使用Java DeepJavaLibrary ONNXRuntime 运行TinyLlama进行文本生成_onnxruntime java

1. 转换模型格式为onnx optimum-cli export onnx -m TinyLlama/TinyLlama-1.1B-Chat-v1.0  --monolith  TinyLlama-onnx

2. 执行量化 optimum-cli onnxruntime quantize --onnx_model  TinyLlama-onnx  -o llama_quantize --avx2

package com.yucl.demo.djl;
 
import java.nio.FloatBuffer;
import java.nio.LongBuffer;
import java.nio.file.Paths;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
 
import ai.djl.huggingface.tokenizers.Encoding;
import ai.djl.huggingface.tokenizers.HuggingFaceTokenizer;
import ai.djl.ndarray.NDArray;
import ai.djl.ndarray.NDManager;
import ai.djl.ndarray.types.Shape;
import ai.onnxruntime.OnnxTensor;
import ai.onnxruntime.OnnxValue;
import ai.onnxruntime.OrtEnvironment;
import ai.onnxruntime.OrtLoggingLevel;
import ai.onnxruntime.OrtSession;
 
public class OnnxTextGenerateDemo {
 
    public static void main(String[] args) throws Exception {
        String TOKENIZER_URI = "D:\\llm\\llama_quantize\\tokenizer.json";
        String MODEL_URI = "D:\\llm\\llama_quantize\\model_quantized.onnx";
        OrtSession.SessionOptions sessionOptions = new OrtSession.SessionOptions();
        sessionOptions.setSessionLogLevel(OrtLoggingLevel.ORT_LOGGING_LEVEL_ERROR);
        sessionOptions.setMemoryPatternOptimization(true);
        try (OrtEnvironment env = OrtEnvironment.getEnvironment();
                OrtSession session = env.createSession(MODEL_URI, sessionOptions);) {
            String sentences = "How to learn ai program ? ";
            HuggingFaceTokenizer tokenizer = HuggingFaceTokenizer.newInstance(Paths.get(TOKENIZER_URI), Map.of());
 
            Encoding encodings = tokenizer.encode(sentences);
            long[] input_ids = encodings.getIds();
            List<Long> generatedIds = new ArrayList<>();
            for (long id : input_ids) {
                generatedIds.add(id);
            }
            int totalLength = 100;
            while (generatedIds.size() < totalLength) {
                long[] currentInputIds = new long[generatedIds.size()];
                long[] currentPositionIds = new long[generatedIds.size()];
                long[] attentionMask = new long[generatedIds.size()];
                for (int i = 0; i < generatedIds.size(); i++) {
                    currentInputIds[i] = generatedIds.get(i);
                    currentPositionIds[i] = i;
                    attentionMask[i] = 1;
                }
 
                try (OnnxTensor inputTensor = OnnxTensor.createTensor(env, LongBuffer.wrap(currentInputIds),
                        new long[] { 1, currentInputIds.length });
                        OnnxTensor attentionMaskTensor = OnnxTensor.createTensor(env, LongBuffer.wrap(attentionMask),
                                new long[] { 1, attentionMask.length });
                        OnnxTensor positionTensor = OnnxTensor.createTensor(env, LongBuffer.wrap(currentPositionIds),
                                new long[] { 1, currentPositionIds.length });) {
 
                    Map<String, OnnxTensor> inputs = new HashMap<>();
                    inputs.put("input_ids", inputTensor);
                    inputs.put("attention_mask", attentionMaskTensor);
                    inputs.put("position_ids", positionTensor);
                    try (OrtSession.Result results = session.run(inputs)) {
                        OnnxValue lastHiddenState = results.get(0);
                        float[][][] logits = (float[][][]) lastHiddenState.getValue();
                        long nextTokenId = argmax(logits[0][logits[0].length - 1]);
                        generatedIds.add(nextTokenId);
                        System.out.print(tokenizer.decode(new long[] { nextTokenId }) + " ");
                    }
                    inputs.clear();
                }
 
            }
 
            long[] gen_tokens = new long[generatedIds.size()];
            for (int i = 0; i < generatedIds.size(); i++) {
                gen_tokens[i] = generatedIds.get(i);
            }
 
            String outputText = tokenizer.decode(gen_tokens);
            System.out.println("Generated text: " + outputText);
        }
 
    }
 
    public static NDArray create(float[][][] data, NDManager manager) {
        FloatBuffer buffer = FloatBuffer.allocate(data.length * data[0].length * data[0][0].length);
        for (float[][] data2 : data) {
            for (float[] d : data2) {
                buffer.put(d);
            }
        }
        buffer.rewind();
        return manager.create(buffer, new Shape(data.length, data[0].length, data[0][0].length));
    }
 
    // 选择最大概率对应的 token ID (贪心搜索)
    private static int argmax(float[] array) {
        int maxIndex = 0;
        for (int i = 1; i < array.length; i++) {
            if (array[i] > array[maxIndex]) {
                maxIndex = i;
            }
        }
        return maxIndex;
    }
 
}

源码地址：yucl80/ai-demo-java (github.com)