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src/main/java/org/dromara/easyai/transFormer/CodecBlock.java
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179
src/main/java/org/dromara/easyai/transFormer/CodecBlock.java
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package org.dromara.easyai.transFormer;
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import org.dromara.easyai.function.ReLu;
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import org.dromara.easyai.i.OutBack;
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import org.dromara.easyai.matrixTools.Matrix;
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import org.dromara.easyai.matrixTools.MatrixOperation;
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import org.dromara.easyai.transFormer.model.CodecBlockModel;
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import org.dromara.easyai.transFormer.nerve.HiddenNerve;
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import org.dromara.easyai.transFormer.nerve.Nerve;
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import org.dromara.easyai.transFormer.seflAttention.LayNorm;
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import org.dromara.easyai.transFormer.seflAttention.MultiSelfAttention;
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import java.util.ArrayList;
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import java.util.HashMap;
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import java.util.List;
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import java.util.Map;
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public class CodecBlock {
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private final MultiSelfAttention multiSelfAttention;
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private final LayNorm attentionLayNorm;//注意力层残差归一化
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private final List<HiddenNerve> fistHiddenNerves = new ArrayList<>();//线性层第一层
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private final List<HiddenNerve> secondHiddenNerves = new ArrayList<>();//线性层第二层
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private final LayNorm lineLayNorm;//线性层残差归一化
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private final TransWordVector transWordVector;//内置词向量
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////////////////////////////////////
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private CodecBlock afterEncoderBlock;//后编码模块
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private CodecBlock beforeEncoderBlock;//前编码模块
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private CodecBlock lastEncoderBlock;//最后一层编码器
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private final Map<Long, Matrix> outMatrixMap = new HashMap<>();
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private final boolean encoder;//是否为编码器
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private LineBlock lineBlock;//解码器最后的线性分类器
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private FirstDecoderBlock firstDecoderBlock;//解码器第一层
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private final MatrixOperation matrixOperation;
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private final int coreNumber;
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public CodecBlockModel getModel() throws Exception {
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List<float[][]> firstNerveModel = new ArrayList<>();
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List<float[][]> secondNerveModel = new ArrayList<>();
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for (int i = 0; i < fistHiddenNerves.size(); i++) {
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firstNerveModel.add(fistHiddenNerves.get(i).getModel());
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secondNerveModel.add(secondHiddenNerves.get(i).getModel());
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}
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CodecBlockModel codecBlockModel = new CodecBlockModel();
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codecBlockModel.setMultiSelfAttentionModel(multiSelfAttention.getModel());
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codecBlockModel.setAttentionLayNormModel(attentionLayNorm.getModel());
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codecBlockModel.setFistNervesModel(firstNerveModel);
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codecBlockModel.setSecondNervesModel(secondNerveModel);
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codecBlockModel.setLineLayNormModel(lineLayNorm.getModel());
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return codecBlockModel;
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}
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public void insertModel(CodecBlockModel codecBlockModel) throws Exception {
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multiSelfAttention.insertModel(codecBlockModel.getMultiSelfAttentionModel());
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attentionLayNorm.insertModel(codecBlockModel.getAttentionLayNormModel());
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List<float[][]> firstNerveModel = codecBlockModel.getFistNervesModel();
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List<float[][]> secondNerveModel = codecBlockModel.getSecondNervesModel();
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for (int i = 0; i < fistHiddenNerves.size(); i++) {
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fistHiddenNerves.get(i).insertModel(firstNerveModel.get(i));
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secondHiddenNerves.get(i).insertModel(secondNerveModel.get(i));
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}
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lineLayNorm.insertModel(codecBlockModel.getLineLayNormModel());
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}
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public void setFirstDecoderBlock(FirstDecoderBlock firstDecoderBlock) {
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this.firstDecoderBlock = firstDecoderBlock;
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}
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public void setLineBlock(LineBlock lineBlock) {
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this.lineBlock = lineBlock;
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}
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public void setLastEncoderBlock(CodecBlock lastEncoderBlock) {
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this.lastEncoderBlock = lastEncoderBlock;
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}
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public void setAfterEncoderBlock(CodecBlock afterEncoderBlock) {
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this.afterEncoderBlock = afterEncoderBlock;
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}
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public void setBeforeEncoderBlock(CodecBlock beforeEncoderBlock) {
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this.beforeEncoderBlock = beforeEncoderBlock;
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}
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public CodecBlock(int multiNumber, int featureDimension, float studyPoint, int depth,
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boolean encoder, int regularModel, float regular, int coreNumber, TransWordVector transWordVector) throws Exception {//进行初始化
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matrixOperation = new MatrixOperation(coreNumber);
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this.encoder = encoder;
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this.transWordVector = transWordVector;
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this.coreNumber = coreNumber;
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attentionLayNorm = new LayNorm(1, featureDimension, this, null, studyPoint, coreNumber, encoder, depth);
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lineLayNorm = new LayNorm(2, featureDimension, this, null, studyPoint, coreNumber, encoder, depth);
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multiSelfAttention = new MultiSelfAttention(multiNumber, studyPoint, depth, featureDimension, encoder, this, coreNumber,
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null);
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multiSelfAttention.setLayNorm(attentionLayNorm);
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attentionLayNorm.setMultiSelfAttention(multiSelfAttention);
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initLine(featureDimension, studyPoint, regularModel, regular);
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attentionLayNorm.setHiddenNerves(fistHiddenNerves);
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lineLayNorm.setHiddenNerves(secondHiddenNerves);
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}
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public void backError(long eventID, Matrix errorMatrix) throws Exception {//最后线性层返回误差
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lineLayNorm.backErrorFromLine(errorMatrix, eventID);
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}
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public void removeOutMatrix(long eventID) {
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outMatrixMap.remove(eventID);
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}
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public Matrix getOutMatrix(long eventID) {
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return outMatrixMap.get(eventID);
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}
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public void sendOutputMatrix(long eventID, Matrix out, boolean isStudy, OutBack outBack,
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List<Integer> E, Matrix encoderFeature, boolean outAllPro) throws Exception {//参数正向出口
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if (beforeEncoderBlock != null) {
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beforeEncoderBlock.sendInputMatrix(eventID, out, isStudy, outBack, E, encoderFeature, outAllPro);
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} else if (encoder) {//编码器走到末尾 保存输出矩阵
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outMatrixMap.put(eventID, out);
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} else {//解码器走到头了 输出线性分类层
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lineBlock.sendParameter(eventID, out, isStudy, outBack, E, outAllPro);
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}
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}
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public void backCodecError(Matrix errorMatrix, long eventID, Matrix allFeature) throws Exception {//本层最终误差返回
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Matrix error = matrixOperation.add(errorMatrix, allFeature);
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if (afterEncoderBlock != null) {
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afterEncoderBlock.backError(eventID, error);
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} else if (firstDecoderBlock != null) {//将误差反给第一层解码器
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firstDecoderBlock.backError(eventID, error);
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} else {//返回给词向量
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transWordVector.backEncoderError(error);
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}
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}
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public void backLastEncoderError(Matrix error) throws Exception {//给最后一层编码器返回误差
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lastEncoderBlock.backLastError(error);
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}
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private void backLastError(Matrix error) throws Exception {//最后一层编码器接收error
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lineLayNorm.backLastError(error);
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}
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public void encoderBackStart(long eventID) throws Exception {//给最后一层编码器发送back指令
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lineLayNorm.encoderBackStart(eventID);
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}
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//Encoder 参数正向入口
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public void sendInputMatrix(long eventID, Matrix feature, boolean isStudy, OutBack outBack, List<Integer> E
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, Matrix encoderFeature, boolean outAllPro) throws Exception {
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multiSelfAttention.sendMatrixMessage(eventID, feature, isStudy, outBack, E, encoderFeature, outAllPro);
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}
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private void initLine(int featureDimension, float studyPoint, int regularModel, float regular) throws Exception {
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List<Nerve> firstNerves = new ArrayList<>();
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List<Nerve> secondNerves = new ArrayList<>();
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for (int i = 0; i < featureDimension; i++) {
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HiddenNerve hiddenNerve1 = new HiddenNerve(i + 1, 1, studyPoint, new ReLu(), featureDimension,
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featureDimension, null, regularModel, regular, coreNumber);
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fistHiddenNerves.add(hiddenNerve1);
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hiddenNerve1.setAfterLayNorm(attentionLayNorm);
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firstNerves.add(hiddenNerve1);
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}
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for (int i = 0; i < featureDimension; i++) {
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HiddenNerve hiddenNerve2 = new HiddenNerve(i + 1, 2, studyPoint, null,
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featureDimension, 1, null, regularModel, regular, coreNumber);
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hiddenNerve2.setBeforeLayNorm(lineLayNorm);
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secondHiddenNerves.add(hiddenNerve2);
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secondNerves.add(hiddenNerve2);
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}
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for (Nerve hiddenNerve : firstNerves) {
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hiddenNerve.connect(secondNerves);
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}
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for (Nerve hiddenNerve : secondNerves) {
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hiddenNerve.connectFather(firstNerves);
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}
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}
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}
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