dl_advanced.mbt

// 深度学习高级（v52.0.0）
// Transformer、注意力机制、预训练模型、微调、迁移学习、自监督学习

///|
/// 深度学习高级任务类型
pub(all) enum DLAdvancedTaskType {
  Transformer
  AttentionMechanism
  PretrainedModel
  FineTuning
  TransferLearning
  SelfSupervisedLearning
  ContrastiveLearning
  Unknown
} derive(Eq)

///|
/// Transformer架构类型
pub(all) enum TransformerArchitecture {
  EncoderOnly
  DecoderOnly
  EncoderDecoder
  VisionTransformer
  DataEfficient
  SwinTransformer
  Unknown
} derive(Eq)

///|
/// 注意力机制类型
pub(all) enum AttentionType {
  SelfAttention
  MultiHeadAttention
  CrossAttention
  SparseAttention
  LinearAttention
  FlashAttention
  Performer
  Unknown
} derive(Eq)

///|
/// 预训练任务类型
pub(all) enum PretrainingTask {
  MaskedLanguageModeling
  NextSentencePrediction
  MaskedImageModeling
  ContrastiveLearning
  DenoisingAutoencoder
  RotationPrediction
  Colorization
  Unknown
} derive(Eq)

///|
/// Transformer配置
pub(all) struct TransformerConfig {
  architecture : TransformerArchitecture
  attention_type : AttentionType
  pretraining_task : PretrainingTask
  num_layers : Int
  hidden_dim : Int
  num_heads : Int
  intermediate_dim : Int
  dropout_rate : Double
  attention_dropout : Double
  activation : String
  max_position_embeddings : Int
  vocab_size : Int
  use_layer_norm : Bool
  use_residual : Bool
  use_gradient_checkpointing : Bool
}

///|
/// 注意力权重
pub(all) struct AttentionWeights {
  weights : Array[Array[Double]]
  num_heads : Int
  seq_length : Int
  head_dim : Int
  attention_type : AttentionType
}

///|
/// Transformer层输出
pub(all) struct TransformerLayerOutput {
  hidden_states : Array[Array[Double]]
  attention_weights : AttentionWeights
  layer_norm_output : Array[Array[Double]]
  seq_length : Int
  hidden_dim : Int
}

///|
/// 预训练模型配置
pub(all) struct PretrainedModelConfig {
  model_name : String
  model_type : String
  num_parameters : Int
  hidden_dim : Int
  num_layers : Int
  num_heads : Int
  vocab_size : Int
  max_seq_length : Int
  pretraining_data : String
  pretraining_steps : Int
  batch_size : Int
  learning_rate : Double
  optimizer : String
  training_time_hours : Double
}

///|
/// 微调配置
pub(all) struct FineTuningConfig {
  model_name : String
  task_type : String
  num_classes : Int
  learning_rate : Double
  num_epochs : Int
  batch_size : Int
  warmup_ratio : Double
  weight_decay : Double
  max_grad_norm : Double
  use_gradient_accumulation : Bool
  gradient_accumulation_steps : Int
  use_mixed_precision : Bool
  freeze_layers : Int
  use_lora : Bool
  lora_r : Int
  lora_alpha : Double
  lora_dropout : Double
}

///|
/// 迁移学习结果
pub(all) struct TransferLearningResult {
  source_model : String
  target_task : String
  source_accuracy : Double
  target_accuracy : Double
  transfer_gain : Double
  num_frozen_layers : Int
  num_trainable_params : Int
  training_time_s : Double
  convergence_epoch : Int
}

///|
/// 自监督学习配置
pub(all) struct SelfSupervisedConfig {
  task_type : PretrainingTask
  mask_ratio : Double
  temperature : Double
  num_negatives : Int
  use_momentum : Bool
  momentum : Double
  use_ema : Bool
  ema_decay : Double
  projection_dim : Int
  prediction_dim : Int
}

///|
/// 创建默认Transformer配置
pub fn create_default_transformer_config() -> TransformerConfig {
  TransformerConfig::{
    architecture: TransformerArchitecture::EncoderOnly,
    attention_type: AttentionType::MultiHeadAttention,
    pretraining_task: PretrainingTask::MaskedLanguageModeling,
    num_layers: 12,
    hidden_dim: 768,
    num_heads: 12,
    intermediate_dim: 3072,
    dropout_rate: 0.1,
    attention_dropout: 0.1,
    activation: "gelu",
    max_position_embeddings: 512,
    vocab_size: 30522,
    use_layer_norm: true,
    use_residual: true,
    use_gradient_checkpointing: false,
  }
}

///|
/// 创建默认预训练模型配置
pub fn create_default_pretrained_config() -> PretrainedModelConfig {
  PretrainedModelConfig::{
    model_name: "bert-base-uncased",
    model_type: "bert",
    num_parameters: 110000000,
    hidden_dim: 768,
    num_layers: 12,
    num_heads: 12,
    vocab_size: 30522,
    max_seq_length: 512,
    pretraining_data: "Wikipedia + BookCorpus",
    pretraining_steps: 1000000,
    batch_size: 256,
    learning_rate: 0.0001,
    optimizer: "adamw",
    training_time_hours: 72.0,
  }
}

///|
/// 创建默认微调配置
pub fn create_default_finetuning_config() -> FineTuningConfig {
  FineTuningConfig::{
    model_name: "bert-base-uncased",
    task_type: "classification",
    num_classes: 10,
    learning_rate: 0.00002,
    num_epochs: 3,
    batch_size: 32,
    warmup_ratio: 0.1,
    weight_decay: 0.01,
    max_grad_norm: 1.0,
    use_gradient_accumulation: false,
    gradient_accumulation_steps: 1,
    use_mixed_precision: true,
    freeze_layers: 0,
    use_lora: false,
    lora_r: 8,
    lora_alpha: 16.0,
    lora_dropout: 0.1,
  }
}

///|
/// 计算自注意力（简化版）
pub fn compute_self_attention(
  query : Array[Array[Double]],
  key : Array[Array[Double]],
  value : Array[Array[Double]],
  seq_length : Int,
  head_dim : Int,
) -> AttentionWeights {
  ignore(value)
  let weights : Array[Array[Double]] = []

  // 简化的注意力计算
  for i in 0..<seq_length {
    let row : Array[Double] = []
    let mut sum = 0.0
    for j in 0..<seq_length {
      // 简化的点积
      let mut score = 0.0
      for d in 0..<head_dim {
        if i < query.length() &&
          j < key.length() &&
          d < query[i].length() &&
          d < key[j].length() {
          score += query[i][d] * key[j][d]
        }
      }
      // 简化的softmax
      let exp_score = if score > 0.0 { score } else { 0.0 }
      row.push(exp_score)
      sum += exp_score
    }
    // 归一化
    if sum > 0.0 {
      for j in 0..<row.length() {
        row[j] /= sum
      }
    }
    weights.push(row)
  }

  AttentionWeights::{
    weights,
    num_heads: 1,
    seq_length,
    head_dim,
    attention_type: AttentionType::SelfAttention,
  }
}

///|
/// 模拟Transformer前向传播（简化版）
pub fn transformer_forward(
  input_embeddings : Array[Array[Double]],
  config : TransformerConfig,
) -> TransformerLayerOutput {
  let seq_length = input_embeddings.length()
  let hidden_dim = config.hidden_dim

  // 简化的前向传播
  let hidden_states : Array[Array[Double]] = []
  for _ in 0..<seq_length {
    let state : Array[Double] = []
    for _ in 0..<hidden_dim {
      state.push(0.1)
    }
    hidden_states.push(state)
  }

  let attention = compute_self_attention(
    input_embeddings,
    input_embeddings,
    input_embeddings,
    seq_length,
    if hidden_dim > 64 {
      64
    } else {
      hidden_dim
    },
  )

  TransformerLayerOutput::{
    hidden_states,
    attention_weights: attention,
    layer_norm_output: hidden_states,
    seq_length,
    hidden_dim,
  }
}

///|
/// 模拟微调过程（简化版）
pub fn simulate_finetuning(
  config : FineTuningConfig,
  initial_accuracy : Double,
) -> TransferLearningResult {
  let mut accuracy = initial_accuracy
  let mut convergence_epoch = config.num_epochs

  for epoch in 0..<config.num_epochs {
    let progress = (epoch + 1).to_double() / config.num_epochs.to_double()
    accuracy = initial_accuracy + (1.0 - initial_accuracy) * progress * 0.8

    if accuracy > 0.95 && convergence_epoch == config.num_epochs {
      convergence_epoch = epoch + 1
    }
  }

  let transfer_gain = accuracy - initial_accuracy
  let num_trainable_params = if config.use_lora {
    config.lora_r * config.lora_r.to_double().to_int() * 2
  } else {
    110000000 / (config.freeze_layers + 1)
  }

  TransferLearningResult::{
    source_model: config.model_name,
    target_task: config.task_type,
    source_accuracy: initial_accuracy,
    target_accuracy: accuracy,
    transfer_gain,
    num_frozen_layers: config.freeze_layers,
    num_trainable_params,
    training_time_s: config.num_epochs.to_double() * 100.0,
    convergence_epoch,
  }
}

///|
/// 生成DL高级报告
pub fn dl_advanced_report_to_string(
  task_type : DLAdvancedTaskType,
  config : TransformerConfig,
  result_description : String,
) -> String {
  let mut report = "## 深度学习高级报告\n\n"

  report = report + "### 任务信息\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report +
    "| 任务类型 | " +
    (match task_type {
      DLAdvancedTaskType::Transformer => "Transformer"
      DLAdvancedTaskType::AttentionMechanism => "注意力机制"
      DLAdvancedTaskType::PretrainedModel => "预训练模型"
      DLAdvancedTaskType::FineTuning => "微调"
      DLAdvancedTaskType::TransferLearning => "迁移学习"
      DLAdvancedTaskType::SelfSupervisedLearning => "自监督学习"
      DLAdvancedTaskType::ContrastiveLearning => "对比学习"
      _ => "未知"
    }) +
    " |\n"
  report = report +
    "| 架构 | " +
    (match config.architecture {
      TransformerArchitecture::EncoderOnly => "仅编码器"
      TransformerArchitecture::DecoderOnly => "仅解码器"
      TransformerArchitecture::EncoderDecoder => "编码器-解码器"
      TransformerArchitecture::VisionTransformer => "视觉Transformer"
      TransformerArchitecture::SwinTransformer => "Swin Transformer"
      _ => "未知"
    }) +
    " |\n"
  report = report +
    "| 注意力类型 | " +
    (match config.attention_type {
      AttentionType::SelfAttention => "自注意力"
      AttentionType::MultiHeadAttention => "多头注意力"
      AttentionType::CrossAttention => "交叉注意力"
      AttentionType::SparseAttention => "稀疏注意力"
      AttentionType::LinearAttention => "线性注意力"
      AttentionType::FlashAttention => "FlashAttention"
      _ => "未知"
    }) +
    " |\n"
  report = report + "| 层数 | " + config.num_layers.to_string() + " |\n"
  report = report + "| 隐藏维度 | " + config.hidden_dim.to_string() + " |\n"
  report = report +
    "| 注意力头数 | " +
    config.num_heads.to_string() +
    " |\n"
  report = report +
    "| 中间维度 | " +
    config.intermediate_dim.to_string() +
    " |\n"
  report = report + "| Dropout率 | " + config.dropout_rate.to_string() + " |\n"
  report = report +
    "| 最大位置嵌入 | " +
    config.max_position_embeddings.to_string() +
    " |\n"

  report = report + "\n### 结果\n\n"
  report = report + result_description + "\n"

  report
}
src\process\dl_advanced\dl_advanced.mbt

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