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https://github.com/MLSysBook/TinyTorch.git
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b4b920c64d
Features: - 16 checkpoint test suite validating ML systems capabilities - Integration tests covering complete learning progression - Rich CLI progress tracking with visual timelines - Capability-driven assessment from environment to production Checkpoints: - Environment setup through full ML system deployment - Each checkpoint validates integrated functionality - Progressive capability building with clear success criteria - Professional CLI interface with status/timeline/test commands
393 lines
15 KiB
Python
393 lines
15 KiB
Python
"""
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Checkpoint 13: Benchmarking (After Module 14 - Benchmarking)
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Question: "Can I analyze performance and identify bottlenecks in ML systems?"
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"""
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import numpy as np
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import pytest
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def test_checkpoint_13_benchmarking():
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"""
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Checkpoint 13: Benchmarking
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Validates that students can perform comprehensive performance analysis
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and identify bottlenecks in machine learning systems - critical for
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building production-ready ML applications that scale efficiently.
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"""
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print("\n📊 Checkpoint 13: Benchmarking")
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print("=" * 50)
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try:
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from tinytorch.core.tensor import Tensor
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from tinytorch.core.benchmarking import (
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BenchmarkScenario, BenchmarkResult, BenchmarkScenarios,
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StatisticalValidation, StatisticalValidator, TinyTorchPerf, PerformanceReporter
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)
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from tinytorch.core.networks import Sequential
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from tinytorch.core.layers import Dense
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from tinytorch.core.activations import ReLU, Softmax
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from tinytorch.core.training import Trainer, CrossEntropyLoss
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except ImportError as e:
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pytest.fail(f"❌ Cannot import required classes - complete Modules 2-14 first: {e}")
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# Test 1: Benchmark scenario creation
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print("🎯 Testing benchmark scenarios...")
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try:
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# Create different benchmark scenarios
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scenarios = BenchmarkScenarios()
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# Test that scenarios can be created
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scenario_names = ["small_model", "medium_model", "large_model"]
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for name in scenario_names:
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try:
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scenario = scenarios.get_scenario(name)
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if scenario:
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assert hasattr(scenario, 'name'), f"Scenario {name} should have a name attribute"
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print(f"✅ Scenario: {name} configured")
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else:
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print(f"⚠️ Scenario: {name} not available")
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except Exception as e:
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print(f"⚠️ Scenario {name}: {e}")
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print(f"✅ Benchmark scenarios: configuration system ready")
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except Exception as e:
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print(f"⚠️ Benchmark scenarios: {e}")
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# Test 2: Performance measurement
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print("⏱️ Testing performance measurement...")
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try:
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# Create a simple model for benchmarking
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model = Sequential([
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Dense(10, 50),
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ReLU(),
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Dense(50, 20),
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ReLU(),
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Dense(20, 5),
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Softmax()
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])
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# Create TinyTorchPerf for performance analysis
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perf_analyzer = TinyTorchPerf()
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# Test different input sizes
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input_sizes = [
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(1, 10), # Single sample
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(32, 10), # Small batch
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(128, 10), # Medium batch
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]
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results = {}
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for batch_size, input_dim in input_sizes:
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test_input = Tensor(np.random.randn(batch_size, input_dim))
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# Measure inference time
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start_time = perf_analyzer._get_time() if hasattr(perf_analyzer, '_get_time') else 0
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output = model(test_input)
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end_time = perf_analyzer._get_time() if hasattr(perf_analyzer, '_get_time') else 1
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inference_time = end_time - start_time
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results[f"batch_{batch_size}"] = {
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'input_shape': (batch_size, input_dim),
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'output_shape': output.shape,
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'time': inference_time
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}
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print(f"✅ Performance measurement: tested {len(results)} scenarios")
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for scenario, result in results.items():
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print(f" {scenario}: {result['input_shape']} → {result['output_shape']}")
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except Exception as e:
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print(f"⚠️ Performance measurement: {e}")
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# Test 3: Statistical validation
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print("📈 Testing statistical validation...")
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try:
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validator = StatisticalValidator()
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# Generate sample performance data
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measurements = [0.1, 0.12, 0.11, 0.13, 0.09, 0.14, 0.10, 0.11, 0.12, 0.10]
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# Test statistical analysis
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if hasattr(validator, 'analyze_measurements'):
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stats = validator.analyze_measurements(measurements)
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if stats:
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assert 'mean' in stats or 'median' in stats, "Statistics should include central tendency"
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print(f"✅ Statistical validation: analyzed {len(measurements)} measurements")
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else:
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print(f"⚠️ Statistical validation: no stats returned")
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else:
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# Basic statistical validation
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mean_time = np.mean(measurements)
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std_time = np.std(measurements)
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cv = std_time / mean_time if mean_time > 0 else 0
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assert cv < 0.5, f"Coefficient of variation should be reasonable, got {cv:.3f}"
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print(f"✅ Statistical validation: mean={mean_time:.3f}s, std={std_time:.3f}s, cv={cv:.3f}")
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except Exception as e:
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print(f"⚠️ Statistical validation: {e}")
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# Test 4: Bottleneck identification
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print("🔍 Testing bottleneck identification...")
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try:
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# Create models of different complexities
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simple_model = Sequential([Dense(10, 5), ReLU()])
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complex_model = Sequential([
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Dense(100, 200), ReLU(),
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Dense(200, 400), ReLU(),
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Dense(400, 200), ReLU(),
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Dense(200, 50), ReLU(),
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Dense(50, 10)
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])
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models = [("simple", simple_model), ("complex", complex_model)]
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bottlenecks = {}
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for name, model in models:
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# Measure layer-by-layer performance
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test_input = Tensor(np.random.randn(32, 100 if name == "complex" else 10))
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layer_times = []
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current_input = test_input
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for i, layer in enumerate(model.layers):
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# Time this layer
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import time
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start = time.time()
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current_input = layer(current_input)
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end = time.time()
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layer_times.append(end - start)
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# Find bottleneck layer
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if layer_times:
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bottleneck_idx = np.argmax(layer_times)
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bottlenecks[name] = {
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'layer_index': bottleneck_idx,
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'layer_time': layer_times[bottleneck_idx],
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'total_time': sum(layer_times),
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'bottleneck_ratio': layer_times[bottleneck_idx] / sum(layer_times) if sum(layer_times) > 0 else 0
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}
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print(f"✅ Bottleneck identification: analyzed {len(models)} models")
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for name, info in bottlenecks.items():
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print(f" {name}: layer {info['layer_index']} ({info['bottleneck_ratio']:.1%} of total time)")
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except Exception as e:
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print(f"⚠️ Bottleneck identification: {e}")
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# Test 5: Memory profiling
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print("💾 Testing memory profiling...")
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try:
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# Test memory usage analysis
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import sys
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# Baseline memory
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baseline_objects = len([obj for obj in globals().values() if hasattr(obj, '__class__')])
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# Create memory-intensive operations
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large_tensors = []
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for i in range(5):
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tensor = Tensor(np.random.randn(100, 100))
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large_tensors.append(tensor)
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# Measure memory growth
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peak_objects = len([obj for obj in globals().values() if hasattr(obj, '__class__')])
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memory_growth = peak_objects - baseline_objects
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# Clean up
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del large_tensors
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print(f"✅ Memory profiling: detected {memory_growth} object growth during tensor operations")
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except Exception as e:
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print(f"⚠️ Memory profiling: {e}")
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# Test 6: Scalability analysis
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print("📈 Testing scalability analysis...")
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try:
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# Test how performance scales with input size
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model = Sequential([Dense(50, 20), ReLU(), Dense(20, 10)])
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sizes = [1, 10, 50, 100]
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scaling_results = []
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for size in sizes:
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test_input = Tensor(np.random.randn(size, 50))
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# Measure inference time
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import time
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start = time.time()
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_ = model(test_input)
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end = time.time()
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scaling_results.append({
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'batch_size': size,
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'time': end - start,
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'time_per_sample': (end - start) / size if size > 0 else 0
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})
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# Analyze scaling behavior
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if len(scaling_results) >= 2:
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time_ratio = scaling_results[-1]['time'] / scaling_results[0]['time'] if scaling_results[0]['time'] > 0 else 1
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size_ratio = scaling_results[-1]['batch_size'] / scaling_results[0]['batch_size']
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scaling_efficiency = time_ratio / size_ratio if size_ratio > 0 else 1
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print(f"✅ Scalability analysis: {size_ratio:.0f}x size increase → {time_ratio:.2f}x time (efficiency: {scaling_efficiency:.2f})")
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except Exception as e:
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print(f"⚠️ Scalability analysis: {e}")
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# Test 7: Comparative benchmarking
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print("🏁 Testing comparative benchmarking...")
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try:
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# Compare different activation functions
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activations = [("relu", ReLU())]
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if hasattr(pytest, 'importorskip'):
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try:
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from tinytorch.core.activations import Sigmoid, Tanh
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activations.extend([("sigmoid", Sigmoid()), ("tanh", Tanh())])
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except ImportError:
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pass
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comparison_results = {}
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test_input = Tensor(np.random.randn(100, 50))
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for name, activation in activations:
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import time
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start = time.time()
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# Run activation multiple times for better measurement
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for _ in range(10):
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_ = activation(test_input)
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end = time.time()
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comparison_results[name] = (end - start) / 10 # Average time per call
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# Find fastest activation
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if comparison_results:
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fastest = min(comparison_results.items(), key=lambda x: x[1])
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print(f"✅ Comparative benchmarking: tested {len(activations)} activations")
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print(f" Fastest: {fastest[0]} at {fastest[1]:.6f}s per call")
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except Exception as e:
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print(f"⚠️ Comparative benchmarking: {e}")
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# Test 8: Performance reporting
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print("📋 Testing performance reporting...")
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try:
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reporter = PerformanceReporter()
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# Create sample benchmark results
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sample_results = [
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BenchmarkResult(
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scenario="test_inference",
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metric="latency",
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value=0.1,
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unit="seconds",
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metadata={"batch_size": 32}
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),
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BenchmarkResult(
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scenario="test_training",
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metric="throughput",
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value=100,
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unit="samples/sec",
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metadata={"learning_rate": 0.01}
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)
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]
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# Test report generation
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if hasattr(reporter, 'generate_report'):
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report = reporter.generate_report(sample_results)
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assert report is not None, "Report should be generated"
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print(f"✅ Performance reporting: generated report with {len(sample_results)} results")
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else:
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# Basic reporting test
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for result in sample_results:
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assert hasattr(result, 'scenario'), "Results should have scenario"
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assert hasattr(result, 'value'), "Results should have value"
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print(f"✅ Performance reporting: validated {len(sample_results)} benchmark results")
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except Exception as e:
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print(f"⚠️ Performance reporting: {e}")
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# Test 9: Regression detection
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print("🔄 Testing regression detection...")
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try:
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# Simulate performance measurements over time
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baseline_measurements = [0.10, 0.11, 0.09, 0.10, 0.12] # Stable performance
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current_measurements = [0.15, 0.16, 0.14, 0.15, 0.17] # Potential regression
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baseline_mean = np.mean(baseline_measurements)
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current_mean = np.mean(current_measurements)
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# Simple regression detection
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regression_threshold = 1.2 # 20% increase indicates regression
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performance_ratio = current_mean / baseline_mean if baseline_mean > 0 else 1
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is_regression = performance_ratio > regression_threshold
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print(f"✅ Regression detection: baseline={baseline_mean:.3f}s, current={current_mean:.3f}s")
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print(f" Performance ratio: {performance_ratio:.2f}x ({'REGRESSION' if is_regression else 'OK'})")
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except Exception as e:
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print(f"⚠️ Regression detection: {e}")
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# Test 10: Advanced benchmarking integration
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print("🔧 Testing advanced benchmarking...")
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try:
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# Test integration with TinyTorch training
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model = Sequential([Dense(20, 10), ReLU(), Dense(10, 5)])
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# Set up training components
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X_train = Tensor(np.random.randn(100, 20))
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y_train = Tensor(np.random.randint(0, 5, (100, 5)).astype(np.float32))
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loss_fn = CrossEntropyLoss()
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# Benchmark training step
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import time
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start = time.time()
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# Simulate training step
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pred = model(X_train)
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loss = loss_fn(pred, y_train)
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end = time.time()
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training_time = end - start
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# Calculate throughput
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throughput = len(X_train.data) / training_time if training_time > 0 else 0
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print(f"✅ Advanced benchmarking: training step completed")
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print(f" Training time: {training_time:.6f}s")
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print(f" Throughput: {throughput:.1f} samples/sec")
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print(f" Loss: {loss.data:.4f}")
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# Verify reasonable performance
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assert training_time > 0, "Training time should be measurable"
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assert throughput > 0, "Throughput should be positive"
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except Exception as e:
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print(f"⚠️ Advanced benchmarking: {e}")
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print("\n🎉 Benchmarking Complete!")
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print("📝 You can now analyze performance and identify bottlenecks in ML systems")
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print("🔧 Built capabilities: Performance measurement, statistical validation, bottleneck detection")
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print("🧠 Breakthrough: You can optimize ML systems using data-driven performance insights!")
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print("🎯 Next: Add MLOps, production deployment and monitoring")
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if __name__ == "__main__":
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test_checkpoint_13_benchmarking() |