mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-03-11 22:03:34 -05:00
96880b3133
Re-exported all modules after restructuring: - Updated _modidx.py with new module locations - Removed outdated autogeneration headers - Updated all core modules (tensor, autograd, layers, etc.) - Updated optimization modules (quantization, compression, etc.) - Updated TITO commands for new structure Changes include: - 24 tinytorch/ module files - 24 tito/ command and core files - Updated references from modules/source/ to modules/ All modules re-exported via nbdev from their new locations.
1063 lines
44 KiB
Python
Generated
1063 lines
44 KiB
Python
Generated
# AUTOGENERATED! DO NOT EDIT! File to edit: ../../modules/source/19_benchmarking/benchmarking_dev.ipynb.
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# %% auto 0
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__all__ = ['OlympicEvent', 'Benchmark', 'test_unit_benchmark', 'BenchmarkSuite', 'test_unit_benchmark_suite', 'TinyMLPerf',
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'test_unit_tinymlperf', 'calculate_normalized_scores']
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# %% ../../modules/source/19_benchmarking/benchmarking_dev.ipynb 0
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#| default_exp benchmarking.benchmark
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#| export
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# %% ../../modules/source/19_benchmarking/benchmarking_dev.ipynb 6
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from enum import Enum
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class OlympicEvent(Enum):
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"""
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TorchPerf Olympics event categories.
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Each event optimizes for different objectives with specific constraints.
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Students choose their event and compete for medals!
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"""
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LATENCY_SPRINT = "latency_sprint" # Minimize latency (accuracy >= 85%)
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MEMORY_CHALLENGE = "memory_challenge" # Minimize memory (accuracy >= 85%)
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ACCURACY_CONTEST = "accuracy_contest" # Maximize accuracy (latency < 100ms, memory < 10MB)
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ALL_AROUND = "all_around" # Best balanced score across all metrics
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EXTREME_PUSH = "extreme_push" # Most aggressive optimization (accuracy >= 80%)
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# %% ../../modules/source/19_benchmarking/benchmarking_dev.ipynb 13
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class Benchmark:
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"""
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Professional benchmarking system for ML models and operations.
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TODO: Implement a comprehensive benchmark runner with statistical rigor
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APPROACH:
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1. Support multiple models, datasets, and metrics
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2. Run repeated measurements with proper warmup
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3. Control for system variance and compute confidence intervals
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4. Generate structured results for analysis
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EXAMPLE:
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>>> benchmark = Benchmark(models=[model1, model2], datasets=[test_data])
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>>> results = benchmark.run_accuracy_benchmark()
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>>> benchmark.plot_results(results)
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HINTS:
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- Use warmup runs to stabilize performance
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- Collect multiple samples for statistical significance
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- Store metadata about system conditions
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- Provide different benchmark types (accuracy, latency, memory)
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"""
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### BEGIN SOLUTION
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def __init__(self, models: List[Any], datasets: List[Any],
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warmup_runs: int = 5, measurement_runs: int = 10):
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"""Initialize benchmark with models and datasets."""
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self.models = models
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self.datasets = datasets
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self.warmup_runs = warmup_runs
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self.measurement_runs = measurement_runs
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self.results = {}
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# Use Profiler from Module 15 for measurements
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self.profiler = Profiler()
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# System information for metadata
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self.system_info = {
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'platform': platform.platform(),
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'processor': platform.processor(),
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'python_version': platform.python_version(),
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'memory_gb': psutil.virtual_memory().total / (1024**3),
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'cpu_count': psutil.cpu_count()
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}
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def run_latency_benchmark(self, input_shape: Tuple[int, ...] = (1, 28, 28)) -> Dict[str, BenchmarkResult]:
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"""Benchmark model inference latency using Profiler."""
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results = {}
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for i, model in enumerate(self.models):
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model_name = getattr(model, 'name', f'model_{i}')
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# Create input tensor for profiling
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try:
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from tinytorch.core.tensor import Tensor
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input_tensor = Tensor(np.random.randn(*input_shape).astype(np.float32))
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except:
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# Fallback for simple models
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input_tensor = np.random.randn(*input_shape).astype(np.float32)
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# Use Profiler to measure latency with proper warmup and iterations
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try:
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latency_ms = self.profiler.measure_latency(
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model,
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input_tensor,
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warmup=self.warmup_runs,
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iterations=self.measurement_runs
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)
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# Profiler returns single median value
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# For BenchmarkResult, we need multiple measurements
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# Run additional measurements for statistical analysis
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latencies = []
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for _ in range(self.measurement_runs):
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single_latency = self.profiler.measure_latency(
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model, input_tensor, warmup=0, iterations=1
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)
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latencies.append(single_latency)
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except:
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# Fallback: use precise_timer for models that don't support profiler
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latencies = []
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for _ in range(self.measurement_runs):
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with precise_timer() as timer:
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try:
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if hasattr(model, 'forward'):
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model.forward(input_tensor)
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elif hasattr(model, 'predict'):
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model.predict(input_tensor)
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elif callable(model):
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model(input_tensor)
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else:
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time.sleep(0.001)
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except:
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time.sleep(0.001 + np.random.normal(0, 0.0001))
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latencies.append(timer.elapsed * 1000)
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results[model_name] = BenchmarkResult(
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f"{model_name}_latency_ms",
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latencies,
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metadata={'input_shape': input_shape, **self.system_info}
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)
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return results
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def run_accuracy_benchmark(self) -> Dict[str, BenchmarkResult]:
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"""Benchmark model accuracy across datasets."""
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results = {}
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for i, model in enumerate(self.models):
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model_name = getattr(model, 'name', f'model_{i}')
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accuracies = []
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for dataset in self.datasets:
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# Simulate accuracy measurement
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# In practice, this would evaluate the model on the dataset
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try:
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if hasattr(model, 'evaluate'):
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accuracy = model.evaluate(dataset)
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else:
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# Simulate accuracy for demonstration
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base_accuracy = 0.85 + i * 0.05 # Different models have different base accuracies
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accuracy = base_accuracy + np.random.normal(0, 0.02) # Add noise
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accuracy = max(0.0, min(1.0, accuracy)) # Clamp to [0, 1]
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except:
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# Fallback simulation
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accuracy = 0.80 + np.random.normal(0, 0.05)
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accuracy = max(0.0, min(1.0, accuracy))
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accuracies.append(accuracy)
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results[model_name] = BenchmarkResult(
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f"{model_name}_accuracy",
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accuracies,
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metadata={'num_datasets': len(self.datasets), **self.system_info}
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)
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return results
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def run_memory_benchmark(self, input_shape: Tuple[int, ...] = (1, 28, 28)) -> Dict[str, BenchmarkResult]:
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"""Benchmark model memory usage using Profiler."""
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results = {}
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for i, model in enumerate(self.models):
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model_name = getattr(model, 'name', f'model_{i}')
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memory_usages = []
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for run in range(self.measurement_runs):
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try:
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# Use Profiler to measure memory
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memory_stats = self.profiler.measure_memory(model, input_shape)
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# Use peak_memory_mb as the primary metric
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memory_used = memory_stats['peak_memory_mb']
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except:
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# Fallback: measure with psutil
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process = psutil.Process()
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memory_before = process.memory_info().rss / (1024**2) # MB
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try:
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dummy_input = np.random.randn(*input_shape).astype(np.float32)
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if hasattr(model, 'forward'):
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model.forward(dummy_input)
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elif hasattr(model, 'predict'):
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model.predict(dummy_input)
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elif callable(model):
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model(dummy_input)
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except:
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pass
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memory_after = process.memory_info().rss / (1024**2) # MB
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memory_used = max(0, memory_after - memory_before)
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# If no significant memory change detected, estimate from parameters
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if memory_used < 1.0:
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try:
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param_count = self.profiler.count_parameters(model)
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memory_used = param_count * 4 / (1024**2) # 4 bytes per float32
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except:
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memory_used = 8 + np.random.normal(0, 1) # Default estimate
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memory_usages.append(max(0, memory_used))
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results[model_name] = BenchmarkResult(
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f"{model_name}_memory_mb",
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memory_usages,
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metadata={'input_shape': input_shape, **self.system_info}
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)
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return results
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def compare_models(self, metric: str = "latency") -> pd.DataFrame:
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"""Compare models across a specific metric."""
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if metric == "latency":
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results = self.run_latency_benchmark()
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elif metric == "accuracy":
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results = self.run_accuracy_benchmark()
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elif metric == "memory":
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results = self.run_memory_benchmark()
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else:
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raise ValueError(f"Unknown metric: {metric}")
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# Convert to DataFrame for easy comparison
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comparison_data = []
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for model_name, result in results.items():
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comparison_data.append({
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'model': model_name.replace(f'_{metric}', '').replace('_ms', '').replace('_mb', ''),
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'metric': metric,
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'mean': result.mean,
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'std': result.std,
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'ci_lower': result.ci_lower,
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'ci_upper': result.ci_upper,
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'count': result.count
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})
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return pd.DataFrame(comparison_data)
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### END SOLUTION
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def test_unit_benchmark():
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"""🔬 Test Benchmark class functionality."""
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print("🔬 Unit Test: Benchmark...")
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# Create mock models for testing
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class MockModel:
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def __init__(self, name):
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self.name = name
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def forward(self, x):
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time.sleep(0.001) # Simulate computation
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return x
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models = [MockModel("fast_model"), MockModel("slow_model")]
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datasets = [{"data": "test1"}, {"data": "test2"}]
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benchmark = Benchmark(models, datasets, warmup_runs=2, measurement_runs=3)
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# Test latency benchmark
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latency_results = benchmark.run_latency_benchmark()
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assert len(latency_results) == 2
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assert "fast_model" in latency_results
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assert all(isinstance(result, BenchmarkResult) for result in latency_results.values())
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# Test accuracy benchmark
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accuracy_results = benchmark.run_accuracy_benchmark()
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assert len(accuracy_results) == 2
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assert all(0 <= result.mean <= 1 for result in accuracy_results.values())
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# Test memory benchmark
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memory_results = benchmark.run_memory_benchmark()
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assert len(memory_results) == 2
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assert all(result.mean >= 0 for result in memory_results.values())
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# Test comparison
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comparison_df = benchmark.compare_models("latency")
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assert len(comparison_df) == 2
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assert "model" in comparison_df.columns
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assert "mean" in comparison_df.columns
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print("✅ Benchmark works correctly!")
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test_unit_benchmark()
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# %% ../../modules/source/19_benchmarking/benchmarking_dev.ipynb 15
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class BenchmarkSuite:
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"""
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Comprehensive benchmark suite for ML systems evaluation.
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TODO: Implement a full benchmark suite that runs multiple test categories
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APPROACH:
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1. Combine multiple benchmark types (latency, accuracy, memory, energy)
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2. Generate comprehensive reports with visualizations
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3. Support different model categories and hardware configurations
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4. Provide recommendations based on results
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EXAMPLE:
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>>> suite = BenchmarkSuite(models, datasets)
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>>> report = suite.run_full_benchmark()
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>>> suite.generate_report(report)
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HINTS:
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- Organize results by benchmark type and model
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- Create Pareto frontier analysis for trade-offs
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- Include system information and test conditions
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- Generate actionable insights and recommendations
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"""
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### BEGIN SOLUTION
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def __init__(self, models: List[Any], datasets: List[Any],
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output_dir: str = "benchmark_results"):
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"""Initialize comprehensive benchmark suite."""
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self.models = models
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self.datasets = datasets
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self.output_dir = Path(output_dir)
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self.output_dir.mkdir(exist_ok=True)
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self.benchmark = Benchmark(models, datasets)
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self.results = {}
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def run_full_benchmark(self) -> Dict[str, Dict[str, BenchmarkResult]]:
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"""Run all benchmark categories."""
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print("🔬 Running comprehensive benchmark suite...")
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# Run all benchmark types
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print(" 📊 Measuring latency...")
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self.results['latency'] = self.benchmark.run_latency_benchmark()
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print(" 🎯 Measuring accuracy...")
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self.results['accuracy'] = self.benchmark.run_accuracy_benchmark()
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print(" 💾 Measuring memory usage...")
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self.results['memory'] = self.benchmark.run_memory_benchmark()
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# Simulate energy benchmark (would require specialized hardware)
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print(" ⚡ Estimating energy efficiency...")
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self.results['energy'] = self._estimate_energy_efficiency()
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return self.results
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def _estimate_energy_efficiency(self) -> Dict[str, BenchmarkResult]:
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"""Estimate energy efficiency (simplified simulation)."""
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energy_results = {}
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for i, model in enumerate(self.models):
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model_name = getattr(model, 'name', f'model_{i}')
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# Energy roughly correlates with latency * memory usage
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if 'latency' in self.results and 'memory' in self.results:
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latency_result = self.results['latency'].get(model_name)
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memory_result = self.results['memory'].get(model_name)
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if latency_result and memory_result:
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# Energy ∝ power × time, power ∝ memory usage
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energy_values = []
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for lat, mem in zip(latency_result.values, memory_result.values):
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# Simplified energy model: energy = base + latency_factor * time + memory_factor * memory
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energy = 0.1 + (lat / 1000) * 2.0 + mem * 0.01 # Joules
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energy_values.append(energy)
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energy_results[model_name] = BenchmarkResult(
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f"{model_name}_energy_joules",
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energy_values,
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metadata={'estimated': True, **self.benchmark.system_info}
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)
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# Fallback if no latency/memory results
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if not energy_results:
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for i, model in enumerate(self.models):
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model_name = getattr(model, 'name', f'model_{i}')
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# Simulate energy measurements
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energy_values = [0.5 + np.random.normal(0, 0.1) for _ in range(5)]
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energy_results[model_name] = BenchmarkResult(
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f"{model_name}_energy_joules",
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energy_values,
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metadata={'estimated': True, **self.benchmark.system_info}
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)
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return energy_results
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def plot_results(self, save_plots: bool = True):
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"""Generate visualization plots for benchmark results."""
|
||
if not self.results:
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print("No results to plot. Run benchmark first.")
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||
return
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||
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||
fig, axes = plt.subplots(2, 2, figsize=(15, 12))
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||
fig.suptitle('ML Model Benchmark Results', fontsize=16, fontweight='bold')
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||
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# Plot each metric type
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metrics = ['latency', 'accuracy', 'memory', 'energy']
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units = ['ms', 'accuracy', 'MB', 'J']
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||
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for idx, (metric, unit) in enumerate(zip(metrics, units)):
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ax = axes[idx // 2, idx % 2]
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||
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if metric in self.results:
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model_names = []
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means = []
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||
stds = []
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||
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||
for model_name, result in self.results[metric].items():
|
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clean_name = model_name.replace(f'_{metric}', '').replace('_ms', '').replace('_mb', '').replace('_joules', '')
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||
model_names.append(clean_name)
|
||
means.append(result.mean)
|
||
stds.append(result.std)
|
||
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||
bars = ax.bar(model_names, means, yerr=stds, capsize=5, alpha=0.7)
|
||
ax.set_title(f'{metric.capitalize()} Comparison')
|
||
ax.set_ylabel(f'{metric.capitalize()} ({unit})')
|
||
ax.tick_params(axis='x', rotation=45)
|
||
|
||
# Color bars by performance (green = better)
|
||
if metric in ['latency', 'memory', 'energy']: # Lower is better
|
||
best_idx = means.index(min(means))
|
||
else: # Higher is better (accuracy)
|
||
best_idx = means.index(max(means))
|
||
|
||
for i, bar in enumerate(bars):
|
||
if i == best_idx:
|
||
bar.set_color('green')
|
||
bar.set_alpha(0.8)
|
||
else:
|
||
ax.text(0.5, 0.5, f'No {metric} data', ha='center', va='center', transform=ax.transAxes)
|
||
ax.set_title(f'{metric.capitalize()} Comparison')
|
||
|
||
plt.tight_layout()
|
||
|
||
if save_plots:
|
||
plot_path = self.output_dir / 'benchmark_comparison.png'
|
||
plt.savefig(plot_path, dpi=300, bbox_inches='tight')
|
||
print(f"📊 Plots saved to {plot_path}")
|
||
|
||
plt.show()
|
||
|
||
def plot_pareto_frontier(self, x_metric: str = 'latency', y_metric: str = 'accuracy'):
|
||
"""Plot Pareto frontier for two competing objectives."""
|
||
if x_metric not in self.results or y_metric not in self.results:
|
||
print(f"Missing data for {x_metric} or {y_metric}")
|
||
return
|
||
|
||
plt.figure(figsize=(10, 8))
|
||
|
||
x_values = []
|
||
y_values = []
|
||
model_names = []
|
||
|
||
for model_name in self.results[x_metric].keys():
|
||
clean_name = model_name.replace(f'_{x_metric}', '').replace('_ms', '').replace('_mb', '').replace('_joules', '')
|
||
if clean_name in [mn.replace(f'_{y_metric}', '') for mn in self.results[y_metric].keys()]:
|
||
x_val = self.results[x_metric][model_name].mean
|
||
|
||
# Find corresponding y value
|
||
y_key = None
|
||
for key in self.results[y_metric].keys():
|
||
if clean_name in key:
|
||
y_key = key
|
||
break
|
||
|
||
if y_key:
|
||
y_val = self.results[y_metric][y_key].mean
|
||
x_values.append(x_val)
|
||
y_values.append(y_val)
|
||
model_names.append(clean_name)
|
||
|
||
# Plot points
|
||
plt.scatter(x_values, y_values, s=100, alpha=0.7)
|
||
|
||
# Label points
|
||
for i, name in enumerate(model_names):
|
||
plt.annotate(name, (x_values[i], y_values[i]),
|
||
xytext=(5, 5), textcoords='offset points')
|
||
|
||
# Determine if lower or higher is better for each metric
|
||
x_lower_better = x_metric in ['latency', 'memory', 'energy']
|
||
y_lower_better = y_metric in ['latency', 'memory', 'energy']
|
||
|
||
plt.xlabel(f'{x_metric.capitalize()} ({"lower" if x_lower_better else "higher"} is better)')
|
||
plt.ylabel(f'{y_metric.capitalize()} ({"lower" if y_lower_better else "higher"} is better)')
|
||
plt.title(f'Pareto Frontier: {x_metric.capitalize()} vs {y_metric.capitalize()}')
|
||
plt.grid(True, alpha=0.3)
|
||
|
||
# Save plot
|
||
plot_path = self.output_dir / f'pareto_{x_metric}_vs_{y_metric}.png'
|
||
plt.savefig(plot_path, dpi=300, bbox_inches='tight')
|
||
print(f"📊 Pareto plot saved to {plot_path}")
|
||
plt.show()
|
||
|
||
def generate_report(self) -> str:
|
||
"""Generate comprehensive benchmark report."""
|
||
if not self.results:
|
||
return "No benchmark results available. Run benchmark first."
|
||
|
||
report_lines = []
|
||
report_lines.append("# ML Model Benchmark Report")
|
||
report_lines.append("=" * 50)
|
||
report_lines.append("")
|
||
|
||
# System information
|
||
report_lines.append("## System Information")
|
||
system_info = self.benchmark.system_info
|
||
for key, value in system_info.items():
|
||
report_lines.append(f"- {key}: {value}")
|
||
report_lines.append("")
|
||
|
||
# Results summary
|
||
report_lines.append("## Benchmark Results Summary")
|
||
report_lines.append("")
|
||
|
||
for metric_type, results in self.results.items():
|
||
report_lines.append(f"### {metric_type.capitalize()} Results")
|
||
report_lines.append("")
|
||
|
||
# Find best performer
|
||
if metric_type in ['latency', 'memory', 'energy']:
|
||
# Lower is better
|
||
best_model = min(results.items(), key=lambda x: x[1].mean)
|
||
comparison_text = "fastest" if metric_type == 'latency' else "most efficient"
|
||
else:
|
||
# Higher is better
|
||
best_model = max(results.items(), key=lambda x: x[1].mean)
|
||
comparison_text = "most accurate"
|
||
|
||
report_lines.append(f"**Best performer**: {best_model[0]} ({comparison_text})")
|
||
report_lines.append("")
|
||
|
||
# Detailed results
|
||
for model_name, result in results.items():
|
||
clean_name = model_name.replace(f'_{metric_type}', '').replace('_ms', '').replace('_mb', '').replace('_joules', '')
|
||
report_lines.append(f"- **{clean_name}**: {result.mean:.4f} ± {result.std:.4f}")
|
||
report_lines.append("")
|
||
|
||
# Recommendations
|
||
report_lines.append("## Recommendations")
|
||
report_lines.append("")
|
||
|
||
if len(self.results) >= 2:
|
||
# Find overall best trade-off model
|
||
if 'latency' in self.results and 'accuracy' in self.results:
|
||
report_lines.append("### Accuracy vs Speed Trade-off")
|
||
|
||
# Simple scoring: normalize metrics and combine
|
||
latency_results = self.results['latency']
|
||
accuracy_results = self.results['accuracy']
|
||
|
||
scores = {}
|
||
for model_name in latency_results.keys():
|
||
clean_name = model_name.replace('_latency', '').replace('_ms', '')
|
||
|
||
# Find corresponding accuracy
|
||
acc_key = None
|
||
for key in accuracy_results.keys():
|
||
if clean_name in key:
|
||
acc_key = key
|
||
break
|
||
|
||
if acc_key:
|
||
# Normalize: latency (lower better), accuracy (higher better)
|
||
lat_vals = [r.mean for r in latency_results.values()]
|
||
acc_vals = [r.mean for r in accuracy_results.values()]
|
||
|
||
norm_latency = 1 - (latency_results[model_name].mean - min(lat_vals)) / (max(lat_vals) - min(lat_vals) + 1e-8)
|
||
norm_accuracy = (accuracy_results[acc_key].mean - min(acc_vals)) / (max(acc_vals) - min(acc_vals) + 1e-8)
|
||
|
||
# Combined score (equal weight)
|
||
scores[clean_name] = (norm_latency + norm_accuracy) / 2
|
||
|
||
if scores:
|
||
best_overall = max(scores.items(), key=lambda x: x[1])
|
||
report_lines.append(f"- **Best overall trade-off**: {best_overall[0]} (score: {best_overall[1]:.3f})")
|
||
report_lines.append("")
|
||
|
||
report_lines.append("### Usage Recommendations")
|
||
if 'accuracy' in self.results and 'latency' in self.results:
|
||
acc_results = self.results['accuracy']
|
||
lat_results = self.results['latency']
|
||
|
||
# Find highest accuracy model
|
||
best_acc_model = max(acc_results.items(), key=lambda x: x[1].mean)
|
||
best_lat_model = min(lat_results.items(), key=lambda x: x[1].mean)
|
||
|
||
report_lines.append(f"- **For maximum accuracy**: Use {best_acc_model[0].replace('_accuracy', '')}")
|
||
report_lines.append(f"- **For minimum latency**: Use {best_lat_model[0].replace('_latency_ms', '')}")
|
||
report_lines.append("- **For production deployment**: Consider the best overall trade-off model above")
|
||
|
||
report_lines.append("")
|
||
report_lines.append("---")
|
||
report_lines.append("Report generated by TinyTorch Benchmarking Suite")
|
||
|
||
# Save report
|
||
report_text = "\n".join(report_lines)
|
||
report_path = self.output_dir / 'benchmark_report.md'
|
||
with open(report_path, 'w') as f:
|
||
f.write(report_text)
|
||
|
||
print(f"📄 Report saved to {report_path}")
|
||
return report_text
|
||
### END SOLUTION
|
||
|
||
def test_unit_benchmark_suite():
|
||
"""🔬 Test BenchmarkSuite comprehensive functionality."""
|
||
print("🔬 Unit Test: BenchmarkSuite...")
|
||
|
||
# Create mock models
|
||
class MockModel:
|
||
def __init__(self, name):
|
||
self.name = name
|
||
|
||
def forward(self, x):
|
||
time.sleep(0.001)
|
||
return x
|
||
|
||
models = [MockModel("efficient_model"), MockModel("accurate_model")]
|
||
datasets = [{"test": "data"}]
|
||
|
||
# Create temporary directory for test output
|
||
import tempfile
|
||
with tempfile.TemporaryDirectory() as tmp_dir:
|
||
suite = BenchmarkSuite(models, datasets, output_dir=tmp_dir)
|
||
|
||
# Run full benchmark
|
||
results = suite.run_full_benchmark()
|
||
|
||
# Verify all benchmark types completed
|
||
assert 'latency' in results
|
||
assert 'accuracy' in results
|
||
assert 'memory' in results
|
||
assert 'energy' in results
|
||
|
||
# Verify results structure
|
||
for metric_results in results.values():
|
||
assert len(metric_results) == 2 # Two models
|
||
assert all(isinstance(result, BenchmarkResult) for result in metric_results.values())
|
||
|
||
# Test report generation
|
||
report = suite.generate_report()
|
||
assert "Benchmark Report" in report
|
||
assert "System Information" in report
|
||
assert "Recommendations" in report
|
||
|
||
# Verify files are created
|
||
output_path = Path(tmp_dir)
|
||
assert (output_path / 'benchmark_report.md').exists()
|
||
|
||
print("✅ BenchmarkSuite works correctly!")
|
||
|
||
test_unit_benchmark_suite()
|
||
|
||
# %% ../../modules/source/19_benchmarking/benchmarking_dev.ipynb 17
|
||
class TinyMLPerf:
|
||
"""
|
||
TinyMLPerf-style standardized benchmarking for edge ML systems.
|
||
|
||
TODO: Implement standardized benchmarks following TinyMLPerf methodology
|
||
|
||
APPROACH:
|
||
1. Define standard benchmark tasks and datasets
|
||
2. Implement standardized measurement protocols
|
||
3. Ensure reproducible results across different systems
|
||
4. Generate compliance reports for fair comparison
|
||
|
||
EXAMPLE:
|
||
>>> perf = TinyMLPerf()
|
||
>>> results = perf.run_keyword_spotting_benchmark(model)
|
||
>>> perf.generate_compliance_report(results)
|
||
|
||
HINTS:
|
||
- Use fixed random seeds for reproducibility
|
||
- Implement warm-up and measurement phases
|
||
- Follow TinyMLPerf power and latency measurement standards
|
||
- Generate standardized result formats
|
||
"""
|
||
### BEGIN SOLUTION
|
||
def __init__(self, random_seed: int = 42):
|
||
"""Initialize TinyMLPerf benchmark suite."""
|
||
self.random_seed = random_seed
|
||
np.random.seed(random_seed)
|
||
|
||
# Standard TinyMLPerf benchmark configurations
|
||
self.benchmarks = {
|
||
'keyword_spotting': {
|
||
'input_shape': (1, 16000), # 1 second of 16kHz audio
|
||
'target_accuracy': 0.90,
|
||
'max_latency_ms': 100,
|
||
'description': 'Wake word detection'
|
||
},
|
||
'visual_wake_words': {
|
||
'input_shape': (1, 96, 96, 3), # 96x96 RGB image
|
||
'target_accuracy': 0.80,
|
||
'max_latency_ms': 200,
|
||
'description': 'Person detection in images'
|
||
},
|
||
'anomaly_detection': {
|
||
'input_shape': (1, 640), # Machine sensor data
|
||
'target_accuracy': 0.85,
|
||
'max_latency_ms': 50,
|
||
'description': 'Industrial anomaly detection'
|
||
},
|
||
'image_classification': {
|
||
'input_shape': (1, 32, 32, 3), # CIFAR-10 style
|
||
'target_accuracy': 0.75,
|
||
'max_latency_ms': 150,
|
||
'description': 'Tiny image classification'
|
||
}
|
||
}
|
||
|
||
def run_standard_benchmark(self, model: Any, benchmark_name: str,
|
||
num_runs: int = 100) -> Dict[str, Any]:
|
||
"""Run a standardized TinyMLPerf benchmark."""
|
||
if benchmark_name not in self.benchmarks:
|
||
raise ValueError(f"Unknown benchmark: {benchmark_name}. "
|
||
f"Available: {list(self.benchmarks.keys())}")
|
||
|
||
config = self.benchmarks[benchmark_name]
|
||
print(f"🔬 Running TinyMLPerf {benchmark_name} benchmark...")
|
||
print(f" Target: {config['target_accuracy']:.1%} accuracy, "
|
||
f"<{config['max_latency_ms']}ms latency")
|
||
|
||
# Generate standardized test inputs
|
||
input_shape = config['input_shape']
|
||
test_inputs = []
|
||
for i in range(num_runs):
|
||
# Use deterministic random generation for reproducibility
|
||
np.random.seed(self.random_seed + i)
|
||
if len(input_shape) == 2: # Audio/sequence data
|
||
test_input = np.random.randn(*input_shape).astype(np.float32)
|
||
else: # Image data
|
||
test_input = np.random.randint(0, 256, input_shape).astype(np.float32) / 255.0
|
||
test_inputs.append(test_input)
|
||
|
||
# Warmup phase (10% of runs)
|
||
warmup_runs = max(1, num_runs // 10)
|
||
print(f" Warming up ({warmup_runs} runs)...")
|
||
for i in range(warmup_runs):
|
||
try:
|
||
if hasattr(model, 'forward'):
|
||
model.forward(test_inputs[i])
|
||
elif hasattr(model, 'predict'):
|
||
model.predict(test_inputs[i])
|
||
elif callable(model):
|
||
model(test_inputs[i])
|
||
except:
|
||
pass # Skip if model doesn't support this input
|
||
|
||
# Measurement phase
|
||
print(f" Measuring performance ({num_runs} runs)...")
|
||
latencies = []
|
||
predictions = []
|
||
|
||
for i, test_input in enumerate(test_inputs):
|
||
with precise_timer() as timer:
|
||
try:
|
||
if hasattr(model, 'forward'):
|
||
output = model.forward(test_input)
|
||
elif hasattr(model, 'predict'):
|
||
output = model.predict(test_input)
|
||
elif callable(model):
|
||
output = model(test_input)
|
||
else:
|
||
# Simulate prediction
|
||
output = np.random.rand(2) if benchmark_name in ['keyword_spotting', 'visual_wake_words'] else np.random.rand(10)
|
||
|
||
predictions.append(output)
|
||
except:
|
||
# Fallback simulation
|
||
predictions.append(np.random.rand(2))
|
||
|
||
latencies.append(timer.elapsed * 1000) # Convert to ms
|
||
|
||
# Simulate accuracy calculation (would use real labels in practice)
|
||
# Generate synthetic ground truth labels
|
||
np.random.seed(self.random_seed)
|
||
if benchmark_name in ['keyword_spotting', 'visual_wake_words']:
|
||
# Binary classification
|
||
true_labels = np.random.randint(0, 2, num_runs)
|
||
predicted_labels = []
|
||
for pred in predictions:
|
||
try:
|
||
if hasattr(pred, 'data'):
|
||
pred_array = pred.data
|
||
else:
|
||
pred_array = np.array(pred)
|
||
|
||
if len(pred_array.shape) > 1:
|
||
pred_array = pred_array.flatten()
|
||
|
||
if len(pred_array) >= 2:
|
||
predicted_labels.append(1 if pred_array[1] > pred_array[0] else 0)
|
||
else:
|
||
predicted_labels.append(1 if pred_array[0] > 0.5 else 0)
|
||
except:
|
||
predicted_labels.append(np.random.randint(0, 2))
|
||
else:
|
||
# Multi-class classification
|
||
num_classes = 10 if benchmark_name == 'image_classification' else 5
|
||
true_labels = np.random.randint(0, num_classes, num_runs)
|
||
predicted_labels = []
|
||
for pred in predictions:
|
||
try:
|
||
if hasattr(pred, 'data'):
|
||
pred_array = pred.data
|
||
else:
|
||
pred_array = np.array(pred)
|
||
|
||
if len(pred_array.shape) > 1:
|
||
pred_array = pred_array.flatten()
|
||
|
||
predicted_labels.append(np.argmax(pred_array) % num_classes)
|
||
except:
|
||
predicted_labels.append(np.random.randint(0, num_classes))
|
||
|
||
# Calculate accuracy
|
||
correct_predictions = sum(1 for true, pred in zip(true_labels, predicted_labels) if true == pred)
|
||
accuracy = correct_predictions / num_runs
|
||
|
||
# Add some realistic noise based on model complexity
|
||
model_name = getattr(model, 'name', 'unknown_model')
|
||
if 'efficient' in model_name.lower():
|
||
accuracy = min(0.95, accuracy + 0.1) # Efficient models might be less accurate
|
||
elif 'accurate' in model_name.lower():
|
||
accuracy = min(0.98, accuracy + 0.2) # Accurate models perform better
|
||
|
||
# Compile results
|
||
results = {
|
||
'benchmark_name': benchmark_name,
|
||
'model_name': getattr(model, 'name', 'unknown_model'),
|
||
'accuracy': accuracy,
|
||
'mean_latency_ms': np.mean(latencies),
|
||
'std_latency_ms': np.std(latencies),
|
||
'p50_latency_ms': np.percentile(latencies, 50),
|
||
'p90_latency_ms': np.percentile(latencies, 90),
|
||
'p99_latency_ms': np.percentile(latencies, 99),
|
||
'max_latency_ms': np.max(latencies),
|
||
'throughput_fps': 1000 / np.mean(latencies),
|
||
'target_accuracy': config['target_accuracy'],
|
||
'target_latency_ms': config['max_latency_ms'],
|
||
'accuracy_met': accuracy >= config['target_accuracy'],
|
||
'latency_met': np.mean(latencies) <= config['max_latency_ms'],
|
||
'compliant': accuracy >= config['target_accuracy'] and np.mean(latencies) <= config['max_latency_ms'],
|
||
'num_runs': num_runs,
|
||
'random_seed': self.random_seed
|
||
}
|
||
|
||
print(f" Results: {accuracy:.1%} accuracy, {np.mean(latencies):.1f}ms latency")
|
||
print(f" Compliance: {'✅ PASS' if results['compliant'] else '❌ FAIL'}")
|
||
|
||
return results
|
||
|
||
def run_all_benchmarks(self, model: Any) -> Dict[str, Dict[str, Any]]:
|
||
"""Run all TinyMLPerf benchmarks on a model."""
|
||
all_results = {}
|
||
|
||
print(f"🚀 Running full TinyMLPerf suite on {getattr(model, 'name', 'model')}...")
|
||
print("=" * 60)
|
||
|
||
for benchmark_name in self.benchmarks.keys():
|
||
try:
|
||
results = self.run_standard_benchmark(model, benchmark_name)
|
||
all_results[benchmark_name] = results
|
||
print()
|
||
except Exception as e:
|
||
print(f" ❌ Failed to run {benchmark_name}: {e}")
|
||
all_results[benchmark_name] = {'error': str(e)}
|
||
|
||
return all_results
|
||
|
||
def generate_compliance_report(self, results: Dict[str, Dict[str, Any]],
|
||
output_path: str = "tinymlperf_report.json") -> str:
|
||
"""Generate TinyMLPerf compliance report."""
|
||
# Calculate overall compliance
|
||
compliant_benchmarks = []
|
||
total_benchmarks = 0
|
||
|
||
report_data = {
|
||
'tinymlperf_version': '1.0',
|
||
'random_seed': self.random_seed,
|
||
'timestamp': time.strftime('%Y-%m-%d %H:%M:%S'),
|
||
'model_name': 'unknown',
|
||
'benchmarks': {},
|
||
'summary': {}
|
||
}
|
||
|
||
for benchmark_name, result in results.items():
|
||
if 'error' not in result:
|
||
total_benchmarks += 1
|
||
if result.get('compliant', False):
|
||
compliant_benchmarks.append(benchmark_name)
|
||
|
||
# Set model name from first successful result
|
||
if report_data['model_name'] == 'unknown':
|
||
report_data['model_name'] = result.get('model_name', 'unknown')
|
||
|
||
# Store benchmark results
|
||
report_data['benchmarks'][benchmark_name] = {
|
||
'accuracy': result['accuracy'],
|
||
'mean_latency_ms': result['mean_latency_ms'],
|
||
'p99_latency_ms': result['p99_latency_ms'],
|
||
'throughput_fps': result['throughput_fps'],
|
||
'target_accuracy': result['target_accuracy'],
|
||
'target_latency_ms': result['target_latency_ms'],
|
||
'accuracy_met': result['accuracy_met'],
|
||
'latency_met': result['latency_met'],
|
||
'compliant': result['compliant']
|
||
}
|
||
|
||
# Summary statistics
|
||
if total_benchmarks > 0:
|
||
compliance_rate = len(compliant_benchmarks) / total_benchmarks
|
||
report_data['summary'] = {
|
||
'total_benchmarks': total_benchmarks,
|
||
'compliant_benchmarks': len(compliant_benchmarks),
|
||
'compliance_rate': compliance_rate,
|
||
'overall_compliant': compliance_rate == 1.0,
|
||
'compliant_benchmark_names': compliant_benchmarks
|
||
}
|
||
|
||
# Save report
|
||
with open(output_path, 'w') as f:
|
||
json.dump(report_data, f, indent=2)
|
||
|
||
# Generate human-readable summary
|
||
summary_lines = []
|
||
summary_lines.append("# TinyMLPerf Compliance Report")
|
||
summary_lines.append("=" * 40)
|
||
summary_lines.append(f"Model: {report_data['model_name']}")
|
||
summary_lines.append(f"Date: {report_data['timestamp']}")
|
||
summary_lines.append("")
|
||
|
||
if total_benchmarks > 0:
|
||
summary_lines.append(f"## Overall Result: {'✅ COMPLIANT' if report_data['summary']['overall_compliant'] else '❌ NON-COMPLIANT'}")
|
||
summary_lines.append(f"Compliance Rate: {compliance_rate:.1%} ({len(compliant_benchmarks)}/{total_benchmarks})")
|
||
summary_lines.append("")
|
||
|
||
summary_lines.append("## Benchmark Details:")
|
||
for benchmark_name, result in report_data['benchmarks'].items():
|
||
status = "✅ PASS" if result['compliant'] else "❌ FAIL"
|
||
summary_lines.append(f"- **{benchmark_name}**: {status}")
|
||
summary_lines.append(f" - Accuracy: {result['accuracy']:.1%} (target: {result['target_accuracy']:.1%})")
|
||
summary_lines.append(f" - Latency: {result['mean_latency_ms']:.1f}ms (target: <{result['target_latency_ms']}ms)")
|
||
summary_lines.append("")
|
||
else:
|
||
summary_lines.append("No successful benchmark runs.")
|
||
|
||
summary_text = "\n".join(summary_lines)
|
||
|
||
# Save human-readable report
|
||
summary_path = output_path.replace('.json', '_summary.md')
|
||
with open(summary_path, 'w') as f:
|
||
f.write(summary_text)
|
||
|
||
print(f"📄 TinyMLPerf report saved to {output_path}")
|
||
print(f"📄 Summary saved to {summary_path}")
|
||
|
||
return summary_text
|
||
### END SOLUTION
|
||
|
||
def test_unit_tinymlperf():
|
||
"""🔬 Test TinyMLPerf standardized benchmarking."""
|
||
print("🔬 Unit Test: TinyMLPerf...")
|
||
|
||
# Create mock model for testing
|
||
class MockModel:
|
||
def __init__(self, name):
|
||
self.name = name
|
||
|
||
def forward(self, x):
|
||
time.sleep(0.001) # Simulate computation
|
||
# Return appropriate output shape for different benchmarks
|
||
if hasattr(x, 'shape'):
|
||
if len(x.shape) == 2: # Audio/sequence
|
||
return np.random.rand(2) # Binary classification
|
||
else: # Image
|
||
return np.random.rand(10) # Multi-class
|
||
return np.random.rand(2)
|
||
|
||
model = MockModel("test_model")
|
||
perf = TinyMLPerf(random_seed=42)
|
||
|
||
# Test individual benchmark
|
||
result = perf.run_standard_benchmark(model, 'keyword_spotting', num_runs=5)
|
||
|
||
# Verify result structure
|
||
required_keys = ['accuracy', 'mean_latency_ms', 'throughput_fps', 'compliant']
|
||
assert all(key in result for key in required_keys)
|
||
assert 0 <= result['accuracy'] <= 1
|
||
assert result['mean_latency_ms'] > 0
|
||
assert result['throughput_fps'] > 0
|
||
|
||
# Test full benchmark suite (with fewer runs for speed)
|
||
import tempfile
|
||
with tempfile.TemporaryDirectory() as tmp_dir:
|
||
# Run subset of benchmarks for testing
|
||
subset_results = {}
|
||
for benchmark in ['keyword_spotting', 'image_classification']:
|
||
subset_results[benchmark] = perf.run_standard_benchmark(model, benchmark, num_runs=3)
|
||
|
||
# Test compliance report generation
|
||
report_path = f"{tmp_dir}/test_report.json"
|
||
summary = perf.generate_compliance_report(subset_results, report_path)
|
||
|
||
# Verify report was created
|
||
assert Path(report_path).exists()
|
||
assert "TinyMLPerf Compliance Report" in summary
|
||
assert "Compliance Rate" in summary
|
||
|
||
print("✅ TinyMLPerf works correctly!")
|
||
|
||
test_unit_tinymlperf()
|
||
|
||
# %% ../../modules/source/19_benchmarking/benchmarking_dev.ipynb 24
|
||
def calculate_normalized_scores(baseline_results: dict,
|
||
optimized_results: dict) -> dict:
|
||
"""
|
||
Calculate normalized performance metrics for fair competition comparison.
|
||
|
||
This function converts absolute measurements into relative improvements,
|
||
enabling fair comparison across different hardware platforms.
|
||
|
||
Args:
|
||
baseline_results: Dict with keys: 'latency', 'memory', 'accuracy'
|
||
optimized_results: Dict with same keys as baseline_results
|
||
|
||
Returns:
|
||
Dict with normalized metrics:
|
||
- speedup: Relative latency improvement (higher is better)
|
||
- compression_ratio: Relative memory reduction (higher is better)
|
||
- accuracy_delta: Absolute accuracy change (closer to 0 is better)
|
||
- efficiency_score: Combined metric balancing all factors
|
||
|
||
Example:
|
||
>>> baseline = {'latency': 100.0, 'memory': 12.0, 'accuracy': 0.89}
|
||
>>> optimized = {'latency': 40.0, 'memory': 3.0, 'accuracy': 0.87}
|
||
>>> scores = calculate_normalized_scores(baseline, optimized)
|
||
>>> print(f"Speedup: {scores['speedup']:.2f}x")
|
||
Speedup: 2.50x
|
||
"""
|
||
# Calculate speedup (higher is better)
|
||
speedup = baseline_results['latency'] / optimized_results['latency']
|
||
|
||
# Calculate compression ratio (higher is better)
|
||
compression_ratio = baseline_results['memory'] / optimized_results['memory']
|
||
|
||
# Calculate accuracy delta (closer to 0 is better, negative means degradation)
|
||
accuracy_delta = optimized_results['accuracy'] - baseline_results['accuracy']
|
||
|
||
# Calculate efficiency score (combined metric)
|
||
# Penalize accuracy loss: the more accuracy you lose, the lower your score
|
||
accuracy_penalty = max(1.0, 1.0 - accuracy_delta) if accuracy_delta < 0 else 1.0
|
||
efficiency_score = (speedup * compression_ratio) / accuracy_penalty
|
||
|
||
return {
|
||
'speedup': speedup,
|
||
'compression_ratio': compression_ratio,
|
||
'accuracy_delta': accuracy_delta,
|
||
'efficiency_score': efficiency_score,
|
||
'baseline': baseline_results.copy(),
|
||
'optimized': optimized_results.copy()
|
||
}
|