Package io.github.kirstenali.deepj.tensor

Interface TensorBackend

All Known Implementing Classes:

CpuBackend, MetalBackend

public interface TensorBackend

Method Summary

Modifier and Type	Method	Description
void	adamWUpdate(Tensor w, Tensor g, Tensor mt, Tensor vt, float lr, float beta1, float beta2, float eps, float weightDecay, float bc1, float bc2)	In-place AdamW update.
Tensor	add(Tensor a, Tensor b)
Tensor	addBroadcastCols(Tensor a, Tensor colVector)
Tensor	addBroadcastRows(Tensor a, Tensor rowVector)
void	addInPlace(Tensor a, Tensor b)
Tensor	addRowVector(Tensor a, Tensor rowVector)
Tensor	addScalar(Tensor a, float scalar)
void	addScalarInPlace(Tensor a, float s)
Tensor	clamp(Tensor a, float min, float max)
Tensor	crossEntropyGradient(Tensor logits, int[] targets)
float	crossEntropyLoss(Tensor logits, int[] targets)
Tensor	divide(Tensor a, Tensor b)
Tensor	divideBroadcastCols(Tensor a, Tensor colVector)
void	divideInPlace(Tensor a, Tensor b)
Tensor	divideScalar(Tensor a, float scalar)
void	divideScalarInPlace(Tensor a, float s)
Tensor	exp(Tensor a)
void	expInPlace(Tensor a)
Tensor	gelu(Tensor a)
Tensor	geluBackward(Tensor input, Tensor gradOutput)
void	geluInPlace(Tensor a)
Tensor	layerNormBackward(Tensor dXHat, Tensor xHat, Tensor std, int dim)	LayerNorm backward through normalization (given dXHat, xHat, std).
Tensor	log(Tensor a)
void	logInPlace(Tensor a)
default void	materializeTensor(Tensor t)	Materialize a tensor: flush any pending GPU computation and download the result to the tensor's CPU data[].
Tensor	matmul(Tensor a, Tensor b)
Tensor	maxAlongRows(Tensor a)
Tensor	meanAlongRows(Tensor a)
Tensor	multiply(Tensor a, Tensor b)
Tensor	multiplyBroadcastCols(Tensor a, Tensor colVector)
Tensor	multiplyBroadcastRows(Tensor a, Tensor rowVector)
void	multiplyInPlace(Tensor a, Tensor b)
Tensor	multiplyScalar(Tensor a, float scalar)
void	multiplyScalarInPlace(Tensor a, float s)
Tensor	neg(Tensor a)
void	negInPlace(Tensor a)
Tensor	pow(Tensor a, float exponent)
default void	releaseResources()	Release backend-owned resources (GPU buffers, native handles, etc.).
Tensor	relu(Tensor a)
Tensor	reluBackward(Tensor input, Tensor gradOutput)
void	reluInPlace(Tensor a)
void	scatterAddRows(Tensor target, int[] indices, Tensor grad)
Tensor	sigmoid(Tensor a)
void	sigmoidInPlace(Tensor a)
Tensor	softmaxBackward(Tensor gradOutput, Tensor softmaxOut)
Tensor	softmaxRows(Tensor logits)
Tensor	sqrt(Tensor a)
void	sqrtInPlace(Tensor a)
Tensor	subtract(Tensor a, Tensor b)
Tensor	subtractBroadcastCols(Tensor a, Tensor colVector)
void	subtractInPlace(Tensor a, Tensor b)
float	sum(Tensor a)
float	sumAbs(Tensor a)
Tensor	sumAlongCols(Tensor a)
Tensor	sumAlongRows(Tensor a)
Tensor	sumRows(Tensor a)
Tensor	tanh(Tensor a)
void	tanhInPlace(Tensor a)
Tensor	transpose(Tensor a)
Tensor	varianceAlongRows(Tensor a)

Method Details

matmul

Tensor matmul(Tensor a,
 Tensor b)

add

Tensor add(Tensor a,
 Tensor b)

subtract

Tensor subtract(Tensor a,
 Tensor b)

multiply

Tensor multiply(Tensor a,
 Tensor b)

divide

Tensor divide(Tensor a,
 Tensor b)

addRowVector

Tensor addRowVector(Tensor a,
 Tensor rowVector)

addBroadcastCols

Tensor addBroadcastCols(Tensor a,
 Tensor colVector)

divideBroadcastCols

Tensor divideBroadcastCols(Tensor a,
 Tensor colVector)

subtractBroadcastCols

Tensor subtractBroadcastCols(Tensor a,
 Tensor colVector)

multiplyBroadcastCols

Tensor multiplyBroadcastCols(Tensor a,
 Tensor colVector)

addBroadcastRows

Tensor addBroadcastRows(Tensor a,
 Tensor rowVector)

multiplyBroadcastRows

Tensor multiplyBroadcastRows(Tensor a,
 Tensor rowVector)

multiplyScalar

Tensor multiplyScalar(Tensor a,
 float scalar)

addScalar

Tensor addScalar(Tensor a,
 float scalar)

divideScalar

Tensor divideScalar(Tensor a,
 float scalar)

sumRows

Tensor sumRows(Tensor a)

sumAlongRows

Tensor sumAlongRows(Tensor a)

sumAlongCols

Tensor sumAlongCols(Tensor a)

meanAlongRows

Tensor meanAlongRows(Tensor a)

varianceAlongRows

Tensor varianceAlongRows(Tensor a)

maxAlongRows

Tensor maxAlongRows(Tensor a)

sum

float sum(Tensor a)

sumAbs

float sumAbs(Tensor a)

transpose

Tensor transpose(Tensor a)

clamp

Tensor clamp(Tensor a,
 float min,
 float max)

sqrt

Tensor sqrt(Tensor a)

pow

Tensor pow(Tensor a,
 float exponent)

neg

Tensor neg(Tensor a)

exp

Tensor exp(Tensor a)

log

Tensor log(Tensor a)

tanh

Tensor tanh(Tensor a)

sigmoid

Tensor sigmoid(Tensor a)

relu

Tensor relu(Tensor a)

reluBackward

Tensor reluBackward(Tensor input,
 Tensor gradOutput)

gelu

Tensor gelu(Tensor a)

geluBackward

Tensor geluBackward(Tensor input,
 Tensor gradOutput)

softmaxRows

Tensor softmaxRows(Tensor logits)

softmaxBackward

Tensor softmaxBackward(Tensor gradOutput,
 Tensor softmaxOut)

crossEntropyLoss

float crossEntropyLoss(Tensor logits,
 int[] targets)

crossEntropyGradient

Tensor crossEntropyGradient(Tensor logits,
 int[] targets)

adamWUpdate

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

In-place AdamW update. Mutates w, mt, vt.

layerNormBackward

Tensor layerNormBackward(Tensor dXHat,
 Tensor xHat,
 Tensor std,
 int dim)

LayerNorm backward through normalization (given dXHat, xHat, std).

scatterAddRows

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

addInPlace

void addInPlace(Tensor a,
 Tensor b)

subtractInPlace

void subtractInPlace(Tensor a,
 Tensor b)

multiplyInPlace

void multiplyInPlace(Tensor a,
 Tensor b)

divideInPlace

void divideInPlace(Tensor a,
 Tensor b)

multiplyScalarInPlace

void multiplyScalarInPlace(Tensor a,
 float s)

addScalarInPlace

void addScalarInPlace(Tensor a,
 float s)

divideScalarInPlace

void divideScalarInPlace(Tensor a,
 float s)

sqrtInPlace

void sqrtInPlace(Tensor a)

negInPlace

void negInPlace(Tensor a)

expInPlace

void expInPlace(Tensor a)

logInPlace

void logInPlace(Tensor a)

reluInPlace

void reluInPlace(Tensor a)

geluInPlace

void geluInPlace(Tensor a)

tanhInPlace

void tanhInPlace(Tensor a)

sigmoidInPlace

void sigmoidInPlace(Tensor a)

materializeTensor

default void materializeTensor(Tensor t)

Materialize a tensor: flush any pending GPU computation and download the result to the tensor's CPU data[]. Default is a no-op (for CpuBackend).

releaseResources

default void releaseResources()

Release backend-owned resources (GPU buffers, native handles, etc.). Default is a no-op for backends without external resources.