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Implement MLPerf Edu Competition module (Module 20)
Complete capstone competition implementation: - Two division tracks: Closed (optimize) and Open (innovate) - Baseline CNN model for CIFAR-10 - Validation and submission generation system - Integration with Module 19 normalized scoring - Honor code and GitHub repo submission workflow - Worked examples and student templates Module 20 is now a pedagogically sound capstone that applies all Optimization Tier techniques in a fair competition format.
This commit is contained in:
Vijay Janapa Reddi
@@ -1618,7 +1618,41 @@ class CompleteTinyGPTPipeline:
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def __init__(self, vocab_size: int = 100, embed_dim: int = 128,
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num_layers: int = 4, num_heads: int = 4):
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"""Initialize complete pipeline with model architecture."""
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"""
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Initialize complete end-to-end TinyGPT pipeline integrating all 19 modules.
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TODO: Set up a complete ML pipeline with tokenization, model, training,
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profiling, and benchmarking components
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APPROACH:
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1. Store model architecture parameters (vocab_size, embed_dim, num_layers, num_heads)
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2. Initialize tokenizer using CharTokenizer from Module 10 with printable ASCII (32-127)
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3. Create TinyGPT model instance with stored parameters and max_seq_len=256
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4. Setup TinyGPTTrainer for training orchestration with learning_rate=3e-4
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5. Initialize Profiler (Module 15) and Benchmark (Module 19) for performance analysis
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6. Initialize pipeline state tracking (is_trained flag, training_history list)
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7. Print pipeline initialization summary with parameter count and memory usage
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EXAMPLE:
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>>> pipeline = CompleteTinyGPTPipeline(vocab_size=100, embed_dim=128,
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... num_layers=4, num_heads=4)
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🏗️ Complete TinyGPT Pipeline Initialized
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Model: 419,300 parameters
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Memory: 1.6MB
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>>> pipeline.model.count_parameters()
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419300
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>>> pipeline.is_trained
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False
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>>> len(pipeline.training_history)
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0
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HINTS:
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- CharTokenizer needs list of characters: [chr(i) for i in range(32, 127)]
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- TinyGPT requires vocab_size, embed_dim, num_layers, num_heads, max_seq_len
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- TinyGPTTrainer takes model, tokenizer, and learning_rate as arguments
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- Benchmark expects (models_list, datasets_list, metrics_list) format
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- Memory calculation: parameters * 4 bytes / 1024 / 1024 for MB
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"""
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### BEGIN SOLUTION
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self.vocab_size = vocab_size
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1083
modules/source/20_competition/competition_dev.ipynb
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1083
modules/source/20_competition/competition_dev.ipynb
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File diff suppressed because it is too large
Load Diff
@@ -18,52 +18,53 @@
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"""
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# Module 20: TinyMLPerf Competition - Your Capstone Challenge
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Welcome to the capstone! You've built an entire ML system from scratch (M01-13) and learned optimization techniques (M14-19). Now it's time to compete and show what you can do! 🏅
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Welcome to the capstone! You've built an entire ML system (M01-13) and learned optimization techniques (M14-19). Now compete in **TinyMLPerf** - a competition inspired by industry-standard MLPerf benchmarking!
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## 🔗 Your Journey
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## 🔗 Prerequisites & Progress
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**You've Built**: Complete ML framework with all optimization techniques
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**You've Learned**: MLPerf principles and benchmarking methodology (Module 19)
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**You'll Do**: Compete in TinyMLPerf following Closed Division rules
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**You'll Produce**: Standardized TinyMLPerf submission
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**The Journey So Far**:
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```
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Modules 01-13: Build ML System (tensors → transformers)
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Modules 14-18: Learn Optimization Techniques
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Module 19: Learn Benchmarking
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Module 19: Learn MLPerf-Style Benchmarking
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Module 20: Compete in TinyMLPerf! 🏅
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```
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## 🏅 TinyMLPerf: Two Ways to Compete
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## 🏅 TinyMLPerf: MLPerf for Educational Systems
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Inspired by industry-standard MLPerf (which you learned about in Module 19), TinyMLPerf offers **two competition tracks**:
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TinyMLPerf follows MLPerf principles adapted for educational ML systems:
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### 🔒 Closed Division - "Optimization Challenge"
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**What you do:**
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- Start with provided baseline model (everyone gets the same)
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- Apply optimization techniques from Modules 14-18
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- Compete on: Who optimizes best?
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**Closed Division Rules (What You'll Do):**
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- ✅ Use provided baseline models (fair comparison)
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- ✅ Use provided test datasets (standardized evaluation)
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- ✅ Apply optimization techniques from Modules 14-18
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- ✅ Report all metrics (accuracy, latency, memory)
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- ✅ Document your optimization strategy
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**Best for:** Most students - clear rules, fair comparison
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**Focus:** Your optimization skills
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**Why Closed Division?**
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- Fair apples-to-apples comparison
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- Tests your optimization skills (not model design)
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- Mirrors real-world MLPerf Inference competitions
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- Professionally credible methodology
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### 🔓 Open Division - "Innovation Challenge"
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**What you do:**
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- Modify anything! Improve your implementations from M01-19
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- Design better architectures
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- Novel approaches encouraged
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**Competition Categories:**
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- 🏃 Latency Sprint: Minimize inference time
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- 🏋️ Memory Challenge: Minimize model footprint
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- 🎯 Accuracy Contest: Maximize accuracy within constraints
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- 🏋️♂️ All-Around: Best balanced performance
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- 🚀 Extreme Push: Most aggressive optimization
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**Best for:** Advanced students who want more creative freedom
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**Focus:** Your systems innovations
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This module provides:
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1. **Validation**: Verify your TinyTorch installation
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2. **Baseline**: Official reference performance
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3. **Worked Example**: Complete optimization workflow
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4. **Competition Template**: Your submission workspace
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## Competition Categories (Both Divisions)
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- 🏃 **Latency Sprint**: Fastest inference
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- 🏋️ **Memory Challenge**: Smallest model
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- 🎯 **Accuracy Contest**: Best accuracy within constraints
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- 🏋️♂️ **All-Around**: Best balanced performance
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- 🚀 **Extreme Push**: Most aggressive optimization
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## What This Module Provides
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1. **Validation**: Check your TinyTorch works
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2. **Baseline**: Starting point for Closed Division
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3. **Examples**: See both tracks in action
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4. **Template**: Your competition workspace
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Pick your track, optimize, and compete! 🔥
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🔥 Let's compete following professional MLPerf methodology! 🏅
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"""
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# %% [markdown]
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@@ -90,36 +91,47 @@ from tinytorch.competition.submit import load_baseline_model, generate_submissio
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# %% [markdown]
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"""
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# 1. Pick Your Track & Validate
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# 1. TinyMLPerf Rules & System Validation
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Before competing, choose your track and make sure your TinyTorch installation works!
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Before competing, let's understand TinyMLPerf rules and validate your environment. Following MLPerf methodology (learned in Module 19) ensures fair competition and reproducible results.
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## Two Tracks, Two Styles
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## TinyMLPerf Closed Division Rules
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### 🔒 Closed Division - "The Optimization Challenge"
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- Everyone starts with the same baseline model
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- Apply techniques from Modules 14-18 (quantization, pruning, etc.)
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- Fair comparison: who optimizes best?
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- **Choose this if:** You want clear rules and direct competition
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**You learned in Module 19 that MLPerf Closed Division requires:**
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1. **Fixed Models**: Use provided baseline architectures
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2. **Fixed Datasets**: Use provided test data
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3. **Fair Comparison**: Same starting point for everyone
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4. **Reproducibility**: Document all optimizations
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5. **Multiple Metrics**: Report accuracy, latency, memory
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### 🔓 Open Division - "The Innovation Challenge"
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- Modify anything! Improve YOUR TinyTorch implementations
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- Better Conv2d? Faster matmul? Novel architecture? All allowed!
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- Compete on innovation and creativity
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- **Choose this if:** You want freedom to explore and innovate
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**In TinyMLPerf Closed Division, you CAN:**
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- ✅ Apply quantization (Module 17)
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- ✅ Apply pruning/compression (Module 18)
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- ✅ Enable KV caching for transformers (Module 14)
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- ✅ Combine techniques in any order
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- ✅ Tune hyperparameters
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**Can I do both?** Absolutely! Submit to both tracks.
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**In TinyMLPerf Closed Division, you CANNOT:**
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- ❌ Change baseline model architecture
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- ❌ Train on different data
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- ❌ Use external pretrained weights
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- ❌ Modify test dataset
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**Which is "better"?** Neither - they test different skills:
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- Closed = Optimization mastery
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- Open = Systems innovation
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**Why these rules?**
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- Tests your OPTIMIZATION skills (not model design)
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- Fair apples-to-apples comparison
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- Mirrors professional MLPerf competitions
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- Results are meaningful and reproducible
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## Quick Validation
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## System Validation
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Before competing, let's verify everything works:
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- ✅ All modules imported successfully
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- ✅ Optimization techniques available
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- ✅ Benchmarking tools ready
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Let's verify your TinyTorch installation works correctly before competing. MLPerf requires documenting your environment, so validation ensures reproducibility.
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**Validation checks:**
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- ✅ All 19 modules imported successfully
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- ✅ Core operations work (tensor, autograd, layers)
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- ✅ Optimization techniques available (M14-18)
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- ✅ Benchmarking tools functional (M19)
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"""
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# %%
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@@ -129,8 +141,6 @@ import json
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import time
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from pathlib import Path
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from typing import Dict, List, Tuple, Any, Optional
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from tinytorch.benchmarking.benchmark import Benchmark, calculate_normalized_scores
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from tinytorch.profiling.profiler import Profiler
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def validate_installation() -> Dict[str, bool]:
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"""
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@@ -215,19 +225,24 @@ def validate_installation() -> Dict[str, bool]:
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# %% [markdown]
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"""
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# 2. The Baseline (For Closed Division)
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# 2. TinyMLPerf Baseline - Official Reference Performance
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If you're competing in **Closed Division**, everyone starts with this baseline model. If you're in **Open Division**, you can skip this or use it as a reference!
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Following MLPerf Closed Division rules, everyone starts with the SAME baseline model. This ensures fair comparison - we're measuring your optimization skills, not model design.
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## Baseline Model: Simple CNN on CIFAR-10
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## What is a TinyMLPerf Baseline?
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We provide a simple CNN as the starting point for Closed Division:
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- **Architecture:** Conv → Pool → Conv → Pool → FC → FC
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- **Dataset:** CIFAR-10 (standardized test set)
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- **Metrics:** Accuracy, latency, memory (we'll measure together)
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In MLPerf competitions, the baseline is the official reference implementation:
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- **Fixed Architecture:** Provided CNN (everyone uses the same)
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- **Fixed Dataset:** CIFAR-10 test set (standardized evaluation)
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- **Measured Metrics:** Accuracy, latency, memory (reproducible)
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- **Your Goal:** Beat baseline using optimization techniques from M14-18
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**Closed Division:** Optimize THIS model using M14-18 techniques
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**Open Division:** Build/modify whatever you want!
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**This is MLPerf Closed Division:**
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- Everyone starts here ← Fair comparison
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- Apply YOUR optimizations ← Your skill
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- Measure improvement ← Objective scoring
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We provide a simple CNN on CIFAR-10 as the TinyMLPerf baseline. This gives everyone the same starting point.
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### Baseline Components
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@@ -392,35 +407,38 @@ def generate_baseline(model_name: str = "cifar10_cnn", quick: bool = True) -> Di
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# %% [markdown]
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"""
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# 3. Complete Example - See Both Tracks in Action
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# 3. TinyMLPerf Closed Division Workflow - Complete Example
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Let's see complete examples for BOTH competition tracks!
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Let's see a complete TinyMLPerf submission following Closed Division rules. This example demonstrates the professional MLPerf methodology you learned in Module 19.
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## Example 1: Closed Division - Optimization Master
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**TinyMLPerf Closed Division Workflow:**
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1. **Load Official Baseline** (MLPerf requirement)
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2. **Apply Optimizations** (Modules 14-18 techniques)
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3. **Benchmark Systematically** (Module 19 tools)
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4. **Generate Submission** (MLPerf-compliant format)
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5. **Document Strategy** (Reproducibility requirement)
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**Goal:** Compete in All-Around category using provided baseline
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This is your template - study it, then implement your own optimization strategy!
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**Strategy:**
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1. Load baseline CNN
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2. Apply quantization (INT8) → 4x memory reduction
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3. Apply pruning (60%) → Speed boost
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4. Benchmark and submit
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## Example Strategy: All-Around Category
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**Why this order?** Quantize first preserves more accuracy than pruning first.
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For this worked example, we'll compete in the **All-Around** category (best balanced performance across all metrics).
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## Example 2: Open Division - Innovation Master
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**Our Optimization Strategy:**
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- **Step 1:** Quantization (INT8) → 4x memory reduction
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- **Step 2:** Magnitude Pruning (60%) → Faster inference
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- **Step 3:** Systematic Benchmarking → Measure impact
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**Goal:** Beat everyone with a novel approach
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**Why this order?**
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- Quantize FIRST: Preserves more accuracy than pruning first
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- Prune SECOND: Reduces what needs to be quantized
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- Benchmark: Following MLPerf measurement methodology
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**Strategy:**
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1. Improve YOUR Conv2d implementation (faster algorithm)
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2. OR design a better architecture (MobileNet-style)
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3. OR novel quantization (mixed precision per layer)
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4. Benchmark and submit
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**Freedom:** Modify anything in your TinyTorch implementation!
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Let's see the Closed Division example in detail below:
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**This follows MLPerf Closed Division rules:**
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- ✅ Uses provided baseline CNN
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- ✅ Applies optimization techniques (not architecture changes)
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- ✅ Documents strategy clearly
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- ✅ Reports all required metrics
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"""
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# %%
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@@ -531,66 +549,80 @@ def worked_example_optimization():
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# %% [markdown]
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"""
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# 4. Your Turn - Pick Your Track!
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# 4. Your TinyMLPerf Submission Template
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Now it's time to compete! Choose your track and implement your strategy.
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Now it's your turn! Below is your TinyMLPerf Closed Division submission template. Following MLPerf methodology ensures your results are reproducible and fairly comparable.
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## Choose Your Track
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## TinyMLPerf Closed Division Submission Process
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### 🔒 Closed Division Template
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**If you choose Closed Division:**
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1. Pick a category (Latency Sprint, Memory Challenge, etc.)
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2. Design your optimization strategy
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3. Implement in `optimize_for_competition()` below
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4. Use techniques from Modules 14-18 only
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5. Generate submission
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**Good for:** Clear path, fair comparison, most students
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### 🔓 Open Division Template
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**If you choose Open Division:**
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1. Pick a category
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2. Modify YOUR TinyTorch implementations (go edit earlier modules!)
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3. OR design novel architectures
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4. Re-export with `tito export` and benchmark
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5. Generate submission
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**Good for:** Creative freedom, systems innovation, advanced students
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## Competition Categories (Pick ONE)
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- 🏃 **Latency Sprint:** Fastest inference
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- 🏋️ **Memory Challenge:** Smallest model
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- 🎯 **Accuracy Contest:** Best accuracy within constraints
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**Step 1: Choose Your Category**
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Pick ONE category to optimize for:
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- 🏃 **Latency Sprint:** Minimize inference time
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- 🏋️ **Memory Challenge:** Minimize model footprint
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- 🎯 **Accuracy Contest:** Maximize accuracy within constraints
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- 🏋️♂️ **All-Around:** Best balanced performance
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- 🚀 **Extreme Push:** Most aggressive optimization
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## Template Below
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**Step 2: Design Your Optimization Strategy**
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- Review Module 19, Section 4.5 for combination strategies
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- Consider optimization order (quantize→prune vs prune→quantize)
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- Plan ablation study to understand each technique's impact
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- Document your reasoning (MLPerf reproducibility requirement)
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Use the `optimize_for_competition()` function to implement your strategy:
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- **Closed Division:** Apply M14-18 techniques
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- **Open Division:** Do whatever you want, document it!
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**Step 3: Implement in Template**
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- Write optimization code in `optimize_for_competition()`
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- Apply techniques from Modules 14-18
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- Follow TinyMLPerf Closed Division rules (no architecture changes!)
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**Step 4: Benchmark Systematically**
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- Use Module 19 benchmarking tools
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- Measure all required metrics (accuracy, latency, memory)
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- Run multiple times for statistical validity (MLPerf requirement)
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**Step 5: Generate MLPerf-Compliant Submission**
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- Run `generate_submission()` to create `submission.json`
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- Includes baseline comparison (MLPerf requirement)
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- Documents optimization strategy (reproducibility)
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- Ready for TinyMLPerf leaderboard upload
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## Submission Guidelines (MLPerf Inspired)
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- ✅ **Start with baseline:** Load provided CNN (don't modify architecture)
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- ✅ **Apply optimizations:** Use M14-18 techniques only
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- ✅ **Measure fairly:** Same hardware, same test data
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- ✅ **Document everything:** Strategy writeup required
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- ✅ **Report all metrics:** Accuracy, latency, memory (not just best one!)
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**Remember:** TinyMLPerf Closed Division tests your OPTIMIZATION skills, not model design. Work within the rules! 🏅
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"""
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# %%
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#| export
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def optimize_for_competition(baseline_model, event: str = "all_around", division: str = "closed"):
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def optimize_for_competition(baseline_model, event: str = "all_around"):
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"""
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🏅 YOUR COMPETITION ENTRY - IMPLEMENT YOUR STRATEGY HERE!
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This is where you apply optimization techniques from Modules 14-18.
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Available techniques:
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- Module 14: KV Caching (for transformers) - enable_kv_cache()
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- Module 16: Acceleration (vectorization, fusion)
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- Module 17: Quantization (INT8, INT4) - quantize_model()
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- Module 18: Compression (pruning) - magnitude_prune()
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Args:
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baseline_model: Starting model (use for Closed, optional for Open)
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event: Category you're competing in
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baseline_model: The unoptimized model
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event: Which Olympic event you're competing in
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- "latency_sprint": Minimize latency
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- "memory_challenge": Minimize memory
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- "accuracy_contest": Maximize accuracy
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- "all_around": Best balance
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- "extreme_push": Most aggressive
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division: "closed" or "open" - which track you chose
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Returns:
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Your optimized model
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🔒 CLOSED DIVISION Example:
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Example:
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from tinytorch.optimization.quantization import quantize_model
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from tinytorch.optimization.compression import magnitude_prune
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@@ -598,15 +630,6 @@ def optimize_for_competition(baseline_model, event: str = "all_around", division
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optimized = quantize_model(optimized, bits=8)
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optimized = magnitude_prune(optimized, sparsity=0.7)
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return optimized
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🔓 OPEN DIVISION Example:
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# Build your own model OR
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# Use your improved implementations from earlier modules
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# (after you've modified and re-exported them)
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from tinytorch.models import YourCustomArchitecture
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optimized = YourCustomArchitecture()
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return optimized
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"""
|
||||
|
||||
print(f"🏅 YOUR OPTIMIZATION STRATEGY FOR: {event}")
|
||||
@@ -651,201 +674,74 @@ def optimize_for_competition(baseline_model, event: str = "all_around", division
|
||||
|
||||
return optimized_model
|
||||
|
||||
#| export
|
||||
def validate_submission(submission: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
Validate competition submission with sanity checks.
|
||||
|
||||
This catches honest mistakes like unrealistic speedups or accidental training.
|
||||
Honor code system - we trust but verify basic reasonableness.
|
||||
|
||||
Args:
|
||||
submission: Submission dictionary to validate
|
||||
|
||||
Returns:
|
||||
Dict with validation results and warnings
|
||||
"""
|
||||
checks = []
|
||||
warnings = []
|
||||
errors = []
|
||||
|
||||
# Extract metrics
|
||||
normalized = submission.get("normalized_scores", {})
|
||||
speedup = normalized.get("speedup", 1.0)
|
||||
compression = normalized.get("compression_ratio", 1.0)
|
||||
accuracy_delta = normalized.get("accuracy_delta", 0.0)
|
||||
|
||||
# Check 1: Speedup is reasonable (not claiming impossible gains)
|
||||
if speedup > 50:
|
||||
errors.append(f"❌ Speedup {speedup:.1f}x seems unrealistic (>50x)")
|
||||
elif speedup > 20:
|
||||
warnings.append(f"⚠️ Speedup {speedup:.1f}x is very high - please verify measurements")
|
||||
else:
|
||||
checks.append(f"✅ Speedup {speedup:.2f}x is reasonable")
|
||||
|
||||
# Check 2: Compression is reasonable
|
||||
if compression > 32:
|
||||
errors.append(f"❌ Compression {compression:.1f}x seems unrealistic (>32x)")
|
||||
elif compression > 16:
|
||||
warnings.append(f"⚠️ Compression {compression:.1f}x is very high - please verify")
|
||||
else:
|
||||
checks.append(f"✅ Compression {compression:.2f}x is reasonable")
|
||||
|
||||
# Check 3: Accuracy didn't improve (Closed Division rule - no training allowed!)
|
||||
division = submission.get("division", "closed")
|
||||
if division == "closed" and accuracy_delta > 1.0:
|
||||
errors.append(f"❌ Accuracy improved by {accuracy_delta:.1f}pp - did you accidentally train the model?")
|
||||
elif accuracy_delta > 0.5:
|
||||
warnings.append(f"⚠️ Accuracy improved by {accuracy_delta:.1f}pp - verify no training occurred")
|
||||
else:
|
||||
checks.append(f"✅ Accuracy change {accuracy_delta:+.2f}pp is reasonable")
|
||||
|
||||
# Check 4: GitHub repo provided
|
||||
github_repo = submission.get("github_repo", "")
|
||||
if not github_repo or github_repo == "":
|
||||
warnings.append("⚠️ No GitHub repo provided - required for verification")
|
||||
else:
|
||||
checks.append(f"✅ GitHub repo provided: {github_repo}")
|
||||
|
||||
# Check 5: Required fields present
|
||||
required_fields = ["division", "event", "athlete_name", "baseline", "optimized", "normalized_scores"]
|
||||
missing = [f for f in required_fields if f not in submission]
|
||||
if missing:
|
||||
errors.append(f"❌ Missing required fields: {', '.join(missing)}")
|
||||
else:
|
||||
checks.append("✅ All required fields present")
|
||||
|
||||
# Check 6: Techniques documented
|
||||
techniques = submission.get("techniques_applied", [])
|
||||
if not techniques or "TODO" in str(techniques):
|
||||
warnings.append("⚠️ No optimization techniques listed")
|
||||
else:
|
||||
checks.append(f"✅ Techniques documented: {', '.join(techniques[:3])}...")
|
||||
|
||||
return {
|
||||
"valid": len(errors) == 0,
|
||||
"checks": checks,
|
||||
"warnings": warnings,
|
||||
"errors": errors
|
||||
}
|
||||
|
||||
#| export
|
||||
def generate_submission(baseline_model, optimized_model,
|
||||
division: str = "closed",
|
||||
event: str = "all_around",
|
||||
athlete_name: str = "YourName",
|
||||
github_repo: str = "",
|
||||
techniques: List[str] = None) -> Dict[str, Any]:
|
||||
"""
|
||||
Generate standardized TinyMLPerf competition submission with normalized scoring.
|
||||
Generate standardized competition submission.
|
||||
|
||||
Args:
|
||||
baseline_model: Original unoptimized model
|
||||
optimized_model: Your optimized model
|
||||
division: "closed" or "open"
|
||||
event: Competition category (latency_sprint, memory_challenge, all_around, etc.)
|
||||
athlete_name: Your name for submission
|
||||
github_repo: GitHub repository URL for code verification
|
||||
techniques: List of optimization techniques applied
|
||||
event: Olympic event name
|
||||
athlete_name: Your name for leaderboard
|
||||
techniques: List of techniques applied
|
||||
|
||||
Returns:
|
||||
Submission dictionary (will be saved as JSON)
|
||||
"""
|
||||
print("📤 Generating TinyMLPerf Competition Submission...")
|
||||
print("📤 Generating Competition Submission...")
|
||||
print("=" * 70)
|
||||
|
||||
# Get baseline metrics
|
||||
baseline_metrics = generate_baseline(quick=True)
|
||||
|
||||
# Benchmark optimized model
|
||||
# For demonstration, estimate optimized metrics
|
||||
# In real competition, this would benchmark the actual optimized model
|
||||
print("🔬 Benchmarking optimized model...")
|
||||
|
||||
# Use Profiler and Benchmark from Module 19
|
||||
profiler = Profiler()
|
||||
|
||||
# For demonstration, we'll use placeholder metrics
|
||||
# In real competition, students would measure their actual optimized model
|
||||
# Placeholder: Students' actual optimizations would be measured here
|
||||
optimized_metrics = {
|
||||
"model": getattr(optimized_model, 'name', 'Optimized_Model'),
|
||||
"accuracy": 84.0, # Would be measured with actual test set
|
||||
"latency_ms": 28.0, # Would be measured with profiler
|
||||
"memory_mb": 4.0, # Would be measured with profiler
|
||||
"parameters": 2000000, # Would be counted
|
||||
"model": "Your_Optimized_Model",
|
||||
"accuracy": 84.0, # Measured
|
||||
"latency_ms": 28.0, # Measured
|
||||
"memory_mb": 4.0, # Measured
|
||||
"parameters": 2000000, # Measured
|
||||
}
|
||||
|
||||
# Calculate normalized scores using Module 19's function
|
||||
baseline_for_norm = {
|
||||
"latency": baseline_metrics["latency_ms"],
|
||||
"memory": baseline_metrics["memory_mb"],
|
||||
"accuracy": baseline_metrics["accuracy"]
|
||||
# Calculate improvements
|
||||
improvements = {
|
||||
"accuracy_change": optimized_metrics["accuracy"] - baseline_metrics["accuracy"],
|
||||
"latency_speedup": baseline_metrics["latency_ms"] / optimized_metrics["latency_ms"],
|
||||
"memory_reduction": baseline_metrics["memory_mb"] / optimized_metrics["memory_mb"],
|
||||
}
|
||||
|
||||
optimized_for_norm = {
|
||||
"latency": optimized_metrics["latency_ms"],
|
||||
"memory": optimized_metrics["memory_mb"],
|
||||
"accuracy": optimized_metrics["accuracy"]
|
||||
}
|
||||
|
||||
normalized_scores = calculate_normalized_scores(baseline_for_norm, optimized_for_norm)
|
||||
|
||||
# Create submission with all required fields
|
||||
# Create submission
|
||||
submission = {
|
||||
"division": division,
|
||||
"event": event,
|
||||
"athlete_name": athlete_name,
|
||||
"github_repo": github_repo,
|
||||
"baseline": baseline_metrics,
|
||||
"optimized": optimized_metrics,
|
||||
"normalized_scores": {
|
||||
"speedup": normalized_scores["speedup"],
|
||||
"compression_ratio": normalized_scores["compression_ratio"],
|
||||
"accuracy_delta": normalized_scores["accuracy_delta"],
|
||||
"efficiency_score": normalized_scores["efficiency_score"]
|
||||
},
|
||||
"techniques_applied": techniques or ["TODO: Document your optimization techniques"],
|
||||
"improvements": improvements,
|
||||
"techniques_applied": techniques or ["TODO: List your techniques"],
|
||||
"timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
|
||||
"tinytorch_version": "0.1.0",
|
||||
"honor_code": False # Must be explicitly set to True after validation
|
||||
}
|
||||
|
||||
# Validate submission
|
||||
print("\n🔍 Validating submission...")
|
||||
validation = validate_submission(submission)
|
||||
|
||||
# Display validation results
|
||||
print("\n📋 Validation Results:")
|
||||
for check in validation["checks"]:
|
||||
print(f" {check}")
|
||||
for warning in validation["warnings"]:
|
||||
print(f" {warning}")
|
||||
for error in validation["errors"]:
|
||||
print(f" {error}")
|
||||
|
||||
if not validation["valid"]:
|
||||
print("\n❌ Submission has errors - please fix before submitting")
|
||||
return submission
|
||||
|
||||
# Save to JSON
|
||||
output_file = Path("submission.json")
|
||||
with open(output_file, "w") as f:
|
||||
json.dump(submission, f, indent=2)
|
||||
|
||||
print(f"\n✅ Submission saved to: {output_file}")
|
||||
print(f"✅ Submission saved to: {output_file}")
|
||||
print()
|
||||
print("📊 Your Normalized Scores (MLPerf-style):")
|
||||
print(f" Division: {division.upper()}")
|
||||
print(f" Event: {event.replace('_', ' ').title()}")
|
||||
print(f" Speedup: {normalized_scores['speedup']:.2f}x faster ⚡")
|
||||
print(f" Compression: {normalized_scores['compression_ratio']:.2f}x smaller 💾")
|
||||
print(f" Accuracy: {optimized_metrics['accuracy']:.1f}% (Δ {normalized_scores['accuracy_delta']:+.2f}pp)")
|
||||
print(f" Efficiency: {normalized_scores['efficiency_score']:.2f}")
|
||||
print()
|
||||
print("📤 Next Steps:")
|
||||
print(" 1. Verify all metrics are correct")
|
||||
print(" 2. Push your code to GitHub (if not done)")
|
||||
print(" 3. Run: tito submit submission.json")
|
||||
print(" (This will validate and prepare final submission)")
|
||||
print("📊 Your Results:")
|
||||
print(f" Event: {event}")
|
||||
print(f" Accuracy: {optimized_metrics['accuracy']:.1f}% (Δ {improvements['accuracy_change']:+.1f}pp)")
|
||||
print(f" Latency: {optimized_metrics['latency_ms']:.1f}ms ({improvements['latency_speedup']:.2f}x faster)")
|
||||
print(f" Memory: {optimized_metrics['memory_mb']:.2f}MB ({improvements['memory_reduction']:.2f}x smaller)")
|
||||
print()
|
||||
print("📤 Upload submission.json to TorchPerf Olympics platform!")
|
||||
print("=" * 70)
|
||||
|
||||
return submission
|
||||
|
||||
59
modules/source/20_competition/module.yaml
Normal file
59
modules/source/20_competition/module.yaml
Normal file
@@ -0,0 +1,59 @@
|
||||
name: "Competition & Validation"
|
||||
module_number: "20"
|
||||
description: "TorchPerf Olympics preparation - validation, baseline, and competition submission"
|
||||
difficulty: "⭐⭐⭐" # 3 stars - capstone integration
|
||||
estimated_time: "1-2 hours"
|
||||
|
||||
prerequisites:
|
||||
- "Module 19: Benchmarking"
|
||||
- "Modules 14-18: Optimization techniques"
|
||||
|
||||
learning_objectives:
|
||||
- "Validate TinyTorch installation and environment"
|
||||
- "Generate baseline performance metrics"
|
||||
- "Understand complete optimization workflow"
|
||||
- "Create standardized competition submissions"
|
||||
|
||||
key_concepts:
|
||||
- "System validation and environment checks"
|
||||
- "Baseline generation and reference metrics"
|
||||
- "End-to-end optimization workflow"
|
||||
- "Competition submission format"
|
||||
|
||||
skills_developed:
|
||||
- "Systematic validation and testing"
|
||||
- "Performance measurement and comparison"
|
||||
- "Integration of multiple optimization techniques"
|
||||
- "Professional submission preparation"
|
||||
|
||||
exports_to: "tinytorch/competition/submit.py"
|
||||
|
||||
test_coverage:
|
||||
- "Installation validation"
|
||||
- "Baseline generation"
|
||||
- "Worked example workflow"
|
||||
- "Competition template structure"
|
||||
|
||||
connections:
|
||||
builds_on:
|
||||
- "Module 19 for benchmarking tools"
|
||||
- "Modules 14-18 for optimization techniques"
|
||||
enables:
|
||||
- "TorchPerf Olympics competition participation"
|
||||
- "Systematic performance optimization"
|
||||
- "Professional ML systems workflow"
|
||||
|
||||
notes: |
|
||||
This is the capstone module that brings together all previous modules.
|
||||
It's lightweight (no new techniques) but shows the complete workflow from
|
||||
validation through optimization to submission.
|
||||
|
||||
Students learn:
|
||||
1. How to validate their environment works
|
||||
2. What baseline performance looks like
|
||||
3. How to apply optimizations systematically
|
||||
4. How to package work for competition
|
||||
|
||||
The module includes a complete worked example and a template for students
|
||||
to implement their own optimization strategies.
|
||||
|
||||
Reference in New Issue
Block a user