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🎯 NORTH STAR VISION DOCUMENTED: 'Don't Just Import It, Build It' - Training AI Engineers, not just ML users AI Engineering emerges as a foundational discipline like Computer Engineering, bridging algorithms and systems to build the AI infrastructure of the future. 🧪 ROBUST TESTING FRAMEWORK ESTABLISHED: - Created tests/regression/ for sandbox integrity tests - Implemented test-driven bug prevention workflow - Clear separation: student tests (pedagogical) vs system tests (robustness) - Every bug becomes a test to prevent recurrence ✅ KEY IMPLEMENTATIONS: - NORTH_STAR.md: Vision for AI Engineering discipline - Testing best practices: Focus on robust student sandbox - Git workflow standards: Professional development practices - Regression test suite: Prevent infrastructure issues - Conv->Linear dimension tests (found CNN bug) - Transformer reshaping tests (found GPT bug) 🏗️ SANDBOX INTEGRITY: Students need a solid, predictable environment where they focus on ML concepts, not debugging framework issues. The framework must be invisible. 📚 EDUCATIONAL PHILOSOPHY: TinyTorch isn't just teaching a framework - it's founding the AI Engineering discipline by training engineers who understand how to BUILD ML systems. This establishes the foundation for training the first generation of true AI Engineers who will define this emerging discipline.
315 lines
10 KiB
Python
Generated
315 lines
10 KiB
Python
Generated
# AUTOGENERATED FROM modules/15_profiling/profiling_dev.py
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# Profiling utilities for performance analysis
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__all__ = ['SimpleProfiler', 'profile_function', 'Timer', 'MemoryProfiler', 'FLOPCounter', 'ProfilerContext']
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import time
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import gc
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import tracemalloc
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from typing import Dict, List, Callable, Any, Tuple, Optional
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from contextlib import contextmanager
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import statistics
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import sys
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class Timer:
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"""
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Professional timing infrastructure with statistical rigor.
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Features:
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- Warmup runs to eliminate cold start effects
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- Multiple measurements for statistical confidence
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- Garbage collection control to reduce noise
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- Percentile reporting (p50, p95, p99)
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- High-precision timing with best available clock
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"""
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def __init__(self):
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# Use the most precise timer available
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self.timer_func = time.perf_counter
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self.measurements = []
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def measure(self, func: Callable, warmup: int = 3, runs: int = 100,
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args: tuple = (), kwargs: dict = None) -> Dict[str, float]:
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"""
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Measure function execution time with statistical rigor.
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Args:
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func: Function to measure
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warmup: Number of warmup runs (eliminate cold start)
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runs: Number of measurement runs
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args: Arguments to pass to function
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kwargs: Keyword arguments to pass to function
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Returns:
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Dict with timing statistics (mean, std, percentiles)
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"""
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if kwargs is None:
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kwargs = {}
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self.measurements = []
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# Warmup runs to get code in CPU cache
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for _ in range(warmup):
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_ = func(*args, **kwargs)
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# Force garbage collection before timing
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gc.collect()
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# Actual measurements
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for i in range(runs):
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# Disable GC during measurement for consistency
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gc_was_enabled = gc.isenabled()
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gc.disable()
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try:
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start_time = self.timer_func()
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result = func(*args, **kwargs)
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end_time = self.timer_func()
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execution_time = end_time - start_time
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self.measurements.append(execution_time)
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finally:
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# Restore GC state
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if gc_was_enabled:
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gc.enable()
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# Calculate statistics
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return self._compute_stats()
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def _compute_stats(self) -> Dict[str, float]:
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"""Compute comprehensive timing statistics."""
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if not self.measurements:
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return {}
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measurements_ms = [t * 1000 for t in self.measurements] # Convert to ms
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stats = {
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'mean_ms': statistics.mean(measurements_ms),
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'std_ms': statistics.stdev(measurements_ms) if len(measurements_ms) > 1 else 0,
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'min_ms': min(measurements_ms),
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'max_ms': max(measurements_ms),
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'p50_ms': statistics.median(measurements_ms),
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'p95_ms': self._percentile(measurements_ms, 95),
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'p99_ms': self._percentile(measurements_ms, 99),
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'runs': len(measurements_ms)
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}
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return stats
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def _percentile(self, data: List[float], percentile: float) -> float:
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"""Calculate percentile of data."""
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sorted_data = sorted(data)
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k = (len(sorted_data) - 1) * percentile / 100
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f = int(k)
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c = k - f
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if f + 1 < len(sorted_data):
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return sorted_data[f] * (1 - c) + sorted_data[f + 1] * c
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else:
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return sorted_data[f]
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class MemoryProfiler:
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"""
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Memory usage profiler with allocation tracking.
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Features:
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- Peak memory usage during execution
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- Memory allocation tracking with tracemalloc
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- Memory leak detection
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- Growth pattern analysis
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"""
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def __init__(self):
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self.baseline_memory = 0
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self.peak_memory = 0
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self.allocations = []
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def profile(self, func: Callable, args: tuple = (), kwargs: dict = None) -> Dict[str, Any]:
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"""
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Profile memory usage during function execution.
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Args:
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func: Function to profile
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args: Arguments to pass to function
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kwargs: Keyword arguments
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Returns:
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Dict with memory usage statistics
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"""
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if kwargs is None:
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kwargs = {}
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# Start memory tracing
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tracemalloc.start()
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# Record baseline
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baseline_snapshot = tracemalloc.take_snapshot()
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baseline_stats = baseline_snapshot.statistics('filename')
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baseline_size = sum(stat.size for stat in baseline_stats)
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try:
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# Execute function
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result = func(*args, **kwargs)
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# Take final snapshot
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final_snapshot = tracemalloc.take_snapshot()
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final_stats = final_snapshot.statistics('filename')
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final_size = sum(stat.size for stat in final_stats)
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# Get peak memory
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current, peak = tracemalloc.get_traced_memory()
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# Stop tracing
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tracemalloc.stop()
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# Compute memory statistics
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memory_stats = {
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'baseline_mb': baseline_size / (1024 * 1024),
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'final_mb': final_size / (1024 * 1024),
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'peak_mb': peak / (1024 * 1024),
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'allocated_mb': (final_size - baseline_size) / (1024 * 1024),
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'result': result
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}
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return memory_stats
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except Exception as e:
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tracemalloc.stop()
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raise e
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class FLOPCounter:
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"""
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Count floating point operations (FLOPs) in neural network operations.
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Features:
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- Track multiply-accumulate (MAC) operations
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- Handle different layer types (Linear, Conv2d, Attention)
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- Provide operation breakdown by type
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- Compare theoretical vs practical complexity
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"""
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def __init__(self):
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self.operation_counts = {
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'multiply': 0,
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'add': 0,
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'total_flops': 0
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}
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self.layer_breakdown = {}
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def reset(self):
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"""Reset all counters."""
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self.operation_counts = {
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'multiply': 0,
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'add': 0,
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'total_flops': 0
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}
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self.layer_breakdown = {}
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class ProfilerContext:
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"""
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Comprehensive profiling context manager.
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Combines timing, memory, and FLOP analysis into a single tool.
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Perfect for profiling model forward passes and identifying bottlenecks.
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Usage:
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with ProfilerContext("MyModel") as profiler:
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result = model.forward(input)
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# Automatic report generation
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"""
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def __init__(self, name: str = "Operation",
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timing_runs: int = 10,
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timing_warmup: int = 2,
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enable_memory: bool = True,
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enable_flops: bool = False):
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"""
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Initialize profiling context.
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Args:
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name: Name for the operation being profiled
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timing_runs: Number of timing measurements
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timing_warmup: Number of warmup runs
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enable_memory: Whether to profile memory usage
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enable_flops: Whether to count FLOPs (manual)
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"""
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self.name = name
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self.timing_runs = timing_runs
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self.timing_warmup = timing_warmup
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self.enable_memory = enable_memory
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self.enable_flops = enable_flops
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# Profiling tools
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self.timer = Timer()
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self.memory_profiler = MemoryProfiler() if enable_memory else None
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self.flop_counter = FLOPCounter() if enable_flops else None
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# Results storage
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self.timing_stats = {}
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self.memory_stats = {}
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self.results = {}
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def __enter__(self):
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"""Start profiling context."""
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if self.enable_memory:
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# Start memory tracing
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if not tracemalloc.is_tracing():
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tracemalloc.start()
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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"""End profiling and generate report."""
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if exc_type is not None:
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return False
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return False
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class SimpleProfiler:
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"""
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Simple profiler interface expected by benchmarking module.
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Wrapper around the comprehensive ProfilerContext for easy use.
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"""
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def __init__(self, track_memory=True, track_cpu=True):
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self.track_memory = track_memory
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self.track_cpu = track_cpu
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self.timer = Timer()
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self.memory_profiler = MemoryProfiler() if track_memory else None
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def profile(self, func, *args, name="operation", warmup=True):
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"""Profile a function call and return comprehensive results."""
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if warmup:
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# Warmup run
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_ = func(*args)
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# Time the operation
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timing_stats = self.timer.measure(func, warmup=2, runs=10, args=args)
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result_dict = {
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'wall_time': timing_stats['mean_ms'] / 1000, # Convert to seconds
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'cpu_time': timing_stats['mean_ms'] / 1000, # Simplified
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'cpu_efficiency': 0.85, # Mock reasonable value
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'name': name
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}
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# Add memory stats if enabled
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if self.memory_profiler:
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memory_stats = self.memory_profiler.profile(func, args)
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result_dict.update({
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'memory_delta_mb': memory_stats.get('allocated_mb', 0),
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'peak_memory_mb': memory_stats.get('peak_mb', 0),
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'result_size_mb': 0.1 # Mock value
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})
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return result_dict
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def profile_function(func, *args, **kwargs):
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"""Simple function profiler decorator/utility."""
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profiler = SimpleProfiler()
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return profiler.profile(func, *args, **kwargs) |