From dadd15e1f68d1c6cbbc1b71b4113f147fe6c7f34 Mon Sep 17 00:00:00 2001 From: inter Date: Thu, 4 Sep 2025 14:09:18 +0800 Subject: [PATCH] Add File --- .../seflAttention/MultiSelfAttention.java | 215 ++++++++++++++++++ 1 file changed, 215 insertions(+) create mode 100644 src/main/java/org/dromara/easyai/transFormer/seflAttention/MultiSelfAttention.java diff --git a/src/main/java/org/dromara/easyai/transFormer/seflAttention/MultiSelfAttention.java b/src/main/java/org/dromara/easyai/transFormer/seflAttention/MultiSelfAttention.java new file mode 100644 index 0000000..17948b4 --- /dev/null +++ b/src/main/java/org/dromara/easyai/transFormer/seflAttention/MultiSelfAttention.java @@ -0,0 +1,215 @@ +package org.dromara.easyai.transFormer.seflAttention; + +import org.dromara.easyai.matrixTools.Matrix; +import org.dromara.easyai.matrixTools.MatrixOperation; +import org.dromara.easyai.i.OutBack; +import org.dromara.easyai.transFormer.CodecBlock; +import org.dromara.easyai.transFormer.TransWordVector; +import org.dromara.easyai.transFormer.model.MultiSelfAttentionModel; +import org.dromara.easyai.transFormer.model.QKVModel; + +import java.util.*; + +public class MultiSelfAttention {//多头自注意力层 + private final CodecBlock codecBlock;//本层壳子 + private final List selfAttentions = new ArrayList<>(); + private LayNorm layNorm; + private final float studyPoint; + private Matrix powerMatrix;//权重矩阵 + private final int multiNumber;//头数 + private final int wordVectorDimension;//维度 + private Matrix featureMatrix;//接受到的特征矩阵 + private final int depth;//深度 + private final boolean encoder; + private final MatrixOperation matrixOperation; + private final TransWordVector transWordVector; + + public void setLayNorm(LayNorm layNorm) { + this.layNorm = layNorm; + } + + public int getDepth() { + return depth; + } + + private QKVModel getQKV(List qkvModelList, int selfID) { + QKVModel myQKV = null; + for (QKVModel qkvModel : qkvModelList) { + if (qkvModel.getSelfID() == selfID) { + myQKV = qkvModel; + break; + } + } + return myQKV; + } + + public void insertModel(MultiSelfAttentionModel multiSelfAttentionModel) throws Exception { + insertPower(multiSelfAttentionModel.getPowerModel(), powerMatrix); + List qkvModelList = multiSelfAttentionModel.getQkvModelList(); + for (int i = 0; i < selfAttentions.size(); i++) { + QKVModel qkvModel = getQKV(qkvModelList, i); + if (qkvModel != null) { + selfAttentions.get(i).insertModel(qkvModel); + } else { + throw new Exception("模型与激活参数不匹配!内存与模型文件的多头数量不一致！"); + } + } + } + + private void insertPower(float[][] modelPower, Matrix power) throws Exception { + for (int i = 0; i < power.getX(); i++) { + for (int j = 0; j < power.getY(); j++) { + power.setNub(i, j, modelPower[i][j]); + } + } + } + + public MultiSelfAttentionModel getModel() throws Exception { + MultiSelfAttentionModel multiSelfAttentionModel = new MultiSelfAttentionModel(); + List qkvModelList = new ArrayList<>(); + for (SelfAttention selfAttention : selfAttentions) { + qkvModelList.add(selfAttention.getModel()); + } + multiSelfAttentionModel.setPowerModel(powerMatrix.getMatrix()); + multiSelfAttentionModel.setQkvModelList(qkvModelList); + multiSelfAttentionModel.setDepth(depth); + return multiSelfAttentionModel; + } + + private void mergeFeatureMatrix(Matrix myMultiFeature, Matrix matrix, int index) throws Exception {//拼接多头特征 + int startY = wordVectorDimension * index; + int endY = startY + wordVectorDimension; + for (int i = 0; i < matrix.getX(); i++) { + for (int j = startY; j < endY; j++) { + myMultiFeature.setNub(i, j, matrix.getNumber(i, j - startY)); + } + } + } + + private List splitMatrix(Matrix subFeature) {//对多头矩阵进行拆分 + List matrixList = new ArrayList<>(); + int maxDeep = subFeature.getX(); + for (int i = 0; i < selfAttentions.size(); i++) { + Matrix matrix = subFeature.getSonOfMatrix(0, i * wordVectorDimension, maxDeep, wordVectorDimension); + matrixList.add(matrix); + } + return matrixList; + } + + public void backError(Matrix allErrorMatrix, long eventID) throws Exception { + Matrix error = matrixOperation.mathMulBySelf(allErrorMatrix, studyPoint); + //求多头自注意力层权重矩阵的偏导矩阵 + Matrix subPower = matrixOperation.matrixMulPd(error, featureMatrix, powerMatrix, false); + Matrix subFeature = matrixOperation.matrixMulPd(allErrorMatrix, featureMatrix, powerMatrix, true); + powerMatrix = matrixOperation.add(powerMatrix, subPower);//更新权重矩阵 + List matrixList = splitMatrix(subFeature);//拆分矩阵 + Matrix allNextFeatureError = null; + Matrix allLastEncoderError = null; + for (int i = 0; i < selfAttentions.size(); i++) {//将误差回传到每一个自注意力层 + AttentionError attentionError = getSefAttentionBySelfID(i).backError(matrixList.get(i), eventID); + Matrix nextFeatureError = attentionError.getNextFeatureError(); + if (allNextFeatureError == null) { + allNextFeatureError = nextFeatureError; + } else { + allNextFeatureError = matrixOperation.add(allNextFeatureError, nextFeatureError); + } + if (!encoder && depth > 1) {//深度大于1的解码器 + Matrix lastEncoderError = attentionError.getLastEncoderError(); + if (allLastEncoderError == null) { + allLastEncoderError = lastEncoderError; + } else { + allLastEncoderError = matrixOperation.add(allLastEncoderError, lastEncoderError); + } + } + } + if (!encoder && depth > 1) {//给最后一层编码器传送特征，等待回传 + codecBlock.backLastEncoderError(allLastEncoderError); + } + if (codecBlock != null) { + codecBlock.backCodecError(allNextFeatureError, eventID, allErrorMatrix);//将下层误差发送 + } else {//第一层解码器，回传调整词向量 + transWordVector.backDecoderError(allNextFeatureError, allErrorMatrix); + } + } + + + private SelfAttention getSefAttentionBySelfID(int selfID) { + SelfAttention mySelfAttention = null; + for (SelfAttention selfAttention : selfAttentions) { + if (selfAttention.getSelfID() == selfID) { + mySelfAttention = selfAttention; + break; + } + } + return mySelfAttention; + } + + private Matrix countMultiSelfAttention(List eventBodies, boolean isStudy) throws Exception { + int one = wordVectorDimension * multiNumber; + Matrix myMultiFeature = null; + for (int i = 0; i < eventBodies.size(); i++) { + EventBody eventBody = getEventBodyBySelfID(i, eventBodies); + Matrix matrix = eventBody.getFeatureMatrix(); + if (i == 0) { + myMultiFeature = new Matrix(matrix.getX(), one); + } + mergeFeatureMatrix(myMultiFeature, matrix, i); + } + Matrix out = matrixOperation.mulMatrix(myMultiFeature, powerMatrix); + if (isStudy) { + featureMatrix = myMultiFeature;//保存训练时输入的特征矩阵 + } + return out; + } + + private EventBody getEventBodyBySelfID(int selfID, List eventBodies) { + EventBody eventBody = null; + for (EventBody myEventBody : eventBodies) { + if (myEventBody.getSelfID() == selfID) { + eventBody = myEventBody; + break; + } + } + return eventBody; + } + + + public void sendMatrixMessage(long eventID, Matrix feature, boolean isStudy + , OutBack outBack, List E, Matrix encoderFeature, boolean outAllPro) throws Exception {//从输入神经元 + List eventBodies = new ArrayList<>(); + for (SelfAttention selfAttention : selfAttentions) { + EventBody eventBody = selfAttention.sendMatrixFeature(eventID, isStudy, feature, encoderFeature); + eventBodies.add(eventBody); + } + Matrix matrix = countMultiSelfAttention(eventBodies, isStudy);//多头输出 + layNorm.addNorm(feature, matrix, eventID, isStudy, outBack, E, encoderFeature, outAllPro);//进第一个残差层 + } + + + public MultiSelfAttention(int multiNumber, float studyPoint, int depth, int wordVectorDimension, boolean encoder, + CodecBlock codecBlock, int coreNumber, TransWordVector transWordVector) throws Exception { + Random random = new Random(); + matrixOperation = new MatrixOperation(coreNumber); + this.transWordVector = transWordVector; + this.codecBlock = codecBlock; + this.encoder = encoder; + int yiZhi = wordVectorDimension * multiNumber; + this.studyPoint = studyPoint; + this.wordVectorDimension = wordVectorDimension; + this.multiNumber = multiNumber; + this.depth = depth; + for (int k = 0; k < multiNumber; k++) { + SelfAttention selfAttention = new SelfAttention(studyPoint, depth, wordVectorDimension, k, encoder, coreNumber); + selfAttentions.add(selfAttention); + } + powerMatrix = new Matrix(yiZhi, wordVectorDimension); + int x = powerMatrix.getX(); + int y = powerMatrix.getY(); + for (int i = 0; i < x; i++) { + for (int j = 0; j < y; j++) { + powerMatrix.setNub(i, j, random.nextFloat() / yiZhi); + } + } + } + +}