mirror of
https://github.com/MLSysBook/TinyTorch.git
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863cde8e1a
- Import calculate_normalized_scores from Module 19 for fair comparison - Implement validate_submission() with sanity checks for submissions - Check for reasonable speedup (<50x), compression (<32x), accuracy preservation - Verify GitHub repo and required fields are present - Update generate_submission() to use normalized MLPerf-style scoring - Add division parameter for Closed/Open Division tracking - Include github_repo and honor_code fields in submission - Display normalized scores: speedup, compression ratio, accuracy delta - Guide students to use 'tito submit' for final submission workflow
977 lines
34 KiB
Python
977 lines
34 KiB
Python
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# ---
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#| default_exp competition.submit
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# %% [markdown]
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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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## 🔗 Your Journey
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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 20: Compete in TinyMLPerf! 🏅
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```
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## 🏅 TinyMLPerf: Two Ways to Compete
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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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### 🔒 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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**Best for:** Most students - clear rules, fair comparison
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**Focus:** Your optimization skills
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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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**Best for:** Advanced students who want more creative freedom
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**Focus:** Your systems innovations
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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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"""
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# %% [markdown]
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"""
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## 📦 Where This Code Lives in the Final Package
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**Learning Side:** You work in `modules/20_competition/competition_dev.py`
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**Building Side:** Code exports to `tinytorch.competition.submit`
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```python
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# Validation and baseline tools:
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from tinytorch.competition.submit import validate_installation, generate_baseline
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# Competition helpers:
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from tinytorch.competition.submit import load_baseline_model, generate_submission
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```
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**Why this matters:**
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- **Validation:** Ensures your TinyTorch installation works correctly
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- **Baseline:** Establishes reference performance for fair comparison
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- **Competition:** Provides standardized framework for submissions
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- **Integration:** Brings together all 19 modules into one complete workflow
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"""
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# %% [markdown]
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"""
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# 1. Pick Your Track & Validate
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Before competing, choose your track and make sure your TinyTorch installation works!
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## Two Tracks, Two Styles
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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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### 🔓 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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**Can I do both?** Absolutely! Submit to both tracks.
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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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## Quick 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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"""
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# %%
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#| export
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import numpy as np
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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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Validate TinyTorch installation and return status of each component.
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Returns:
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Dictionary mapping module names to validation status (True = working)
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Example:
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>>> status = validate_installation()
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>>> print(status)
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{'tensor': True, 'autograd': True, 'layers': True, ...}
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"""
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validation_results = {}
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print("🔧 Validating TinyTorch Installation...")
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print("=" * 60)
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# Core modules (M01-13)
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core_modules = [
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("tensor", "tinytorch.core.tensor", "Tensor"),
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("autograd", "tinytorch.core.autograd", "enable_autograd"),
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("layers", "tinytorch.core.layers", "Linear"),
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("activations", "tinytorch.core.activations", "ReLU"),
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("losses", "tinytorch.core.training", "MSELoss"),
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("optimizers", "tinytorch.core.optimizers", "SGD"),
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("spatial", "tinytorch.core.spatial", "Conv2d"),
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("attention", "tinytorch.core.attention", "MultiHeadAttention"),
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("transformers", "tinytorch.models.transformer", "GPT"),
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]
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for name, module_path, class_name in core_modules:
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try:
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exec(f"from {module_path} import {class_name}")
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validation_results[name] = True
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print(f"✅ {name.capitalize()}: Working")
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except Exception as e:
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validation_results[name] = False
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print(f"❌ {name.capitalize()}: Failed - {str(e)}")
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# Optimization modules (M14-18)
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opt_modules = [
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("kv_caching", "tinytorch.generation.kv_cache", "enable_kv_cache"),
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("profiling", "tinytorch.profiling.profiler", "Profiler"),
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("quantization", "tinytorch.optimization.quantization", "quantize_model"),
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("compression", "tinytorch.optimization.compression", "magnitude_prune"),
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]
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for name, module_path, func_name in opt_modules:
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try:
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exec(f"from {module_path} import {func_name}")
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validation_results[name] = True
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print(f"✅ {name.replace('_', ' ').capitalize()}: Working")
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except Exception as e:
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validation_results[name] = False
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print(f"❌ {name.replace('_', ' ').capitalize()}: Failed - {str(e)}")
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# Benchmarking (M19)
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try:
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from tinytorch.benchmarking.benchmark import Benchmark, OlympicEvent
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validation_results["benchmarking"] = True
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print(f"✅ Benchmarking: Working")
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except Exception as e:
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validation_results["benchmarking"] = False
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print(f"❌ Benchmarking: Failed - {str(e)}")
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print("=" * 60)
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# Summary
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total = len(validation_results)
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working = sum(validation_results.values())
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if working == total:
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print(f"🎉 Perfect! All {total}/{total} modules working!")
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print("✅ You're ready to compete in TorchPerf Olympics!")
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else:
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print(f"⚠️ {working}/{total} modules working")
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print(f"❌ {total - working} modules need attention")
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print("\nPlease run: pip install -e . (in TinyTorch root)")
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return validation_results
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# %% [markdown]
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"""
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# 2. The Baseline (For Closed Division)
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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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## Baseline Model: Simple CNN on CIFAR-10
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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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**Closed Division:** Optimize THIS model using M14-18 techniques
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**Open Division:** Build/modify whatever you want!
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### Baseline Components
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1. **Model:** Standard CNN (no optimizations)
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2. **Metrics:** Accuracy, latency, memory, parameters
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3. **Test Data:** CIFAR-10 test set (standardized)
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4. **Hardware:** Your local machine (reported for reproducibility)
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The baseline establishes what "unoptimized" looks like. Your job: beat it!
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"""
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# %%
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#| export
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def load_baseline_model(model_name: str = "cifar10_cnn"):
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"""
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Load a baseline model for TorchPerf Olympics competition.
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Args:
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model_name: Name of baseline model to load
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- "cifar10_cnn": Simple CNN for CIFAR-10 classification
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Returns:
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Baseline model instance
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Example:
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>>> model = load_baseline_model("cifar10_cnn")
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>>> print(f"Parameters: {sum(p.size for p in model.parameters())}")
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"""
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from tinytorch.core.layers import Linear
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from tinytorch.core.spatial import Conv2d, MaxPool2d, Flatten
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from tinytorch.core.activations import ReLU
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if model_name == "cifar10_cnn":
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# Simple CNN: Conv -> Pool -> Conv -> Pool -> FC -> FC
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class BaselineCNN:
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def __init__(self):
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self.name = "Baseline_CIFAR10_CNN"
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# Convolutional layers
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self.conv1 = Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1)
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self.relu1 = ReLU()
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self.pool1 = MaxPool2d(kernel_size=2, stride=2)
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self.conv2 = Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1)
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self.relu2 = ReLU()
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self.pool2 = MaxPool2d(kernel_size=2, stride=2)
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# Fully connected layers
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self.flatten = Flatten()
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self.fc1 = Linear(64 * 8 * 8, 128)
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self.relu3 = ReLU()
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self.fc2 = Linear(128, 10) # 10 classes for CIFAR-10
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def forward(self, x):
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# Forward pass
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x = self.conv1.forward(x)
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x = self.relu1.forward(x)
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x = self.pool1.forward(x)
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x = self.conv2.forward(x)
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x = self.relu2.forward(x)
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x = self.pool2.forward(x)
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x = self.flatten.forward(x)
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x = self.fc1.forward(x)
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x = self.relu3.forward(x)
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x = self.fc2.forward(x)
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return x
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def __call__(self, x):
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return self.forward(x)
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return BaselineCNN()
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else:
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raise ValueError(f"Unknown baseline model: {model_name}")
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def generate_baseline(model_name: str = "cifar10_cnn", quick: bool = True) -> Dict[str, Any]:
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"""
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Generate baseline performance metrics for a model.
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Args:
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model_name: Name of baseline model
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quick: If True, use quick estimates instead of full benchmarks
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Returns:
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Baseline scorecard with metrics
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Example:
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>>> baseline = generate_baseline("cifar10_cnn", quick=True)
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>>> print(f"Baseline latency: {baseline['latency_ms']}ms")
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"""
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print("📊 Generating Baseline Scorecard...")
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print("=" * 60)
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# Load model
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model = load_baseline_model(model_name)
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print(f"✅ Loaded baseline model: {model.name}")
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# Count parameters
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def count_parameters(model):
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total = 0
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for attr_name in dir(model):
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attr = getattr(model, attr_name)
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if hasattr(attr, 'weights') and attr.weights is not None:
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total += attr.weights.size
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if hasattr(attr, 'bias') and attr.bias is not None:
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total += attr.bias.size
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return total
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params = count_parameters(model)
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memory_mb = params * 4 / (1024 * 1024) # Assuming float32
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if quick:
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# Quick estimates for fast validation
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print("⚡ Using quick estimates (set quick=False for full benchmark)")
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baseline = {
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"model": model_name,
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"accuracy": 85.0, # Typical for this architecture
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"latency_ms": 45.2,
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"memory_mb": memory_mb,
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"parameters": params,
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"mode": "quick_estimate"
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}
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else:
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# Full benchmark (requires more time)
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from tinytorch.benchmarking.benchmark import Benchmark
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print("🔬 Running full benchmark (this may take a minute)...")
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benchmark = Benchmark([model], [{"name": "baseline"}],
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warmup_runs=5, measurement_runs=20)
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# Measure latency
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input_shape = (1, 3, 32, 32) # CIFAR-10 input
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latency_results = benchmark.run_latency_benchmark(input_shape=input_shape)
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latency_ms = list(latency_results.values())[0].mean * 1000
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baseline = {
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"model": model_name,
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"accuracy": 85.0, # Would need actual test set evaluation
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"latency_ms": latency_ms,
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"memory_mb": memory_mb,
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"parameters": params,
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"mode": "full_benchmark"
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}
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# Display baseline
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print("\n📋 BASELINE SCORECARD")
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print("=" * 60)
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print(f"Model: {baseline['model']}")
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print(f"Accuracy: {baseline['accuracy']:.1f}%")
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print(f"Latency: {baseline['latency_ms']:.1f}ms")
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print(f"Memory: {baseline['memory_mb']:.2f}MB")
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print(f"Parameters: {baseline['parameters']:,}")
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print("=" * 60)
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print("📌 This is your starting point. Optimize to compete!")
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print()
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return baseline
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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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Let's see complete examples for BOTH competition tracks!
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## Example 1: Closed Division - Optimization Master
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**Goal:** Compete in All-Around category using provided baseline
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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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**Why this order?** Quantize first preserves more accuracy than pruning first.
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## Example 2: Open Division - Innovation Master
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**Goal:** Beat everyone with a novel approach
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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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"""
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# %%
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#| export
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def worked_example_optimization():
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"""
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Complete worked example showing full optimization workflow.
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This demonstrates:
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- Loading baseline model
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- Applying multiple optimization techniques
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- Benchmarking systematically
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- Generating submission
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Students should study this and adapt for their own strategies!
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"""
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print("🏅 WORKED EXAMPLE: Complete Optimization Workflow")
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print("=" * 70)
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print("Target: All-Around Event (balanced performance)")
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print("Strategy: Quantization (INT8) → Pruning (60%)")
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print("=" * 70)
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print()
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# Step 1: Load Baseline
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print("📦 Step 1: Load Baseline Model")
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print("-" * 70)
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baseline = load_baseline_model("cifar10_cnn")
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baseline_metrics = generate_baseline("cifar10_cnn", quick=True)
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print()
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# Step 2: Apply Quantization
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print("🔧 Step 2: Apply INT8 Quantization (Module 17)")
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print("-" * 70)
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print("💡 Why quantize? Reduces memory 4x (FP32 → INT8)")
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# For demonstration, we'll simulate quantization
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# In real competition, students would use:
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# from tinytorch.optimization.quantization import quantize_model
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# optimized = quantize_model(baseline, bits=8)
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print("✅ Quantized model (simulated)")
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print(" - Memory: 12.4MB → 3.1MB (4x reduction)")
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print()
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# Step 3: Apply Pruning
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print("✂️ Step 3: Apply Magnitude Pruning (Module 18)")
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print("-" * 70)
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print("💡 Why prune? Removes 60% of weights for faster inference")
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# For demonstration, we'll simulate pruning
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# In real competition, students would use:
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# from tinytorch.optimization.compression import magnitude_prune
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# optimized = magnitude_prune(optimized, sparsity=0.6)
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print("✅ Pruned model (simulated)")
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print(" - Active parameters: 3.2M → 1.28M (60% removed)")
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print()
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# Step 4: Benchmark Results
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print("📊 Step 4: Benchmark Optimized Model (Module 19)")
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print("-" * 70)
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# Simulated optimized metrics
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optimized_metrics = {
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"model": "Optimized_CIFAR10_CNN",
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"accuracy": 83.5, # Slight drop from aggressive optimization
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"latency_ms": 22.1,
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"memory_mb": 1.24, # 4x quantization + 60% pruning
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"parameters": 1280000,
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"techniques": ["quantization_int8", "magnitude_prune_0.6"]
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}
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print("Baseline vs Optimized:")
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print(f" Accuracy: {baseline_metrics['accuracy']:.1f}% → {optimized_metrics['accuracy']:.1f}% (-1.5pp)")
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print(f" Latency: {baseline_metrics['latency_ms']:.1f}ms → {optimized_metrics['latency_ms']:.1f}ms (2.0x faster ✅)")
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print(f" Memory: {baseline_metrics['memory_mb']:.2f}MB → {optimized_metrics['memory_mb']:.2f}MB (10.0x smaller ✅)")
|
|
print(f" Parameters: {baseline_metrics['parameters']:,} → {optimized_metrics['parameters']:,} (60% fewer ✅)")
|
|
print()
|
|
|
|
# Step 5: Generate Submission
|
|
print("📤 Step 5: Generate Competition Submission")
|
|
print("-" * 70)
|
|
|
|
submission = {
|
|
"event": "all_around",
|
|
"athlete_name": "Example_Submission",
|
|
"baseline": baseline_metrics,
|
|
"optimized": optimized_metrics,
|
|
"improvements": {
|
|
"accuracy_drop": -1.5,
|
|
"latency_speedup": 2.0,
|
|
"memory_reduction": 10.0
|
|
},
|
|
"techniques_applied": ["quantization_int8", "magnitude_prune_0.6"],
|
|
"technique_order": "quantize_first_then_prune"
|
|
}
|
|
|
|
print("✅ Submission generated!")
|
|
print(f" Event: {submission['event']}")
|
|
print(f" Techniques: {', '.join(submission['techniques_applied'])}")
|
|
print()
|
|
print("=" * 70)
|
|
print("🎯 This is the complete workflow!")
|
|
print(" Now it's your turn to implement your own optimization strategy.")
|
|
print("=" * 70)
|
|
|
|
return submission
|
|
|
|
# %% [markdown]
|
|
"""
|
|
# 4. Your Turn - Pick Your Track!
|
|
|
|
Now it's time to compete! Choose your track and implement your strategy.
|
|
|
|
## Choose Your Track
|
|
|
|
### 🔒 Closed Division Template
|
|
**If you choose Closed Division:**
|
|
1. Pick a category (Latency Sprint, Memory Challenge, etc.)
|
|
2. Design your optimization strategy
|
|
3. Implement in `optimize_for_competition()` below
|
|
4. Use techniques from Modules 14-18 only
|
|
5. Generate submission
|
|
|
|
**Good for:** Clear path, fair comparison, most students
|
|
|
|
### 🔓 Open Division Template
|
|
**If you choose Open Division:**
|
|
1. Pick a category
|
|
2. Modify YOUR TinyTorch implementations (go edit earlier modules!)
|
|
3. OR design novel architectures
|
|
4. Re-export with `tito export` and benchmark
|
|
5. Generate submission
|
|
|
|
**Good for:** Creative freedom, systems innovation, advanced students
|
|
|
|
## Competition Categories (Pick ONE)
|
|
- 🏃 **Latency Sprint:** Fastest inference
|
|
- 🏋️ **Memory Challenge:** Smallest model
|
|
- 🎯 **Accuracy Contest:** Best accuracy within constraints
|
|
- 🏋️♂️ **All-Around:** Best balanced performance
|
|
- 🚀 **Extreme Push:** Most aggressive optimization
|
|
|
|
## Template Below
|
|
|
|
Use the `optimize_for_competition()` function to implement your strategy:
|
|
- **Closed Division:** Apply M14-18 techniques
|
|
- **Open Division:** Do whatever you want, document it!
|
|
"""
|
|
|
|
# %%
|
|
#| export
|
|
def optimize_for_competition(baseline_model, event: str = "all_around", division: str = "closed"):
|
|
"""
|
|
🏅 YOUR COMPETITION ENTRY - IMPLEMENT YOUR STRATEGY HERE!
|
|
|
|
Args:
|
|
baseline_model: Starting model (use for Closed, optional for Open)
|
|
event: Category you're competing in
|
|
- "latency_sprint": Minimize latency
|
|
- "memory_challenge": Minimize memory
|
|
- "accuracy_contest": Maximize accuracy
|
|
- "all_around": Best balance
|
|
- "extreme_push": Most aggressive
|
|
division: "closed" or "open" - which track you chose
|
|
|
|
Returns:
|
|
Your optimized model
|
|
|
|
🔒 CLOSED DIVISION Example:
|
|
from tinytorch.optimization.quantization import quantize_model
|
|
from tinytorch.optimization.compression import magnitude_prune
|
|
|
|
optimized = baseline_model
|
|
optimized = quantize_model(optimized, bits=8)
|
|
optimized = magnitude_prune(optimized, sparsity=0.7)
|
|
return optimized
|
|
|
|
🔓 OPEN DIVISION Example:
|
|
# Build your own model OR
|
|
# Use your improved implementations from earlier modules
|
|
# (after you've modified and re-exported them)
|
|
|
|
from tinytorch.models import YourCustomArchitecture
|
|
optimized = YourCustomArchitecture()
|
|
return optimized
|
|
"""
|
|
|
|
print(f"🏅 YOUR OPTIMIZATION STRATEGY FOR: {event}")
|
|
print("=" * 70)
|
|
|
|
# Start with baseline
|
|
optimized_model = baseline_model
|
|
|
|
# ============================================================
|
|
# YOUR CODE BELOW - Apply optimization techniques here!
|
|
# ============================================================
|
|
|
|
# TODO: Students implement their optimization strategy
|
|
#
|
|
# Example strategies by event:
|
|
#
|
|
# Latency Sprint (speed priority):
|
|
# - Heavy quantization (INT4 or INT8)
|
|
# - Aggressive pruning (80-90%)
|
|
# - Kernel fusion if applicable
|
|
#
|
|
# Memory Challenge (size priority):
|
|
# - INT8 or INT4 quantization
|
|
# - Aggressive pruning (70-90%)
|
|
# - Compression techniques
|
|
#
|
|
# All-Around (balanced):
|
|
# - INT8 quantization
|
|
# - Moderate pruning (50-70%)
|
|
# - Selective optimization
|
|
#
|
|
# Your strategy:
|
|
|
|
|
|
|
|
# ============================================================
|
|
# YOUR CODE ABOVE
|
|
# ============================================================
|
|
|
|
print("✅ Optimization complete!")
|
|
print("💡 Tip: Benchmark your result to see the impact!")
|
|
|
|
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.
|
|
|
|
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
|
|
|
|
Returns:
|
|
Submission dictionary (will be saved as JSON)
|
|
"""
|
|
print("📤 Generating TinyMLPerf Competition Submission...")
|
|
print("=" * 70)
|
|
|
|
# Get baseline metrics
|
|
baseline_metrics = generate_baseline(quick=True)
|
|
|
|
# Benchmark 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
|
|
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
|
|
}
|
|
|
|
# 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"]
|
|
}
|
|
|
|
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
|
|
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"],
|
|
"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()
|
|
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()
|
|
print("=" * 70)
|
|
|
|
return submission
|
|
|
|
# %% [markdown]
|
|
"""
|
|
# 5. Module Integration Test
|
|
|
|
Complete validation and competition workflow test.
|
|
"""
|
|
|
|
# %% nbgrader={"grade": true, "grade_id": "test-module", "locked": true, "points": 10}
|
|
def test_module():
|
|
"""
|
|
Complete test of Module 20 functionality.
|
|
|
|
This validates:
|
|
- Installation validation works
|
|
- Baseline generation works
|
|
- Worked example runs successfully
|
|
- Competition template is ready
|
|
"""
|
|
print("=" * 70)
|
|
print("MODULE 20 INTEGRATION TEST")
|
|
print("=" * 70)
|
|
print()
|
|
|
|
# Test 1: Validation
|
|
print("🔧 Test 1: System Validation")
|
|
validation_status = validate_installation()
|
|
assert len(validation_status) > 0, "Validation should return status dict"
|
|
print("✅ Validation working!")
|
|
print()
|
|
|
|
# Test 2: Baseline Generation
|
|
print("📊 Test 2: Baseline Generation")
|
|
baseline = generate_baseline(quick=True)
|
|
assert "accuracy" in baseline, "Baseline should include accuracy"
|
|
assert "latency_ms" in baseline, "Baseline should include latency"
|
|
assert "memory_mb" in baseline, "Baseline should include memory"
|
|
print("✅ Baseline generation working!")
|
|
print()
|
|
|
|
# Test 3: Worked Example
|
|
print("🏅 Test 3: Worked Example")
|
|
example_submission = worked_example_optimization()
|
|
assert "event" in example_submission, "Submission should include event"
|
|
assert "baseline" in example_submission, "Submission should include baseline"
|
|
assert "optimized" in example_submission, "Submission should include optimized"
|
|
print("✅ Worked example working!")
|
|
print()
|
|
|
|
# Test 4: Competition Template
|
|
print("🎯 Test 4: Competition Template")
|
|
baseline_model = load_baseline_model("cifar10_cnn")
|
|
optimized = optimize_for_competition(baseline_model, event="all_around")
|
|
assert optimized is not None, "Optimization should return model"
|
|
print("✅ Competition template working!")
|
|
print()
|
|
|
|
print("=" * 70)
|
|
print("✅ ALL TESTS PASSED!")
|
|
print("=" * 70)
|
|
print()
|
|
print("🎉 You're ready for TorchPerf Olympics!")
|
|
print(" Next steps:")
|
|
print(" 1. Implement your optimization strategy in optimize_for_competition()")
|
|
print(" 2. Run this module to generate submission.json")
|
|
print(" 3. Upload to competition platform")
|
|
print()
|
|
print("🔥 Good luck! May the best optimizer win! 🏅")
|
|
|
|
test_module()
|
|
|
|
# %% [markdown]
|
|
"""
|
|
## 🤔 ML Systems Thinking: Competition as Learning
|
|
|
|
TorchPerf Olympics isn't just about winning - it's about understanding trade-offs:
|
|
|
|
**The Meta-Lesson**: Every optimization involves trade-offs:
|
|
- Quantization: Speed vs Accuracy
|
|
- Pruning: Size vs Performance
|
|
- Caching: Memory vs Speed
|
|
|
|
Professional ML engineers navigate these trade-offs daily. The competition forces you to:
|
|
1. **Think systematically** about optimization strategies
|
|
2. **Measure rigorously** using benchmarking tools
|
|
3. **Make data-driven decisions** based on actual measurements
|
|
4. **Document and justify** your choices
|
|
|
|
The best submission isn't always the "fastest" or "smallest" - it's the one that best understands and navigates the trade-off space for their chosen event.
|
|
|
|
What will your strategy be? 🤔
|
|
"""
|
|
|
|
# %% [markdown]
|
|
"""
|
|
## 🎯 MODULE SUMMARY: Competition & Validation
|
|
|
|
**What You've Learned:**
|
|
- ✅ How to validate your TinyTorch installation
|
|
- ✅ How to generate baseline performance metrics
|
|
- ✅ How to combine optimization techniques systematically
|
|
- ✅ How to benchmark and measure impact
|
|
- ✅ How to generate standardized competition submissions
|
|
|
|
**The Complete Workflow:**
|
|
```
|
|
1. Validate → Ensure environment works
|
|
2. Baseline → Establish reference performance
|
|
3. Optimize → Apply techniques from M14-18
|
|
4. Benchmark → Measure impact using M19
|
|
5. Submit → Generate standardized submission
|
|
```
|
|
|
|
**Key Takeaway**: Competition teaches systematic optimization thinking. The goal isn't just winning - it's understanding the entire optimization process from baseline to submission.
|
|
|
|
**Next Steps:**
|
|
1. Study the worked example
|
|
2. Implement your own optimization strategy
|
|
3. Benchmark your results
|
|
4. Generate submission.json
|
|
5. Compete in TorchPerf Olympics!
|
|
|
|
🔥 Now go optimize and win gold! 🏅
|
|
"""
|
|
|