TinyTorch/COMPRESSION_AUDIT.md

Module 16: Compression - Integration Test & Warning Audit

Date: 2025-11-25 Module Path: /Users/VJ/GitHub/TinyTorch/src/16_compression/16_compression.py Test Path: /Users/VJ/GitHub/TinyTorch/tests/17_compression/

---

Executive Summary

Module 16 (Compression) is functionally complete with all core implementations working. However, it has:

• ✅ 6 unit tests covering all major functionality
• ✅ 1 comprehensive integration test (test_module())
• ⚠️ Missing external integration tests in tests/17_compression/
• 🚨 7 critical issues requiring warnings/documentation
• 💡 4 educational gaps where students might get confused

---

Current Test Coverage

Existing Unit Tests (6 tests, all embedded in module)

1. test_unit_measure_sparsity() (Line 414-435)

   • Tests sparsity calculation on dense and sparse models
   • Coverage: ✅ Dense model, ✅ Manually sparse model
   • Status: PASSING

2. test_unit_magnitude_prune() (Line 556-592)

   • Tests magnitude-based weight pruning
   • Coverage: ✅ 50% sparsity target, ✅ Large weights survive
   • Status: PASSING

3. test_unit_structured_prune() (Line 725-765)

   • Tests channel-wise structured pruning
   • Coverage: ✅ Channel removal, ✅ Block sparsity pattern
   • Status: PASSING

4. test_unit_low_rank_approximate() (Line 881-913)

   • Tests SVD-based low-rank approximation
   • Coverage: ✅ Dimension check, ✅ Compression ratio, ✅ Reconstruction error
   • Status: PASSING

5. test_unit_knowledge_distillation() (Line 1127-1162)

   • Tests teacher-student distillation setup
   • Coverage: ✅ Loss calculation, ✅ Temperature scaling, ✅ Alpha balancing
   • Status: PASSING

6. test_unit_compress_model() (Line 1295-1331)

   • Tests comprehensive compression pipeline
   • Coverage: ✅ Multiple techniques, ✅ Statistics tracking
   • Status: PASSING

Existing Integration Test (1 test)

7. test_module() (Line 1534-1637)
   • Comprehensive end-to-end module test
   • Coverage: ✅ All unit tests, ✅ Pipeline integration, ✅ Distillation setup, ✅ Low-rank approximation
   • Status: PASSING

External Integration Tests (MISSING)

File: /Users/VJ/GitHub/TinyTorch/tests/17_compression/test_compression_integration.py

• Status: STUB ONLY (24 lines, TODO placeholder)
• No actual tests implemented
• Missing integration with other modules

---

Critical Issues Identified

🔥 SEVERITY: CRITICAL - Data Loss / Silent Failures

Issue 1: In-Place Pruning Without Warning

Location: magnitude_prune() (Line 501-553)

Problem:

def magnitude_prune(model, sparsity=0.9):
    # ...
    for param in weight_params:
        mask = np.abs(param.data) >= threshold
        param.data = param.data * mask  # ← MUTATES ORIGINAL MODEL!
    return model

Why Critical:

• Students may expect a new model, get mutated original
• No way to recover original weights after pruning
• Common ML pattern: non-destructive operations
• Similar functions (PyTorch's prune) use masks, not mutations

Student Impact:

• Lost hours debugging "why did my model forget everything?"
• Confusion when trying to compare before/after
• Breaking production code that assumes immutability

Where to Document:

• Top of magnitude_prune() docstring
• Beginning of "Magnitude-Based Pruning" section (Line 439)

---

Issue 2: Structured Pruning Also Mutates In-Place

Location: structured_prune() (Line 668-722)

Problem:

def structured_prune(model, prune_ratio=0.5):
    for layer in model.layers:
        if isinstance(layer, Linear):
            # ...
            weight[:, prune_indices] = 0  # ← MUTATES ORIGINAL!
            if layer.bias is not None:
                layer.bias.data[prune_indices] = 0  # ← MUTATES BIAS TOO!

Why Critical:

• Same mutation issue as magnitude pruning
• Additionally mutates bias terms (students might not expect this)
• Changes model behavior permanently

Student Impact: Same as Issue 1

Where to Document: Top of structured_prune() docstring

---

🚨 SEVERITY: HIGH - Incorrect Results / Accuracy Loss

Issue 3: Low-Rank Approximation Not Integrated Into Model

Location: low_rank_approximate() (Line 839-878)

Problem:

def low_rank_approximate(weight_matrix, rank_ratio=0.5):
    # ...
    return U_truncated, S_truncated, V_truncated
    # ← Returns decomposed matrices, but model still uses original weights!

Why Critical:

• Function returns decomposed matrices but doesn't update the model
• Students call it thinking model is compressed, but nothing changes
• No guidance on how to actually use the returned U, S, V matrices
• compress_model() only records it as "applied" but doesn't actually apply it (Line 1281-1284)

Student Impact:

• "Why is my model still the same size after low-rank compression?"
• Confusion about what to do with returned matrices
• False sense that compression happened when it didn't

Where to Document:

• Top of low_rank_approximate() docstring
• Warning in "Low-Rank Approximation" section (Line 767)
• Fix in compress_model() integration

---

Issue 4: Sparse Storage Not Actually Implemented

Location: Throughout module, especially analysis sections

Problem:

# From demo_compression_with_profiler (Line 1398):
print(f"   Memory: {memory_after['parameter_memory_mb']:.2f} MB (same storage)")
#                                                               ^^^^^^^^^^^^

The module correctly notes that pruning doesn't reduce memory without sparse storage, but:

• Never implements or demonstrates actual sparse storage
• Students might think pruning alone saves memory
• All memory calculations assume dense storage

Why Critical:

• MAJOR EDUCATIONAL MISCONCEPTION: 90% sparse ≠ 90% memory savings
• Students will be confused when their "compressed" models use same memory
• Disconnect between theoretical compression and actual benefits

Student Impact:

• "I pruned 90% of weights, why is my model file still 100MB?"
• Frustration with "compression that doesn't compress"
• Misunderstanding fundamental CS concept (sparse vs dense storage)

Where to Document:

• Create WARNING box in "Sparsity Measurement" section (Line 342)
• Add WARNING in motivation section (Line 142)
• Add practical guidance on when sparse storage helps

---

⚠️ SEVERITY: MEDIUM - Confusion / Unexpected Behavior

Issue 5: Knowledge Distillation is Incomplete

Location: KnowledgeDistillation class (Line 1012-1125)

Problem:

class KnowledgeDistillation:
    def __init__(self, teacher_model, student_model, temperature=3.0, alpha=0.7):
        # Stores models but no training loop!

    def distillation_loss(self, student_logits, teacher_logits, true_labels):
        # Computes loss but doesn't train the student

Why Medium (not High):

• Class correctly states it's for loss calculation, not training
• But students expect a complete distillation system
• No guidance on how to actually train the student

Student Impact:

• "How do I use this to compress my model?"
• Unclear what to do with the loss value
• Missing integration with training loop

Where to Document:

• Top of KnowledgeDistillation class docstring
• Example showing integration with training loop
• Link to Module 07 (Training) for training patterns

---

Issue 6: Bias Measurement Inconsistency

Location: measure_sparsity() (Line 367-411)

Problem:

def measure_sparsity(model) -> float:
    for param in model.parameters():
        # Only count weight matrices (2D), not biases (1D)
        # Biases are often initialized to zero, which would skew sparsity
        if len(param.shape) > 1:
            total_params += param.size
            zero_params += np.sum(param.data == 0)

Why Problematic:

• Comment says biases initialized to zero, but Linear initializes biases to zero (Module 03)
• Excluding biases makes sense, but rationale is misleading
• Students might think biases don't matter for compression

Student Impact:

• Confusion about why biases aren't counted
• Potential misunderstanding of bias initialization

Where to Document:

• Fix the comment to be accurate
• Add note about why biases are excluded (small fraction of params)

---

Issue 7: Temperature Scaling Edge Cases

Location: KnowledgeDistillation.distillation_loss() (Line 1061-1107)

Problem:

def distillation_loss(self, student_logits, teacher_logits, true_labels):
    # Soften distributions with temperature
    student_soft = self._softmax(student_logits / self.temperature)
    teacher_soft = self._softmax(teacher_logits / self.temperature)

Edge Cases Not Handled:

• temperature = 0 → Division by zero
• temperature < 0 → Meaningless negative temperatures
• Very large temperatures (>20) → Numerical instability in softmax

Student Impact:

• Cryptic errors if they experiment with extreme temperatures
• No guidance on valid temperature ranges

Where to Document:

• Add validation in __init__
• Add WARNING about valid temperature ranges (1-10 typical)

---

💡 SEVERITY: LOW - Educational Gaps

Issue 8: Missing Integration with Quantization (Module 15)

Location: Entire module

Problem:

• Module 15 (Quantization) and Module 16 (Compression) should work together
• No examples combining quantization + pruning
• Students miss the powerful combination of techniques

Student Impact:

• Missing knowledge of production compression pipelines
• Don't realize techniques can be combined

Where to Document:

• Add section showing quantization + compression pipeline
• Update compression_config to include quantization options

---

Issue 9: No Gradient-Based Pruning

Location: "Structured Pruning" section (Line 595)

Problem:

• Module mentions gradient-based importance (Line 286-288) but never implements it
• Only implements L2 norm importance
• Students might wonder how to do gradient-based pruning

Student Impact:

• Limited understanding of importance metrics
• Missing a powerful pruning technique

Where to Document:

• Add note that gradient-based is advanced/optional
• Point to research papers for interested students

---

Issue 10: Compression Ratio vs Sparsity Confusion

Location: Analysis functions (Lines 1429-1484)

Problem:

compression_ratio = 1.0 / (1.0 - sparsity)  # This is backwards!

Correct Definition:

• Compression ratio = original_size / compressed_size
• For 90% sparsity: ratio = 10x (not 1/(1-0.9)=10)
• But the formula happens to give the right answer for the wrong reason

Student Impact:

• Confusion about what compression ratio means
• Wrong mental model for future work

Where to Document:

• Fix the comment to explain the formula correctly
• Add clear definition of compression ratio

---

Proposed Integration Tests

Test Suite for /tests/17_compression/test_compression_integration.py

Test 1: Compression Pipeline Integration

What it validates: End-to-end compression workflow

def test_compression_pipeline_integration():
    """Test complete compression pipeline with multiple techniques."""
    # Create model from modules 01-03
    from tinytorch.core.tensor import Tensor
    from tinytorch.core.layers import Linear

    # Build multi-layer model
    model = SimpleModel(
        Linear(128, 64),
        Linear(64, 32),
        Linear(32, 10)
    )

    # Apply compression pipeline
    config = {
        'magnitude_prune': 0.7,
        'structured_prune': 0.3
    }

    original_params = count_active_params(model)
    compressed_model = compress_model(model, config)
    final_params = count_active_params(compressed_model)

    # Validate compression
    assert final_params < original_params * 0.5
    assert measure_sparsity(compressed_model) > 60

Why needed: Validates that multiple techniques compose correctly

---

Test 2: Cross-Module Integration (Profiler + Compression)

What it validates: Integration with Module 14 (Profiling)

def test_profiler_compression_integration():
    """Test compression with profiler measurements."""
    from tinytorch.profiling.profiler import Profiler

    profiler = Profiler()
    model = Linear(256, 128)

    # Measure before
    baseline = profiler.count_parameters(model)

    # Compress
    magnitude_prune(model, sparsity=0.8)

    # Measure after
    # Should show same param count but higher sparsity
    after = profiler.count_parameters(model)
    assert after == baseline  # Same total params
    assert measure_sparsity(model) >= 75  # But mostly zeros

Why needed: Validates integration with profiling tools

---

Test 3: Accuracy Preservation Test

What it validates: Model still produces reasonable outputs after compression

def test_compression_preserves_functionality():
    """Test that compressed model still produces valid outputs."""
    model = Linear(10, 5)
    input_data = Tensor(np.random.randn(2, 10))

    # Get baseline output
    baseline_output = model.forward(input_data)

    # Compress (moderate sparsity)
    magnitude_prune(model, sparsity=0.5)

    # Check output still valid
    compressed_output = model.forward(input_data)

    assert compressed_output.shape == baseline_output.shape
    assert not np.isnan(compressed_output.data).any()
    # Outputs should be similar (not identical)
    assert np.allclose(compressed_output.data, baseline_output.data, rtol=0.5)

Why needed: Validates that compression doesn't break model completely

---

Test 4: Knowledge Distillation Training Loop

What it validates: Complete distillation workflow

def test_knowledge_distillation_training():
    """Test full distillation training loop."""
    # Create teacher and student
    teacher = SimpleModel(Linear(20, 50), Linear(50, 10))
    student = SimpleModel(Linear(20, 10))  # Smaller

    kd = KnowledgeDistillation(teacher, student)

    # Dummy training data
    X = Tensor(np.random.randn(32, 20))
    y = np.random.randint(0, 10, 32)

    # Get initial loss
    teacher_out = teacher.forward(X)
    student_out = student.forward(X)
    initial_loss = kd.distillation_loss(student_out, teacher_out, y)

    # Simulate training step (would need optimizer from Module 06)
    # This test just validates loss computation works
    assert initial_loss > 0
    assert not np.isnan(initial_loss)

Why needed: Shows complete usage pattern for distillation

---

Test 5: Low-Rank Decomposition Application

What it validates: How to actually use low-rank approximation

def test_low_rank_decomposition_application():
    """Test applying low-rank decomposition to actual weights."""
    layer = Linear(100, 50)
    original_weight = layer.weight.data.copy()

    # Decompose
    U, S, V = low_rank_approximate(original_weight, rank_ratio=0.3)

    # Reconstruct and apply
    reconstructed = U @ np.diag(S) @ V
    layer.weight.data = reconstructed

    # Validate
    assert layer.weight.shape == original_weight.shape

    # Check compression achieved
    original_params = original_weight.size
    compressed_params = U.size + S.size + V.size
    assert compressed_params < original_params

Why needed: Shows how to actually use low-rank results

---

Test 6: Sparsity Pattern Validation

What it validates: Structured vs unstructured sparsity patterns

def test_sparsity_patterns():
    """Test that structured pruning creates block sparsity."""
    model = SimpleModel(Linear(10, 20))

    # Apply structured pruning
    structured_prune(model, prune_ratio=0.5)

    # Check that entire channels are zero
    weight = model.layers[0].weight.data
    for col in range(weight.shape[1]):
        channel = weight[:, col]
        # Each channel should be either all-zero or no-zeros
        if np.any(channel == 0):
            assert np.all(channel == 0), "Structured pruning should zero entire channels"

Why needed: Validates structured vs unstructured difference

---

Test 7: Edge Case Testing

What it validates: Robustness to edge cases

def test_compression_edge_cases():
    """Test compression with edge cases."""
    # Test 1: Already sparse model
    model = SimpleModel(Linear(5, 5))
    model.layers[0].weight.data[:] = 0  # All zeros
    initial_sparsity = measure_sparsity(model)
    magnitude_prune(model, sparsity=0.9)
    assert measure_sparsity(model) >= initial_sparsity

    # Test 2: Very small model
    tiny_model = SimpleModel(Linear(2, 2))
    magnitude_prune(tiny_model, sparsity=0.5)
    assert tiny_model.layers[0].weight.data.size > 0

    # Test 3: Extreme sparsity (99%)
    large_model = SimpleModel(Linear(100, 100))
    magnitude_prune(large_model, sparsity=0.99)
    assert measure_sparsity(large_model) >= 95

Why needed: Validates robustness

---

Proposed Documentation Additions

WARNING Block 1: In-Place Mutation

Location: After line 497 (before magnitude_prune function)

### ⚠️ CRITICAL WARNING: In-Place Mutation

**Both `magnitude_prune()` and `structured_prune()` modify your model DIRECTLY!**

```python
# ❌ WRONG: Expecting original model to be preserved
original_model = MyModel()
compressed_model = magnitude_prune(original_model, sparsity=0.9)
# original_model is NOW PRUNED! Both variables point to same model!

# ✅ CORRECT: Make a copy first if you need the original
import copy
original_model = MyModel()
compressed_model = magnitude_prune(copy.deepcopy(original_model), sparsity=0.9)
# original_model is preserved, compressed_model is pruned

Why this matters:

• You CANNOT undo pruning after it's applied
• If you need to compare before/after, copy BEFORE pruning
• Production code: Always keep original checkpoint before compression

When in-place is OK:

• One-time compression for deployment
• You've already saved the original model
• You're experimenting and don't need the original

When to copy first:

• Comparing compression techniques
• Tuning sparsity thresholds
• Experimenting with different configurations
• Production pipelines where you might need to roll back

---

### WARNING Block 2: Sparse Storage Misconception
**Location**: After line 363 (in "Understanding Sparsity" section)

```markdown
### 🚨 CRITICAL MISCONCEPTION: Sparsity ≠ Automatic Memory Savings

**90% sparsity does NOT mean 90% memory reduction in TinyTorch (or standard NumPy)!**

```python
# The harsh truth:
model = Linear(1000, 1000)  # 1M parameters = 4MB
magnitude_prune(model, sparsity=0.9)  # 90% weights now zero

print(f"Sparsity: {measure_sparsity(model):.1f}%")  # 90.0%
print(f"Memory: {model.weight.data.nbytes / 1024**2:.1f} MB")  # Still 4MB! 😱

Why sparsity doesn't reduce memory automatically:

• NumPy arrays use dense storage: Every zero still takes 4 bytes
• Pruning sets values to zero but doesn't change storage format
• Need sparse matrix formats (CSR, COO) to get memory savings

When you DO get memory savings:

from scipy.sparse import csr_matrix  # Sparse format

dense_weight = model.weight.data  # 1M × 4 bytes = 4MB
sparse_weight = csr_matrix(dense_weight)  # Only stores non-zeros!

# With 90% sparsity:
# - Dense: 1M values × 4 bytes = 4MB
# - Sparse: 100K values × 4 bytes + indices = ~0.5MB
# Savings: 8x memory reduction

The compression reality check:

Technique	Memory Savings	Speed Savings	Accuracy
Pruning (dense storage)	❌ None	❌ None	✅ Good
Pruning (sparse storage)	✅ 5-10x	⚠️ Variable*	✅ Good
Structured pruning	✅ Moderate	✅ 2-5x	⚠️ Moderate
Quantization	✅ 2-4x	✅ 2-4x	✅ Good
Distillation	✅ 10x+	✅ 10x+	⚠️ -5%

*Depends on hardware support for sparse operations

What this means for you:

• Learning: Understand sparsity patterns (this module's goal) ✅
• Deployment: Need sparse libraries (scipy, PyTorch sparse) for actual savings
• Production: Combine pruning + quantization + sparse storage for best results

---

### WARNING Block 3: Low-Rank Limitations
**Location**: After line 836 (before `low_rank_approximate` function)

```markdown
### ⚠️ IMPORTANT: Low-Rank Approximation Doesn't Auto-Update Model

**This function returns decomposed matrices but DOESN'T compress your model automatically!**

```python
# ❌ WRONG: Expecting model to be compressed
model = Linear(100, 50)
U, S, V = low_rank_approximate(model.weight.data, rank_ratio=0.5)
# Model still uses original 100×50 weight matrix!
# U, S, V just sitting there unused

# ✅ CORRECT: You must manually apply the decomposition
model = Linear(100, 50)
original_weight = model.weight.data

# Step 1: Decompose
U, S, V = low_rank_approximate(original_weight, rank_ratio=0.5)

# Step 2: Create low-rank layer (you need to implement this!)
# Option A: Replace with two smaller Linear layers
model_compressed = SimpleModel(
    LinearLowRank(100, rank, 50)  # U and V as separate layers
)

# Option B: Reconstruct and replace weight (loses compression benefits)
model.weight.data = U @ np.diag(S) @ V  # Same size, approximation error

Why this is tricky:

• Low-rank compression requires architecture changes
• One big layer → Two small layers in sequence
• TinyTorch's Linear doesn't support low-rank mode
• This is a research-level technique, not plug-and-play

When low-rank is worth it:

• ✅ Very large weight matrices (>1000×1000)
• ✅ Matrices with low intrinsic rank (redundant information)
• ✅ You can modify the architecture
• ❌ Small matrices (overhead exceeds benefits)
• ❌ Full-rank matrices (can't compress without huge error)

Production approach:

1. Profile which layers are large (Module 14)
2. Apply low-rank to largest layers only
3. Replace architecture with factored layers
4. Fine-tune the compressed model

---

### WARNING Block 4: Knowledge Distillation Incompleteness
**Location**: After line 1008 (before `KnowledgeDistillation` class)

```markdown
### 💡 IMPORTANT: This is a Loss Function, Not a Training Loop

**`KnowledgeDistillation` computes the loss but DOESN'T train the student model!**

```python
# This class provides:
kd = KnowledgeDistillation(teacher, student)
loss = kd.distillation_loss(student_out, teacher_out, labels)  # ✅ Just a number

# This class DOES NOT provide:
kd.train()  # ❌ No training loop
kd.fit(data)  # ❌ No fit method
kd.compress_model()  # ❌ No one-click compression

To actually train a student model, you need (from Module 06-07):

# Step 1: Setup (this module)
teacher = BigModel()  # Pre-trained
student = SmallModel()  # Random initialization
kd = KnowledgeDistillation(teacher, student, temperature=4.0, alpha=0.7)

# Step 2: Training (Module 06-07)
optimizer = SGD(student.parameters(), lr=0.01)  # Module 06

for epoch in range(num_epochs):
    for batch_x, batch_y in dataloader:  # Module 09
        # Forward passes
        teacher_out = teacher.forward(batch_x)  # No gradients needed
        student_out = student.forward(batch_x)  # Student learns here

        # Distillation loss (THIS MODULE)
        loss = kd.distillation_loss(student_out, teacher_out, batch_y)

        # Backprop and update (Module 05-06)
        student_out.backward()  # Module 05
        optimizer.step()  # Module 06
        optimizer.zero_grad()

# Now student is trained to mimic teacher!

Why it's designed this way:

• Modularity: Separation of concerns (loss ≠ training)
• Flexibility: You control the training loop
• Reusability: Works with any optimizer (SGD, Adam, etc.)
• Educational: You see every step of the process

What you get from this module:

• ✅ Distillation loss calculation with temperature scaling
• ✅ Understanding of soft targets vs hard targets
• ✅ Alpha balancing between teacher and ground truth

What you need from other modules:

• Module 05: backward() for gradients
• Module 06: Optimizers (SGD, Adam) for weight updates
• Module 07: Training loop patterns
• Module 09: DataLoader for batching

---

### WARNING Block 5: Temperature Edge Cases
**Location**: In `KnowledgeDistillation.__init__` docstring (after line 1046)

```markdown
⚠️ **VALID TEMPERATURE RANGES**:
- Typical range: 3-5 (good balance of softening)
- Minimum: 1.0 (no softening, standard softmax)
- Maximum: ~10 (very soft, may lose information)
- NEVER: ≤0 (division by zero or negative temperatures)

Invalid temperatures cause:
- T=0: ZeroDivisionError
- T<0: Nonsensical negative probabilities
- T>20: Numerical instability (underflow in exp)

---

Summary Statistics

Test Coverage Summary

• Unit Tests: 6 functions tested ✅
• Integration Test: 1 comprehensive test ✅
• External Tests: 0 implemented ⚠️ (stubs only)
• Coverage Gaps:
  • No cross-module integration tests
  • No accuracy preservation tests
  • No edge case testing
  • No production workflow examples

Critical Issue Summary

• 🔥 Critical (2): In-place mutation (2 functions)
• 🚨 High (2): Low-rank not integrated, sparse storage misconception
• ⚠️ Medium (3): Distillation incomplete, bias inconsistency, temperature edges
• 💡 Low (3): Quantization integration, gradient pruning, compression ratio

Documentation Gaps

• Missing warnings: 5 critical warning blocks needed
• Unclear patterns: Knowledge distillation usage, low-rank application
• Misconceptions: Sparse storage, compression ratios
• Missing examples: Cross-module integration, production pipelines

---

Recommendations

Immediate Actions (Priority 1)

1. ✅ Add WARNING blocks for in-place mutation (Issues 1, 2)
2. ✅ Add WARNING for sparse storage misconception (Issue 4)
3. ✅ Fix compress_model() to properly handle low-rank (Issue 3)
4. ✅ Add temperature validation in KnowledgeDistillation.__init__ (Issue 7)

Short-term Actions (Priority 2)

5. Implement external integration tests (all 7 proposed tests)
6. Add complete distillation training example (Issue 5)
7. Fix bias measurement comment (Issue 6)
8. Add compression ratio explanation (Issue 10)

Long-term Enhancements (Priority 3)

9. Add quantization + compression pipeline example (Issue 8)
10. Add gradient-based pruning (optional) (Issue 9)
11. Add sparse storage example with scipy
12. Add production deployment examples

---

Quality Gate

Module 16 should NOT be marked "complete" until:

• All 5 critical WARNING blocks added
• In-place mutation documented clearly
• Sparse storage misconception addressed
• At least 3 integration tests implemented
• Knowledge distillation usage example added
• Temperature validation added

Current Status: ⚠️ FUNCTIONAL BUT NEEDS WARNINGS

---

Audit completed by: Claude Code (TinyTorch QA) Next steps: Review with education-reviewer for warning placement and wording.