model_compression.mbt

// 模型压缩与优化（v42.0.0）
// 剪枝、量化、知识蒸馏、低秩分解、模型部署优化

///|
/// 模型压缩方法类型
pub(all) enum ModelCompressionMethod {
  Pruning
  Quantization
  KnowledgeDistillation
  LowRankFactorization
  WeightSharing
  NetworkSlimming
  StructuredPruning
  UnstructuredPruning
  Unknown
} derive(Eq)

///|
/// 量化类型
pub(all) enum QuantizationType {
  INT8
  INT4
  INT16
  FP16
  BF16
  MixedPrecision
  DynamicQuantization
  StaticQuantization
  Unknown
} derive(Eq)

///|
/// 剪枝类型
pub(all) enum PruningType {
  MagnitudePruning
  GradientPruning
  MovementPruning
  LotteryTicket
  StructuredChannel
  StructuredFilter
  UnstructuredWeight
  BlockPruning
  Unknown
} derive(Eq)

///|
/// 模型压缩结果
pub(all) struct ModelCompressionResult {
  original_size_mb : Double
  compressed_size_mb : Double
  compression_ratio : Double
  original_parameters : Int
  compressed_parameters : Int
  parameter_reduction : Double
  original_flops : Int
  compressed_flops : Int
  flops_reduction : Double
  accuracy_drop : Double
  method_type : ModelCompressionMethod
}

///|
/// 量化配置
pub(all) struct QuantizationConfig {
  quantization_type : QuantizationType
  num_bits : Int
  symmetric : Bool
  per_channel : Bool
  calibration_samples : Int
  use_quant_aware_training : Bool
  activation_quantization : Bool
  weight_quantization : Bool
}

///|
/// 剪枝配置
pub(all) struct PruningConfig {
  pruning_type : PruningType
  pruning_ratio : Double
  threshold : Double
  structured : Bool
  global_pruning : Bool
  iterative_pruning : Bool
  num_iterations : Int
  fine_tune_epochs : Int
}

///|
/// 知识蒸馏配置
pub(all) struct KnowledgeDistillationConfig {
  temperature : Double
  alpha : Double
  beta : Double
  use_hint_loss : Bool
  use_attention_transfer : Bool
  use_feature_distillation : Bool
  teacher_layers : Array[Int]
  student_layers : Array[Int]
}

///|
/// 低秩分解配置
pub(all) struct LowRankConfig {
  rank_ratio : Double
  min_rank : Int
  max_rank : Int
  use_svd : Bool
  use_cp_decomposition : Bool
  use_tucker_decomposition : Bool
  threshold : Double
}

///|
/// 模型优化配置
pub(all) struct ModelOptimizationConfig {
  use_operator_fusion : Bool
  use_memory_optimization : Bool
  use_inference_acceleration : Bool
  use_batch_optimization : Bool
  use_graph_optimization : Bool
  use_constant_folding : Bool
  use_dead_code_elimination : Bool
  target_device : String
  optimization_level : Int
}

///|
/// 部署目标
pub(all) enum DeploymentTarget {
  ONNX
  TensorRT
  OpenVINO
  CoreML
  NNAPI
  TFLite
  TorchScript
  WebAssembly
  Unknown
} derive(Eq)

///|
/// 部署配置
pub(all) struct DeploymentConfig {
  target : DeploymentTarget
  optimize_for_inference : Bool
  use_dynamic_shapes : Bool
  batch_size : Int
  input_shape : Array[Int]
  output_names : Array[String]
  opset_version : Int
  use_half_precision : Bool
}

///|
/// 部署结果
pub(all) struct DeploymentResult {
  target : DeploymentTarget
  output_path : String
  model_size_mb : Double
  inference_time_ms : Double
  memory_usage_mb : Double
  throughput_fps : Double
  success : Bool
  error_message : String
}

///|
/// 创建默认量化配置
pub fn create_default_quantization_config() -> QuantizationConfig {
  QuantizationConfig::{
    quantization_type: QuantizationType::INT8,
    num_bits: 8,
    symmetric: true,
    per_channel: true,
    calibration_samples: 100,
    use_quant_aware_training: false,
    activation_quantization: true,
    weight_quantization: true,
  }
}

///|
/// 创建默认剪枝配置
pub fn create_default_pruning_config() -> PruningConfig {
  PruningConfig::{
    pruning_type: PruningType::MagnitudePruning,
    pruning_ratio: 0.5,
    threshold: 0.01,
    structured: false,
    global_pruning: true,
    iterative_pruning: false,
    num_iterations: 1,
    fine_tune_epochs: 10,
  }
}

///|
/// 创建默认知识蒸馏配置
pub fn create_default_distillation_config() -> KnowledgeDistillationConfig {
  KnowledgeDistillationConfig::{
    temperature: 4.0,
    alpha: 0.5,
    beta: 0.5,
    use_hint_loss: false,
    use_attention_transfer: false,
    use_feature_distillation: true,
    teacher_layers: [],
    student_layers: [],
  }
}

///|
/// 创建默认低秩分解配置
pub fn create_default_lowrank_config() -> LowRankConfig {
  LowRankConfig::{
    rank_ratio: 0.5,
    min_rank: 8,
    max_rank: 256,
    use_svd: true,
    use_cp_decomposition: false,
    use_tucker_decomposition: false,
    threshold: 0.01,
  }
}

///|
/// 创建默认模型优化配置
pub fn create_default_optimization_config() -> ModelOptimizationConfig {
  ModelOptimizationConfig::{
    use_operator_fusion: true,
    use_memory_optimization: true,
    use_inference_acceleration: true,
    use_batch_optimization: true,
    use_graph_optimization: true,
    use_constant_folding: true,
    use_dead_code_elimination: true,
    target_device: "cpu",
    optimization_level: 3,
  }
}

///|
/// 创建默认部署配置
pub fn create_default_deployment_config() -> DeploymentConfig {
  DeploymentConfig::{
    target: DeploymentTarget::ONNX,
    optimize_for_inference: true,
    use_dynamic_shapes: false,
    batch_size: 1,
    input_shape: [1, 3, 224, 224],
    output_names: [],
    opset_version: 17,
    use_half_precision: false,
  }
}

///|
/// 量化模型（简化版）
pub fn quantize_model(
  original_size_mb : Double,
  original_parameters : Int,
  original_flops : Int,
  config : QuantizationConfig,
) -> ModelCompressionResult {
  let compression_factor = match config.quantization_type {
    QuantizationType::INT8 => 4.0
    QuantizationType::INT4 => 8.0
    QuantizationType::INT16 => 2.0
    QuantizationType::FP16 => 2.0
    QuantizationType::BF16 => 2.0
    QuantizationType::MixedPrecision => 3.0
    _ => 2.0
  }

  let compressed_size = original_size_mb / compression_factor
  let compressed_parameters = original_parameters
  let compressed_flops = original_flops
  let accuracy_drop = match config.quantization_type {
    QuantizationType::INT8 => 0.02
    QuantizationType::INT4 => 0.05
    QuantizationType::INT16 => 0.005
    QuantizationType::FP16 => 0.005
    _ => 0.01
  }

  ModelCompressionResult::{
    original_size_mb,
    compressed_size_mb: compressed_size,
    compression_ratio: compression_factor,
    original_parameters,
    compressed_parameters,
    parameter_reduction: 0.0,
    original_flops,
    compressed_flops,
    flops_reduction: 0.0,
    accuracy_drop,
    method_type: ModelCompressionMethod::Quantization,
  }
}

///|
/// 剪枝模型（简化版）
pub fn prune_model(
  original_size_mb : Double,
  original_parameters : Int,
  original_flops : Int,
  config : PruningConfig,
) -> ModelCompressionResult {
  let pruning_ratio = config.pruning_ratio
  let compressed_size = original_size_mb * (1.0 - pruning_ratio)
  let compressed_parameters = (original_parameters.to_double() *
  (1.0 - pruning_ratio)).to_int()
  let compressed_flops = (original_flops.to_double() *
  (1.0 - pruning_ratio * 0.8)).to_int()
  let accuracy_drop = pruning_ratio * 0.1

  ModelCompressionResult::{
    original_size_mb,
    compressed_size_mb: compressed_size,
    compression_ratio: original_size_mb / compressed_size,
    original_parameters,
    compressed_parameters,
    parameter_reduction: pruning_ratio,
    original_flops,
    compressed_flops,
    flops_reduction: pruning_ratio * 0.8,
    accuracy_drop,
    method_type: ModelCompressionMethod::Pruning,
  }
}

///|
/// 知识蒸馏（简化版）
pub fn knowledge_distillation(
  teacher_size_mb : Double,
  student_size_mb : Double,
  teacher_parameters : Int,
  student_parameters : Int,
  config : KnowledgeDistillationConfig,
) -> ModelCompressionResult {
  let compression_ratio = teacher_size_mb / student_size_mb
  let parameter_reduction = 1.0 -
    student_parameters.to_double() / teacher_parameters.to_double()
  let accuracy_drop = 0.01 / config.temperature

  ModelCompressionResult::{
    original_size_mb: teacher_size_mb,
    compressed_size_mb: student_size_mb,
    compression_ratio,
    original_parameters: teacher_parameters,
    compressed_parameters: student_parameters,
    parameter_reduction,
    original_flops: 0,
    compressed_flops: 0,
    flops_reduction: parameter_reduction * 0.7,
    accuracy_drop,
    method_type: ModelCompressionMethod::KnowledgeDistillation,
  }
}

///|
/// 低秩分解（简化版）
pub fn low_rank_factorization(
  original_size_mb : Double,
  original_parameters : Int,
  original_flops : Int,
  config : LowRankConfig,
) -> ModelCompressionResult {
  let rank_ratio = config.rank_ratio
  let compressed_size = original_size_mb * (0.3 + rank_ratio * 0.4)
  let compressed_parameters = (original_parameters.to_double() *
  (0.3 + rank_ratio * 0.4)).to_int()
  let compressed_flops = (original_flops.to_double() * (0.4 + rank_ratio * 0.3)).to_int()
  let accuracy_drop = (1.0 - rank_ratio) * 0.03

  ModelCompressionResult::{
    original_size_mb,
    compressed_size_mb: compressed_size,
    compression_ratio: original_size_mb / compressed_size,
    original_parameters,
    compressed_parameters,
    parameter_reduction: 1.0 -
    compressed_parameters.to_double() / original_parameters.to_double(),
    original_flops,
    compressed_flops,
    flops_reduction: 1.0 -
    compressed_flops.to_double() / original_flops.to_double(),
    accuracy_drop,
    method_type: ModelCompressionMethod::LowRankFactorization,
  }
}

///|
/// 优化模型推理（简化版）
pub fn optimize_model_inference(
  original_inference_time_ms : Double,
  original_memory_mb : Double,
  config : ModelOptimizationConfig,
) -> (Double, Double, Double) {
  let mut speedup = 1.0
  let mut memory_reduction = 1.0

  if config.use_operator_fusion {
    speedup *= 1.2
  }
  if config.use_memory_optimization {
    memory_reduction *= 0.8
  }
  if config.use_inference_acceleration {
    speedup *= 1.5
  }
  if config.use_batch_optimization {
    speedup *= 1.1
  }
  if config.use_graph_optimization {
    speedup *= 1.1
  }
  if config.use_constant_folding {
    speedup *= 1.05
  }
  if config.use_dead_code_elimination {
    memory_reduction *= 0.95
  }

  let optimized_time = original_inference_time_ms / speedup
  let optimized_memory = original_memory_mb * memory_reduction
  let throughput = 1000.0 / optimized_time

  (optimized_time, optimized_memory, throughput)
}

///|
/// 部署模型（简化版）
pub fn deploy_model(
  model_size_mb : Double,
  inference_time_ms : Double,
  config : DeploymentConfig,
) -> DeploymentResult {
  let target_name = match config.target {
    DeploymentTarget::ONNX => "ONNX"
    DeploymentTarget::TensorRT => "TensorRT"
    DeploymentTarget::OpenVINO => "OpenVINO"
    DeploymentTarget::CoreML => "Core ML"
    DeploymentTarget::NNAPI => "NNAPI"
    DeploymentTarget::TFLite => "TFLite"
    DeploymentTarget::TorchScript => "TorchScript"
    DeploymentTarget::WebAssembly => "WebAssembly"
    _ => "未知"
  }

  let mut deployed_size = model_size_mb
  let mut deployed_time = inference_time_ms

  if config.optimize_for_inference {
    deployed_time *= 0.7
  }
  if config.use_half_precision {
    deployed_size *= 0.5
    deployed_time *= 0.8
  }

  DeploymentResult::{
    target: config.target,
    output_path: "model_" + target_name + ".onnx",
    model_size_mb: deployed_size,
    inference_time_ms: deployed_time,
    memory_usage_mb: deployed_size * 2.0,
    throughput_fps: 1000.0 / deployed_time,
    success: true,
    error_message: "",
  }
}

///|
/// 生成模型压缩报告
pub fn model_compression_report_to_string(
  result : ModelCompressionResult,
  method_name : String,
) -> String {
  let mut report = "## 模型压缩与优化报告\n\n"
  report = report + "### 压缩信息\n\n"
  report = report +
    "| 指标 | 原始 | 压缩后 | 变化 |\n|------|------|------|------|\n"
  report = report +
    "| 模型大小 | " +
    result.original_size_mb.to_string() +
    " MB | " +
    result.compressed_size_mb.to_string() +
    " MB | " +
    result.compression_ratio.to_string() +
    "x |\n"
  report = report +
    "| 参数数量 | " +
    result.original_parameters.to_string() +
    " | " +
    result.compressed_parameters.to_string() +
    " | " +
    (result.parameter_reduction * 100.0).to_string() +
    "% |\n"
  report = report +
    "| FLOPs | " +
    result.original_flops.to_string() +
    " | " +
    result.compressed_flops.to_string() +
    " | " +
    (result.flops_reduction * 100.0).to_string() +
    "% |\n"
  report = report +
    "| 精度下降 | - | - | " +
    (result.accuracy_drop * 100.0).to_string() +
    "% |\n"
  report = report + "| 压缩方法 | " + method_name + " | | |\n"

  report
}
src\process\model_compression\model_compression.mbt

72.86%
