Package io.github.kirstenali.deepj.layers.transformer.blocks

Class LlamaTransformerBlock

java.lang.Object

io.github.kirstenali.deepj.layers.transformer.blocks.LlamaTransformerBlock

All Implemented Interfaces:

Layer, Trainable

public final class LlamaTransformerBlock
extends Object

Pre-LN transformer block with Llama-style full-head attention:

   x = x + RoPE-Attn( RMSNorm(x) )
   x = x + SwiGLU(    RMSNorm(x) )
 

Composes RMSNorm1D, RoPEMultiHeadSelfAttention, and SwiGLULayer.

Constructor Summary

Constructors

Constructor	Description
LlamaTransformerBlock(int dModel, int nHeads, int dFF, int maxSeqLen, Random rnd)

Method Summary

Modifier and Type	Method	Description
Tensor	backward(Tensor gradOut)
Tensor	forward(Tensor x)
List<Parameter>	parameters()
protected Layer[]	subLayers()	Return the ordered sub-layers that own trainable parameters.

Methods inherited from class java.lang.Object

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

Methods inherited from interface io.github.kirstenali.deepj.training.Trainable

zeroGrad

Constructor Details

LlamaTransformerBlock

public LlamaTransformerBlock(int dModel,
 int nHeads,
 int dFF,
 int maxSeqLen,
 Random rnd)

Parameters:

dModel - model dimension

nHeads - attention heads (must divide dModel)

dFF - SwiGLU intermediate dimension (typically ≈ 8/3 × dModel)

maxSeqLen - maximum sequence length for the RoPE table

rnd - random source for weight initialisation

Method Details

subLayers

protected Layer[] subLayers()

Return the ordered sub-layers that own trainable parameters.

forward

public Tensor forward(Tensor x)

backward

public Tensor backward(Tensor gradOut)

parameters

public List<Parameter> parameters()

Specified by:

parameters in interface Trainable