Add multi head attention

[zhenyu-02]

🚀 Add Multi-Head Attention Implementation

Overview

This PR implements a complete Multi-Head Self-Attention mechanism and makes it the default architecture for the RustGPT project, replacing the previous single-head attention implementation.

🎯 Motivation

As mentioned in README.md#L177, multi-head attention was listed as a desired feature under "Areas for Improvement". Multi-head attention is a core component of modern Transformer architectures (GPT, BERT, etc.) and provides:

• Better representation learning: Multiple heads can attend to different aspects of the input simultaneously
• Improved model capacity: More parameters dedicated to attention computation
• Standard architecture: Aligns with mainstream Transformer implementations

📋 Changes

New Files

• ✨ src/multi_head_attention.rs (405 lines) - Complete multi-head attention implementation with forward/backward passes
• ✅ tests/multi_head_attention_test.rs (278 lines) - Comprehensive test suite with 13 test cases
• 📚 MULTI_HEAD_ATTENTION.md - Technical documentation and usage guide
• 📚 MIGRATION_TO_MULTI_HEAD.md - Migration guide and architecture comparison

Modified Files

• src/lib.rs - Added NUM_HEADS constant (default: 8)
• src/llm.rs - Updated to use MultiHeadTransformerBlock by default
• src/main.rs - All 3 transformer layers now use multi-head attention
• src/transformer.rs - Added MultiHeadTransformerBlock (legacy TransformerBlock kept for backward compatibility)
• tests/llm_test.rs - Updated tests to match new architecture
• tests/transformer_test.rs - Added 5 new tests for MultiHeadTransformerBlock
• README.md - Updated documentation to reflect multi-head attention implementation

🔧 Technical Details

Architecture

Input [seq_len, embedding_dim=128]
  ↓
Multi-Head Attention (8 heads, head_dim=16)
  ├─ Head 1: Q₁, K₁, V₁ → Attention₁
  ├─ Head 2: Q₂, K₂, V₂ → Attention₂
  ├─ ...
  └─ Head 8: Q₈, K₈, V₈ → Attention₈
  ↓
Concatenate → Output Projection (W_o)
  ↓
Residual Connection + Layer Norm
  ↓
Feed-Forward Network
  ↓
Residual Connection + Layer Norm

Key Features

• ✅ Configurable number of heads via NUM_HEADS constant
• ✅ Causal masking for autoregressive generation
• ✅ Residual connections for training stability
• ✅ Full gradient computation for backpropagation
• ✅ Adam optimizer integration for all weight matrices
• ✅ Numerical stability (tested with extreme values)

Parameter Changes

Before (Single-Head):

• 3 weight matrices (W_q, W_k, W_v): 49,152 parameters per transformer block

After (Multi-Head):

• 4 weight matrices (W_q, W_k, W_v, W_o): 65,536 parameters per transformer block
• +33% parameters dedicated to attention mechanism

✅ Testing

All tests pass (49/49):

$ cargo test
...
running 13 tests  # multi_head_attention_test
running 6 tests   # transformer_test (including 5 new tests)
running 5 tests   # llm_test
...
test result: ok. 49 passed; 0 failed; 0 ignored

Test Coverage

• ✅ Forward/backward propagation
• ✅ Different sequence lengths (1-10)
• ✅ Different head counts (1, 2, 4, 8, 16, 32, 64, 128)
• ✅ Residual connections
• ✅ Causal masking
• ✅ Parameter counting
• ✅ Numerical stability
• ✅ Head splitting/concatenation
• ✅ Training step simulation
• ✅ Edge cases (should panic when heads don't divide embedding_dim)

🔄 Backward Compatibility

The legacy SelfAttention and TransformerBlock implementations are preserved but marked with #[allow(dead_code)] for reference. Users can still access them if needed, but the default behavior now uses multi-head attention.

📊 Performance Impact

• Increased parameters: +26% total model size (due to additional W_o matrix)
• Improved expressiveness: 8 heads can learn different attention patterns
• Training time: Slightly increased due to more computations per forward pass

🎨 Code Quality

• ✅ No compilation errors
• ✅ All clippy warnings addressed
• ✅ Code formatted with cargo fmt
• ✅ Comprehensive documentation with examples
• ✅ Clear comments explaining complex logic

📚 Documentation

Extensive documentation provided:

• MULTI_HEAD_ATTENTION.md: Technical implementation details, API documentation, usage examples
• MIGRATION_TO_MULTI_HEAD.md: Migration guide, architecture comparison, rollback instructions
• Inline code comments: Detailed explanations of the attention mechanism
• README updates: Reflected new architecture and marked feature as complete ✅

🚀 Usage Example

use llm::{LLM, EMBEDDING_DIM, HIDDEN_DIM, NUM_HEADS};
use llm::multi_head_attention::MultiHeadAttention;

// Default LLM now uses multi-head attention (8 heads)
let llm = LLM::default();

// Or create custom multi-head attention layer
let mut mha = MultiHeadAttention::new(EMBEDDING_DIM, NUM_HEADS);
let output = mha.forward(&input);

🔍 Related Issues

Addresses the "multi-head attention" item mentioned in README.md under "Areas for Improvement" (line 177).

---

Model Output Example:

=== MODEL INFORMATION ===
Network architecture: Embeddings -> MultiHeadTransformerBlock -> MultiHeadTransformerBlock -> MultiHeadTransformerBlock -> OutputProjection
Model configuration -> max_seq_len: 80, embedding_dim: 128, hidden_dim: 256, num_heads: 8
Total parameters: ~234,000

[zhenyu-02]

@tekaratzas please check, Thanks!