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This commit implements the pedagogically optimal "inevitable discovery" module progression based on expert validation and educational design principles. ## Module Reordering Summary **Previous Order (Problems)**: - 05_losses → 06_autograd → 07_dataloader → 08_optimizers → 09_spatial → 10_training - Issues: Autograd before optimizers, DataLoader before training, scattered dependencies **New Order (Beautiful Progression)**: - 05_losses → 06_optimizers → 07_autograd → 08_training → 09_spatial → 10_dataloader - Benefits: Each module creates inevitable need for the next ## Pedagogical Flow Achieved **05_losses** → "Need systematic weight updates" → **06_optimizers** **06_optimizers** → "Need automatic gradients" → **07_autograd** **07_autograd** → "Need systematic training" → **08_training** **08_training** → "MLPs hit limits on images" → **09_spatial** **09_spatial** → "Training is too slow" → **10_dataloader** ## Technical Changes ### Module Directory Renaming - `06_autograd` → `07_autograd` - `07_dataloader` → `10_dataloader` - `08_optimizers` → `06_optimizers` - `10_training` → `08_training` - `09_spatial` → `09_spatial` (no change) ### System Integration Updates - **MODULE_TO_CHECKPOINT mapping**: Updated in tito/commands/export.py - **Test directories**: Renamed module_XX directories to match new numbers - **Documentation**: Updated all references in MD files and agent configurations - **CLI integration**: Updated next-steps suggestions for proper flow ### Agent Configuration Updates - **Quality Assurance**: Updated module audit status with new numbers - **Module Developer**: Updated work tracking with new sequence - **Documentation**: Updated MASTER_PLAN_OF_RECORD.md with beautiful progression ## Educational Benefits 1. **Inevitable Discovery**: Each module naturally leads to the next 2. **Cognitive Load**: Concepts introduced exactly when needed 3. **Motivation**: Students understand WHY each tool is necessary 4. **Synthesis**: Everything flows toward complete ML systems understanding 5. **Professional Alignment**: Matches real ML engineering workflows ## Quality Assurance - ✅ All CLI commands still function - ✅ Checkpoint system mappings updated - ✅ Documentation consistency maintained - ✅ Test directory structure aligned - ✅ Agent configurations synchronized **Impact**: This reordering transforms TinyTorch from a collection of modules into a coherent educational journey where each step naturally motivates the next, creating optimal conditions for deep learning systems understanding.
83 lines
2.7 KiB
Markdown
83 lines
2.7 KiB
Markdown
# Module 17: Precision - Numerical Optimization through Quantization
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## Overview
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Reduce model size by 75% and accelerate inference by 2-4x through INT8 quantization. Learn how production systems deploy billion-parameter models on edge devices.
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## What You'll Build
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- **INT8 Quantizer**: Convert FP32 models to INT8
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- **Calibration System**: Find optimal scaling factors
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- **Quantized Operations**: Fast integer arithmetic
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- **Accuracy Validator**: Measure precision/performance tradeoffs
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## Learning Objectives
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1. **Numerical Representation**: FP32 vs FP16 vs INT8 tradeoffs
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2. **Post-Training Quantization**: Convert trained models efficiently
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3. **Calibration Techniques**: Minimize accuracy loss
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4. **Hardware Acceleration**: Why INT8 is 4x faster on modern hardware
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## Prerequisites
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- Module 15: Acceleration (backend dispatch)
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- Module 10: Training (trained models to quantize)
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## Key Concepts
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### The Problem: Model Size and Speed
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```python
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# FP32 Model - High precision, slow, large
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model = TinyGPT() # 400MB, 100ms/token
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# After quantization - Lower precision, fast, small
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quantized = quantize_int8(model) # 100MB, 25ms/token
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```
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### Quantization Process
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```python
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# 1. Calibration - Find scale factors
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scales = calibrate(model, calibration_data)
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# 2. Quantization - Convert weights
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quantized_weights = (weights / scales).round().clip(-128, 127)
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# 3. Inference - Use integer ops
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output = quantized_forward(input, quantized_weights, scales)
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```
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## Performance Impact
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- **Model Size**: 4x reduction (FP32 → INT8)
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- **Inference Speed**: 2-4x faster on CPU/GPU
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- **Accuracy**: Typically <1% loss with good calibration
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- **Memory Bandwidth**: 4x reduction
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## Real-World Applications
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- **Mobile Deployment**: Run LLMs on phones
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- **Edge AI**: Raspberry Pi inference
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- **Datacenter Efficiency**: 4x more models per GPU
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- **TensorFlow Lite**: Production quantization
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## Module Structure
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1. **Numerical Basics**: Understanding precision and range
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2. **Quantization Math**: Scale factors and rounding
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3. **Calibration**: Finding optimal quantization parameters
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4. **Implementation**: Building quantized operations
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5. **Evaluation**: Accuracy vs performance analysis
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## Hands-On Examples
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```python
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# Quantize your trained CNN
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cnn = load_trained_model("cifar10_cnn.pt")
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quantized = quantize_model(cnn, calibration_loader)
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# Compare accuracy
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original_acc = evaluate(cnn, test_loader) # 75.2%
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quantized_acc = evaluate(quantized, test_loader) # 74.8%
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# Measure speedup
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original_time = benchmark(cnn) # 45ms/batch
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quantized_time = benchmark(quantized) # 12ms/batch (3.75x faster!)
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```
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## Success Criteria
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- ✅ Quantize models to INT8 with <1% accuracy loss
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- ✅ Achieve 2-4x inference speedup
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- ✅ Reduce model size by 75%
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- ✅ Understand hardware acceleration principles |