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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.
114 lines
3.4 KiB
Markdown
114 lines
3.4 KiB
Markdown
# Module 19: Benchmarking - Performance Measurement & Analysis
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## Overview
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Learn to scientifically measure, analyze, and optimize ML system performance. Build profiling tools that identify bottlenecks and guide optimization decisions.
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## What You'll Build
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- **Performance Profiler**: Measure time, memory, and compute
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- **Bottleneck Analyzer**: Identify optimization opportunities
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- **Comparison Framework**: A/B test different approaches
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- **Visualization Tools**: Performance dashboards
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## Learning Objectives
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1. **Scientific Measurement**: Reproducible performance testing
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2. **Profiling Techniques**: Time, memory, and operation profiling
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3. **Bottleneck Analysis**: Find and fix performance issues
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4. **Optimization Validation**: Prove improvements work
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## Prerequisites
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- Modules 15-18: All optimization techniques
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- Module 10: Training (baseline for comparison)
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## Key Concepts
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### Comprehensive Profiling
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```python
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@profile
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def model_forward(model, input):
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with Timer() as t:
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with MemoryTracker() as m:
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output = model(input)
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print(f"Time: {t.elapsed}ms")
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print(f"Memory: {m.peak_usage}MB")
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print(f"FLOPs: {count_flops(model, input)}")
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```
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### Bottleneck Identification
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```python
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profiler = Profiler()
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with profiler:
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model.train(data_loader)
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# Find top time consumers
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profiler.print_top_operations(n=10)
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# 45% - Matrix multiplication
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# 23% - Attention computation
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# 15% - Data loading
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# ...
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```
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### A/B Testing
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```python
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# Compare optimization techniques
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baseline = measure_performance(original_model)
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optimized = measure_performance(quantized_model)
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improvement = {
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'speedup': optimized.time / baseline.time,
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'memory_reduction': baseline.memory / optimized.memory,
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'accuracy_delta': optimized.accuracy - baseline.accuracy
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}
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```
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## Tools You'll Master
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- **Time Profiling**: Where cycles are spent
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- **Memory Profiling**: Peak usage and allocation patterns
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- **Operation Counting**: FLOPs and memory bandwidth
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- **Statistical Analysis**: Confidence intervals and significance
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## Real-World Skills
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- **Production Profiling**: Tools used at Meta, Google
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- **Performance Debugging**: Find unexpected slowdowns
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- **Optimization Planning**: Data-driven decisions
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- **Regression Testing**: Ensure optimizations persist
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## Module Structure
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1. **Measurement Fundamentals**: Accurate timing and memory tracking
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2. **Building Profilers**: Hook-based profiling system
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3. **Analysis Tools**: Statistical analysis of results
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4. **Visualization**: Performance dashboards
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5. **Case Studies**: Profile and optimize real models
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## Practical Examples
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```python
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# Profile your optimizations
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models = {
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'baseline': original_model,
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'quantized': quantized_model,
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'pruned': pruned_model,
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'cached': cached_transformer
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}
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results = benchmark_suite(models, test_data)
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plot_performance_comparison(results)
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# Output:
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# Model Time Memory Accuracy
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# baseline 100ms 400MB 75.0%
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# quantized 25ms 100MB 74.5%
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# pruned 30ms 40MB 73.8%
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# cached 20ms 450MB 75.0%
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```
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## Advanced Topics
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- **Roofline Analysis**: Hardware utilization
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- **Memory Bandwidth**: Identifying memory-bound operations
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- **Cache Analysis**: L1/L2/L3 cache behavior
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- **Distributed Profiling**: Multi-GPU systems
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## Success Criteria
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- ✅ Build complete profiling system from scratch
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- ✅ Identify and fix 3+ performance bottlenecks
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- ✅ Create reproducible benchmark suite
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- ✅ Generate professional performance reports |