mirror of
https://github.com/MLSysBook/TinyTorch.git
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f4fdf968c5
- tinytorch/benchmarking/: Benchmark class for Module 19 - tinytorch/competition/: Submission utilities for Module 20 - tinytorch/data/: Data loading utilities - tinytorch/utils/data/: Additional data helpers Exported from modules 19-20 and module 08
643 lines
25 KiB
Python
Generated
643 lines
25 KiB
Python
Generated
# ╔═══════════════════════════════════════════════════════════════════════════════╗
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# ║ 🚨 CRITICAL WARNING 🚨 ║
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# ║ AUTOGENERATED! DO NOT EDIT! ║
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# ║ ║
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# ║ This file is AUTOMATICALLY GENERATED from source modules. ║
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# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║
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# ║ ║
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# ║ ✅ TO EDIT: modules/source/XX_submit/submit_dev.py ║
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# ║ ✅ TO EXPORT: Run 'tito module complete <module_name>' ║
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# ║ ║
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# ║ 🛡️ STUDENT PROTECTION: This file contains optimized implementations. ║
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# ║ Editing it directly may break module functionality and training. ║
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# ║ ║
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# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║
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# ║ happens! The tinytorch/ directory is just the compiled output. ║
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# ╚═══════════════════════════════════════════════════════════════════════════════╝
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# %% auto 0
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__all__ = ['validate_installation', 'load_baseline_model', 'generate_baseline', 'worked_example_optimization',
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'optimize_for_competition', 'validate_submission', 'generate_submission']
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# %% ../../modules/source/20_competition/competition_dev.ipynb 4
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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 ..benchmarking.benchmark import Benchmark, calculate_normalized_scores
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from ..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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# %% ../../modules/source/20_competition/competition_dev.ipynb 6
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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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# %% ../../modules/source/20_competition/competition_dev.ipynb 8
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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 ✅)")
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print(f" Parameters: {baseline_metrics['parameters']:,} → {optimized_metrics['parameters']:,} (60% fewer ✅)")
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print()
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# Step 5: Generate Submission
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print("📤 Step 5: Generate Competition Submission")
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print("-" * 70)
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submission = {
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"event": "all_around",
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"athlete_name": "Example_Submission",
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"baseline": baseline_metrics,
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"optimized": optimized_metrics,
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"improvements": {
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"accuracy_drop": -1.5,
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"latency_speedup": 2.0,
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"memory_reduction": 10.0
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},
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"techniques_applied": ["quantization_int8", "magnitude_prune_0.6"],
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"technique_order": "quantize_first_then_prune"
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}
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print("✅ Submission generated!")
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print(f" Event: {submission['event']}")
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print(f" Techniques: {', '.join(submission['techniques_applied'])}")
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print()
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print("=" * 70)
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print("🎯 This is the complete workflow!")
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print(" Now it's your turn to implement your own optimization strategy.")
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print("=" * 70)
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return submission
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# %% ../../modules/source/20_competition/competition_dev.ipynb 10
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def optimize_for_competition(baseline_model, event: str = "all_around", division: str = "closed"):
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"""
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🏅 YOUR COMPETITION ENTRY - IMPLEMENT YOUR STRATEGY HERE!
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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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- "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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from tinytorch.optimization.quantization import quantize_model
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from tinytorch.optimization.compression import magnitude_prune
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optimized = baseline_model
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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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"""
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print(f"🏅 YOUR OPTIMIZATION STRATEGY FOR: {event}")
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print("=" * 70)
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# Start with baseline
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optimized_model = baseline_model
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# ============================================================
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# YOUR CODE BELOW - Apply optimization techniques here!
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# ============================================================
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# TODO: Students implement their optimization strategy
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#
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# Example strategies by event:
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#
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# Latency Sprint (speed priority):
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# - Heavy quantization (INT4 or INT8)
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# - Aggressive pruning (80-90%)
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# - Kernel fusion if applicable
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#
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# Memory Challenge (size priority):
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# - INT8 or INT4 quantization
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# - Aggressive pruning (70-90%)
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# - Compression techniques
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#
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# All-Around (balanced):
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# - INT8 quantization
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# - Moderate pruning (50-70%)
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# - Selective optimization
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#
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# Your strategy:
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# ============================================================
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# YOUR CODE ABOVE
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# ============================================================
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print("✅ Optimization complete!")
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print("💡 Tip: Benchmark your result to see the impact!")
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return optimized_model
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#| export
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def validate_submission(submission: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Validate competition submission with sanity checks.
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This catches honest mistakes like unrealistic speedups or accidental training.
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Honor code system - we trust but verify basic reasonableness.
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Args:
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submission: Submission dictionary to validate
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Returns:
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Dict with validation results and warnings
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"""
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checks = []
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warnings = []
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errors = []
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# Extract metrics
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normalized = submission.get("normalized_scores", {})
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speedup = normalized.get("speedup", 1.0)
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compression = normalized.get("compression_ratio", 1.0)
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accuracy_delta = normalized.get("accuracy_delta", 0.0)
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# Check 1: Speedup is reasonable (not claiming impossible gains)
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if speedup > 50:
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errors.append(f"❌ Speedup {speedup:.1f}x seems unrealistic (>50x)")
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elif speedup > 20:
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warnings.append(f"⚠️ Speedup {speedup:.1f}x is very high - please verify measurements")
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else:
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checks.append(f"✅ Speedup {speedup:.2f}x is reasonable")
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# Check 2: Compression is reasonable
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if compression > 32:
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errors.append(f"❌ Compression {compression:.1f}x seems unrealistic (>32x)")
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elif compression > 16:
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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
|