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 WAVE 26 P1.2: MULTI-HEAD SELF-ATTENTION - QUICK REFERENCE
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📦 NEW FILES
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  ml/src/dqn/attention.rs                              580 lines, 8 tests

📝 MODIFIED FILES
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  ml/src/dqn/mod.rs                                    +2 lines (L10, L62)

🎯 IMPLEMENTATION HIGHLIGHTS
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  ✅ Multi-head self-attention for temporal patterns
  ✅ Scaled dot-product: Attention(Q,K,V) = softmax(QK^T/√dk)V
  ✅ Optional causal/padding masks
  ✅ Xavier initialization (all layers)
  ✅ Optional layer normalization
  ✅ Optional residual connections
  ✅ Full GPU acceleration (CUDA)
  ✅ 8 comprehensive TDD tests

🔧 USAGE EXAMPLE
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  use ml::dqn::{MultiHeadAttention, MultiHeadAttentionConfig};

  // Create config (embed_dim divisible by num_heads)
  let config = MultiHeadAttentionConfig::new(64, 4)?;

  // Initialize layer
  let attn = MultiHeadAttention::new(config, &vb, &device)?;

  // Forward pass: (batch, seq_len, embed_dim)
  let output = attn.forward(&input, None)?;

  // With causal mask
  let mask = create_causal_mask(seq_len, &device)?;
  let output = attn.forward(&input, Some(&mask))?;

⚙️  CONFIGURATION OPTIONS
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  embed_dim        64              Embedding dimension (divisible by num_heads)
  num_heads        4               Number of attention heads
  dropout          0.1             Dropout probability
  use_layer_norm   true            Enable layer normalization
  layer_norm_eps   1e-5            LayerNorm epsilon
  use_residual     true            Enable residual connections

  head_dim = embed_dim / num_heads = 64 / 4 = 16

🧪 TDD TEST COVERAGE (8 TESTS)
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  ✅ test_config_validation         Config validation (embed_dim % num_heads)
  ✅ test_default_config            Default values
  ✅ test_attention_creation        Layer instantiation
  ✅ test_forward_pass_shape        Output shape (2,8,64) → (2,8,64)
  ✅ test_forward_with_mask         Causal mask application
  ✅ test_dimension_mismatch        Error handling
  ✅ test_residual_connection       Residual connections
  ✅ test_multiple_heads            1,2,4,8 heads

🏗️  ARCHITECTURE DIAGRAM
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  Input (batch, seq_len, embed_dim)
    |
    ├─> Query (WQ) ─┐
    ├─> Key (WK) ───┤
    └─> Value (WV) ─┴─> Scaled Dot-Product Attention
                          |
                          v
                     Multi-Head Concat
                          |
                          v
                     Output Linear (WO)
                          |
                          v
                     LayerNorm (optional)
                          |
                          v
                     Residual Add (optional)
                          |
                          v
                     Output (batch, seq_len, embed_dim)

📊 PERFORMANCE CHARACTERISTICS
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  Memory:      O(batch × seq_len² × num_heads)
  Computation: O(batch × seq_len² × embed_dim × num_heads)

  Typical: batch=32, seq_len=8, embed_dim=64, heads=4
    → ~16KB attention scores per batch
    → ~524K FLOPs per sample

  GPU Memory: Manageable for seq_len ≤ 32

🔗 INTEGRATION PATH
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  1. Add to QNetworkConfig:
     pub use_attention: bool
     pub attention_config: Option<MultiHeadAttentionConfig>

  2. Add to QNetwork:
     attention: Option<MultiHeadAttention>

  3. In forward():
     if self.config.use_attention {
         x = self.attention.forward(&x, None)?;
     }

⚠️  CONSTRAINTS & LIMITATIONS
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  ⚠️  embed_dim MUST be divisible by num_heads
  ⚠️  Quadratic memory with sequence length (manageable for trading)
  ⚠️  Recommend batch_size ≤ 64 for GPU memory
  ⚠️  Pre-existing codebase compilation errors (unrelated to this change)

📚 ERROR TYPES
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  MLError::ConfigurationError    Invalid config (embed_dim % num_heads ≠ 0)
  MLError::DimensionMismatch     Input shape mismatch
  MLError::InitializationError   Parameter initialization failed
  MLError::ModelError            Forward pass failed
  MLError::TensorOperationError  Tensor manipulation failed

🎓 REFERENCES
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  • Vaswani et al. (2017): "Attention Is All You Need"
  • Glorot & Bengio (2010): Xavier initialization
  • Ba et al. (2016): Layer normalization

✅ STATUS: IMPLEMENTATION COMPLETE
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  ✅ Full multi-head attention implementation
  ✅ TDD test suite (8 tests)
  ✅ Documentation
  ✅ Module integration (mod.rs)
  ⏳ Network integration (next phase)
  ⏳ Training validation (next phase)

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