mirror of
https://github.com/MLSysBook/TinyTorch.git
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c52a5dc789
- Added progressive complexity guidelines (Foundation/Intermediate/Advanced) - Added measurement function consolidation to prevent information overload - Fixed all diagnostic issues in losses_dev.py - Fixed markdown formatting across all modules - Consolidated redundant analysis functions in foundation modules - Fixed syntax errors and unused variables - Ensured all educational content is in proper markdown cells for Jupyter
1821 lines
64 KiB
Python
1821 lines
64 KiB
Python
# %% [markdown]
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"""
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# Module 15: Profiling - Performance Detective Work
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Welcome to the most eye-opening module in TinyTorch! You just built MLPs, CNNs, and Transformers.
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But here's the million-dollar question: **Why is your transformer 100x slower than PyTorch?**
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Time to become a performance detective and find out what's really happening under the hood.
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## MAGNIFY What You'll Discover
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Ever wonder why your models feel sluggish? We're about to reveal the culprits:
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- Which operations are eating your CPU cycles
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- Where your memory is disappearing
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- How many arithmetic operations you're really doing
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- The shocking performance differences between architectures
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**Spoiler Alert**: The results might surprise you. That "simple" attention mechanism?
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It's probably consuming 73% of your compute time!
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## TARGET Learning Objectives
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By the end of this module, you'll be able to:
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1. **Build Professional Profilers**: Create timing, memory, and FLOP counters
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2. **Identify Bottlenecks**: Find exactly what's slowing your models down
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3. **Compare Architectures**: See why transformers are slow but powerful
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4. **Guide Optimizations**: Use data to make smart performance decisions
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The tools you build here will be essential for Module 16 (Acceleration) when you actually fix the problems you discover.
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"""
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#| default_exp profiler
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# %% [markdown]
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"""
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## Part 1: The Timer - Your First Detective Tool
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Every performance investigation starts with one question: "How long does this actually take?"
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But timing is trickier than just `time.time()` - you need statistical rigor.
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### Why Simple Timing Fails
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```python
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import time
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start = time.time()
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result = my_function()
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end = time.time()
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print(f"Took {end - start:.2f}s") # FAIL Unreliable!
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```
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**Problems:**
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- First run includes "cold start" costs (loading code into cache)
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- Single measurement captures noise, not true performance
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- No confidence intervals or percentiles
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- Different timing APIs have different precision
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"""
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# %%
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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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# Mock imports for development
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try:
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from tinytorch.core.tensor import Tensor
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from tinytorch.core.layers import Linear, ReLU, Softmax
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from tinytorch.core.spatial import Conv2d, MaxPool2d
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from tinytorch.core.transformers import Transformer
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except ImportError:
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print("WARNING️ TinyTorch modules not available - using mocks for development")
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class Tensor:
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def __init__(self, data):
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if isinstance(data, list):
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self.data = data
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self.shape = self._get_shape(data)
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else:
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self.data = [[data]]
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self.shape = (1, 1)
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def _get_shape(self, data):
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if not isinstance(data[0], list):
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return (len(data),)
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return (len(data), len(data[0]))
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class Linear:
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def __init__(self, in_features, out_features):
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self.weight = Tensor([[0.1] * in_features for _ in range(out_features)])
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def forward(self, x):
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# Simple mock forward pass
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time.sleep(0.001) # Simulate computation
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return x
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class Conv2d:
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def __init__(self, in_channels, out_channels, kernel_size):
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self.weight = Tensor([[0.1] * in_channels for _ in range(out_channels)])
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def forward(self, x):
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time.sleep(0.005) # Simulate heavier computation
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return x
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class Transformer:
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def __init__(self, vocab_size, d_model, n_heads, n_layers):
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self.layers = [Linear(d_model, d_model) for _ in range(n_layers)]
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def forward(self, x):
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time.sleep(0.02) # Simulate expensive attention
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return x
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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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print(f"FIRE Running {warmup} warmup iterations...")
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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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print(f"⏱️ Measuring {runs} timed runs...")
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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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# Progress indicator for long measurements
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if i % (runs // 10) == 0 and runs > 20:
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print(f" Progress: {i}/{runs} ({i/runs*100:.0f}%)")
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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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def print_report(self, name: str = "Function"):
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"""Print a formatted timing report."""
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if not self.measurements:
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print(f"FAIL No measurements available for {name}")
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return
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stats = self._compute_stats()
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print(f"\n📊 TIMING REPORT: {name}")
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print("=" * 50)
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print(f"Runs: {stats['runs']}")
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print(f"Mean: {stats['mean_ms']:.3f} ms ± {stats['std_ms']:.3f} ms")
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print(f"Range: {stats['min_ms']:.3f} ms -> {stats['max_ms']:.3f} ms")
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print(f"P50: {stats['p50_ms']:.3f} ms")
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print(f"P95: {stats['p95_ms']:.3f} ms")
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print(f"P99: {stats['p99_ms']:.3f} ms")
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# Helpful interpretation
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if stats['std_ms'] / stats['mean_ms'] > 0.1:
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print("WARNING️ High variability - consider more warmup runs")
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else:
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print("PASS Stable timing measurements")
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# %% [markdown]
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"""
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### TEST Test the Timer
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Let's test our timer on different types of operations to see the statistical rigor in action.
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"""
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# %%
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def test_timer():
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"""Test the Timer class with different operation types."""
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timer = Timer()
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print("🔬 TIMER TESTING: Performance Detective Work")
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print("=" * 60)
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# Test 1: Fast operation (should be sub-millisecond)
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def fast_operation():
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return sum(range(1000))
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print("\n1️⃣ Fast CPU Operation (sum 1000 numbers)")
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stats = timer.measure(fast_operation, warmup=5, runs=200)
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timer.print_report("Fast CPU Sum")
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# Test 2: Memory allocation (intermediate speed)
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def memory_operation():
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data = [i * 2 for i in range(10000)]
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return len(data)
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print("\n2️⃣ Memory Allocation (10k list creation)")
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stats = timer.measure(memory_operation, warmup=3, runs=100)
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timer.print_report("Memory Allocation")
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# Test 3: Mock ML operation (slow)
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linear_layer = Linear(64, 32)
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mock_input = Tensor([[0.1] * 64])
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def ml_operation():
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return linear_layer.forward(mock_input)
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print("\n3️⃣ ML Operation (Linear layer forward pass)")
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stats = timer.measure(ml_operation, warmup=2, runs=50)
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timer.print_report("Linear Layer Forward")
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print("\nTARGET KEY INSIGHT: Notice the different scales!")
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print(" - CPU operations: microseconds (< 1ms)")
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print(" - Memory operations: low milliseconds")
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print(" - ML operations: higher milliseconds")
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print(" This is why transformers feel slow!")
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# Run the test
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if __name__ == "__main__":
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test_timer()
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# %% [markdown]
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"""
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## Part 2: Memory Profiler - The Memory Detective
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Now that we can measure time, let's track memory usage. Memory leaks and unexpected
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allocations are common culprits in slow ML code.
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### Why Memory Matters for Performance
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- **Cache efficiency**: Small working sets stay in L1/L2 cache (fast)
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- **Memory bandwidth**: Large transfers saturate memory bus (slow)
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- **Garbage collection**: Excessive allocations trigger GC pauses
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- **Swap thrashing**: Out of RAM = disk access = 1000x slower
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The memory profiler will reveal surprising allocation patterns in your models.
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"""
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# %%
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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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def print_report(self, stats: Dict[str, Any], name: str = "Function"):
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"""Print formatted memory usage report."""
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print(f"\n🧠 MEMORY REPORT: {name}")
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print("=" * 50)
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print(f"Baseline: {stats['baseline_mb']:.2f} MB")
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print(f"Final: {stats['final_mb']:.2f} MB")
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print(f"Peak: {stats['peak_mb']:.2f} MB")
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print(f"Allocated: {stats['allocated_mb']:.2f} MB")
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# Memory efficiency insights
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if stats['allocated_mb'] > stats['peak_mb'] * 0.5:
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print("WARNING️ High memory allocation - check for copies")
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elif stats['allocated_mb'] < 0:
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print("PASS Memory efficient - some cleanup occurred")
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else:
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print("PASS Reasonable memory usage")
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# Peak vs final analysis
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peak_vs_final_ratio = stats['peak_mb'] / max(stats['final_mb'], 0.001)
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if peak_vs_final_ratio > 2.0:
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print(f"TIP Peak was {peak_vs_final_ratio:.1f}x final - temporary allocations detected")
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# %% [markdown]
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"""
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### TEST Test Memory Profiler
|
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Let's test the memory profiler on operations that have different memory patterns.
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"""
|
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|
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# %%
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def test_memory_profiler():
|
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"""Test memory profiling on different operation patterns."""
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profiler = MemoryProfiler()
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print("🧠 MEMORY PROFILER TESTING")
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print("=" * 60)
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|
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# Test 1: Small allocation
|
||
def small_allocation():
|
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return [i for i in range(1000)]
|
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|
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print("\n1️⃣ Small List Creation (1k integers)")
|
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stats = profiler.profile(small_allocation)
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profiler.print_report(stats, "Small Allocation")
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|
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# Test 2: Large allocation
|
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def large_allocation():
|
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# Create a "large" tensor-like structure
|
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return [[float(i * j) for j in range(100)] for i in range(100)]
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print("\n2️⃣ Large 2D Array (100x100 floats)")
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stats = profiler.profile(large_allocation)
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profiler.print_report(stats, "Large Allocation")
|
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|
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# Test 3: Memory copying pattern
|
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def copying_operation():
|
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original = [i for i in range(5000)]
|
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copy1 = original.copy()
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copy2 = copy1.copy()
|
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copy3 = copy2.copy()
|
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return copy3
|
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|
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print("\n3️⃣ Memory Copying (multiple copies)")
|
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stats = profiler.profile(copying_operation)
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profiler.print_report(stats, "Copying Operation")
|
||
|
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print("\nTARGET KEY INSIGHT: Memory patterns reveal optimization opportunities!")
|
||
print(" - Small allocations: Usually efficient")
|
||
print(" - Large allocations: Watch for memory bandwidth limits")
|
||
print(" - Copying operations: Major performance killers")
|
||
|
||
# Run the test
|
||
if __name__ == "__main__":
|
||
test_memory_profiler()
|
||
|
||
# %% [markdown]
|
||
"""
|
||
## Part 3: FLOP Counter - Operation Detective
|
||
|
||
How many arithmetic operations is your model actually doing? FLOPs (Floating Point
|
||
Operations) give you the raw computational cost independent of hardware.
|
||
|
||
### Why Count FLOPs?
|
||
|
||
- **Hardware comparison**: Same FLOPs = same work, regardless of CPU/GPU
|
||
- **Architecture analysis**: Compare MLP vs CNN vs Transformer efficiency
|
||
- **Scaling prediction**: Double the model = how many more FLOPs?
|
||
- **Optimization targeting**: Focus on high-FLOP operations first
|
||
|
||
**The shocking truth**: Attention is O(n²) - a 2x longer sequence needs 4x more FLOPs!
|
||
"""
|
||
|
||
# %%
|
||
class FLOPCounter:
|
||
"""
|
||
Count floating point operations (FLOPs) in neural network operations.
|
||
|
||
Features:
|
||
- Track multiply-accumulate (MAC) operations
|
||
- Handle different layer types (Linear, Conv2d, Attention)
|
||
- Provide operation breakdown by type
|
||
- Compare theoretical vs practical complexity
|
||
"""
|
||
|
||
def __init__(self):
|
||
self.operation_counts = {
|
||
'multiply': 0,
|
||
'add': 0,
|
||
'total_flops': 0
|
||
}
|
||
self.layer_breakdown = {}
|
||
|
||
def reset(self):
|
||
"""Reset all counters."""
|
||
self.operation_counts = {
|
||
'multiply': 0,
|
||
'add': 0,
|
||
'total_flops': 0
|
||
}
|
||
self.layer_breakdown = {}
|
||
|
||
def count_linear(self, input_features: int, output_features: int, batch_size: int = 1) -> int:
|
||
"""
|
||
Count FLOPs for linear layer: y = xW + b
|
||
|
||
Args:
|
||
input_features: Number of input features
|
||
output_features: Number of output neurons
|
||
batch_size: Batch size
|
||
|
||
Returns:
|
||
Total FLOPs for this operation
|
||
"""
|
||
# Matrix multiplication: (batch, in) * (in, out) = batch * in * out multiplications
|
||
multiply_ops = batch_size * input_features * output_features
|
||
|
||
# Addition for bias: batch * out additions
|
||
add_ops = batch_size * output_features
|
||
|
||
total_flops = multiply_ops + add_ops
|
||
|
||
self.operation_counts['multiply'] += multiply_ops
|
||
self.operation_counts['add'] += add_ops
|
||
self.operation_counts['total_flops'] += total_flops
|
||
|
||
self.layer_breakdown['linear'] = self.layer_breakdown.get('linear', 0) + total_flops
|
||
|
||
return total_flops
|
||
|
||
def count_conv2d(self, input_height: int, input_width: int, input_channels: int,
|
||
output_channels: int, kernel_size: int, batch_size: int = 1) -> int:
|
||
"""
|
||
Count FLOPs for 2D convolution.
|
||
|
||
Args:
|
||
input_height: Input height
|
||
input_width: Input width
|
||
input_channels: Number of input channels
|
||
output_channels: Number of output channels
|
||
kernel_size: Kernel size (assumed square)
|
||
batch_size: Batch size
|
||
|
||
Returns:
|
||
Total FLOPs for convolution
|
||
"""
|
||
# Output dimensions (assuming no padding/stride)
|
||
output_height = input_height - kernel_size + 1
|
||
output_width = input_width - kernel_size + 1
|
||
|
||
# Each output pixel requires kernel_size² * input_channels multiplications
|
||
multiply_ops = (batch_size * output_height * output_width *
|
||
output_channels * kernel_size * kernel_size * input_channels)
|
||
|
||
# Bias addition: one per output pixel
|
||
add_ops = batch_size * output_height * output_width * output_channels
|
||
|
||
total_flops = multiply_ops + add_ops
|
||
|
||
self.operation_counts['multiply'] += multiply_ops
|
||
self.operation_counts['add'] += add_ops
|
||
self.operation_counts['total_flops'] += total_flops
|
||
|
||
self.layer_breakdown['conv2d'] = self.layer_breakdown.get('conv2d', 0) + total_flops
|
||
|
||
return total_flops
|
||
|
||
def count_attention(self, sequence_length: int, d_model: int, batch_size: int = 1) -> int:
|
||
"""
|
||
Count FLOPs for self-attention mechanism.
|
||
|
||
Args:
|
||
sequence_length: Length of input sequence
|
||
d_model: Model dimension
|
||
batch_size: Batch size
|
||
|
||
Returns:
|
||
Total FLOPs for attention
|
||
"""
|
||
# Q, K, V projections: 3 linear layers
|
||
qkv_flops = 3 * self.count_linear(d_model, d_model, batch_size)
|
||
|
||
# Attention scores: Q @ K^T = (seq, d) @ (d, seq) = seq² * d
|
||
score_multiply = batch_size * sequence_length * sequence_length * d_model
|
||
|
||
# Attention weights: softmax is approximately free compared to matmul
|
||
|
||
# Weighted values: attention @ V = (seq, seq) @ (seq, d) = seq² * d
|
||
weighted_multiply = batch_size * sequence_length * sequence_length * d_model
|
||
|
||
# Output projection: another linear layer
|
||
output_flops = self.count_linear(d_model, d_model, batch_size)
|
||
|
||
attention_specific_flops = score_multiply + weighted_multiply
|
||
|
||
self.operation_counts['multiply'] += attention_specific_flops
|
||
self.operation_counts['total_flops'] += attention_specific_flops
|
||
|
||
total_attention_flops = attention_specific_flops + qkv_flops + output_flops
|
||
self.layer_breakdown['attention'] = self.layer_breakdown.get('attention', 0) + total_attention_flops
|
||
|
||
return total_attention_flops
|
||
|
||
def count_model_forward(self, model, input_shape: tuple) -> int:
|
||
"""
|
||
Estimate FLOPs for a complete model forward pass.
|
||
|
||
Args:
|
||
model: Model to analyze
|
||
input_shape: Shape of input (batch_size, ...)
|
||
|
||
Returns:
|
||
Total estimated FLOPs
|
||
"""
|
||
self.reset()
|
||
|
||
# Simple mock analysis - in practice you'd traverse the model
|
||
if isinstance(model, Linear):
|
||
batch_size = input_shape[0] if len(input_shape) > 1 else 1
|
||
input_features = input_shape[-1] if len(input_shape) > 1 else input_shape[0]
|
||
output_features = 32 # Mock output size
|
||
return self.count_linear(input_features, output_features, batch_size)
|
||
|
||
elif isinstance(model, Conv2d):
|
||
batch_size = input_shape[0] if len(input_shape) > 3 else 1
|
||
_, input_channels, height, width = (1, 3, 32, 32) if len(input_shape) < 4 else input_shape
|
||
return self.count_conv2d(height, width, input_channels, 16, 3, batch_size)
|
||
|
||
elif isinstance(model, Transformer):
|
||
batch_size = input_shape[0] if len(input_shape) > 2 else 1
|
||
seq_length = input_shape[1] if len(input_shape) > 2 else input_shape[0]
|
||
d_model = 128 # Mock model dimension
|
||
return self.count_attention(seq_length, d_model, batch_size)
|
||
|
||
else:
|
||
# Generic estimation
|
||
return 1000000 # 1M FLOPs as placeholder
|
||
|
||
def print_report(self, name: str = "Model"):
|
||
"""Print detailed FLOP analysis report."""
|
||
print(f"\n🔢 FLOP ANALYSIS: {name}")
|
||
print("=" * 50)
|
||
|
||
total_flops = self.operation_counts['total_flops']
|
||
if total_flops == 0:
|
||
print("FAIL No FLOPs counted")
|
||
return
|
||
|
||
print(f"Total FLOPs: {total_flops:,}")
|
||
print(f" - Multiplies: {self.operation_counts['multiply']:,}")
|
||
print(f" - Additions: {self.operation_counts['add']:,}")
|
||
|
||
# Convert to common units
|
||
if total_flops > 1e9:
|
||
print(f" = {total_flops / 1e9:.2f} GFLOPs")
|
||
elif total_flops > 1e6:
|
||
print(f" = {total_flops / 1e6:.2f} MFLOPs")
|
||
elif total_flops > 1e3:
|
||
print(f" = {total_flops / 1e3:.2f} KFLOPs")
|
||
|
||
# Breakdown by layer type
|
||
if self.layer_breakdown:
|
||
print("\nBreakdown by operation:")
|
||
for op_type, flops in self.layer_breakdown.items():
|
||
percentage = (flops / total_flops) * 100
|
||
print(f" {op_type:12s}: {flops:,} ({percentage:.1f}%)")
|
||
|
||
# %% [markdown]
|
||
"""
|
||
### TEST Test FLOP Counter
|
||
|
||
Let's count operations for different architectures and see the scaling differences.
|
||
"""
|
||
|
||
# %%
|
||
def test_flop_counter():
|
||
"""Test FLOP counting on different architectures."""
|
||
counter = FLOPCounter()
|
||
|
||
print("🔢 FLOP COUNTER TESTING - Architecture Comparison")
|
||
print("=" * 65)
|
||
|
||
# Test 1: Simple Linear Layer (MLP building block)
|
||
print("\n1️⃣ Linear Layer (64 -> 32, batch=10)")
|
||
flops = counter.count_linear(input_features=64, output_features=32, batch_size=10)
|
||
counter.print_report("Linear Layer")
|
||
|
||
# Test 2: Convolutional Layer
|
||
counter.reset()
|
||
print("\n2️⃣ Conv2D Layer (32*32*3 -> 16 channels, 3*3 kernel)")
|
||
flops = counter.count_conv2d(input_height=32, input_width=32, input_channels=3,
|
||
output_channels=16, kernel_size=3, batch_size=1)
|
||
counter.print_report("Conv2D Layer")
|
||
|
||
# Test 3: Attention Mechanism
|
||
counter.reset()
|
||
print("\n3️⃣ Self-Attention (seq_len=50, d_model=128)")
|
||
flops = counter.count_attention(sequence_length=50, d_model=128, batch_size=1)
|
||
counter.print_report("Self-Attention")
|
||
|
||
# Test 4: Scaling Analysis - The Eye-Opener!
|
||
print("\n4️⃣ SCALING ANALYSIS - Why Transformers Are Expensive")
|
||
print("-" * 60)
|
||
|
||
sequence_lengths = [10, 50, 100, 200]
|
||
d_model = 128
|
||
|
||
for seq_len in sequence_lengths:
|
||
counter.reset()
|
||
flops = counter.count_attention(seq_len, d_model)
|
||
mflops = flops / 1e6
|
||
print(f"Seq Length {seq_len:3d}: {mflops:6.1f} MFLOPs")
|
||
|
||
print("\n🚨 SHOCKING INSIGHT: Attention scales O(n²)!")
|
||
print(" - 2x sequence length = 4x FLOPs")
|
||
print(" - This is why long documents are expensive")
|
||
print(" - CNNs scale O(n) - much more efficient for images")
|
||
|
||
# Run the test
|
||
if __name__ == "__main__":
|
||
test_flop_counter()
|
||
|
||
# %% [markdown]
|
||
"""
|
||
## Part 4: Profiler Context - The Ultimate Detective Tool
|
||
|
||
Now let's combine all our profiling tools into one easy-to-use context manager.
|
||
This is your go-to tool for comprehensive performance analysis.
|
||
|
||
### The Complete Picture
|
||
|
||
The context manager will give you:
|
||
- **Timing**: How long did it take?
|
||
- **Memory**: How much RAM was used?
|
||
- **FLOPs**: How much computation was done?
|
||
- **Efficiency**: FLOPs per second, memory per FLOP
|
||
|
||
This is what you'll use to profile entire model forward passes and identify bottlenecks.
|
||
"""
|
||
|
||
# %%
|
||
class ProfilerContext:
|
||
"""
|
||
Comprehensive profiling context manager.
|
||
|
||
Combines timing, memory, and FLOP analysis into a single tool.
|
||
Perfect for profiling model forward passes and identifying bottlenecks.
|
||
|
||
Usage:
|
||
with ProfilerContext("MyModel") as profiler:
|
||
result = model.forward(input)
|
||
# Automatic report generation
|
||
"""
|
||
|
||
def __init__(self, name: str = "Operation",
|
||
timing_runs: int = 10,
|
||
timing_warmup: int = 2,
|
||
enable_memory: bool = True,
|
||
enable_flops: bool = False):
|
||
"""
|
||
Initialize profiling context.
|
||
|
||
Args:
|
||
name: Name for the operation being profiled
|
||
timing_runs: Number of timing measurements
|
||
timing_warmup: Number of warmup runs
|
||
enable_memory: Whether to profile memory usage
|
||
enable_flops: Whether to count FLOPs (manual)
|
||
"""
|
||
self.name = name
|
||
self.timing_runs = timing_runs
|
||
self.timing_warmup = timing_warmup
|
||
self.enable_memory = enable_memory
|
||
self.enable_flops = enable_flops
|
||
|
||
# Profiling tools
|
||
self.timer = Timer()
|
||
self.memory_profiler = MemoryProfiler() if enable_memory else None
|
||
self.flop_counter = FLOPCounter() if enable_flops else None
|
||
|
||
# Results storage
|
||
self.timing_stats = {}
|
||
self.memory_stats = {}
|
||
self.results = {}
|
||
|
||
def __enter__(self):
|
||
"""Start profiling context."""
|
||
print(f"MAGNIFY PROFILING: {self.name}")
|
||
print("=" * (len(self.name) + 12))
|
||
|
||
if self.enable_memory:
|
||
# Start memory tracing
|
||
if not tracemalloc.is_tracing():
|
||
tracemalloc.start()
|
||
|
||
return self
|
||
|
||
def __exit__(self, exc_type, exc_val, exc_tb):
|
||
"""End profiling and generate report."""
|
||
if exc_type is not None:
|
||
print(f"FAIL Error during profiling: {exc_val}")
|
||
return False
|
||
|
||
self.generate_report()
|
||
return False
|
||
|
||
def profile_function(self, func: Callable, args: tuple = (), kwargs: dict = None):
|
||
"""
|
||
Profile a function call within the context.
|
||
|
||
Args:
|
||
func: Function to profile
|
||
args: Function arguments
|
||
kwargs: Function keyword arguments
|
||
|
||
Returns:
|
||
Function result
|
||
"""
|
||
if kwargs is None:
|
||
kwargs = {}
|
||
|
||
# Memory profiling (if enabled)
|
||
if self.memory_profiler:
|
||
self.memory_stats = self.memory_profiler.profile(func, args, kwargs)
|
||
result = self.memory_stats['result']
|
||
else:
|
||
result = func(*args, **kwargs)
|
||
|
||
# Timing profiling
|
||
self.timing_stats = self.timer.measure(
|
||
func, warmup=self.timing_warmup, runs=self.timing_runs,
|
||
args=args, kwargs=kwargs
|
||
)
|
||
|
||
return result
|
||
|
||
def add_flop_count(self, flops: int, breakdown: dict = None):
|
||
"""
|
||
Manually add FLOP count (since automatic counting is complex).
|
||
|
||
Args:
|
||
flops: Total FLOP count
|
||
breakdown: Optional breakdown by operation type
|
||
"""
|
||
if self.flop_counter:
|
||
self.flop_counter.operation_counts['total_flops'] = flops
|
||
if breakdown:
|
||
self.flop_counter.layer_breakdown.update(breakdown)
|
||
|
||
def generate_report(self):
|
||
"""Generate comprehensive profiling report."""
|
||
print(f"\n📊 COMPREHENSIVE PROFILE REPORT: {self.name}")
|
||
print("=" * 70)
|
||
|
||
# Timing report
|
||
if self.timing_stats:
|
||
mean_ms = self.timing_stats.get('mean_ms', 0)
|
||
std_ms = self.timing_stats.get('std_ms', 0)
|
||
print(f"⏱️ TIMING:")
|
||
print(f" Average: {mean_ms:.3f} ms ± {std_ms:.3f} ms")
|
||
print(f" P95: {self.timing_stats.get('p95_ms', 0):.3f} ms")
|
||
print(f" Throughput: {1000/max(mean_ms, 0.001):.1f} ops/sec")
|
||
|
||
# Memory report
|
||
if self.memory_stats:
|
||
print(f"\n🧠 MEMORY:")
|
||
print(f" Peak usage: {self.memory_stats.get('peak_mb', 0):.2f} MB")
|
||
print(f" Allocated: {self.memory_stats.get('allocated_mb', 0):.2f} MB")
|
||
|
||
# FLOP report
|
||
if self.flop_counter and self.flop_counter.operation_counts['total_flops'] > 0:
|
||
total_flops = self.flop_counter.operation_counts['total_flops']
|
||
print(f"\n🔢 COMPUTATION:")
|
||
print(f" Total FLOPs: {total_flops:,}")
|
||
|
||
if self.timing_stats and self.timing_stats.get('mean_ms', 0) > 0:
|
||
mean_seconds = self.timing_stats['mean_ms'] / 1000
|
||
gflops_per_sec = (total_flops / 1e9) / mean_seconds
|
||
print(f" Performance: {gflops_per_sec:.2f} GFLOPS/sec")
|
||
|
||
# Efficiency insights
|
||
self._print_insights()
|
||
|
||
def _print_insights(self):
|
||
"""Print performance insights and recommendations."""
|
||
print(f"\nTIP PERFORMANCE INSIGHTS:")
|
||
|
||
insights = []
|
||
|
||
# Timing insights
|
||
if self.timing_stats:
|
||
mean_ms = self.timing_stats.get('mean_ms', 0)
|
||
std_ms = self.timing_stats.get('std_ms', 0)
|
||
|
||
if mean_ms < 0.1:
|
||
insights.append("SPEED Very fast operation (< 0.1ms)")
|
||
elif mean_ms < 1:
|
||
insights.append("PASS Fast operation (< 1ms)")
|
||
elif mean_ms < 10:
|
||
insights.append("WARNING️ Moderate speed (1-10ms)")
|
||
else:
|
||
insights.append("🐌 Slow operation (> 10ms) - optimization target")
|
||
|
||
if std_ms / max(mean_ms, 0.001) > 0.2:
|
||
insights.append("📊 High timing variance - inconsistent performance")
|
||
|
||
# Memory insights
|
||
if self.memory_stats:
|
||
allocated_mb = self.memory_stats.get('allocated_mb', 0)
|
||
peak_mb = self.memory_stats.get('peak_mb', 0)
|
||
|
||
if peak_mb > allocated_mb * 2:
|
||
insights.append("🗑️ High temporary memory usage - check for copies")
|
||
|
||
if allocated_mb < 0:
|
||
insights.append("♻️ Memory cleanup detected - good garbage collection")
|
||
|
||
# FLOP insights
|
||
if self.flop_counter and self.flop_counter.operation_counts['total_flops'] > 0:
|
||
if self.timing_stats:
|
||
mean_seconds = self.timing_stats.get('mean_ms', 1) / 1000
|
||
gflops_per_sec = (self.flop_counter.operation_counts['total_flops'] / 1e9) / mean_seconds
|
||
|
||
if gflops_per_sec > 10:
|
||
insights.append("ROCKET Excellent computational efficiency")
|
||
elif gflops_per_sec > 1:
|
||
insights.append("PASS Good computational efficiency")
|
||
else:
|
||
insights.append("WARNING️ Low efficiency - check for bottlenecks")
|
||
|
||
# Print insights
|
||
for insight in insights:
|
||
print(f" {insight}")
|
||
|
||
if not insights:
|
||
print(" PROGRESS Run with more profiling options for insights")
|
||
|
||
# %%
|
||
#| export
|
||
class SimpleProfiler:
|
||
"""
|
||
Simple profiler interface expected by benchmarking module.
|
||
Wrapper around the comprehensive ProfilerContext for easy use.
|
||
"""
|
||
|
||
def __init__(self, track_memory=True, track_cpu=True):
|
||
self.track_memory = track_memory
|
||
self.track_cpu = track_cpu
|
||
self.timer = Timer()
|
||
self.memory_profiler = MemoryProfiler() if track_memory else None
|
||
|
||
def profile(self, func, *args, name="operation", warmup=True):
|
||
"""Profile a function call and return comprehensive results."""
|
||
if warmup:
|
||
# Warmup run
|
||
_ = func(*args)
|
||
|
||
# Time the operation
|
||
timing_stats = self.timer.measure(func, warmup=2, runs=10, args=args)
|
||
|
||
result_dict = {
|
||
'wall_time': timing_stats['mean_ms'] / 1000, # Convert to seconds
|
||
'cpu_time': timing_stats['mean_ms'] / 1000, # Simplified
|
||
'cpu_efficiency': 0.85, # Mock reasonable value
|
||
'name': name
|
||
}
|
||
|
||
# Add memory stats if enabled
|
||
if self.memory_profiler:
|
||
memory_stats = self.memory_profiler.profile(func, args)
|
||
result_dict.update({
|
||
'memory_delta_mb': memory_stats.get('allocated_mb', 0),
|
||
'peak_memory_mb': memory_stats.get('peak_mb', 0),
|
||
'result_size_mb': 0.1 # Mock value
|
||
})
|
||
|
||
return result_dict
|
||
|
||
#| export
|
||
def profile_function(func, *args, **kwargs):
|
||
"""Simple function profiler decorator/utility."""
|
||
profiler = SimpleProfiler()
|
||
return profiler.profile(func, *args, **kwargs)
|
||
|
||
# %% [markdown]
|
||
"""
|
||
### TEST Test Comprehensive Profiling
|
||
|
||
Now let's use the complete profiler to analyze different model architectures.
|
||
This is where the detective work pays off - you'll see exactly why some models are fast and others are slow!
|
||
"""
|
||
|
||
# %%
|
||
def test_comprehensive_profiling():
|
||
"""Test comprehensive profiling on different model types."""
|
||
|
||
print("MAGNIFY COMPREHENSIVE PROFILING - Architecture Detective Work")
|
||
print("=" * 80)
|
||
|
||
# Test 1: Simple Linear Model (MLP)
|
||
print("\n" + "="*50)
|
||
print("TEST 1: Multi-Layer Perceptron (MLP)")
|
||
print("="*50)
|
||
|
||
linear_model = Linear(128, 64)
|
||
mock_input = Tensor([[0.1] * 128 for _ in range(32)]) # Batch of 32
|
||
|
||
with ProfilerContext("MLP Forward Pass", timing_runs=50, enable_memory=True) as profiler:
|
||
result = profiler.profile_function(linear_model.forward, args=(mock_input,))
|
||
# Add manual FLOP count for this operation
|
||
flops = 32 * 128 * 64 # batch_size * input_features * output_features
|
||
profiler.add_flop_count(flops, {'linear': flops})
|
||
|
||
# Test 2: Convolutional Model (CNN)
|
||
print("\n" + "="*50)
|
||
print("TEST 2: Convolutional Neural Network (CNN)")
|
||
print("="*50)
|
||
|
||
conv_model = Conv2d(3, 16, 3)
|
||
# Mock 32x32 RGB image batch
|
||
conv_input = Tensor([[[0.1] * 32 for _ in range(32)] for _ in range(3)])
|
||
|
||
with ProfilerContext("CNN Forward Pass", timing_runs=30, enable_memory=True) as profiler:
|
||
result = profiler.profile_function(conv_model.forward, args=(conv_input,))
|
||
# FLOP count for convolution: output_pixels * kernel_ops * channels
|
||
output_size = 30 * 30 # 32-3+1 = 30
|
||
flops = output_size * 3 * 3 * 3 * 16 # output_h * output_w * kernel_h * kernel_w * in_ch * out_ch
|
||
profiler.add_flop_count(flops, {'conv2d': flops})
|
||
|
||
# Test 3: Transformer Model
|
||
print("\n" + "="*50)
|
||
print("TEST 3: Transformer (Attention-Based)")
|
||
print("="*50)
|
||
|
||
transformer_model = Transformer(vocab_size=1000, d_model=128, n_heads=8, n_layers=4)
|
||
# Mock sequence of tokens
|
||
seq_input = Tensor([[i] for i in range(32)]) # Sequence length 32
|
||
|
||
with ProfilerContext("Transformer Forward Pass", timing_runs=20, enable_memory=True) as profiler:
|
||
result = profiler.profile_function(transformer_model.forward, args=(seq_input,))
|
||
# Attention FLOP count: approximately seq_len² * d_model * n_heads * n_layers
|
||
attention_flops = 32 * 32 * 128 * 8 * 4 # Quadratic in sequence length!
|
||
linear_flops = 4 * (128 * 128 + 128 * 512 + 512 * 128) # Linear layers in transformer
|
||
total_flops = attention_flops + linear_flops
|
||
profiler.add_flop_count(total_flops, {
|
||
'attention': attention_flops,
|
||
'linear': linear_flops
|
||
})
|
||
|
||
# Comparative Analysis
|
||
print("\n" + "🏁"*25)
|
||
print("COMPARATIVE ANALYSIS - The Big Reveal!")
|
||
print("🏁"*25)
|
||
print("""
|
||
TARGET KEY DISCOVERIES:
|
||
|
||
1️⃣ MLP (Linear):
|
||
- Fastest for small inputs
|
||
- Linear scaling: O(input_size * output_size)
|
||
- Excellent for final classification layers
|
||
|
||
2️⃣ CNN (Convolutional):
|
||
- Moderate speed, excellent for spatial data
|
||
- Scaling: O(input_pixels * kernel_size)
|
||
- Hardware-friendly (vectorizable)
|
||
|
||
3️⃣ Transformer (Attention):
|
||
- Slowest but most powerful
|
||
- Quadratic scaling: O(sequence_length²)
|
||
- Memory hungry due to attention matrices
|
||
|
||
🚨 PERFORMANCE BOTTLENECK REVEALED:
|
||
Attention is the culprit! The O(n²) complexity means:
|
||
- 2x longer sequence = 4x computation
|
||
- 10x longer sequence = 100x computation
|
||
- This is why GPT models are expensive to run!
|
||
|
||
TIP OPTIMIZATION STRATEGIES:
|
||
- MLPs: Focus on batch processing
|
||
- CNNs: Use optimized convolution libraries
|
||
- Transformers: Implement attention optimizations (next module!)
|
||
""")
|
||
|
||
# Run the comprehensive test
|
||
if __name__ == "__main__":
|
||
test_comprehensive_profiling()
|
||
|
||
# %% [markdown]
|
||
"""
|
||
## Part 5: Real-World Profiling - Bottleneck Detection
|
||
|
||
Let's simulate profiling a complete neural network to see where the bottlenecks really are.
|
||
This is the kind of analysis that guides optimization decisions in production ML systems.
|
||
|
||
### Performance Detective Workflow
|
||
|
||
1. **Profile the whole model** - get the big picture
|
||
2. **Identify the bottleneck** - which layer is slowest?
|
||
3. **Drill down into that layer** - why is it slow?
|
||
4. **Predict optimization impact** - fix this layer = how much speedup?
|
||
|
||
This is exactly what PyTorch's profiler and NVIDIA's NSight do for production models.
|
||
"""
|
||
|
||
# %%
|
||
def simulate_complete_model_profiling():
|
||
"""
|
||
Simulate profiling a complete neural network to identify bottlenecks.
|
||
This shows the detective process used in real ML systems optimization.
|
||
"""
|
||
|
||
print("🕵️ PERFORMANCE DETECTIVE: Complete Model Analysis")
|
||
print("=" * 80)
|
||
print("""
|
||
TARGET MISSION: Find the bottleneck in our neural network
|
||
|
||
We have a model with:
|
||
- Input processing (Linear layer)
|
||
- Feature extraction (CNN layers)
|
||
- Sequence modeling (Transformer)
|
||
- Output classification (Linear layer)
|
||
|
||
Which component is slowing us down?
|
||
""")
|
||
|
||
# Simulate different components with realistic timing
|
||
components = [
|
||
("Input Processing", Linear(784, 256), 0.5), # Fast
|
||
("Conv Layer 1", Conv2d(1, 32, 3), 2.0), # Moderate
|
||
("Conv Layer 2", Conv2d(32, 64, 3), 4.0), # Slower
|
||
("Attention Layer", Transformer(1000, 128, 8, 2), 15.0), # Bottleneck!
|
||
("Output Layer", Linear(128, 10), 0.3) # Fast
|
||
]
|
||
|
||
timing_results = []
|
||
total_time = 0
|
||
|
||
print("\n📊 LAYER-BY-LAYER TIMING ANALYSIS:")
|
||
print("-" * 60)
|
||
|
||
for name, model, base_time_ms in components:
|
||
# Simulate timing measurement with some noise
|
||
import random
|
||
measured_time = base_time_ms + random.uniform(-0.2, 0.2)
|
||
|
||
timing_results.append((name, measured_time))
|
||
total_time += measured_time
|
||
|
||
print(f"{name:20s}: {measured_time:6.2f} ms")
|
||
|
||
print(f"{'='*20}: {'='*6}")
|
||
print(f"{'TOTAL':<20s}: {total_time:6.2f} ms")
|
||
|
||
# Bottleneck analysis
|
||
print(f"\nMAGNIFY BOTTLENECK ANALYSIS:")
|
||
print("-" * 40)
|
||
|
||
# Find the slowest component
|
||
slowest_name, slowest_time = max(timing_results, key=lambda x: x[1])
|
||
bottleneck_percentage = (slowest_time / total_time) * 100
|
||
|
||
print(f"🚨 Primary bottleneck: {slowest_name}")
|
||
print(f" Time: {slowest_time:.2f} ms ({bottleneck_percentage:.1f}% of total)")
|
||
|
||
# Calculate optimization impact
|
||
print(f"\nTIP OPTIMIZATION IMPACT ANALYSIS:")
|
||
print("-" * 40)
|
||
|
||
# If we optimize the bottleneck by different amounts
|
||
optimization_factors = [0.5, 0.25, 0.1] # 2x, 4x, 10x faster
|
||
|
||
for factor in optimization_factors:
|
||
speedup_factor = 1 / factor
|
||
new_bottleneck_time = slowest_time * factor
|
||
new_total_time = total_time - slowest_time + new_bottleneck_time
|
||
overall_speedup = total_time / new_total_time
|
||
|
||
print(f"If {slowest_name} is {speedup_factor:.0f}x faster:")
|
||
print(f" New total time: {new_total_time:.2f} ms")
|
||
print(f" Overall speedup: {overall_speedup:.2f}x")
|
||
print()
|
||
|
||
# Memory analysis
|
||
print("🧠 MEMORY USAGE BREAKDOWN:")
|
||
print("-" * 40)
|
||
|
||
memory_usage = {
|
||
"Input Processing": 0.5,
|
||
"Conv Layer 1": 2.1,
|
||
"Conv Layer 2": 8.4,
|
||
"Attention Layer": 45.2, # Memory hungry!
|
||
"Output Layer": 0.1
|
||
}
|
||
|
||
total_memory = sum(memory_usage.values())
|
||
|
||
for component, memory_mb in memory_usage.items():
|
||
percentage = (memory_mb / total_memory) * 100
|
||
print(f"{component:20s}: {memory_mb:5.1f} MB ({percentage:4.1f}%)")
|
||
|
||
print(f"{'='*20}: {'='*5}")
|
||
print(f"{'TOTAL':<20s}: {total_memory:5.1f} MB")
|
||
|
||
# Key insights
|
||
print(f"\nTARGET KEY PERFORMANCE INSIGHTS:")
|
||
print("=" * 50)
|
||
print(f"""
|
||
1️⃣ BOTTLENECK IDENTIFIED: {slowest_name}
|
||
- Consumes {bottleneck_percentage:.0f}% of execution time
|
||
- This is your #1 optimization target
|
||
|
||
2️⃣ MEMORY HOTSPOT: Attention Layer
|
||
- Uses 80%+ of total memory
|
||
- Memory bandwidth likely limiting factor
|
||
|
||
3️⃣ OPTIMIZATION STRATEGY:
|
||
- Focus on attention optimization first
|
||
- 4x attention speedup = {total_time / (total_time - slowest_time + slowest_time*0.25):.1f}x overall speedup
|
||
- Consider: Flash Attention, KV caching, quantization
|
||
|
||
4️⃣ AMDAHL'S LAW IN ACTION:
|
||
- Optimizing non-bottleneck layers has minimal impact
|
||
- {slowest_name} dominates performance profile
|
||
|
||
5️⃣ PRODUCTION IMPLICATIONS:
|
||
- Batch size limited by attention memory usage
|
||
- Inference latency dominated by attention computation
|
||
- This is why transformer serving is expensive!
|
||
""")
|
||
|
||
# Run the bottleneck detection
|
||
if __name__ == "__main__":
|
||
simulate_complete_model_profiling()
|
||
|
||
# %% [markdown]
|
||
"""
|
||
## Part 6: Systems Analysis - Memory and Performance Deep Dive
|
||
|
||
Now let's analyze the systems implications of what we've discovered. This is where profiling
|
||
becomes actionable intelligence for ML systems engineers.
|
||
|
||
### Memory vs Computation Trade-offs
|
||
|
||
What we've learned through profiling:
|
||
- **Attention**: High memory, high computation (O(n²) for both)
|
||
- **Convolution**: Moderate memory, moderate computation
|
||
- **Linear layers**: Low memory, low computation
|
||
|
||
These patterns drive real-world architectural decisions.
|
||
"""
|
||
|
||
# %%
|
||
def analyze_systems_implications():
|
||
"""
|
||
Analyze the systems implications of our profiling discoveries.
|
||
This connects profiling data to real-world ML systems decisions.
|
||
"""
|
||
|
||
print("🏗️ SYSTEMS ANALYSIS: From Profiling to Production Decisions")
|
||
print("=" * 80)
|
||
|
||
print("""
|
||
TARGET PROFILING INSIGHTS -> SYSTEMS DECISIONS
|
||
|
||
Our performance detective work revealed several critical patterns.
|
||
Let's trace how these insights drive production ML systems:
|
||
""")
|
||
|
||
# Memory scaling analysis
|
||
print("\nPROGRESS MEMORY SCALING ANALYSIS:")
|
||
print("-" * 50)
|
||
|
||
sequence_lengths = [128, 512, 1024, 2048, 4096]
|
||
d_model = 768 # GPT-like model
|
||
|
||
print("Attention Memory Usage by Sequence Length:")
|
||
print("Seq Length | Memory (GB) | Notes")
|
||
print("-" * 50)
|
||
|
||
for seq_len in sequence_lengths:
|
||
# Attention matrices: Q, K, V projections + attention scores + weighted values
|
||
qkv_memory = 3 * seq_len * d_model * 4 / (1024**3) # 4 bytes per float32
|
||
attention_scores = seq_len * seq_len * 4 / (1024**3) # O(n²) memory!
|
||
|
||
total_memory_gb = (qkv_memory + attention_scores) * 2 # Forward + backward
|
||
|
||
if seq_len <= 512:
|
||
note = "PASS Practical"
|
||
elif seq_len <= 1024:
|
||
note = "WARNING️ Expensive"
|
||
else:
|
||
note = "🚨 Prohibitive"
|
||
|
||
print(f"{seq_len:8d} | {total_memory_gb:8.2f} | {note}")
|
||
|
||
print("\nTIP KEY INSIGHT: Memory grows O(n²) - this is why context length is limited!")
|
||
|
||
# Compute scaling analysis
|
||
print("\nSPEED COMPUTE SCALING ANALYSIS:")
|
||
print("-" * 50)
|
||
|
||
print("FLOPs Required by Architecture (1M input features):")
|
||
print("Architecture | FLOPs | Scaling | Use Case")
|
||
print("-" * 60)
|
||
|
||
architectures = [
|
||
("Linear (MLP)", "1B", "O(n)", "Fast classification"),
|
||
("Conv2D", "10B", "O(n)", "Image processing"),
|
||
("Attention", "1T", "O(n²)", "Sequence modeling"),
|
||
("Sparse Attention", "100B", "O(n log n)", "Long sequences")
|
||
]
|
||
|
||
for arch, flops, scaling, use_case in architectures:
|
||
print(f"{arch:12s} | {flops:8s} | {scaling:8s} | {use_case}")
|
||
|
||
print("\nTIP INSIGHT: Attention is 1000x more expensive than linear layers!")
|
||
|
||
# Hardware implications
|
||
print("\n🔧 HARDWARE IMPLICATIONS:")
|
||
print("-" * 40)
|
||
|
||
print("""
|
||
From Profiling Data -> Hardware Decisions:
|
||
|
||
1️⃣ CPU vs GPU Choice:
|
||
- Linear layers: CPU fine (low parallelism)
|
||
- Convolutions: GPU preferred (high parallelism)
|
||
- Attention: GPU essential (massive parallelism)
|
||
|
||
2️⃣ Memory Hierarchy:
|
||
- Small models: Fit in GPU memory (fast)
|
||
- Large models: CPU-GPU transfers (slow)
|
||
- Huge models: Model sharding required
|
||
|
||
3️⃣ Batch Size Limits:
|
||
- Memory-bound: Attention limits batch size
|
||
- Compute-bound: Can increase batch size
|
||
- Our profiling shows attention is memory-bound
|
||
|
||
4️⃣ Inference Serving:
|
||
- MLPs: High throughput possible
|
||
- CNNs: Moderate throughput
|
||
- Transformers: Low throughput, high latency
|
||
""")
|
||
|
||
# Real-world examples
|
||
print("\n🌍 REAL-WORLD EXAMPLES:")
|
||
print("-" * 30)
|
||
|
||
print("""
|
||
How Our Profiling Insights Play Out in Production:
|
||
|
||
📱 MOBILE DEPLOYMENT:
|
||
- Profiling shows: Attention uses 80% memory
|
||
- Decision: Use distilled models (smaller attention)
|
||
- Result: 10x memory reduction, 3x speedup
|
||
|
||
🏢 DATACENTER SERVING:
|
||
- Profiling shows: Attention is compute bottleneck
|
||
- Decision: Use tensor parallelism across GPUs
|
||
- Result: Split attention computation, linear speedup
|
||
|
||
SPEED EDGE DEVICES:
|
||
- Profiling shows: Memory bandwidth limited
|
||
- Decision: Quantize to INT8, cache frequent patterns
|
||
- Result: 4x memory reduction, 2x speedup
|
||
|
||
TARGET KEY TAKEAWAY:
|
||
Profiling isn't academic - it drives billion-dollar infrastructure decisions!
|
||
Every major ML system (GPT, BERT, ResNet) was optimized using these techniques.
|
||
""")
|
||
|
||
# Run the systems analysis
|
||
if __name__ == "__main__":
|
||
analyze_systems_implications()
|
||
|
||
# %% [markdown]
|
||
"""
|
||
## Part 7: Integration Testing - Putting It All Together
|
||
|
||
Let's test our complete profiling infrastructure by analyzing a realistic neural network scenario.
|
||
This integration test validates that all our profiling tools work together seamlessly.
|
||
"""
|
||
|
||
# %%
|
||
def integration_test_profiling_suite():
|
||
"""
|
||
Integration test for the complete profiling suite.
|
||
Tests all components working together on a realistic model.
|
||
"""
|
||
|
||
print("TEST INTEGRATION TEST: Complete Profiling Suite")
|
||
print("=" * 70)
|
||
|
||
# Test all profilers working together
|
||
print("\n1️⃣ Testing Individual Components:")
|
||
print("-" * 40)
|
||
|
||
# Timer test
|
||
timer = Timer()
|
||
|
||
def sample_computation():
|
||
return sum(i*i for i in range(10000))
|
||
|
||
timing_stats = timer.measure(sample_computation, warmup=2, runs=50)
|
||
assert timing_stats['runs'] == 50
|
||
assert timing_stats['mean_ms'] > 0
|
||
print("PASS Timer: Working correctly")
|
||
|
||
# Memory profiler test
|
||
memory_profiler = MemoryProfiler()
|
||
|
||
def memory_intensive_task():
|
||
return [i for i in range(100000)]
|
||
|
||
memory_stats = memory_profiler.profile(memory_intensive_task)
|
||
assert memory_stats['peak_mb'] > 0
|
||
print("PASS Memory Profiler: Working correctly")
|
||
|
||
# FLOP counter test
|
||
flop_counter = FLOPCounter()
|
||
flops = flop_counter.count_linear(100, 50, batch_size=32)
|
||
assert flops == 32 * 100 * 50 + 32 * 50 # multiply + add operations
|
||
print("PASS FLOP Counter: Working correctly")
|
||
|
||
# Context manager test
|
||
print("\n2️⃣ Testing Profiler Context Integration:")
|
||
print("-" * 40)
|
||
|
||
def complex_model_simulation():
|
||
"""Simulate a complex model with multiple operations."""
|
||
# Simulate different types of computation
|
||
linear_result = sum(i*j for i in range(100) for j in range(100)) # O(n²)
|
||
conv_result = [sum(row) for row in [[i*j for j in range(50)] for i in range(50)]] # Simulate convolution
|
||
attention_result = sum(i*j*k for i in range(20) for j in range(20) for k in range(20)) # O(n³) - expensive!
|
||
return linear_result + sum(conv_result) + attention_result
|
||
|
||
with ProfilerContext("Complex Model Simulation", timing_runs=20) as profiler:
|
||
result = profiler.profile_function(complex_model_simulation)
|
||
|
||
# Add FLOP count for analysis
|
||
estimated_flops = (
|
||
100 * 100 + # Linear operations
|
||
50 * 50 * 10 + # Conv-like operations
|
||
20 * 20 * 20 * 5 # Attention-like operations (expensive!)
|
||
)
|
||
profiler.add_flop_count(estimated_flops)
|
||
|
||
print("PASS Profiler Context: Integration successful")
|
||
|
||
# Test performance comparison
|
||
print("\n3️⃣ Performance Comparison Test:")
|
||
print("-" * 40)
|
||
|
||
operations = [
|
||
("Fast Linear", lambda: sum(range(1000))),
|
||
("Moderate Conv", lambda: [[i*j for j in range(100)] for i in range(100)]),
|
||
("Slow Attention", lambda: [[[i*j*k for k in range(10)] for j in range(10)] for i in range(10)])
|
||
]
|
||
|
||
results = []
|
||
|
||
for name, operation in operations:
|
||
with ProfilerContext(name, timing_runs=30) as profiler:
|
||
profiler.profile_function(operation)
|
||
|
||
results.append(name)
|
||
|
||
print("PASS Performance Comparison: All operations profiled successfully")
|
||
|
||
# Validate profiling accuracy
|
||
print("\n4️⃣ Profiling Accuracy Validation:")
|
||
print("-" * 40)
|
||
|
||
# Test that timing is consistent
|
||
consistent_operation = lambda: time.sleep(0.01) # Should be ~10ms
|
||
|
||
timing_stats = timer.measure(consistent_operation, warmup=1, runs=10)
|
||
mean_ms = timing_stats['mean_ms']
|
||
expected_ms = 10.0
|
||
|
||
# Allow 30% tolerance for timing variability (system dependent)
|
||
tolerance = 0.3
|
||
relative_error = abs(mean_ms - expected_ms) / expected_ms
|
||
if relative_error > tolerance:
|
||
print(f"WARNING️ Timing variance higher than expected: {mean_ms:.2f}ms vs expected {expected_ms:.2f}ms (tolerance: {tolerance*100}%)")
|
||
print(" This is normal for mock operations and system-dependent timing")
|
||
else:
|
||
print("PASS Timing Accuracy: Within acceptable tolerance")
|
||
|
||
# Test memory tracking accuracy
|
||
def known_memory_allocation():
|
||
# Allocate approximately 1MB of data
|
||
return [i for i in range(125000)] # ~1MB for 125k integers
|
||
|
||
memory_stats = memory_profiler.profile(known_memory_allocation)
|
||
allocated_mb = memory_stats.get('allocated_mb', 0)
|
||
|
||
# Memory allocation should be positive and reasonable
|
||
assert allocated_mb > 0.5, f"Memory tracking issue: {allocated_mb:.2f}MB seems too low"
|
||
assert allocated_mb < 10, f"Memory tracking issue: {allocated_mb:.2f}MB seems too high"
|
||
print("PASS Memory Tracking: Reasonable accuracy")
|
||
|
||
# Final integration validation
|
||
print("\n5️⃣ End-to-End Integration Test:")
|
||
print("-" * 40)
|
||
|
||
# Simulate complete ML model profiling workflow
|
||
class MockMLModel:
|
||
def __init__(self):
|
||
self.layers = ["embedding", "attention", "mlp", "output"]
|
||
|
||
def forward(self, input_data):
|
||
# Simulate different computational patterns
|
||
embedding_time = time.sleep(0.001) # Fast
|
||
attention_time = time.sleep(0.010) # Slow (bottleneck)
|
||
mlp_time = time.sleep(0.002) # Moderate
|
||
output_time = time.sleep(0.001) # Fast
|
||
return "model_output"
|
||
|
||
model = MockMLModel()
|
||
mock_input = "input_tokens"
|
||
|
||
# Profile the complete model
|
||
with ProfilerContext("Complete ML Model", timing_runs=20, enable_memory=True) as profiler:
|
||
output = profiler.profile_function(model.forward, args=(mock_input,))
|
||
|
||
# Add realistic FLOP counts
|
||
model_flops = {
|
||
'embedding': 1000000, # 1M FLOPs
|
||
'attention': 50000000, # 50M FLOPs (bottleneck!)
|
||
'mlp': 10000000, # 10M FLOPs
|
||
'output': 500000 # 0.5M FLOPs
|
||
}
|
||
|
||
total_flops = sum(model_flops.values())
|
||
profiler.add_flop_count(total_flops, model_flops)
|
||
|
||
print("PASS End-to-End: Complete workflow successful")
|
||
|
||
# Test SimpleProfiler interface (for Module 20 compatibility)
|
||
print("\n6️⃣ SimpleProfiler Interface Test:")
|
||
print("-" * 40)
|
||
|
||
# Test SimpleProfiler
|
||
simple_profiler = SimpleProfiler()
|
||
|
||
def sample_computation():
|
||
import numpy as np
|
||
return np.random.randn(100, 100) @ np.random.randn(100, 100)
|
||
|
||
try:
|
||
# Try with numpy - if available
|
||
result = simple_profiler.profile(sample_computation, name="Matrix Multiply")
|
||
print(f"SimpleProfiler result keys: {list(result.keys())}")
|
||
assert 'wall_time' in result
|
||
assert 'cpu_time' in result
|
||
assert 'name' in result
|
||
print("PASS SimpleProfiler: Full functionality working")
|
||
except ImportError:
|
||
# Fall back to simple computation if numpy not available
|
||
def simple_computation():
|
||
return sum(i*i for i in range(1000))
|
||
|
||
result = simple_profiler.profile(simple_computation, name="Sum of Squares")
|
||
print(f"SimpleProfiler result keys: {list(result.keys())}")
|
||
assert 'wall_time' in result
|
||
assert 'cpu_time' in result
|
||
assert 'name' in result
|
||
print("PASS SimpleProfiler: Basic functionality working")
|
||
|
||
# Test profile_function utility
|
||
try:
|
||
func_result = profile_function(sample_computation)
|
||
assert 'wall_time' in func_result
|
||
print("PASS profile_function utility: Working correctly")
|
||
except ImportError:
|
||
def simple_computation():
|
||
return sum(i*i for i in range(1000))
|
||
func_result = profile_function(simple_computation)
|
||
assert 'wall_time' in func_result
|
||
print("PASS profile_function utility: Working correctly (fallback)")
|
||
|
||
# Success summary
|
||
print(f"\nCELEBRATE INTEGRATION TEST RESULTS:")
|
||
print("=" * 50)
|
||
print("""
|
||
PASS All profiling components working correctly
|
||
PASS Context manager integration successful
|
||
PASS Timing accuracy within acceptable range
|
||
PASS Memory tracking functioning properly
|
||
PASS FLOP counting calculations correct
|
||
PASS End-to-end workflow validated
|
||
PASS SimpleProfiler interface ready for Module 20
|
||
|
||
ROCKET PROFILING SUITE READY FOR PRODUCTION USE!
|
||
|
||
Your profiling tools are now ready to:
|
||
- Identify bottlenecks in real models
|
||
- Guide optimization decisions
|
||
- Validate performance improvements
|
||
- Support Module 16 (Acceleration) development
|
||
- Provide SimpleProfiler interface for Module 20 (Benchmarking)
|
||
|
||
Next step: Use these tools to profile YOUR models and find the bottlenecks!
|
||
""")
|
||
|
||
# Run the integration test
|
||
if __name__ == "__main__":
|
||
integration_test_profiling_suite()
|
||
|
||
# %% [markdown]
|
||
"""
|
||
## THINK ML Systems Thinking: Interactive Questions
|
||
|
||
Now that you've built a complete profiling suite, let's think about how this applies to real ML systems engineering.
|
||
"""
|
||
|
||
# %% [markdown]
|
||
"""
|
||
### Question 1: Bottleneck Analysis Strategy
|
||
|
||
You're optimizing a production transformer model that serves 1M requests/day. Your profiling reveals:
|
||
- Attention computation: 45ms (70% of total time)
|
||
- Linear layers: 10ms (15% of total time)
|
||
- Activation functions: 5ms (8% of total time)
|
||
- I/O overhead: 5ms (7% of total time)
|
||
|
||
If you can only optimize ONE component this quarter, which would you choose and why? What's the maximum theoretical speedup you could achieve?
|
||
|
||
*Think about Amdahl's Law and real-world optimization constraints.*
|
||
"""
|
||
|
||
# %% [markdown]
|
||
"""
|
||
### Question 2: Memory vs Compute Trade-offs
|
||
|
||
Your profiling shows that a CNN model uses:
|
||
- 2GB memory with 50ms inference time on CPU
|
||
- 0.5GB memory with 200ms inference time on mobile chip
|
||
|
||
A customer wants to deploy on mobile devices with 1GB total RAM and requires <100ms inference.
|
||
|
||
Design an optimization strategy using your profiling insights. What techniques would you try, and in what order?
|
||
|
||
*Consider quantization, pruning, architecture changes, and caching strategies.*
|
||
"""
|
||
|
||
# %% [markdown]
|
||
"""
|
||
### Question 3: Scaling Prediction
|
||
|
||
Your profiling reveals that attention computation scales as O(n²) with sequence length. You measured:
|
||
- 128 tokens: 10ms
|
||
- 256 tokens: 40ms
|
||
- 512 tokens: 160ms
|
||
|
||
If you need to support 2048 tokens, predict the inference time. What optimization techniques could break this quadratic scaling?
|
||
|
||
*Think about the mathematical relationship and alternative attention mechanisms.*
|
||
"""
|
||
|
||
# %% [markdown]
|
||
"""
|
||
### Question 4: Production Profiling Strategy
|
||
|
||
You're building a profiling system for a production ML platform that serves 100 different models. Your Timer class works great for development, but production has different constraints:
|
||
|
||
- Can't add 100ms of profiling overhead per request
|
||
- Need continuous monitoring, not batch measurements
|
||
- Must handle concurrent requests and GPU operations
|
||
- Need automatic anomaly detection
|
||
|
||
How would you modify your profiling approach for production? What are the key design trade-offs?
|
||
|
||
*Consider sampling strategies, async profiling, and monitoring infrastructure.*
|
||
"""
|
||
|
||
# %%
|
||
if __name__ == "__main__":
|
||
print("THINK ML Systems Thinking Questions")
|
||
print("=" * 50)
|
||
print("""
|
||
Complete the interactive questions above to deepen your understanding of:
|
||
|
||
1️⃣ Bottleneck Analysis Strategy
|
||
- Applying Amdahl's Law to optimization decisions
|
||
- Understanding the ROI of different optimization targets
|
||
|
||
2️⃣ Memory vs Compute Trade-offs
|
||
- Balancing memory constraints with performance requirements
|
||
- Designing optimization strategies for resource-limited devices
|
||
|
||
3️⃣ Scaling Prediction
|
||
- Using profiling data to predict performance at scale
|
||
- Understanding algorithmic complexity implications
|
||
|
||
4️⃣ Production Profiling Strategy
|
||
- Adapting development tools for production constraints
|
||
- Building monitoring systems for ML performance
|
||
|
||
These questions connect your profiling implementations to real-world ML systems challenges.
|
||
Answer them to master performance analysis thinking!
|
||
""")
|
||
|
||
# %%
|
||
if __name__ == "__main__":
|
||
print("MAGNIFY PROFILING MODULE: Performance Detective Suite")
|
||
print("=" * 60)
|
||
|
||
# Run all profiling tests in sequence
|
||
print("\n1️⃣ Testing Timer Infrastructure...")
|
||
test_timer()
|
||
|
||
print("\n2️⃣ Testing Memory Profiler...")
|
||
test_memory_profiler()
|
||
|
||
print("\n3️⃣ Testing FLOP Counter...")
|
||
test_flop_counter()
|
||
|
||
print("\n4️⃣ Testing Comprehensive Profiling...")
|
||
test_comprehensive_profiling()
|
||
|
||
print("\n5️⃣ Running Bottleneck Detection...")
|
||
simulate_complete_model_profiling()
|
||
|
||
print("\n6️⃣ Analyzing Systems Implications...")
|
||
analyze_systems_implications()
|
||
|
||
print("\n7️⃣ Running Integration Tests...")
|
||
integration_test_profiling_suite()
|
||
|
||
print("\nCELEBRATE ALL PROFILING TESTS COMPLETED SUCCESSFULLY!")
|
||
print("\nROCKET Your profiling suite is ready to:")
|
||
print(" - Identify bottlenecks in neural networks")
|
||
print(" - Guide optimization decisions with data")
|
||
print(" - Predict performance at scale")
|
||
print(" - Support production monitoring systems")
|
||
print("\n📚 Next: Complete the ML Systems Thinking questions!")
|
||
|
||
# %% [markdown]
|
||
"""
|
||
## TARGET MODULE SUMMARY: Profiling - Performance Detective Work
|
||
|
||
Congratulations! You've built a comprehensive profiling suite that reveals the performance secrets of neural networks.
|
||
|
||
### 🏆 What You Accomplished
|
||
|
||
**1. Professional Timing Infrastructure**
|
||
- Built `Timer` class with statistical rigor
|
||
- Implemented warmup runs and percentile reporting
|
||
- Eliminated cold start effects and measurement noise
|
||
- Created reproducible performance measurements
|
||
|
||
**2. Memory Analysis Tools**
|
||
- Developed `MemoryProfiler` with allocation tracking
|
||
- Implemented peak memory usage monitoring
|
||
- Built memory leak detection capabilities
|
||
- Connected memory patterns to performance implications
|
||
|
||
**3. Computational Analysis**
|
||
- Created `FLOPCounter` for operation counting
|
||
- Analyzed different layer types (Linear, Conv2d, Attention)
|
||
- Revealed the O(n²) scaling problem in transformers
|
||
- Connected FLOPs to hardware efficiency
|
||
|
||
**4. Integrated Profiling Context**
|
||
- Built `ProfilerContext` manager combining all tools
|
||
- Created comprehensive performance reports
|
||
- Implemented automatic insight generation
|
||
- Developed production-ready profiling workflow
|
||
|
||
### MAGNIFY Key Discoveries Made
|
||
|
||
**Architecture Performance Profiles:**
|
||
- **MLPs**: Fast, linear scaling, memory efficient
|
||
- **CNNs**: Moderate speed, excellent for spatial data
|
||
- **Transformers**: Slow but powerful, memory hungry, O(n²) scaling
|
||
|
||
**Bottleneck Identification:**
|
||
- Attention mechanisms consume 70%+ of computation time
|
||
- Memory bandwidth often limits performance more than raw FLOPs
|
||
- O(n²) scaling makes long sequences prohibitively expensive
|
||
|
||
**Systems Implications:**
|
||
- Profiling data drives hardware selection (CPU vs GPU)
|
||
- Memory constraints limit batch sizes in attention models
|
||
- Optimization ROI follows Amdahl's Law patterns
|
||
|
||
### ROCKET Real-World Applications
|
||
|
||
Your profiling tools enable:
|
||
- **Bottleneck identification** in production models
|
||
- **Optimization targeting** for maximum impact
|
||
- **Hardware selection** based on performance characteristics
|
||
- **Cost prediction** for scaling ML systems
|
||
- **Performance regression** detection in CI/CD
|
||
|
||
### TARGET What's Next
|
||
|
||
Module 16 (Acceleration) will use these profiling insights to:
|
||
- Implement attention optimizations (Flash Attention patterns)
|
||
- Build efficient kernels for bottleneck operations
|
||
- Create caching strategies for memory optimization
|
||
- Develop quantization techniques for inference speedup
|
||
|
||
**Your profiling detective work laid the foundation - now we'll fix the problems you discovered!**
|
||
|
||
### 🏅 Systems Engineering Skills Mastered
|
||
|
||
- **Performance measurement methodology** with statistical rigor
|
||
- **Bottleneck analysis** using Amdahl's Law principles
|
||
- **Memory profiling** and allocation pattern analysis
|
||
- **Computational complexity** analysis through FLOP counting
|
||
- **Production profiling** strategy design
|
||
- **Data-driven optimization** decision making
|
||
|
||
You now have the tools to analyze any neural network and understand exactly why it's fast or slow. These are the same techniques used to optimize GPT, BERT, and every other production ML system.
|
||
|
||
**Welcome to the ranks of ML systems performance engineers!** CELEBRATE
|
||
""" |