134_边缘推理：TensorFlow Lite - 优化移动端LLM部署技术详解与实战指南

import tensorflow as tf

# 加载TFLite模型
interpreter = tf.lite.Interpreter(model_path="model.tflite")

# 分配张量内存
interpreter.allocate_tensors()

# 获取输入和输出张量
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# 设置输入数据
interpreter.set_tensor(input_details[0]['index'], input_data)

# 运行推理
interpreter.invoke()

# 获取输出结果
output_data = interpreter.get_tensor(output_details[0]['index'])

import tensorflow as tf

# 从Keras模型转换
def convert_keras_model_to_tflite(keras_model, output_path):
    converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
    
    # 应用优化选项
    converter.optimizations = [tf.lite.Optimize.DEFAULT]
    
    # 转换为TFLite格式
    tflite_model = converter.convert()
    
    # 保存模型
    with open(output_path, 'wb') as f:
        f.write(tflite_model)
    
    return output_path

import tensorflow as tf

# 使用GPU委托
interpreter = tf.lite.Interpreter(
    model_path="model.tflite",
    experimental_delegates=[tf.lite.experimental.load_delegate('libtensorflowlite_gpu.so')]
)

# 后训练整数量化示例
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]

# 设置代表性数据集用于校准
def representative_data_gen():
    for input_value in test_images:
        yield [input_value]

converter.representative_dataset = representative_data_gen
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.int8
converter.inference_output_type = tf.int8

quantized_tflite_model = converter.convert()

# 量化感知训练示例
import tensorflow_model_optimization as tfmot

quantize_model = tfmot.quantization.keras.quantize_model
q_aware_model = quantize_model(model)

# 编译和训练量化感知模型
q_aware_model.compile(optimizer='adam',
                      loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                      metrics=['accuracy'])

q_aware_model.fit(train_dataset, epochs=10, validation_data=test_dataset)

# 转换为TFLite量化模型
converter = tf.lite.TFLiteConverter.from_keras_model(q_aware_model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
quantized_tflite_model = converter.convert()

# 混合精度量化示例
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_types = [tf.float16]
quantized_tflite_model = converter.convert()

# 剪枝示例
import tensorflow_model_optimization as tfmot

prune_low_magnitude = tfmot.sparsity.keras.prune_low_magnitude

# 定义剪枝参数
pruning_params = {
    'pruning_schedule': tfmot.sparsity.keras.PolynomialDecay(
        initial_sparsity=0.30,
        final_sparsity=0.80,
        begin_step=0,
        end_step=end_step)
}

# 创建剪枝模型
model_for_pruning = prune_low_magnitude(model, **pruning_params)

# 编译模型
model_for_pruning.compile(
    optimizer='adam',
    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    metrics=['accuracy'])

# 添加日志回调
log_dir = './logs'
callbacks = [
    tfmot.sparsity.keras.UpdatePruningStep(),
    tfmot.sparsity.keras.PruningSummaries(log_dir=log_dir),
]

# 训练模型
model_for_pruning.fit(
    train_dataset,
    epochs=epochs,
    validation_data=test_dataset,
    callbacks=callbacks)

# 移除剪枝包装，准备导出
model_for_export = tfmot.sparsity.keras.strip_pruning(model_for_pruning)

# 蒸馏损失函数示例
def distillation_loss(student_logits, teacher_logits, hard_labels, temperature=2.0, alpha=0.5):
    # 软标签损失 - KL散度
    soft_targets = tf.nn.softmax(teacher_logits / temperature)
    soft_prob = tf.nn.softmax(student_logits / temperature)
    soft_loss = tf.reduce_mean(tf.keras.losses.kld(soft_targets, soft_prob) * (temperature ** 2))
    
    # 硬标签损失 - 交叉熵
    hard_loss = tf.keras.losses.sparse_categorical_crossentropy(hard_labels, student_logits, from_logits=True)
    hard_loss = tf.reduce_mean(hard_loss)
    
    # 组合损失
    return alpha * soft_loss + (1 - alpha) * hard_loss

# 中间层蒸馏示例
def intermediate_distillation_loss(student_outputs, teacher_outputs, temperature=2.0):
    loss = 0
    # 匹配每一层的输出
    for s_out, t_out in zip(student_outputs, teacher_outputs):
        # 可以使用MSE、余弦相似度等
        layer_loss = tf.reduce_mean(tf.square(s_out - t_out))
        loss += layer_loss
    return loss

pip install tensorflow tensorflow-model-optimization transformers torch onnx onnx-tf

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_name = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# 步骤1: 将PyTorch模型转换为TensorFlow SavedModel
def convert_pytorch_to_tensorflow(model, tokenizer, output_path):
    import torch
    import tensorflow as tf
    from transformers import TFAutoModelForSequenceClassification
    
    # 转换模型
    tf_model = TFAutoModelForSequenceClassification.from_pretrained(
        model_name, from_pt=True, torch_dtype=torch.float32
    )
    
    # 保存为SavedModel格式
    tf_model.save_pretrained(output_path, saved_model=True)
    return output_path

# 步骤2: 将TensorFlow SavedModel转换为TFLite
def convert_tensorflow_to_tflite(saved_model_path, tflite_output_path):
    import tensorflow as tf
    
    # 加载SavedModel
    converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_path)
    
    # 应用优化
    converter.optimizations = [tf.lite.Optimize.DEFAULT]
    
    # 定义代表性数据集（用于量化）
    def representative_data_gen():
        import numpy as np
        for i in range(100):
            # 生成示例输入
            text = "This is a sample text for calibration"
            inputs = tokenizer(text, return_tensors="tf", truncation=True, padding=True, max_length=128)
            
            # 按照模型输入格式生成样本
            yield [inputs["input_ids"].numpy(), inputs["attention_mask"].numpy()]
    
    converter.representative_dataset = representative_data_gen
    
    # 针对LLM优化
    converter.target_spec.supported_ops = [
        tf.lite.OpsSet.TFLITE_BUILTINS,
        tf.lite.OpsSet.SELECT_TF_OPS
    ]
    
    # 转换模型
    tflite_model = converter.convert()
    
    # 保存TFLite模型
    with open(tflite_output_path, 'wb') as f:
        f.write(tflite_model)
    
    return tflite_output_path

# 执行转换流程
saved_model_path = "./tf_model"
convert_pytorch_to_tensorflow(model, tokenizer, saved_model_path)
tflite_path = "./model.tflite"
convert_tensorflow_to_tflite(saved_model_path, tflite_path)

# 步骤1: PyTorch模型导出为ONNX
def export_pytorch_to_onnx(model, tokenizer, onnx_path):
    import torch
    
    # 创建示例输入
    text = "This is a sample text for ONNX export"
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
    
    # 导出为ONNX
    torch.onnx.export(
        model,
        (inputs["input_ids"], inputs["attention_mask"]),
        onnx_path,
        opset_version=13,
        do_constant_folding=True,
        input_names=['input_ids', 'attention_mask'],
        output_names=['output'],
        dynamic_axes={
            'input_ids': {0: 'batch_size', 1: 'sequence_length'},
            'attention_mask': {0: 'batch_size', 1: 'sequence_length'},
            'output': {0: 'batch_size'}
        }
    )
    return onnx_path

# 步骤2: ONNX模型转换为TensorFlow
def convert_onnx_to_tensorflow(onnx_path, tf_output_path):
    from onnx_tf.backend import prepare
    import onnx
    import tensorflow as tf
    
    # 加载ONNX模型
    onnx_model = onnx.load(onnx_path)
    
    # 转换为TensorFlow
    tf_rep = prepare(onnx_model)
    
    # 保存为SavedModel
    tf_rep.export_graph(tf_output_path)
    return tf_output_path

# 步骤3: TensorFlow模型转换为TFLite
# (使用前面定义的convert_tensorflow_to_tflite函数)

# 执行转换流程
onnx_path = "./model.onnx"
export_pytorch_to_onnx(model, tokenizer, onnx_path)
tf_path = "./onnx_tf_model"
convert_onnx_to_tensorflow(onnx_path, tf_path)
tflite_path = "./onnx_model.tflite"
convert_tensorflow_to_tflite(tf_path, tflite_path)

def optimize_model_with_quantization(saved_model_path, output_path):
    import tensorflow as tf
    
    converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_path)
    
    # 设置优化级别
    converter.optimizations = [tf.lite.Optimize.DEFAULT]
    
    # 对于BERT类模型的量化配置
    converter.target_spec.supported_ops = [
        tf.lite.OpsSet.TFLITE_BUILTINS,
        tf.lite.OpsSet.SELECT_TF_OPS
    ]
    
    # 针对Transformer架构的特殊优化
    converter._experimental_lower_tensor_list_ops = False
    
    # 混合精度量化配置
    converter.target_spec.supported_types = [tf.float16, tf.int8]
    
    # 设置代表性数据集
    def representative_dataset():
        # 实现数据集生成逻辑
        pass
    
    converter.representative_dataset = representative_dataset
    
    # 转换并保存
    tflite_model = converter.convert()
    with open(output_path, 'wb') as f:
        f.write(tflite_model)
    
    return output_path

def enable_graph_optimizations(converter):
    # 启用所有默认图优化
    converter.optimizations = [tf.lite.Optimize.DEFAULT]
    
    # 启用特定的图优化
    converter.allow_custom_ops = True
    
    # 对于LLM的特殊优化
    converter.experimental_new_converter = True
    
    return converter

def apply_additional_compression(model_path, output_path):
    import tensorflow as tf
    import tensorflow_model_optimization as tfmot
    
    # 加载模型
    model = tf.saved_model.load(model_path)
    
    # 应用剪枝
    prune_low_magnitude = tfmot.sparsity.keras.prune_low_magnitude
    pruning_params = {
        'pruning_schedule': tfmot.sparsity.keras.PolynomialDecay(
            initial_sparsity=0.50,
            final_sparsity=0.80,
            begin_step=0,
            end_step=1000
        )
    }
    
    # 注意：这里需要根据实际模型结构调整剪枝应用方式
    # 对于Hugging Face模型，可能需要更复杂的处理
    
    # 转换为TFLite
    converter = tf.lite.TFLiteConverter.from_saved_model(model_path)
    converter.optimizations = [tf.lite.Optimize.DEFAULT]
    
    # 转换并保存
    tflite_model = converter.convert()
    with open(output_path, 'wb') as f:
        f.write(tflite_model)
    
    return output_path

class OptimizedTokenizer:
    def __init__(self, tokenizer, max_length=128):
        self.tokenizer = tokenizer
        self.max_length = max_length
    
    def preprocess(self, text):
        # 缓存常用词汇的分词结果
        # 批量处理多个文本
        # 优化padding和truncation逻辑
        inputs = self.tokenizer(
            text,
            return_tensors="tf",
            truncation=True,
            padding="max_length",
            max_length=self.max_length
        )
        return inputs["input_ids"], inputs["attention_mask"]
    
    def batch_preprocess(self, texts):
        # 高效批量预处理
        inputs = self.tokenizer(
            texts,
            return_tensors="tf",
            truncation=True,
            padding="max_length",
            max_length=self.max_length
        )
        return inputs["input_ids"], inputs["attention_mask"]

def optimize_output_processing(output_ids, tokenizer):
    # 批处理解码
    # 提前终止条件优化
    # 内存复用
    
    # 高效解码
    decoded_texts = []
    for ids in output_ids:
        # 跳过特殊标记
        ids = [id for id in ids if id not in tokenizer.all_special_ids]
        text = tokenizer.decode(ids, skip_special_tokens=True)
        decoded_texts.append(text)
    
    return decoded_texts

// 1. 添加依赖到build.gradle
dependencies {
    implementation 'org.tensorflow:tensorflow-lite:2.15.0'
    implementation 'org.tensorflow:tensorflow-lite-support:0.4.4'
    implementation 'org.tensorflow:tensorflow-lite-gpu:2.15.0' // 可选，用于GPU加速
}

// 2. 模型加载与推理示例
import org.tensorflow.lite.Interpreter;
import org.tensorflow.lite.support.common.FileUtil;
import org.tensorflow.lite.gpu.CompatibilityList;
import org.tensorflow.lite.gpu.GpuDelegate;

public class TFLiteModelExecutor {
    private Interpreter tflite;
    
    public void init(Context context) {
        try {
            // 加载模型
            MappedByteBuffer modelBuffer = FileUtil.loadMappedFile(context, "model.tflite");
            
            // 配置GPU加速
            CompatibilityList compatList = new CompatibilityList();
            Interpreter.Options options = new Interpreter.Options();
            
            if (compatList.isDelegateSupportedOnThisDevice()) {
                GpuDelegate gpuDelegate = new GpuDelegate();
                options.addDelegate(gpuDelegate);
            } else {
                // 使用CPU，设置线程数
                options.setNumThreads(4);
            }
            
            tflite = new Interpreter(modelBuffer, options);
        } catch (IOException e) {
            Log.e("TFLite", "Error loading model", e);
        }
    }
    
    public float[][] runInference(int[][] inputIds, int[][] attentionMask) {
        // 准备输入和输出张量
        float[][] output = new float[inputIds.length][2]; // 假设二分类任务
        
        // 执行推理
        tflite.runForMultipleInputsOutputs(
            new Object[]{inputIds, attentionMask}, 
            Map.of(0, output)
        );
        
        return output;
    }
    
    public void close() {
        if (tflite != null) {
            tflite.close();
        }
    }
}

// 1. 添加依赖到Podfile
// pod 'TensorFlowLiteSwift', '~> 2.15.0'

// 2. 模型加载与推理示例
import TensorFlowLiteSwift

class TFLiteModelExecutor {
    private var interpreter: Interpreter?
    
    func initialize() {
        guard let modelPath = Bundle.main.path(forResource: "model", ofType: "tflite") else {
            print("Failed to locate model file.")
            return
        }
        
        do {
            // 设置解释器选项
            var options = Interpreter.Options()
            options.threadCount = 4
            
            // 尝试使用Core ML委托（如果可用）
            if let coreMLDelegate = try? CoreMLDelegate() {
                options.addDelegate(coreMLDelegate)
            } else if let metalDelegate = try? MetalDelegate() {
                // 尝试使用Metal委托
                options.addDelegate(metalDelegate)
            }
            
            interpreter = try Interpreter(modelPath: modelPath, options: options)
            try interpreter?.allocateTensors()
        } catch {
            print("Failed to initialize interpreter: \(error.localizedDescription)")
        }
    }
    
    func runInference(inputIds: [[Int32]], attentionMask: [[Int32]]) -> [[Float32]] {
        guard let interpreter = interpreter else {
            return []
        }
        
        do {
            // 设置输入张量
            try interpreter.copy(inputIds, toInputAt: 0)
            try interpreter.copy(attentionMask, toInputAt: 1)
            
            // 执行推理
            try interpreter.invoke()
            
            // 获取输出张量
            let outputTensor = try interpreter.output(at: 0)
            return outputTensor.dataToTensor(ofType: Float32.self)
        } catch {
            print("Failed to run inference: \(error.localizedDescription)")
            return []
        }
    }
}

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

# 选择轻量级模型
model_name = "distilbert-base-uncased-finetuned-sst-2-english"

# 加载模型和分词器
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# 设置为评估模式
model.eval()

import tensorflow as tf
from transformers import TFAutoModelForSequenceClassification

# 转换为TensorFlow模型
tf_model = TFAutoModelForSequenceClassification.from_pretrained(
    model_name, from_pt=True, torch_dtype=torch.float32
)

# 保存为SavedModel
saved_model_path = "./optimized_bert"
tf_model.save_pretrained(saved_model_path, saved_model=True)

# 转换为TFLite并应用优化
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_path)

# 启用所有优化
converter.optimizations = [tf.lite.Optimize.DEFAULT]

# 定义代表性数据集
def representative_dataset():
    import numpy as np
    
    # 使用一些示例文本进行校准
    sample_texts = [
        "This movie is fantastic! I really enjoyed it.",
        "The acting was terrible and the plot was boring.",
        "This is a neutral statement about nothing in particular.",
        # 添加更多样本来提高校准质量
    ]
    
    for text in sample_texts:
        # 分词并准备输入
        inputs = tokenizer(text, return_tensors="tf", truncation=True, padding="max_length", max_length=128)
        yield [inputs["input_ids"].numpy(), inputs["attention_mask"].numpy()]

converter.representative_dataset = representative_dataset

# 设置操作集
converter.target_spec.supported_ops = [
    tf.lite.OpsSet.TFLITE_BUILTINS,
    tf.lite.OpsSet.SELECT_TF_OPS
]

# 对于BERT模型的特殊优化
converter._experimental_lower_tensor_list_ops = False

# 转换模型
tflite_model = converter.convert()

# 保存TFLite模型
with open("./optimized_bert.tflite", "wb") as f:
    f.write(tflite_model)

print("Model converted and optimized successfully!")

import tensorflow as tf
import numpy as np
import time

# 加载TFLite模型
interpreter = tf.lite.Interpreter(model_path="./optimized_bert.tflite")
interpreter.allocate_tensors()

# 获取输入和输出索引
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# 准备测试数据
def test_model_performance(interpreter, tokenizer, test_texts, num_runs=100):
    total_time = 0
    all_predictions = []
    
    for i in range(num_runs):
        for text in test_texts:
            # 预处理输入
            inputs = tokenizer(text, return_tensors="tf", truncation=True, padding="max_length", max_length=128)
            input_ids = inputs["input_ids"].numpy()
            attention_mask = inputs["attention_mask"].numpy()
            
            # 设置输入
            interpreter.set_tensor(input_details[0]['index'], input_ids)
            interpreter.set_tensor(input_details[1]['index'], attention_mask)
            
            # 测量推理时间
            start_time = time.time()
            interpreter.invoke()
            end_time = time.time()
            total_time += (end_time - start_time)
            
            # 获取输出
            output = interpreter.get_tensor(output_details[0]['index'])
            prediction = np.argmax(output, axis=1)
            all_predictions.append((text, prediction[0]))
    
    # 计算平均推理时间
    avg_time = total_time / (num_runs * len(test_texts)) * 1000  # 转换为毫秒
    print(f"Average inference time: {avg_time:.2f} ms")
    
    # 分析内存使用（近似值）
    print("Model size (TFLite):", len(tflite_model) / (1024 * 1024), "MB")
    
    return all_predictions, avg_time

# 测试性能
test_texts = [
    "This product is amazing!",
    "I'm not satisfied with the service.",
    "The quality is acceptable."
]
predictions, avg_time = test_model_performance(interpreter, tokenizer, test_texts)

# 打印部分预测结果
for text, pred in predictions[:5]:
    sentiment = "Positive" if pred == 1 else "Negative"
    print(f"Text: '{text}' -> Sentiment: {sentiment}")

# 性能瓶颈分析示例代码
def analyze_performance_bottlenecks(interpreter, input_ids, attention_mask):
    import time
    import numpy as np
    
    # 测量总体推理时间
    start_time = time.time()
    interpreter.invoke()
    total_time = time.time() - start_time
    print(f"Total inference time: {total_time*1000:.2f} ms")
    
    # 分析内存使用情况
    input_size = input_ids.nbytes + attention_mask.nbytes
    print(f"Input tensors size: {input_size/1024:.2f} KB")
    
    # 分析每层的计算量
    # 注意：这需要更详细的模型分析工具，这里仅作示例
    print("Potential bottlenecks:")
    print("1. Attention mechanisms (high computational intensity)")
    print("2. Feed-forward networks (large matrix multiplications)")
    print("3. Layer normalization (frequent normalization operations)")
    
    return {
        "total_time_ms": total_time * 1000,
        "input_size_kb": input_size / 1024,
        "bottlenecks": ["Attention", "FFN", "LayerNorm"]
    }

# 使用TFLite的委托机制优化计算
import tensorflow as tf

def optimize_with_delegates(model_path, device_type="auto"):
    interpreter = tf.lite.Interpreter(model_path=model_path)
    
    # 获取可用的委托
    delegates = []
    
    try:
        # 尝试GPU委托
        if device_type in ["auto", "gpu"]:
            gpu_delegate = tf.lite.experimental.load_delegate('libtensorflowlite_gpu_delegate.so')
            delegates.append(gpu_delegate)
    except:
        pass
    
    try:
        # 尝试NNAPI委托（Android）
        if device_type in ["auto", "nnapi"]:
            nnapi_delegate = tf.lite.experimental.load_delegate('libtensorflowlite_nnapi_delegate.so')
            delegates.append(nnapi_delegate)
    except:
        pass
    
    try:
        # 尝试Hexagon委托（高通设备）
        if device_type in ["auto", "hexagon"]:
            hexagon_delegate = tf.lite.experimental.load_delegate(
                'libhexagon_delegate.so', 
                {'hexagon_lib_path': 'libhexagon_nn_skel.so'}
            )
            delegates.append(hexagon_delegate)
    except:
        pass
    
    # 使用委托重新创建解释器
    if delegates:
        interpreter = tf.lite.Interpreter(
            model_path=model_path,
            experimental_delegates=delegates
        )
    
    return interpreter

# 内存优化策略示例
def optimize_memory_usage(interpreter, batch_size=1, max_sequence_length=64):
    # 1. 减小批量大小和序列长度
    print(f"Optimizing for batch_size={batch_size}, max_seq_len={max_sequence_length}")
    
    # 2. 使用增量推理（对于生成式模型）
    def incremental_inference(interpreter, initial_input, max_tokens=20):
        import numpy as np
        
        outputs = []
        current_input = initial_input
        
        for _ in range(max_tokens):
            # 设置输入
            interpreter.set_tensor(interpreter.get_input_details()[0]['index'], current_input)
            
            # 执行推理
            interpreter.invoke()
            
            # 获取输出
            output = interpreter.get_tensor(interpreter.get_output_details()[0]['index'])
            next_token = np.argmax(output, axis=-1)
            
            outputs.append(next_token)
            
            # 更新输入（将生成的token添加到序列中）
            # 注意：实际实现需要更复杂的输入更新逻辑
        
        return outputs
    
    # 3. 内存分片加载技术
    def load_model_in_chunks(model_path, chunk_size_mb=50):
        """分段加载大模型以减少峰值内存使用"""
        print(f"Loading model in chunks of {chunk_size_mb}MB")
        # 实际实现需要更复杂的内存管理
        
    return {
        "optimized": True,
        "suggestions": [
            "Use incremental inference for generation tasks",
            "Apply model sharding for larger models",
            "Optimize tokenizer memory usage",
            "Consider on-device quantization"
        ]
    }

// Android设备上的硬件加速优化
public class HardwareAccelerationOptimizer {
    
    public Interpreter getOptimizedInterpreter(Context context, String modelPath) {
        try {
            MappedByteBuffer modelBuffer = FileUtil.loadMappedFile(context, modelPath);
            Interpreter.Options options = new Interpreter.Options();
            
            // 1. 尝试NNAPI委托（神经处理API）
            if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.P) {
                try {
                    NnApiDelegate nnApiDelegate = new NnApiDelegate();
                    options.addDelegate(nnApiDelegate);
                    Log.d("TFLite", "NNAPI delegate enabled");
                } catch (Exception e) {
                    Log.w("TFLite", "NNAPI delegate not available: " + e.getMessage());
                }
            }
            
            // 2. 尝试GPU委托
            try {
                CompatibilityList compatList = new CompatibilityList();
                if (compatList.isDelegateSupportedOnThisDevice()) {
                    GpuDelegate gpuDelegate = new GpuDelegate();
                    options.addDelegate(gpuDelegate);
                    Log.d("TFLite", "GPU delegate enabled");
                }
            } catch (Exception e) {
                Log.w("TFLite", "GPU delegate not available: " + e.getMessage());
            }
            
            // 3. 尝试使用多线程CPU
            options.setNumThreads(Math.max(2, Runtime.getRuntime().availableProcessors() / 2));
            
            return new Interpreter(modelBuffer, options);
        } catch (Exception e) {
            Log.e("TFLite", "Error creating optimized interpreter", e);
            return null;
        }
    }
}

# 在Android上使用BatteryManager监控功耗
# 注意：这需要通过JNI或平台特定API实现
def monitor_battery_consumption():
    print("Monitoring battery consumption during model inference")
    # 1. 记录推理前后的电量百分比
    # 2. 分析每推理100个token的电量消耗
    # 3. 建立功耗模型，预测不同负载下的电池消耗

def optimize_for_battery_life(interpreter, battery_level):
    # 根据电池电量动态调整推理参数
    options = {}
    
    if battery_level < 20:
        # 省电模式
        options["cpu_threads"] = 1
        options["batch_size"] = 1
        options["max_length"] = 32
        options["use_gpu"] = False
        options["cache_size"] = 0.2  # 减少缓存使用
    elif battery_level < 50:
        # 平衡模式
        options["cpu_threads"] = 2
        options["batch_size"] = 1
        options["max_length"] = 64
        options["use_gpu"] = True
        options["cache_size"] = 0.5
    else:
        # 性能模式
        options["cpu_threads"] = 4
        options["batch_size"] = 2
        options["max_length"] = 128
        options["use_gpu"] = True
        options["cache_size"] = 0.8
    
    print(f"Battery optimization applied: {options}")
    return options

// Android上的任务调度优化
public class TaskScheduler {
    
    public void scheduleInferenceTask(Runnable inferenceTask) {
        ConnectivityManager cm = (ConnectivityManager) getSystemService(Context.CONNECTIVITY_SERVICE);
        NetworkInfo activeNetwork = cm.getActiveNetworkInfo();
        boolean isCharging = BatteryManager.BATTERY_PLUGGED_AC == 
                            batteryManager.getIntProperty(BatteryManager.BATTERY_PROPERTY_STATUS);
        
        // 创建工作请求
        WorkRequest.Builder workBuilder = new OneTimeWorkRequest.Builder(InferenceWorker.class);
        
        // 添加约束条件
        Constraints constraints = new Constraints.Builder()
            .setRequiresCharging(true)  // 仅在充电时运行
            .setRequiredNetworkType(isCharging ? NetworkType.UNMETERED : NetworkType.CONNECTED)
            .build();
        
        workBuilder.setConstraints(constraints);
        
        // 调度任务
        WorkManager.getInstance().enqueue(workBuilder.build());
    }
}

# 使用TensorFlow Lite Profiler分析性能
import tensorflow as tf

def profile_model_performance(model_path, input_ids, attention_mask, num_runs=10):
    # 启用Profiler
    tf.lite.experimental.enable_profiling()
    
    # 创建解释器
    interpreter = tf.lite.Interpreter(model_path=model_path)
    interpreter.allocate_tensors()
    
    # 设置输入
    interpreter.set_tensor(interpreter.get_input_details()[0]['index'], input_ids)
    if len(interpreter.get_input_details()) > 1:
        interpreter.set_tensor(interpreter.get_input_details()[1]['index'], attention_mask)
    
    # 运行多次以获取准确的性能数据
    for _ in range(num_runs):
        interpreter.invoke()
    
    # 获取性能统计
    profiler = tf.lite.experimental.get_profiler()
    stats = profiler.get_stats()
    
    print("Performance profiling results:")
    print(stats)
    
    return stats

class LLMPerformanceMonitor:
    def __init__(self):
        self.start_time = 0
        self.end_time = 0
        self.memory_usage = {}
        self.battery_before = 0
        self.battery_after = 0
    
    def start_monitoring(self):
        # 记录开始时间
        import time
        self.start_time = time.time()
        
        # 记录当前内存使用
        import psutil
        process = psutil.Process()
        self.memory_usage['start'] = process.memory_info().rss / 1024 / 1024  # MB
        
        # 在实际设备上，可以通过平台API获取电池状态
        
        print(f"Monitoring started at {self.start_time}")
    
    def stop_monitoring(self):
        # 记录结束时间
        import time
        self.end_time = time.time()
        
        # 记录最终内存使用
        import psutil
        process = psutil.Process()
        self.memory_usage['end'] = process.memory_info().rss / 1024 / 1024  # MB
        
        # 计算统计信息
        execution_time = (self.end_time - self.start_time) * 1000  # 转换为毫秒
        memory_delta = self.memory_usage['end'] - self.memory_usage['start']
        
        results = {
            "execution_time_ms": execution_time,
            "peak_memory_mb": max(self.memory_usage.values()),
            "memory_increase_mb": memory_delta
        }
        
        print(f"Monitoring stopped. Results: {results}")
        return results
    
    def log_metrics(self, metrics, log_file="performance_log.csv"):
        import csv
        import os
        import datetime
        
        # 添加时间戳
        metrics['timestamp'] = datetime.datetime.now().isoformat()
        
        # 检查文件是否存在
        file_exists = os.path.isfile(log_file)
        
        # 写入CSV文件
        with open(log_file, 'a', newline='') as csvfile:
            fieldnames = ['timestamp', 'execution_time_ms', 'peak_memory_mb', 'memory_increase_mb']
            writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
            
            # 如果文件不存在，写入表头
            if not file_exists:
                writer.writeheader()
            
            writer.writerow(metrics)
        
        print(f"Metrics logged to {log_file}")

class AdaptiveOptimizer:
    def __init__(self, model_path):
        self.model_path = model_path
        self.device_capabilities = self.detect_device_capabilities()
        self.current_strategy = None
    
    def detect_device_capabilities(self):
        # 检测设备能力
        import psutil
        import platform
        
        capabilities = {
            "os": platform.system(),
            "cpu_count": psutil.cpu_count(),
            "total_memory_gb": psutil.virtual_memory().total / (1024**3),
            "has_gpu": False,  # 需要平台特定检测
            "battery_level": 100,  # 需要平台特定检测
            "is_charging": False  # 需要平台特定检测
        }
        
        return capabilities
    
    def select_optimization_strategy(self, task_type="classification", input_length=64):
        caps = self.device_capabilities
        
        # 根据设备能力选择策略
        if caps["total_memory_gb"] < 4:
            # 低内存设备
            strategy = {
                "quantization": "int8",
                "max_length": min(input_length, 32),
                "batch_size": 1,
                "use_gpu": False,
                "pruning": True
            }
        elif caps["total_memory_gb"] < 8:
            # 中等内存设备
            strategy = {
                "quantization": "float16",
                "max_length": min(input_length, 64),
                "batch_size": 1,
                "use_gpu": caps["has_gpu"],
                "pruning": False
            }
        else:
            # 高内存设备
            strategy = {
                "quantization": "float32",
                "max_length": input_length,
                "batch_size": 2,
                "use_gpu": True,
                "pruning": False
            }
        
        # 根据电池状态调整
        if caps["battery_level"] < 20 and not caps["is_charging"]:
            strategy["use_gpu"] = False
            strategy["batch_size"] = 1
            strategy["max_length"] = min(strategy["max_length"], 32)
        
        self.current_strategy = strategy
        print(f"Selected optimization strategy: {strategy}")
        return strategy
    
    def apply_strategy(self, interpreter):
        # 应用选定的优化策略
        # 实际实现需要更复杂的配置逻辑
        print(f"Applying optimization strategy")
        return interpreter

用户输入 → 文本预处理 → TensorFlow Lite模型(情感分析) → 意图识别模型 → 回复生成 → 用户界面

# 智能客服模型选择与优化代码
def prepare_customer_service_models():
    from transformers import AutoModelForSequenceClassification, AutoTokenizer
    import tensorflow as tf
    
    # 1. 情感分析模型 - 使用轻量级模型
    sentiment_model_name = "distilbert-base-uncased-finetuned-sst-2-english"
    sentiment_tokenizer = AutoTokenizer.from_pretrained(sentiment_model_name)
    sentiment_model = AutoModelForSequenceClassification.from_pretrained(sentiment_model_name)
    
    # 2. 意图识别模型 - 使用多标签分类模型
    intent_model_name = "bert-base-uncased-finetuned-mrpc"
    intent_tokenizer = AutoTokenizer.from_pretrained(intent_model_name)
    intent_model = AutoModelForSequenceClassification.from_pretrained(intent_model_name)
    
    # 3. 转换为TFLite并应用优化
    def convert_and_optimize(model, tokenizer, output_path, num_labels=2):
        # 转换为TensorFlow模型
        import torch
        from transformers import TFAutoModelForSequenceClassification
        
        tf_model = TFAutoModelForSequenceClassification.from_pretrained(
            model.config.name_or_path, 
            from_pt=True, 
            torch_dtype=torch.float32,
            num_labels=num_labels
        )
        
        # 保存为SavedModel
        saved_model_path = f"{output_path}_saved"
        tf_model.save_pretrained(saved_model_path, saved_model=True)
        
        # 转换为TFLite并优化
        converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_path)
        converter.optimizations = [tf.lite.Optimize.DEFAULT]
        
        # 定义代表性数据集
        def representative_dataset():
            sample_texts = [
                "I'm very happy with your service",
                "I want to return my product",
                "How do I track my order?",
                "I'm having technical issues",
                "Your product is amazing!"
            ]
            
            for text in sample_texts:
                inputs = tokenizer(text, return_tensors="tf", truncation=True, padding="max_length", max_length=128)
                yield [inputs["input_ids"].numpy(), inputs["attention_mask"].numpy()]
        
        converter.representative_dataset = representative_dataset
        converter.target_spec.supported_ops = [
            tf.lite.OpsSet.TFLITE_BUILTINS,
            tf.lite.OpsSet.SELECT_TF_OPS
        ]
        
        # 执行转换
        tflite_model = converter.convert()
        
        # 保存优化后的模型
        with open(output_path, "wb") as f:
            f.write(tflite_model)
        
        return output_path
    
    # 转换并优化两个模型
    sentiment_tflite_path = convert_and_optimize(
        sentiment_model, 
        sentiment_tokenizer, 
        "sentiment_model.tflite",
        num_labels=2  # 正面/负面
    )
    
    intent_tflite_path = convert_and_optimize(
        intent_model, 
        intent_tokenizer, 
        "intent_model.tflite",
        num_labels=5  # 示例：5个不同的意图类别
    )
    
    return {
        "sentiment_model": sentiment_tflite_path,
        "intent_model": intent_tflite_path,
        "tokenizers": {
            "sentiment": sentiment_tokenizer,
            "intent": intent_tokenizer
        }
    }

public class CustomerServiceAssistant {
    private Interpreter sentimentModel;
    private Interpreter intentModel;
    private TokenizerTokenizer sentimentTokenizer;
    private TokenizerTokenizer intentTokenizer;
    
    // 意图类别映射
    private final String[] INTENT_CLASSES = {
        "Product Inquiry",
        "Order Tracking",
        "Technical Support",
        "Refund Request",
        "General Feedback"
    };
    
    public void initialize(Context context) {
        try {
            // 加载模型
            MappedByteBuffer sentimentBuffer = FileUtil.loadMappedFile(context, "sentiment_model.tflite");
            MappedByteBuffer intentBuffer = FileUtil.loadMappedFile(context, "intent_model.tflite");
            
            // 配置解释器
            Interpreter.Options options = new Interpreter.Options();
            options.setNumThreads(2);
            
            // 尝试使用GPU加速
            try {
                CompatibilityList compatList = new CompatibilityList();
                if (compatList.isDelegateSupportedOnThisDevice()) {
                    GpuDelegate gpuDelegate = new GpuDelegate();
                    options.addDelegate(gpuDelegate);
                }
            } catch (Exception e) {
                Log.w("CustomerService", "GPU acceleration not available");
            }
            
            sentimentModel = new Interpreter(sentimentBuffer, options);
            intentModel = new Interpreter(intentBuffer, options);
            
            // 初始化分词器（实际实现需要平台特定的分词器）
            // sentimentTokenizer = initializeTokenizer(context, "sentiment_vocab.txt");
            // intentTokenizer = initializeTokenizer(context, "intent_vocab.txt");
            
        } catch (IOException e) {
            Log.e("CustomerService", "Error initializing models", e);
        }
    }
    
    public ServiceResponse processUserQuery(String userQuery) {
        // 1. 情感分析
        float sentimentScore = analyzeSentiment(userQuery);
        String sentiment = sentimentScore > 0.5f ? "Positive" : "Negative";
        
        // 2. 意图识别
        int intentIndex = recognizeIntent(userQuery);
        String intent = INTENT_CLASSES[intentIndex];
        
        // 3. 生成回复
        String response = generateResponse(sentiment, intent, userQuery);
        
        return new ServiceResponse(sentiment, intent, response);
    }
    
    private float analyzeSentiment(String text) {
        // 预处理文本
        int[][] inputIds = preprocessText(text, sentimentTokenizer);
        int[][] attentionMask = generateAttentionMask(inputIds);
        
        // 设置输入
        float[][] output = new float[1][2];
        sentimentModel.runForMultipleInputsOutputs(
            new Object[]{inputIds, attentionMask},
            Map.of(0, output)
        );
        
        // 返回情感分数（这里简化处理，实际可能需要softmax）
        return output[0][1]; // 假设索引1是正面情感
    }
    
    private int recognizeIntent(String text) {
        // 类似情感分析的实现
        // ...
        return 0; // 简化返回
    }
    
    private String generateResponse(String sentiment, String intent, String query) {
        // 根据情感和意图生成适当的回复
        Map<String, Map<String, String>> responseTemplates = new HashMap<>();
        
        // 初始化回复模板（实际应用中可能更复杂）
        Map<String, String> positiveTemplates = new HashMap<>();
        positiveTemplates.put("Product Inquiry", "Thank you for your interest in our products! How can I help you?");
        // 添加更多模板...
        
        Map<String, String> negativeTemplates = new HashMap<>();
        negativeTemplates.put("Technical Support", "I'm sorry you're having issues. Let me help you resolve this.");
        // 添加更多模板...
        
        responseTemplates.put("Positive", positiveTemplates);
        responseTemplates.put("Negative", negativeTemplates);
        
        // 返回对应模板的回复
        return responseTemplates.getOrDefault(sentiment, new HashMap<>())
                .getOrDefault(intent, "I'm here to help. Can you provide more details?");
    }
    
    // 其他辅助方法...
    
    public void close() {
        if (sentimentModel != null) sentimentModel.close();
        if (intentModel != null) intentModel.close();
    }
}

// 异步处理和降级机制实现
public class AsyncServiceHandler {
    private final ExecutorService executorService = Executors.newFixedThreadPool(2);
    private final CustomerServiceAssistant assistant;
    private final SharedPreferences sharedPreferences;
    private boolean isReducedPerformance = false;
    
    public AsyncServiceHandler(Context context) {
        assistant = new CustomerServiceAssistant();
        assistant.initialize(context);
        sharedPreferences = context.getSharedPreferences("service_cache", Context.MODE_PRIVATE);
    }
    
    public void processQuery(String query, ServiceCallback callback) {
        // 检查缓存
        String cachedResponse = sharedPreferences.getString(query, null);
        if (cachedResponse != null) {
            callback.onSuccess(parseResponse(cachedResponse));
            return;
        }
        
        // 异步处理查询
        executorService.submit(() -> {
            try {
                // 检查设备状态，必要时降级
                checkDeviceStatus();
                
                // 处理查询
                ServiceResponse response = assistant.processUserQuery(query);
                
                // 缓存结果
                cacheResponse(query, response);
                
                // 返回结果到主线程
                new Handler(Looper.getMainLooper()).post(() -> callback.onSuccess(response));
            } catch (Exception e) {
                new Handler(Looper.getMainLooper()).post(() -> callback.onError(e));
            }
        });
    }
    
    private void checkDeviceStatus() {
        // 检查内存和电池状态，必要时调整模型性能
        ActivityManager.MemoryInfo memoryInfo = new ActivityManager.MemoryInfo();
        ActivityManager activityManager = (ActivityManager) getSystemService(Context.ACTIVITY_SERVICE);
        activityManager.getMemoryInfo(memoryInfo);
        
        IntentFilter iFilter = new IntentFilter(Intent.ACTION_BATTERY_CHANGED);
        Intent batteryStatus = registerReceiver(null, iFilter);
        int level = batteryStatus.getIntExtra(BatteryManager.EXTRA_LEVEL, -1);
        int scale = batteryStatus.getIntExtra(BatteryManager.EXTRA_SCALE, -1);
        float batteryLevel = level * 100 / (float) scale;
        
        // 如果内存压力大或电池电量低，启用降低性能模式
        if (memoryInfo.lowMemory || batteryLevel < 20) {
            isReducedPerformance = true;
            // 切换到更轻量级的模型或降低精度
        } else {
            isReducedPerformance = false;
        }
    }
    
    private void cacheResponse(String query, ServiceResponse response) {
        // 缓存响应，使用简单的JSON格式
        try {
            JSONObject json = new JSONObject();
            json.put("sentiment", response.sentiment);
            json.put("intent", response.intent);
            json.put("response", response.response);
            
            SharedPreferences.Editor editor = sharedPreferences.edit();
            editor.putString(query, json.toString());
            editor.apply();
        } catch (JSONException e) {
            Log.w("Cache", "Failed to cache response", e);
        }
    }
    
    // 其他方法...
    
    public interface ServiceCallback {
        void onSuccess(ServiceResponse response);
        void onError(Exception e);
    }
    
    public static class ServiceResponse {
        public final String sentiment;
        public final String intent;
        public final String response;
        
        public ServiceResponse(String sentiment, String intent, String response) {
            this.sentiment = sentiment;
            this.intent = intent;
            this.response = response;
        }
    }
}

# 离线翻译模型准备与优化
def prepare_translation_model():
    from transformers import TFAutoModelForSeq2SeqLM, AutoTokenizer
    import tensorflow as tf
    
    # 选择轻量级翻译模型
    model_name = "t5-small"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = TFAutoModelForSeq2SeqLM.from_pretrained(model_name)
    
    # 针对翻译任务的优化
    def optimize_for_translation(saved_model_path, tflite_path):
        # 转换为TFLite
        converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_path)
        
        # 启用优化
        converter.optimizations = [tf.lite.Optimize.DEFAULT]
        
        # 对于翻译模型的特殊配置
        converter.target_spec.supported_ops = [
            tf.lite.OpsSet.TFLITE_BUILTINS,
            tf.lite.OpsSet.SELECT_TF_OPS
        ]
        
        # 动态形状支持
        converter.input_shapes = {'input_ids': [1, 128], 'attention_mask': [1, 128]}
        
        # 定义代表性数据集
        def representative_dataset():
            sample_texts = [
                "translate English to French: Hello, how are you?",
                "translate English to Spanish: I love learning new technologies",
                "translate English to German: The weather is nice today",
                "translate English to Italian: Please help me with this",
                "translate English to Chinese: Artificial intelligence is amazing"
            ]
            
            for text in sample_texts:
                inputs = tokenizer(text, return_tensors="tf", truncation=True, padding="max_length", max_length=128)
                yield [inputs["input_ids"].numpy(), inputs["attention_mask"].numpy()]
        
        converter.representative_dataset = representative_dataset
        
        # 执行转换
        tflite_model = converter.convert()
        
        # 保存模型
        with open(tflite_path, "wb") as f:
            f.write(tflite_model)
        
        return tflite_path
    
    # 保存模型
    saved_model_path = "./translation_model"
    model.save_pretrained(saved_model_path, saved_model=True)
    
    # 优化并转换
    tflite_path = optimize_for_translation(saved_model_path, "translation_model.tflite")
    
    return {
        "model_path": tflite_path,
        "tokenizer": tokenizer
    }

# 翻译质量与速度平衡策略
class TranslationOptimizer:
    def __init__(self, model_path):
        self.model_path = model_path
        self.interpreter = None
        self.config = {
            "beam_width": 1,  # 默认为贪婪解码
            "max_length": 64,  # 最大输出长度
            "temperature": 1.0,  # 解码温度
            "optimization_level": "balanced"  # balanced, quality, speed
        }
    
    def initialize(self, optimization_level="balanced"):
        import tensorflow as tf
        
        # 根据优化级别设置参数
        if optimization_level == "speed":
            self.config["beam_width"] = 1
            self.config["max_length"] = 32
            self.config["temperature"] = 0.8
        elif optimization_level == "quality":
            self.config["beam_width"] = 3
            self.config["max_length"] = 128
            self.config["temperature"] = 1.0
        else:  # balanced
            self.config["beam_width"] = 2
            self.config["max_length"] = 64
            self.config["temperature"] = 0.9
        
        self.config["optimization_level"] = optimization_level
        
        # 加载模型
        self.interpreter = tf.lite.Interpreter(model_path=self.model_path)
        
        # 根据设备能力优化
        self._optimize_for_device()
        
        return self.config
    
    def _optimize_for_device(self):
        # 根据设备能力调整线程数等参数
        import psutil
        cpu_count = psutil.cpu_count()
        
        # 设置线程数
        num_threads = max(1, min(4, cpu_count // 2))
        self.interpreter.set_num_threads(num_threads)
    
    def translate(self, text, source_lang, target_lang, optimization_level=None):
        # 如果指定了新的优化级别，重新初始化
        if optimization_level and optimization_level != self.config["optimization_level"]:
            self.initialize(optimization_level)
        
        # 准备输入文本
        input_text = f"translate {source_lang} to {target_lang}: {text}"
        
        # 这里需要实现完整的翻译逻辑
        # ...
        
        return "Translated text"  # 简化返回

用户历史 → 偏好分析模型 → 内容编码模型 → 相似度计算 → 推荐排序 → 推荐结果

# 内容推荐模型实现
def prepare_recommendation_model():
    import tensorflow as tf
    from transformers import TFAutoModel, AutoTokenizer
    
    # 选择内容编码模型
    model_name = "distilbert-base-uncased"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    encoder_model = TFAutoModel.from_pretrained(model_name)
    
    # 创建简单的推荐模型
    class RecommendationModel(tf.keras.Model):
        def __init__(self, encoder):
            super(RecommendationModel, self).__init__()
            self.encoder = encoder
            
        def call(self, inputs):
            # 提取CLS标记的输出作为文本表示
            outputs = self.encoder(inputs)
            return outputs.last_hidden_state[:, 0, :]  # CLS表示
    
    # 创建推荐模型
    rec_model = RecommendationModel(encoder_model)
    
    # 保存模型
    saved_model_path = "./recommendation_model"
    
    # 保存为SavedModel
    @tf.function(input_signature=[
        tf.TensorSpec(shape=[1, 128], dtype=tf.int32, name="input_ids"),
        tf.TensorSpec(shape=[1, 128], dtype=tf.int32, name="attention_mask")
    ])
    def serve_fn(input_ids, attention_mask):
        return rec_model([input_ids, attention_mask])
    
    tf.saved_model.save(rec_model, saved_model_path, signatures={'serving_default': serve_fn})
    
    # 转换为TFLite
    converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_path)
    converter.optimizations = [tf.lite.Optimize.DEFAULT]
    
    # 定义代表性数据集
    def representative_dataset():
        sample_texts = [
            "Machine learning techniques for beginners",
            "Latest developments in artificial intelligence",
            "Mobile app development best practices",
            "Data science fundamentals and applications",
            "Cloud computing and deployment strategies"
        ]
        
        for text in sample_texts:
            inputs = tokenizer(text, return_tensors="tf", truncation=True, padding="max_length", max_length=128)
            yield [inputs["input_ids"].numpy(), inputs["attention_mask"].numpy()]
    
    converter.representative_dataset = representative_dataset
    
    # 转换模型
    tflite_model = converter.convert()
    
    # 保存TFLite模型
    tflite_path = "recommendation_model.tflite"
    with open(tflite_path, "wb") as f:
        f.write(tflite_model)
    
    return {
        "model_path": tflite_path,
        "tokenizer": tokenizer
    }

public class ContentRecommendationEngine {
    private Interpreter model;
    private Tokenizer tokenizer;
    private List<ContentItem> contentCatalog;
    private List<float[]> contentEmbeddings;
    private SharedPreferences userPreferences;
    
    public void initialize(Context context) {
        try {
            // 加载模型
            MappedByteBuffer modelBuffer = FileUtil.loadMappedFile(context, "recommendation_model.tflite");
            model = new Interpreter(modelBuffer);
            
            // 初始化分词器
            // tokenizer = initializeTokenizer(context, "vocab.txt");
            
            // 加载内容目录
            contentCatalog = loadContentCatalog(context);
            
            // 预计算内容嵌入（应用启动时）
            precomputeEmbeddings();
            
            // 加载用户偏好
            userPreferences = context.getSharedPreferences("user_prefs", Context.MODE_PRIVATE);
            
        } catch (IOException e) {
            Log.e("Recommendation", "Error initializing engine", e);
        }
    }
    
    public List<ContentItem> getRecommendations(String userQuery, int numRecommendations) {
        // 1. 编码用户查询
        float[] queryEmbedding = encodeText(userQuery);
        
        // 2. 计算与所有内容的相似度
        List<Pair<ContentItem, Float>> similarities = new ArrayList<>();
        for (int i = 0; i < contentEmbeddings.size(); i++) {
            float similarity = calculateCosineSimilarity(queryEmbedding, contentEmbeddings.get(i));
            similarities.add(new Pair<>(contentCatalog.get(i), similarity));
        }
        
        // 3. 根据相似度排序
        similarities.sort((a, b) -> b.second.compareTo(a.second));
        
        // 4. 返回前N个推荐
        List<ContentItem> recommendations = new ArrayList<>();
        for (int i = 0; i < Math.min(numRecommendations, similarities.size()); i++) {
            recommendations.add(similarities.get(i).first);
        }
        
        // 5. 更新用户偏好
        updateUserPreferences(userQuery, recommendations);
        
        return recommendations;
    }
    
    private float[] encodeText(String text) {
        // 预处理文本
        int[][] inputIds = preprocessText(text);
        int[][] attentionMask = generateAttentionMask(inputIds);
        
        // 设置输入
        float[][] output = new float[1][768]; // 假设输出维度为768
        model.runForMultipleInputsOutputs(
            new Object[]{inputIds, attentionMask},
            Map.of(0, output)
        );
        
        return output[0];
    }
    
    private void precomputeEmbeddings() {
        contentEmbeddings = new ArrayList<>();
        
        // 为内容目录中的每个项目预计算嵌入
        for (ContentItem item : contentCatalog) {
            float[] embedding = encodeText(item.getTitle() + " " + item.getDescription());
            contentEmbeddings.add(embedding);
        }
    }
    
    private float calculateCosineSimilarity(float[] vec1, float[] vec2) {
        // 计算余弦相似度
        float dotProduct = 0.0f;
        float norm1 = 0.0f;
        float norm2 = 0.0f;
        
        for (int i = 0; i < vec1.length; i++) {
            dotProduct += vec1[i] * vec2[i];
            norm1 += vec1[i] * vec1[i];
            norm2 += vec2[i] * vec2[i];
        }
        
        return dotProduct / (float)(Math.sqrt(norm1) * Math.sqrt(norm2));
    }
    
    private void updateUserPreferences(String query, List<ContentItem> recommendations) {
        // 更新用户偏好（实际实现可能更复杂）
        SharedPreferences.Editor editor = userPreferences.edit();
        long timestamp = System.currentTimeMillis();
        
        // 记录查询
        editor.putString("last_query", query);
        editor.putLong("last_query_time", timestamp);
        
        // 可以记录用户点击的推荐项等
        editor.apply();
    }
    
    // 其他辅助方法...
    
    public static class ContentItem {
        private String id;
        private String title;
        private String description;
        private String category;
        private long timestamp;
        
        // 构造函数、getter和setter方法
        
        public String getTitle() { return title; }
        public String getDescription() { return description; }
    }
}

图像输入 → 图像编码器 → 特征融合 → 文本解码器 → 文本输出

# 多模态图像描述模型实现
def prepare_image_captioning_model():
    import tensorflow as tf
    from transformers import TFVisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
    
    # 选择轻量级多模态模型
    model_name = "nlpconnect/vit-gpt2-image-captioning"
    
    # 加载模型组件
    feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = TFVisionEncoderDecoderModel.from_pretrained(model_name)
    
    # 为移动设备优化模型
    def optimize_model_for_mobile(saved_model_path, tflite_path):
        # 转换为TFLite
        converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_path)
        
        # 启用优化
        converter.optimizations = [tf.lite.Optimize.DEFAULT]
        
        # 对于多模态模型的特殊配置
        converter.target_spec.supported_ops = [
            tf.lite.OpsSet.TFLITE_BUILTINS,
            tf.lite.OpsSet.SELECT_TF_OPS
        ]
        
        # 定义代表性数据集
        def representative_dataset():
            import numpy as np
            from PIL import Image
            import io
            import requests
            
            # 使用一些示例图像URL（实际应用中可能使用本地图像）
            sample_images = [
                "https://images.unsplash.com/photo-1507146153580-69a1fe6d8aa1",
                "https://images.unsplash.com/photo-1535713875002-d1d0cf377fde",
                "https://images.unsplash.com/photo-1526304640581-d334cdbbf45e"
            ]
            
            for img_url in sample_images:
                try:
                    # 下载图像
                    response = requests.get(img_url, stream=True)
                    img = Image.open(io.BytesIO(response.content)).convert("RGB")
                    
                    # 预处理图像
                    pixel_values = feature_extractor(images=img, return_tensors="tf").pixel_values
                    
                    yield [pixel_values.numpy()]
                except:
                    # 如果下载失败，使用随机数据
                    random_image = np.random.rand(1, 224, 224, 3).astype(np.float32)
                    yield [random_image]
        
        converter.representative_dataset = representative_dataset
        
        # 转换模型
        tflite_model = converter.convert()
        
        # 保存TFLite模型
        with open(tflite_path, "wb") as f:
            f.write(tflite_model)
        
        return tflite_path
    
    # 保存模型
    saved_model_path = "./image_captioning_model"
    model.save_pretrained(saved_model_path, saved_model=True)
    
    # 优化并转换
    tflite_path = optimize_model_for_mobile(saved_model_path, "image_captioning_model.tflite")
    
    return {
        "model_path": tflite_path,
        "feature_extractor": feature_extractor,
        "tokenizer": tokenizer
    }

public class ImageCaptioningManager {
    private Interpreter model;
    private FeatureExtractor featureExtractor;
    private Tokenizer tokenizer;
    
    public void initialize(Context context) {
        try {
            // 加载模型
            MappedByteBuffer modelBuffer = FileUtil.loadMappedFile(context, "image_captioning_model.tflite");
            
            // 配置解释器
            Interpreter.Options options = new Interpreter.Options();
            options.setNumThreads(2);
            
            // 尝试使用GPU加速
            try {
                CompatibilityList compatList = new CompatibilityList();
                if (compatList.isDelegateSupportedOnThisDevice()) {
                    GpuDelegate gpuDelegate = new GpuDelegate();
                    options.addDelegate(gpuDelegate);
                }
            } catch (Exception e) {
                Log.w("ImageCaptioning", "GPU acceleration not available");
            }
            
            model = new Interpreter(modelBuffer, options);
            
            // 初始化特征提取器和分词器（实际实现需要平台特定代码）
            // featureExtractor = initializeFeatureExtractor(context);
            // tokenizer = initializeTokenizer(context);
            
        } catch (IOException e) {
            Log.e("ImageCaptioning", "Error initializing model", e);
        }
    }
    
    public String generateCaption(Bitmap image) {
        try {
            // 1. 预处理图像
            float[][] imageFeatures = preprocessImage(image);
            
            // 2. 执行推理
            // 注意：实际实现中，多模态模型的输入输出可能更复杂
            // 这里简化处理
            float[][] outputs = new float[1][100]; // 假设输出100个token
            model.run(imageFeatures, outputs);
            
            // 3. 解码生成的文本
            String caption = decodeOutput(outputs);
            
            return caption;
        } catch (Exception e) {
            Log.e("ImageCaptioning", "Error generating caption", e);
            return "Unable to generate caption. Please try again.";
        }
    }
    
    private float[][] preprocessImage(Bitmap image) {
        // 图像预处理：调整大小、归一化等
        // 实际实现需要根据模型要求进行处理
        Bitmap resizedBitmap = Bitmap.createScaledBitmap(image, 224, 224, true);
        
        // 转换为浮点数组
        float[][] result = new float[1][224 * 224 * 3];
        int pixelIndex = 0;
        
        for (int y = 0; y < 224; y++) {
            for (int x = 0; x < 224; x++) {
                int pixel = resizedBitmap.getPixel(x, y);
                
                // 提取RGB值并归一化
                result[0][pixelIndex++] = Color.red(pixel) / 255.0f;
                result[0][pixelIndex++] = Color.green(pixel) / 255.0f;
                result[0][pixelIndex++] = Color.blue(pixel) / 255.0f;
            }
        }
        
        return result;
    }
    
    private String decodeOutput(float[][] outputs) {
        // 解码模型输出为文本
        // 实际实现需要根据模型的输出格式进行解码
        // 这里返回示例文本
        return "A beautiful scene with interesting elements.";
    }
    
    public void close() {
        if (model != null) {
            model.close();
        }
    }
}

NPU发展趋势: 计算能力(TOPS)每18个月翻倍，能效比提升2倍以上

# 未来TensorFlow Lite硬件加速架构预览
class FutureHardwareAcceleration:
    def __init__(self):
        # 多层次硬件抽象
        self.hardware_layers = {
            "neuromorphic": False,  # 神经形态计算支持
            "photonic": False,      # 光电混合计算支持
            "advanced_npu": True    # 高级NPU支持
        }
        
        # 动态硬件调度
        self.dynamic_scheduler = None
    
    def configure_acceleration(self, model_complexity, device_profile):
        # 根据模型复杂度和设备配置动态选择最佳硬件加速策略
        pass
    
    def enable_heterogeneous_computing(self):
        # 启用异构计算，在不同硬件单元间动态分配计算任务
        pass
    
    def optimize_power_efficiency(self):
        # 根据电池状态和计算需求优化功耗
        pass

# 自动化模型优化流程示例
class AutoModelOptimizer:
    def __init__(self, model, device_profile, task_type):
        self.model = model
        self.device_profile = device_profile  # 设备性能配置
        self.task_type = task_type            # 任务类型
        self.optimization_strategy = None
    
    def analyze_model(self):
        # 分析模型结构和计算特性
        pass
    
    def profile_device(self):
        # 分析目标设备性能特征
        pass
    
    def select_optimization_strategy(self):
        # 选择最佳优化策略组合
        pass
    
    def apply_optimization(self):
        # 应用选定的优化策略
        pass
    
    def evaluate_performance(self):
        # 评估优化后的模型性能
        pass
    
    def auto_tune(self):
        # 自动调优过程
        self.analyze_model()
        self.profile_device()
        self.select_optimization_strategy()
        self.apply_optimization()
        return self.evaluate_performance()

联邦学习流程: 本地训练 → 安全聚合 → 全局更新 → 模型分发

# 未来TensorFlow Lite开发工具链示例
class FutureTFLiteToolchain:
    def __init__(self):
        self.model_analyzer = None      # 模型分析器
        self.performance_profiler = None  # 性能分析器
        self.optimizer = None           # 优化工具
        self.deployer = None            # 部署工具
    
    def analyze_model(self, model_path):
        # 全面分析模型结构和性能特性
        pass
    
    def profile_performance(self, model_path, device_profile):
        # 在目标设备上模拟性能分析
        pass
    
    def suggest_optimizations(self):
        # 基于分析结果推荐优化策略
        pass
    
    def apply_optimizations(self):
        # 应用推荐的优化
        pass
    
    def deploy_to_devices(self, target_devices):
        # 部署到目标设备
        pass