!3 nllp机器翻译支持202种语言互相翻译

Merge pull request !3 from puerile男孩/dev

examples/src/main/java/smartai/examples/nlp/TextTranslationCPU.java

@@ -0,0 +1,57 @@
+package smartai.examples.nlb;
+
+import ai.djl.Device;
+import ai.djl.ModelException;
+import ai.djl.translate.TranslateException;
+import smartai.examples.nlb.generate.SearchConfig;
+import smartai.examples.nlb.model.NllbModel;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.io.IOException;
+
+/**
+ * 文本翻译，支持202种语言互译
+ *
+ * @author Calvin
+ * @mail 179209347@qq.com
+ * @website www.aias.top
+ */
+public final class TextTranslationCPU {
+
+    private static final Logger logger = LoggerFactory.getLogger(TextTranslationCPU.class);
+
+    private TextTranslationCPU() {
+    }
+
+    public static void main(String[] args) throws ModelException, IOException,
+            TranslateException {
+
+        SearchConfig config = new SearchConfig();
+        // 设置输出文字的最大长度
+        config.setMaxSeqLength(128);
+        // 设置源语言：中文 "zho_Hans": 256200
+        config.setSrcLangId(256200);
+        // 设置目标语言：英文 "eng_Latn": 256047
+        config.setForcedBosTokenId(256047);
+        config.setForcedBosTokenId(256201);
+
+        // 输入文字
+        String input = "智利北部的丘基卡马塔矿是世界上最大的露天矿之一，长约4公里，宽3公里，深1公里。";
+
+        String modelPath = "E:\\ai\\models\\nlp\\";
+        String cpuModelName = "traced_translation_cpu.pt";
+        String gpuModelName = "traced_translation_gpu.pt";
+        try (NllbModel nllbModel = new NllbModel(config, modelPath, cpuModelName, Device.cpu())) {
+
+            System.setProperty("ai.djl.pytorch.graph_optimizer", "false");
+
+            // 运行模型，获取翻译结果
+            String result = nllbModel.translate(input);
+
+            logger.info("result========={}", result);
+        } finally {
+            System.clearProperty("ai.djl.pytorch.graph_optimizer");
+        }
+    }
+}

examples/src/main/java/smartai/examples/nlp/generate/BatchTensorList.java

@@ -0,0 +1,174 @@
+/*
+ * Copyright 2023 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance
+ * with the License. A copy of the License is located at
+ *
+ * http://aws.amazon.com/apache2.0/
+ *
+ * or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES
+ * OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
+ * and limitations under the License.
+ */
+package smartai.examples.nlb.generate;
+
+import ai.djl.ndarray.NDArray;
+import ai.djl.ndarray.NDList;
+
+/**
+ * BatchTensorList represents a search state, and the NDArrays inside are updated in each iteration
+ * of the autoregressive loop.
+ *
+ * <p>It is a struct consisting of NDArrays, whose first dimension is batch, and also contains
+ * sequence dimension (whose position in tensor's shape is specified by seqDimOrder). The SeqBatcher
+ * batch operations will operate on these two dimensions.
+ */
+public abstract class BatchTensorList {
+    // [batch, seq_past]. seq-dim-size == |past_seq| + |inputIds|. Will grow.
+    private NDArray pastOutputIds;
+
+    // [batch, seq_past]
+    // The cache of past attentionMask. seq-dim-size == |past_seq| + |inputIds|. Will grow.
+    private NDArray pastAttentionMask;
+
+    // (k, v) * numLayer,
+    // kv: [batch, heads, seq_past, kvfeature]
+    // The cache of past sequence. seq-dim-size == |past_seq| + |inputIds|. Will grow.
+    private NDList pastKeyValues;
+
+    // Sequence dimension order among all dimensions for each element in the batch list.
+    private long[] seqDimOrder;
+
+    BatchTensorList() {}
+
+    /**
+     * Constructs a new {@code BatchTensorList} instance.
+     *
+     * @param list the NDList that contains the serialized version of the batch tensors
+     * @param seqDimOrder the sequence dimension order that specifies where the sequence dimension
+     *     is in a tensor's shape
+     */
+    BatchTensorList(NDList list, long[] seqDimOrder) {
+        this.seqDimOrder = seqDimOrder;
+        pastOutputIds = list.get(0);
+        pastAttentionMask = list.get(1);
+        pastKeyValues = list.subNDList(2);
+    }
+
+    /**
+     * Constructs a new {@code BatchTensorList} instance.
+     *
+     * @param pastOutputIds past output token ids
+     * @param pastAttentionMask past attention mask
+     * @param pastKeyValues past kv cache
+     * @param seqDimOrder the sequence dimension order that specifies where the sequence dimension
+     *     is in a tensor's shape
+     */
+    BatchTensorList(
+            NDArray pastOutputIds,
+            NDArray pastAttentionMask,
+            NDList pastKeyValues,
+            long[] seqDimOrder) {
+        this.pastKeyValues = pastKeyValues;
+        this.pastOutputIds = pastOutputIds;
+        this.pastAttentionMask = pastAttentionMask;
+        this.seqDimOrder = seqDimOrder;
+    }
+
+    /**
+     * Constructs a new {@code BatchTensorList} instance from the serialized version of the batch
+     * tensors.
+     *
+     * <p>The pastOutputIds has to be the first in the output list.
+     *
+     * @param inputList the serialized version of the batch tensors
+     * @param seqDimOrder the sequence dimension order that specifies where the sequence dimension
+     *     is in a tensor's shape
+     * @return BatchTensorList
+     */
+    public abstract BatchTensorList fromList(NDList inputList, long[] seqDimOrder);
+
+    /**
+     * Returns the serialized version of the BatchTensorList. The pastOutputIds has to be the first
+     * in the output list.
+     *
+     * @return the NDList that contains the serialized BatchTensorList
+     */
+    public abstract NDList getList();
+
+    /**
+     * Returns the sequence dimension order which specifies where the sequence dimension is in a
+     * tensor's shape.
+     *
+     * @return the sequence dimension order which specifies where the sequence dimension is in a
+     *     tensor's shape
+     */
+    public long[] getSeqDimOrder() {
+        return seqDimOrder;
+    }
+
+    /**
+     * Returns the value of the pastOutputIds.
+     *
+     * @return the value of pastOutputIds
+     */
+    public NDArray getPastOutputIds() {
+        return pastOutputIds;
+    }
+
+    /**
+     * Sets the past output token ids.
+     *
+     * @param pastOutputIds the past output token ids
+     */
+    public void setPastOutputIds(NDArray pastOutputIds) {
+        this.pastOutputIds = pastOutputIds;
+    }
+
+    /**
+     * Returns the value of the pastAttentionMask.
+     *
+     * @return the value of pastAttentionMask
+     */
+    public NDArray getPastAttentionMask() {
+        return pastAttentionMask;
+    }
+
+    /**
+     * Sets the attention mask.
+     *
+     * @param pastAttentionMask the attention mask
+     */
+    public void setPastAttentionMask(NDArray pastAttentionMask) {
+        this.pastAttentionMask = pastAttentionMask;
+    }
+
+    /**
+     * Returns the value of the pastKeyValues.
+     *
+     * @return the value of pastKeyValues
+     */
+    public NDList getPastKeyValues() {
+        return pastKeyValues;
+    }
+
+    /**
+     * Sets the kv cache.
+     *
+     * @param pastKeyValues the kv cache
+     */
+    public void setPastKeyValues(NDList pastKeyValues) {
+        this.pastKeyValues = pastKeyValues;
+    }
+
+    /**
+     * Sets the sequence dimension order which specifies where the sequence dimension is in a
+     * tensor's shape.
+     *
+     * @param seqDimOrder the sequence dimension order which specifies where the sequence dimension
+     *     is in a tensor's shape
+     */
+    public void setSeqDimOrder(long[] seqDimOrder) {
+        this.seqDimOrder = seqDimOrder;
+    }
+}

examples/src/main/java/smartai/examples/nlp/generate/CausalLMOutput.java

@@ -0,0 +1,32 @@
+package smartai.examples.nlb.generate;
+
+import ai.djl.ndarray.NDArray;
+import ai.djl.ndarray.NDList;
+/**
+ * 解码输出对象
+ *
+ * @author Calvin
+ * @mail 179209347@qq.com
+ * @website www.aias.top
+ */
+public class CausalLMOutput {
+    private NDArray logits;
+    private NDList pastKeyValuesList;
+
+    public CausalLMOutput(NDArray logits, NDList pastKeyValues) {
+        this.logits = logits;
+        this.pastKeyValuesList = pastKeyValues;
+    }
+
+    public NDArray getLogits() {
+        return logits;
+    }
+
+    public void setLogits(NDArray logits) {
+        this.logits = logits;
+    }
+
+    public NDList getPastKeyValuesList() {
+        return pastKeyValuesList;
+    }
+}

examples/src/main/java/smartai/examples/nlp/generate/GreedyBatchTensorList.java

@@ -0,0 +1,84 @@
+package smartai.examples.nlb.generate;
+
+import ai.djl.ndarray.NDArray;
+import ai.djl.ndarray.NDList;
+
+/**
+ * 贪婪搜索张量对象列表
+ *
+ * @author Calvin
+ * @mail 179209347@qq.com
+ * @website www.aias.top
+ */
+public class GreedyBatchTensorList extends BatchTensorList {
+    // [batch, 1]
+    private NDArray nextInputIds;
+
+    private NDArray pastOutputIds;
+
+    private NDArray encoderHiddenStates;
+    private NDArray attentionMask;
+    private NDList pastKeyValues;
+
+    public GreedyBatchTensorList(
+            NDArray nextInputIds,
+            NDArray pastOutputIds,
+            NDList pastKeyValues,
+            NDArray encoderHiddenStates,
+            NDArray attentionMask) {
+        this.nextInputIds = nextInputIds;
+        this.pastKeyValues = pastKeyValues;
+        this.pastOutputIds = pastOutputIds;
+        this.attentionMask = attentionMask;
+        this.encoderHiddenStates = encoderHiddenStates;
+    }
+
+    public GreedyBatchTensorList() {}
+
+    public BatchTensorList fromList(NDList inputList, long[] seqDimOrder) {
+        return new GreedyBatchTensorList();
+    }
+
+    public NDList getList() {
+        return new NDList();
+    }
+
+    public NDArray getNextInputIds() {
+        return nextInputIds;
+    }
+
+    public void setNextInputIds(NDArray nextInputIds) {
+        this.nextInputIds = nextInputIds;
+    }
+    public NDArray getPastOutputIds() {
+        return pastOutputIds;
+    }
+
+    public void setPastOutputIds(NDArray pastOutputIds) {
+        this.pastOutputIds = pastOutputIds;
+    }
+
+    public NDList getPastKeyValues() {
+        return pastKeyValues;
+    }
+
+    public void setPastKeyValues(NDList pastKeyValues) {
+        this.pastKeyValues = pastKeyValues;
+    }
+
+    public NDArray getEncoderHiddenStates() {
+        return encoderHiddenStates;
+    }
+
+    public void setEncoderHiddenStates(NDArray encoderHiddenStates) {
+        this.encoderHiddenStates = encoderHiddenStates;
+    }
+
+    public NDArray getAttentionMask() {
+        return attentionMask;
+    }
+
+    public void setAttentionMask(NDArray attentionMask) {
+        this.attentionMask = attentionMask;
+    }
+}

examples/src/main/java/smartai/examples/nlp/generate/SearchConfig.java

@@ -0,0 +1,104 @@
+package smartai.examples.nlb.generate;
+/**
+ * 配置信息
+ *
+ * @author Calvin
+ * @mail 179209347@qq.com
+ * @website www.aias.top
+ */
+public class SearchConfig {
+
+    private int maxSeqLength;
+    private long padTokenId;
+    private long eosTokenId;
+    private long bosTokenId;
+    private long decoderStartTokenId;
+    private float encoderRepetitionPenalty;
+    private long forcedBosTokenId;
+    private long srcLangId;
+    private float lengthPenalty;
+    public SearchConfig() {
+        this.maxSeqLength = 512;
+        this.eosTokenId = 2;
+        this.bosTokenId = 0;
+        this.padTokenId = 1;
+        this.decoderStartTokenId = 2;
+        this.encoderRepetitionPenalty = 1.0f;
+        this.srcLangId = 0;
+        this.forcedBosTokenId = 0;
+        this.lengthPenalty = 1.0f;
+
+    }
+
+    public long getSrcLangId() {
+        return srcLangId;
+    }
+
+    public void setSrcLangId(long srcLangId) {
+        this.srcLangId = srcLangId;
+    }
+
+    public void setEosTokenId(long eosTokenId) {
+        this.eosTokenId = eosTokenId;
+    }
+
+    public int getMaxSeqLength() {
+        return maxSeqLength;
+    }
+
+    public void setMaxSeqLength(int maxSeqLength) {
+        this.maxSeqLength = maxSeqLength;
+    }
+
+    public long getPadTokenId() {
+        return padTokenId;
+    }
+
+    public void setPadTokenId(long padTokenId) {
+        this.padTokenId = padTokenId;
+    }
+
+    public long getEosTokenId() {
+        return eosTokenId;
+    }
+
+    public long getDecoderStartTokenId() {
+        return decoderStartTokenId;
+    }
+
+    public void setDecoderStartTokenId(long decoderStartTokenId) {
+        this.decoderStartTokenId = decoderStartTokenId;
+    }
+
+    public float getEncoderRepetitionPenalty() {
+        return encoderRepetitionPenalty;
+    }
+
+    public void setEncoderRepetitionPenalty(float encoderRepetitionPenalty) {
+        this.encoderRepetitionPenalty = encoderRepetitionPenalty;
+    }
+
+    public long getForcedBosTokenId() {
+        return forcedBosTokenId;
+    }
+
+    public void setForcedBosTokenId(long forcedBosTokenId) {
+        this.forcedBosTokenId = forcedBosTokenId;
+    }
+
+    public float getLengthPenalty() {
+        return lengthPenalty;
+    }
+
+    public void setLengthPenalty(float lengthPenalty) {
+        this.lengthPenalty = lengthPenalty;
+    }
+
+    public long getBosTokenId() {
+        return bosTokenId;
+    }
+
+    public void setBosTokenId(long bosTokenId) {
+        this.bosTokenId = bosTokenId;
+    }
+}

examples/src/main/java/smartai/examples/nlp/model/Decoder2Translator.java

@@ -0,0 +1,45 @@
+package smartai.examples.nlb.model;
+
+import ai.djl.ndarray.NDArray;
+import ai.djl.ndarray.NDList;
+import ai.djl.translate.NoBatchifyTranslator;
+import ai.djl.translate.TranslatorContext;
+import smartai.examples.nlb.generate.CausalLMOutput;
+
+/**
+ * 解碼器，參數支持 pastKeyValues
+ *
+ * @author Calvin
+ * @mail 179209347@qq.com
+ * @website www.aias.top
+ */
+public class Decoder2Translator implements NoBatchifyTranslator<NDList, CausalLMOutput> {
+    private String tupleName;
+
+    public Decoder2Translator() {
+        tupleName = "past_key_values(" + 12 + ',' + 4 + ')';
+    }
+
+    @Override
+    public NDList processInput(TranslatorContext ctx, NDList input) {
+
+        NDArray placeholder = ctx.getNDManager().create(0);
+        placeholder.setName("module_method:decoder2");
+
+        input.add(placeholder);
+
+        return input;
+    }
+
+    @Override
+    public CausalLMOutput processOutput(TranslatorContext ctx, NDList output) {
+        NDArray logitsOutput = output.get(0);
+        NDList pastKeyValuesOutput = output.subNDList(1, 12 * 4 + 1);
+
+        for (NDArray array : pastKeyValuesOutput) {
+            array.setName(tupleName);
+        }
+
+        return new CausalLMOutput(logitsOutput, pastKeyValuesOutput);
+    }
+}

examples/src/main/java/smartai/examples/nlp/model/DecoderTranslator.java

@@ -0,0 +1,44 @@
+package smartai.examples.nlb.model;
+
+import ai.djl.ndarray.NDArray;
+import ai.djl.ndarray.NDList;
+import ai.djl.translate.NoBatchifyTranslator;
+import ai.djl.translate.TranslatorContext;
+import smartai.examples.nlb.generate.CausalLMOutput;
+/**
+ * 解碼器，參數沒有 pastKeyValues
+ *
+ * @author Calvin
+ * @mail 179209347@qq.com
+ * @website www.aias.top
+ */
+public class DecoderTranslator implements NoBatchifyTranslator<NDList, CausalLMOutput> {
+    private String tupleName;
+
+    public DecoderTranslator() {
+        tupleName = "past_key_values(" + 12 + ',' + 4 + ')';
+    }
+
+    @Override
+    public NDList processInput(TranslatorContext ctx, NDList input) {
+
+        NDArray placeholder = ctx.getNDManager().create(0);
+        placeholder.setName("module_method:decoder");
+
+        input.add(placeholder);
+
+        return input;
+    }
+
+    @Override
+    public CausalLMOutput processOutput(TranslatorContext ctx, NDList output) {
+        NDArray logitsOutput = output.get(0);
+        NDList pastKeyValuesOutput = output.subNDList(1, 12 * 4 + 1);
+
+        for (NDArray array : pastKeyValuesOutput) {
+            array.setName(tupleName);
+        }
+
+        return new CausalLMOutput(logitsOutput, pastKeyValuesOutput);
+    }
+}

examples/src/main/java/smartai/examples/nlp/model/EncoderTranslator.java

@@ -0,0 +1,49 @@
+package smartai.examples.nlb.model;
+
+import ai.djl.ndarray.NDArray;
+import ai.djl.ndarray.NDList;
+import ai.djl.ndarray.NDManager;
+import ai.djl.translate.NoBatchifyTranslator;
+import ai.djl.translate.TranslatorContext;
+
+import java.util.Arrays;
+
+/**
+ * 编码器前后处理
+ *
+ * @author Calvin
+ * @mail 179209347@qq.com
+ * @website www.aias.top
+ */
+public class EncoderTranslator implements NoBatchifyTranslator<long[], NDArray> {
+
+
+    public EncoderTranslator() {
+    }
+
+    @Override
+    public NDList processInput(TranslatorContext ctx, long[] input) throws Exception {
+        NDManager manager = ctx.getNDManager();
+
+        NDArray inputIdArray = manager.create(input).expandDims(0);
+        inputIdArray.setName("input_ids");
+
+        long[] attentionMask = new long[input.length];
+        Arrays.fill(attentionMask, 1);
+        NDArray attentionMaskArray = manager.create(attentionMask).expandDims(0);
+        attentionMaskArray.setName("attention_mask");
+
+        NDArray placeholder = ctx.getNDManager().create(0);
+        placeholder.setName("module_method:encoder");
+
+        return new NDList(inputIdArray, attentionMaskArray, placeholder);
+    }
+
+    @Override
+    public NDArray processOutput(TranslatorContext ctx, NDList list) {
+        NDArray encoderHiddenStates = list.get(0);
+        encoderHiddenStates.detach();
+        return encoderHiddenStates;
+    }
+
+}

examples/src/main/java/smartai/examples/nlp/model/NllbModel.java

@@ -0,0 +1,183 @@
+package smartai.examples.nlb.model;
+
+import ai.djl.Device;
+import ai.djl.ModelException;
+import ai.djl.huggingface.tokenizers.Encoding;
+import ai.djl.huggingface.tokenizers.HuggingFaceTokenizer;
+import ai.djl.inference.Predictor;
+import ai.djl.ndarray.NDArray;
+import ai.djl.ndarray.NDList;
+import ai.djl.ndarray.NDManager;
+import ai.djl.ndarray.index.NDIndex;
+import ai.djl.repository.zoo.Criteria;
+import ai.djl.repository.zoo.ZooModel;
+import ai.djl.translate.NoopTranslator;
+import ai.djl.translate.TranslateException;
+import smartai.examples.nlb.generate.CausalLMOutput;
+import smartai.examples.nlb.generate.GreedyBatchTensorList;
+import smartai.examples.nlb.generate.SearchConfig;
+import smartai.examples.nlb.tokenizer.TokenUtils;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.io.IOException;
+import java.nio.file.Paths;
+import java.util.Arrays;
+/**
+ * 模型载入及推理
+ *
+ * @author Calvin
+ * @mail 179209347@qq.com
+ * @website www.aias.top
+ */
+public class NllbModel implements AutoCloseable {
+    private static final Logger logger = LoggerFactory.getLogger(NllbModel.class);
+    private SearchConfig config;
+    private ZooModel<NDList, NDList> nllbModel;
+    private HuggingFaceTokenizer tokenizer;
+    private Predictor<long[], NDArray> encoderPredictor;
+    private Predictor<NDList, CausalLMOutput> decoderPredictor;
+    private Predictor<NDList, CausalLMOutput> decoder2Predictor;
+    private NDManager manager;
+
+    public NllbModel(SearchConfig config, String modelPath, String modelName, Device device) throws ModelException, IOException {
+        this.config = config;
+        Criteria<NDList, NDList> criteria =
+                Criteria.builder()
+                        .setTypes(NDList.class, NDList.class)
+                        .optModelPath(Paths.get(modelPath + modelName))
+                        .optEngine("PyTorch")
+                        .optDevice(device)
+                        .optTranslator(new NoopTranslator())
+                        .build();
+
+        manager = NDManager.newBaseManager(device);
+        nllbModel = criteria.loadModel();
+        tokenizer = HuggingFaceTokenizer.newInstance(Paths.get(modelPath + "tokenizer.json"));
+        encoderPredictor = nllbModel.newPredictor(new EncoderTranslator());
+        decoderPredictor = nllbModel.newPredictor(new DecoderTranslator());
+        decoder2Predictor = nllbModel.newPredictor(new Decoder2Translator());
+    }
+
+    public NDArray encoder(long[] ids) throws TranslateException {
+        return encoderPredictor.predict(ids);
+    }
+
+    public CausalLMOutput decoder(NDList input) throws TranslateException {
+        return decoderPredictor.predict(input);
+    }
+
+    public CausalLMOutput decoder2(NDList input) throws TranslateException {
+        return decoder2Predictor.predict(input);
+    }
+
+    @Override
+    public void close() {
+        encoderPredictor.close();
+        decoderPredictor.close();
+        decoder2Predictor.close();
+        nllbModel.close();
+        manager.close();
+        tokenizer.close();
+    }
+
+    public String translate(String input) throws TranslateException {
+
+        Encoding encoding = tokenizer.encode(input);
+        long[] ids = encoding.getIds();
+        // 1. Encoder
+        long[] inputIds = new long[ids.length];
+        // 设置源语言编码
+        inputIds[0] = config.getSrcLangId();
+        for (int i = 0; i < ids.length - 1; i++) {
+            inputIds[i + 1] = ids[i];
+        }
+        logger.info("inputIds: " + Arrays.toString(inputIds));
+        long[] attentionMask = encoding.getAttentionMask();
+        NDArray attentionMaskArray = manager.create(attentionMask).expandDims(0);
+
+        NDArray encoderHiddenStates = encoder(inputIds);
+
+        NDArray decoder_input_ids = manager.create(new long[]{config.getDecoderStartTokenId()}).reshape(1, 1);
+        NDList decoderInput = new NDList(decoder_input_ids, encoderHiddenStates, attentionMaskArray);
+
+        // 2. Initial Decoder
+        CausalLMOutput modelOutput = decoder(decoderInput);
+        modelOutput.getLogits().attach(manager);
+        modelOutput.getPastKeyValuesList().attach(manager);
+
+        GreedyBatchTensorList searchState =
+                new GreedyBatchTensorList(null, decoder_input_ids, modelOutput.getPastKeyValuesList(), encoderHiddenStates, attentionMaskArray);
+
+        while (true) {
+//            try (NDScope ignore = new NDScope()) {
+            NDArray pastOutputIds = searchState.getPastOutputIds();
+
+            if (searchState.getNextInputIds() != null) {
+                decoderInput = new NDList(searchState.getNextInputIds(), searchState.getEncoderHiddenStates(), searchState.getAttentionMask());
+                decoderInput.addAll(searchState.getPastKeyValues());
+                // 3. Decoder loop
+                modelOutput = decoder2(decoderInput);
+            }
+
+            NDArray outputIds = greedyStepGen(config, pastOutputIds, modelOutput.getLogits());
+
+            searchState.setNextInputIds(outputIds);
+            pastOutputIds = pastOutputIds.concat(outputIds, 1);
+            searchState.setPastOutputIds(pastOutputIds);
+
+            searchState.setPastKeyValues(modelOutput.getPastKeyValuesList());
+
+            // memory management
+//                NDScope.unregister(outputIds, pastOutputIds);
+//        }
+
+            // Termination Criteria
+            long id = searchState.getNextInputIds().toLongArray()[0];
+            if (config.getEosTokenId() == id) {
+                searchState.setNextInputIds(null);
+                break;
+            }
+            if (searchState.getPastOutputIds() != null && searchState.getPastOutputIds().getShape().get(1) + 1 >= config.getMaxSeqLength()) {
+                break;
+            }
+        }
+
+        if (searchState.getNextInputIds() == null) {
+            NDArray resultIds = searchState.getPastOutputIds();
+            String result = TokenUtils.decode(config, tokenizer, resultIds);
+            return result;
+        } else {
+            NDArray resultIds = searchState.getPastOutputIds(); // .concat(searchState.getNextInputIds(), 1)
+            String result = TokenUtils.decode(config, tokenizer, resultIds);
+            return result;
+        }
+
+    }
+
+    public NDArray greedyStepGen(SearchConfig config, NDArray pastOutputIds, NDArray next_token_scores) {
+        next_token_scores = next_token_scores.get(":, -1, :");
+
+        NDArray new_next_token_scores = manager.create(next_token_scores.getShape(), next_token_scores.getDataType());
+        next_token_scores.copyTo(new_next_token_scores);
+
+        // LogitsProcessor 1. ForcedBOSTokenLogitsProcessor
+        // 设置目标语言
+        long cur_len = pastOutputIds.getShape().getLastDimension();
+        if (cur_len == 1) {
+            long num_tokens = new_next_token_scores.getShape().getLastDimension();
+            for (long i = 0; i < num_tokens; i++) {
+                if (i != config.getForcedBosTokenId()) {
+                    new_next_token_scores.set(new NDIndex(":," + i), Float.NEGATIVE_INFINITY);
+                }
+            }
+            new_next_token_scores.set(new NDIndex(":," + config.getForcedBosTokenId()), 0);
+        }
+
+        NDArray probs = new_next_token_scores.softmax(-1);
+        NDArray next_tokens = probs.argMax(-1);
+
+        return next_tokens.expandDims(0);
+    }
+
+}

examples/src/main/java/smartai/examples/objectdetection/ObjectDetection.java

@@ -111,18 +111,21 @@ public class ObjectDetection {
 
 
     /**
-     * 使用yolo官方模型检测
+     * 使用yolo官方模型检测物品识别
      */
     @Test
     public void objectDetectionWithOfficialModel(){
         DetectorModelConfig config = new DetectorModelConfig();
+        config.setThreshold(0.3f);
         //也支持YoloV8：YOLOV8_OFFICIAL 模型可以从文档中提供的地址下载
         config.setModelEnum(DetectorModelEnum.YOLOV12_OFFICIAL);//检测模型，目前支持19种模型
         // 指定模型路径，需要更改为自己的模型路径
-        config.setModelPath("/Users/xxx/Documents/develop/face_model/yolov12n.onnx");
+        config.setModelPath("E:\\ai\\models\\yolo12m\\yolov12m.onnx");
         DetectorModel detectorModel = ObjectDetectionModelFactory.getInstance().getModel(config);
         //一定要将yolo官方的类别文件：synset.txt（文档中下载）放在模型同目录下，否则报错
-        detectorModel.detectAndDraw("src/main/resources/object_detection.jpg","output/object_detection_detected.png");
+        DetectionResponse detect = detectorModel.detect("E:\\ai\\testimage\\1.jpg");
+        log.info("目标检测结果：{}", JSONObject.toJSONString(detect));
+        detectorModel.detectAndDraw("E:\\ai\\testimage\\1.jpg","E:\\ai\\outimage\\11.png");
     }
 
     /**