Refactor Module 16: Extract verify_pruning_works() function

- Create standalone verify_pruning_works() function (Section 8.5)
- Clean separation: verification logic in reusable function
- test_module() now calls verify_pruning_works() - much cleaner
- Students can call this function on their own pruned models
- Returns dict with verification results (sparsity, zeros, verified)
- Includes example usage in __main__ block
- HONEST messaging: Memory saved = 0 MB (dense storage)
- Educational: Explains compute vs memory savings

Benefits:
- Not tacked on - first-class verification function
- Reusable across different pruning strategies
- Clear educational value about dense vs sparse storage
- Each function has one clear job

modules/16_compression/compression.py

@@ -1330,6 +1330,77 @@ def test_unit_compress_model():
 if __name__ == "__main__":
     test_unit_compress_model()
 
+# %% [markdown]
+"""
+## 8.5 Verification - Proving Pruning Works
+
+Before analyzing compression in production, let's verify that our pruning actually achieves sparsity using real measurements.
+"""
+
+# %% nbgrader={"grade": false, "grade_id": "verify_pruning", "solution": false}
+def verify_pruning_works(model, target_sparsity=0.8):
+    """
+    Verify pruning actually creates zeros using real zero-counting.
+
+    This is NOT a theoretical calculation - we count actual zero values
+    in parameter arrays and honestly report memory footprint (unchanged with dense storage).
+
+    Args:
+        model: Model with pruned parameters
+        target_sparsity: Expected sparsity ratio (default 0.8 = 80%)
+
+    Returns:
+        dict: Verification results with sparsity, zeros, total, verified
+
+    Example:
+        >>> model = SimpleModel(Linear(100, 50))
+        >>> magnitude_prune(model, sparsity=0.8)
+        >>> results = verify_pruning_works(model, target_sparsity=0.8)
+        >>> assert results['verified']  # Pruning actually works!
+    """
+    print("🔬 Verifying pruning sparsity with actual zero-counting...")
+
+    # Count actual zeros in model parameters
+    zeros = sum(np.sum(p.data == 0) for p in model.parameters())
+    total = sum(p.data.size for p in model.parameters())
+    sparsity = zeros / total
+    memory_bytes = sum(p.data.nbytes for p in model.parameters())
+
+    # Display results
+    print(f"   Total parameters: {total:,}")
+    print(f"   Zero parameters: {zeros:,}")
+    print(f"   Active parameters: {total - zeros:,}")
+    print(f"   Sparsity achieved: {sparsity*100:.1f}%")
+    print(f"   Memory footprint: {memory_bytes / MB_TO_BYTES:.2f} MB (unchanged - dense storage)")
+
+    # Verify target met (allow 15% tolerance for structured pruning variations)
+    verified = abs(sparsity - target_sparsity) < 0.15
+    status = '✓' if verified else '✗'
+    print(f"   {status} Meets {target_sparsity*100:.0f}% sparsity target")
+
+    assert verified, f"Sparsity target not met: {sparsity:.2f} vs {target_sparsity:.2f}"
+
+    print(f"\n✅ VERIFIED: {sparsity*100:.1f}% sparsity achieved")
+    print(f"⚠️  Memory saved: 0 MB (dense numpy arrays)")
+    print(f"💡 LEARNING: Compute savings ~{sparsity*100:.1f}% (skip zero multiplications)")
+    print(f"   In production: Use sparse formats (scipy.sparse.csr_matrix) for memory savings")
+
+    return {
+        'sparsity': sparsity,
+        'zeros': zeros,
+        'total': total,
+        'active': total - zeros,
+        'memory_mb': memory_bytes / MB_TO_BYTES,
+        'verified': verified
+    }
+
+# Run verification example when developing
+if __name__ == "__main__":
+    # Create and prune test model
+    test_model = SimpleModel(Linear(100, 50), Linear(50, 25))
+    magnitude_prune(test_model, sparsity=0.8)
+    verify_pruning_works(test_model, target_sparsity=0.8)
+
 # %% [markdown]
 """
 ## 8.6 Systems Analysis - Compression Techniques
@@ -1629,35 +1700,16 @@ def test_module():
 
     print(f"✅ Low-rank: {compression_ratio:.2f}x compression, {error:.3f} error")
 
-    # ✨ VERIFICATION: Actual Optimization Effects
-    print("\n🔬 VERIFICATION: Actual Optimization Effects...")
-    print("=" * 50)
-
-    print("\n✓ Verifying pruning sparsity...")
-    # Count actual zeros in pruned model
-    zeros = sum(np.sum(p.data == 0) for p in model.parameters())
-    total = sum(p.data.size for p in model.parameters())
-    sparsity = zeros / total
-    memory_bytes = sum(p.data.nbytes for p in model.parameters())
-
-    print(f"   Total parameters: {total:,}")
-    print(f"   Zero parameters: {zeros:,}")
-    print(f"   Sparsity achieved: {sparsity*100:.1f}%")
-    print(f"   Memory footprint: {memory_bytes / MB_TO_BYTES:.2f} MB (unchanged - dense storage)")
-
+    # Verify pruning actually works
+    print()
     target_sparsity = compression_config['magnitude_prune']
-    assert abs(sparsity - target_sparsity) < 0.15, f"Sparsity target not met: {sparsity:.2f} vs {target_sparsity:.2f}"
-
-    print(f"\n✅ VERIFIED: {sparsity*100:.1f}% sparsity achieved")
-    print(f"⚠️  Memory saved: 0 MB (dense numpy arrays)")
-    print(f"💡 LEARNING: Compute savings ~{sparsity*100:.1f}% (skip zero multiplications)")
-    print(f"   In production: Use sparse formats (scipy.sparse.csr_matrix) for memory savings")
+    verification_results = verify_pruning_works(model, target_sparsity=target_sparsity)
 
     print("\n" + "=" * 50)
     print("🎉 ALL TESTS PASSED! Module ready for export.")
     print("📈 Compression system provides:")
-    print(f"   • {sparsity*100:.1f}% sparsity")
-    print(f"   • ✓ VERIFIED with actual zero-counting")
+    print(f"   • {verification_results['sparsity']*100:.1f}% sparsity")
+    print(f"   • ✓ VERIFIED: {verification_results['zeros']:,} actual zeros counted")
     print(f"   • Honest: Dense storage = no memory savings (educational limitation)")
     print("Run: tito module complete 16")