int... shape)

from

public static Tensor from(float[] values)

from

public static Tensor from(float[] values,
 DType dtype)

from

public static Tensor from(float[] values,
 DType dtype,
 int... shape)

sum

public float sum()

sumAbs

public float sumAbs()

crossEntropyLoss

public float crossEntropyLoss(int[] targets)

getRow

public Tensor getRow(int row)

setRow

public void setRow(int row,
 Tensor source,
 int srcRow)

sliceRows

public static Tensor sliceRows(Tensor t,
 int[] rowIndices,
 int cols)

sampleRows

public static Tensor sampleRows(Tensor t,
 int n,
 Random rnd)

print

public void print(String label)

zeros

public static Tensor zeros(int rows,
 int cols)

ones

public static Tensor ones(int rows,
 int cols)

random

public static Tensor random(int rows,
 int cols,
 Random rand)

causalMask

public static Tensor causalMask(int size)

requireSameShape

public static void requireSameShape(Tensor a,
 Tensor b,
 String op)

requireTargetsMatchRows

public static void requireTargetsMatchRows(Tensor logits,
 int[] targets)