Update tinytorch and tito with module exports

Re-exported all modules after restructuring:
- Updated _modidx.py with new module locations
- Removed outdated autogeneration headers
- Updated all core modules (tensor, autograd, layers, etc.)
- Updated optimization modules (quantization, compression, etc.)
- Updated TITO commands for new structure

Changes include:
- 24 tinytorch/ module files
- 24 tito/ command and core files
- Updated references from modules/source/ to modules/

All modules re-exported via nbdev from their new locations.

tinytorch/benchmarking/benchmark.py

@@ -1,22 +1,8 @@
-# ╔═══════════════════════════════════════════════════════════════════════════════╗
-# ║                        🚨 CRITICAL WARNING 🚨                                ║
-# ║                     AUTOGENERATED! DO NOT EDIT!                              ║
-# ║                                                                               ║
-# ║  This file is AUTOMATICALLY GENERATED from source modules.                   ║
-# ║  ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported!            ║
-# ║                                                                               ║
-# ║  ✅ TO EDIT: modules/source/XX_benchmark/benchmark_dev.py           ║
-# ║  ✅ TO EXPORT: Run 'tito module complete <module_name>'                      ║
-# ║                                                                               ║
-# ║  🛡️ STUDENT PROTECTION: This file contains optimized implementations.        ║
-# ║     Editing it directly may break module functionality and training.         ║
-# ║                                                                               ║
-# ║  🎓 LEARNING TIP: Work in modules/source/ - that's where real development    ║
-# ║     happens! The tinytorch/ directory is just the compiled output.           ║
-# ╚═══════════════════════════════════════════════════════════════════════════════╝
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../../modules/source/19_benchmarking/benchmarking_dev.ipynb.
+
 # %% auto 0
 __all__ = ['OlympicEvent', 'Benchmark', 'test_unit_benchmark', 'BenchmarkSuite', 'test_unit_benchmark_suite', 'TinyMLPerf',
-           'test_unit_tinymlperf']
+           'test_unit_tinymlperf', 'calculate_normalized_scores']
 
 # %% ../../modules/source/19_benchmarking/benchmarking_dev.ipynb 0
 #| default_exp benchmarking.benchmark
@@ -72,7 +58,7 @@ class Benchmark:
         self.measurement_runs = measurement_runs
         self.results = {}
         
-        # Use Profiler from Module 14 for measurements
+        # Use Profiler from Module 15 for measurements
         self.profiler = Profiler()
 
         # System information for metadata
@@ -1024,3 +1010,53 @@ def test_unit_tinymlperf():
     print("✅ TinyMLPerf works correctly!")
 
 test_unit_tinymlperf()
+
+# %% ../../modules/source/19_benchmarking/benchmarking_dev.ipynb 24
+def calculate_normalized_scores(baseline_results: dict, 
+                                optimized_results: dict) -> dict:
+    """
+    Calculate normalized performance metrics for fair competition comparison.
+    
+    This function converts absolute measurements into relative improvements,
+    enabling fair comparison across different hardware platforms.
+    
+    Args:
+        baseline_results: Dict with keys: 'latency', 'memory', 'accuracy'
+        optimized_results: Dict with same keys as baseline_results
+        
+    Returns:
+        Dict with normalized metrics:
+        - speedup: Relative latency improvement (higher is better)
+        - compression_ratio: Relative memory reduction (higher is better)
+        - accuracy_delta: Absolute accuracy change (closer to 0 is better)
+        - efficiency_score: Combined metric balancing all factors
+        
+    Example:
+        >>> baseline = {'latency': 100.0, 'memory': 12.0, 'accuracy': 0.89}
+        >>> optimized = {'latency': 40.0, 'memory': 3.0, 'accuracy': 0.87}
+        >>> scores = calculate_normalized_scores(baseline, optimized)
+        >>> print(f"Speedup: {scores['speedup']:.2f}x")
+        Speedup: 2.50x
+    """
+    # Calculate speedup (higher is better)
+    speedup = baseline_results['latency'] / optimized_results['latency']
+    
+    # Calculate compression ratio (higher is better)
+    compression_ratio = baseline_results['memory'] / optimized_results['memory']
+    
+    # Calculate accuracy delta (closer to 0 is better, negative means degradation)
+    accuracy_delta = optimized_results['accuracy'] - baseline_results['accuracy']
+    
+    # Calculate efficiency score (combined metric)
+    # Penalize accuracy loss: the more accuracy you lose, the lower your score
+    accuracy_penalty = max(1.0, 1.0 - accuracy_delta) if accuracy_delta < 0 else 1.0
+    efficiency_score = (speedup * compression_ratio) / accuracy_penalty
+    
+    return {
+        'speedup': speedup,
+        'compression_ratio': compression_ratio,
+        'accuracy_delta': accuracy_delta,
+        'efficiency_score': efficiency_score,
+        'baseline': baseline_results.copy(),
+        'optimized': optimized_results.copy()
+    }