# 📋 TinyTorch Master Plan of Record
*Official Development Plan - Last Updated: September 2024*

## Executive Summary
**Status**: 14/15 Core Modules Complete (93%)  
**Goal**: Build ML systems understanding through minimal, working implementations  
**Philosophy**: Just enough code to understand WHY PyTorch works the way it does

---

## 🎯 **OFFICIAL MODULE STRUCTURE**

### **PHASE 1: FOUNDATION** ✅ 100% Complete
*Build minimal working neural network*

| # | Module | Status | Current Location | Milestone Contribution |
|---|--------|--------|------------------|----------------------|
| 01 | Setup | ✅ COMPLETE | `modules/01_setup/` | Development environment |
| 02 | Tensor | ✅ COMPLETE | `modules/02_tensor/` | N-dimensional arrays, operations |
| 03 | Activations | ✅ COMPLETE | `modules/03_activations/` | Nonlinearity (enables learning) |
| 04 | Layers | ✅ COMPLETE | `modules/04_layers/` | Linear transformation, parameters |
| 05 | Losses | ✅ COMPLETE | `modules/05_losses/` | Performance measurement |

**Phase 1 Milestone**: ✅ XOR network inference (proves nonlinearity requirement)

---

### **PHASE 2: LEARNING** ✅ 100% Complete
*Enable automatic training through gradient descent*

| # | Module | Status | Current Location | Milestone Contribution |
|---|--------|--------|------------------|----------------------|
| 06 | Optimizers | ✅ COMPLETE | `modules/06_optimizers/` | SGD, Adam parameter updates |
| 07 | Autograd | ✅ COMPLETE | `modules/07_autograd/` | Automatic differentiation |
| 08 | Training | ✅ COMPLETE | `modules/08_training/` | Loss functions, training loops |
| 09 | Spatial (CNNs) | ✅ COMPLETE | `modules/09_spatial/` | Convolutional operations |
| 10 | DataLoader | ✅ COMPLETE | `modules/10_dataloader/` | Batch processing, data pipeline |

**Phase 2 Milestone**: ✅ CIFAR-10 CNN training to 75% accuracy

---

### **PHASE 3: LANGUAGE** 🟡 80% Complete
*Build modern transformer architectures*

| # | Module | Status | Current Location | Milestone Contribution |
|---|--------|--------|------------------|----------------------|
| 11 | Tokenization | ✅ COMPLETE | `modules/11_tokenization/` | Text to numbers conversion |
| 12 | Embeddings | ✅ COMPLETE | `modules/12_embeddings/` | Learned representations |
| 13 | Attention | ✅ COMPLETE | `modules/13_attention/` | Sequence relationships |
| 14 | Transformers | ✅ COMPLETE | `modules/14_transformers/` | Complete architecture |
| 15 | Generation | 🚧 TODO | *Extract from 14* | Autoregressive text generation |

**Phase 3 Milestone**: 🚧 TinyGPT text generation

---

### **PHASE 4: OPTIMIZATION** (Optional Advanced Track)
*Production-level system optimization*

| # | Module | Status | Current Location | Action Needed |
|---|--------|--------|------------------|---------------|
| 16 | Kernels | 🏠 EXISTS | `temp_holding/13_kernels/` | Move and renumber |
| 17 | Benchmarking | 🏠 EXISTS | `temp_holding/14_benchmarking/` | Move and renumber |
| 18 | MLOps | 🏠 EXISTS | `temp_holding/15_mlops/` | Move and renumber |

**Phase 4 Milestone**: Production-optimized inference

---

## 📊 **CURRENT STATE ASSESSMENT**

### **What's Working** ✅
- **Phases 1-2**: Complete and tested
- **Phase 3**: 4/5 modules complete
- **Integration**: Modules compose correctly for end-to-end training
- **Pedagogical Flow**: Clear progression from tensors to transformers

### **What Needs Fixing** 🔧
1. **Module 15 (Generation)**: Extract from Transformers module
2. **Duplicate Modules**: Clean up 12_attention duplicate
3. **Temp Holding**: Move advanced modules to main structure

### **Implementation Priorities**
| Priority | Task | Impact | Effort |
|----------|------|--------|--------|
| P0 | Extract Generation module | Completes Phase 3 | 2 hours |
| P1 | Fix duplicate attention | Cleans structure | 1 hour |
| P2 | Move temp_holding modules | Enables Phase 4 | 1 hour |

---

## 🎓 **PEDAGOGICAL MILESTONES**

### **Progressive Achievement System**

| Milestone | After Module | What Students Can Do | Validation |
|-----------|-------------|---------------------|------------|
| **Foundation** | 05 | Run neural network inference | XOR outputs correct values |
| **Learning** | 10 | Train models from scratch | Loss decreases, accuracy increases |
| **Vision** | 10 | Build CNNs for images | CIFAR-10 >75% accuracy |
| **Language** | 15 | Generate text with transformers | Coherent text output |

### **Learning Validation Questions**

**After Phase 1**: "Why can't a network without ReLU learn XOR?"  
**After Phase 2**: "How does autograd compute gradients automatically?"  
**After Phase 3**: "Why does attention scale quadratically with sequence length?"  
**After Phase 4**: "What optimizations make transformers production-viable?"

---

## 🔬 **SYSTEMS ENGINEERING EMPHASIS**

### **Core Concepts Taught Through Implementation**

| Module | Primary Systems Concept | Why It Matters |
|--------|------------------------|----------------|
| Tensor | Memory layout, vectorization | 10-100x performance difference |
| Activations | Numerical stability | Prevents gradient explosion/vanishing |
| Layers | Matrix multiplication O(N³) | Dominates neural network compute |
| Networks | Composition patterns | Enables arbitrary depth |
| Autograd | Graph memory retention | Training memory = forward + backward |
| Spatial | Convolution efficiency | Spatial reuse, parameter sharing |
| Optimizers | State memory (Adam 3x) | Memory vs convergence tradeoff |
| DataLoader | I/O bottlenecks | Data loading often limits training |
| Training | Gradient accumulation | Batch size vs memory tradeoffs |
| Attention | O(N²) scaling | Sequence length limitations |
| Transformers | Layer memory accumulation | Deep models memory requirements |

### **Memory Scaling Patterns**

```
Operation         Memory Scaling    Bottleneck At
---------         --------------    -------------
Dense Layer       O(input × output) 10k × 10k = 400MB
Convolution       O(C × H × W × K²) High resolution images
Attention         O(N²)             ~2k sequence length
Transformer       O(layers × N²)    Deep models, long sequences
Adam Optimizer    O(3 × parameters) Large models (3x memory)
```

---

## 📅 **DEVELOPMENT TIMELINE**

### **Completed Work** ✅
- Modules 01-14: Core framework complete
- Testing: All modules pass individual tests
- Integration: End-to-end training verified

### **Remaining Work** 🚧
| Task | Priority | Effort | Dependencies |
|------|----------|--------|--------------|
| Extract Generation module | P0 | 2 hours | Module 14 complete |
| Clean duplicate modules | P1 | 1 hour | None |
| Move temp_holding modules | P2 | 1 hour | None |
| Final integration testing | P0 | 2 hours | All modules complete |

### **Estimated Completion**
- **Phase 3 Completion**: 1 day (Generation module)
- **Full Core Curriculum**: Already 93% complete
- **Phase 4 (Optional)**: Ready in temp_holding

---

## ✅ **DEFINITION OF DONE**

### **Module Completion Criteria**
- [ ] Core implementation with minimal complexity
- [ ] Unit tests passing
- [ ] Memory/performance analysis included
- [ ] Systems engineering insights documented
- [ ] Integration with previous modules verified
- [ ] NBGrader metadata present
- [ ] README with learning objectives

### **Phase Completion Criteria**
- [ ] Milestone achieved (XOR, CIFAR-10, TinyGPT)
- [ ] All module tests passing
- [ ] Integration tests passing
- [ ] Documentation complete
- [ ] No forward dependencies

### **Framework Completion Criteria**
- [ ] Students can train CNN to 75% on CIFAR-10
- [ ] Students can generate text with transformer
- [ ] All modules follow consistent structure
- [ ] Systems concepts emphasized throughout
- [ ] Clean dependency chain (no forward references)

---

## 🎯 **SUCCESS METRICS**

### **Educational Outcomes**
Students completing TinyTorch will:
1. ✅ Understand why neural networks need nonlinearity
2. ✅ Debug gradient flow issues in training
3. ✅ Choose appropriate architectures for data types
4. ✅ Analyze memory/compute tradeoffs
5. ✅ Read PyTorch source code with comprehension

### **Technical Achievements**
- **XOR**: 100% accuracy (Phase 1 validation)
- **CIFAR-10**: >75% accuracy (Phase 2 validation)
- **Text Generation**: Coherent output (Phase 3 validation)
- **Framework**: Complete ML system from scratch

---

## 📝 **NOTES AND DECISIONS**

### **Architectural Decisions**
- **Tensor/Variable Separation**: Keep for pedagogical clarity
- **Module Ordering**: Activations after Layers (better flow)
- **Loss Functions**: Keep within Training module (simpler)
- **Generation**: Extract to separate module (clarity)

### **Deferred Complexity**
- GPU/CUDA support (CPU only for education)
- Dynamic graphs (static is simpler to understand)
- Distributed training (single machine focus)
- Advanced optimizations (clarity over performance)

### **Quality Standards**
- Readable code over optimized code
- Explicit behavior over magic
- Working implementations over complete features
- Systems understanding over algorithm memorization

---

## 🚀 **NEXT ACTIONS**

### **Immediate (This Week)**
1. Extract Generation module from Transformers
2. Clean up duplicate attention modules
3. Update module numbering for consistency
4. Run full integration test suite

### **Short Term (Next Month)**
1. Move temp_holding modules to main structure
2. Create comprehensive test suite
3. Write instructor guide
4. Create student quickstart

### **Long Term (Future)**
1. Video tutorials for each module
2. Interactive notebooks
3. Automated grading integration
4. Community contributions

---

*This Plan of Record represents the official structure and status of the TinyTorch educational framework. It will be updated as modules are completed and the framework evolves.*

**Last Updated**: September 2024  
**Version**: 1.0  
**Status**: ACTIVE DEVELOPMENT