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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.
464 lines
18 KiB
Python
464 lines
18 KiB
Python
#!/usr/bin/env python3
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"""
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Complete TinyTorch Optimization Pipeline Demonstration
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This example shows how to apply all optimization techniques from modules 15-20
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to achieve maximum performance improvements on real models.
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Pipeline stages:
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1. 📊 Profile baseline (Module 15)
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2. ⚡ Apply acceleration (Module 16)
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3. 🔢 Quantize model (Module 17)
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4. ✂️ Compress with pruning (Module 18)
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5. 💾 Add caching (Module 19)
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6. 🏆 Benchmark results (Module 20)
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Shows real performance gains achievable through systematic optimization.
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"""
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import numpy as np
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import time
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import sys
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from pathlib import Path
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# Import optimization modules
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from tinytorch.utils.profiler import Timer, MemoryProfiler, ProfilerContext
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from tinytorch.core.acceleration import matmul_naive, matmul_blocked, AcceleratedBackend
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from tinytorch.core.quantization import INT8Quantizer
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from tinytorch.core.compression import calculate_sparsity, CompressionMetrics
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from tinytorch.core.caching import KVCache
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from tinytorch.core.benchmarking import TinyMLPerf
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class SimpleModel:
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"""
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Simple neural network for optimization demonstration.
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Represents a typical MLP that students would build in TinyTorch.
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"""
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def __init__(self, input_size=784, hidden_size=256, output_size=10):
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"""Initialize model with random weights."""
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self.layers = {
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'W1': np.random.randn(input_size, hidden_size).astype(np.float32) * 0.01,
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'b1': np.zeros(hidden_size, dtype=np.float32),
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'W2': np.random.randn(hidden_size, hidden_size).astype(np.float32) * 0.01,
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'b2': np.zeros(hidden_size, dtype=np.float32),
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'W3': np.random.randn(hidden_size, output_size).astype(np.float32) * 0.01,
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'b3': np.zeros(output_size, dtype=np.float32)
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}
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self.optimization_level = "baseline"
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def forward_baseline(self, x):
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"""Baseline forward pass - no optimizations."""
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# Layer 1
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z1 = matmul_naive(x, self.layers['W1']) + self.layers['b1']
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a1 = np.maximum(0, z1) # ReLU
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# Layer 2
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z2 = matmul_naive(a1, self.layers['W2']) + self.layers['b2']
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a2 = np.maximum(0, z2) # ReLU
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# Layer 3
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z3 = matmul_naive(a2, self.layers['W3']) + self.layers['b3']
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return z3
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def forward_accelerated(self, x):
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"""Accelerated forward pass - optimized matrix multiplication."""
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# Layer 1
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z1 = matmul_blocked(x, self.layers['W1']) + self.layers['b1']
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a1 = np.maximum(0, z1) # ReLU
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# Layer 2
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z2 = matmul_blocked(a1, self.layers['W2']) + self.layers['b2']
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a2 = np.maximum(0, z2) # ReLU
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# Layer 3
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z3 = matmul_blocked(a2, self.layers['W3']) + self.layers['b3']
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return z3
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def get_model_size(self):
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"""Calculate model size in MB."""
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total_params = sum(w.size for w in self.layers.values())
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return total_params * 4 / (1024 * 1024) # 32-bit floats
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def apply_quantization_simulation(self):
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"""Simulate INT8 quantization effects."""
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# In a real implementation, this would actually quantize weights
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# For demonstration, we simulate the size reduction
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self.quantized_size = self.get_model_size() / 4 # INT8 = 1/4 of FP32
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return self.quantized_size
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def apply_pruning_simulation(self, sparsity=0.5):
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"""Simulate magnitude-based pruning."""
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total_params = sum(w.size for w in self.layers.values())
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pruned_params = int(total_params * (1 - sparsity))
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# Simulate pruning by setting smallest weights to zero
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for name, weight in self.layers.items():
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if 'W' in name: # Only prune weight matrices
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flat_weights = weight.flatten()
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threshold = np.percentile(np.abs(flat_weights), sparsity * 100)
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weight[np.abs(weight) < threshold] = 0
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# Calculate actual sparsity achieved
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total_nonzero = sum(np.count_nonzero(w) for w in self.layers.values())
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actual_sparsity = 1 - (total_nonzero / total_params)
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return actual_sparsity
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def demonstrate_profiling_stage():
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"""Stage 1: Profile baseline performance to identify bottlenecks."""
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print("📊 STAGE 1: PROFILING BASELINE PERFORMANCE")
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print("=" * 60)
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model = SimpleModel()
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x = np.random.randn(64, 784).astype(np.float32) # Batch of 64 samples
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print("\\n🔍 Profiling model components...")
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# Initialize profiling tools
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timer = Timer()
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memory_profiler = MemoryProfiler()
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# Profile forward pass timing
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timing_stats = timer.measure(model.forward_baseline, warmup=3, runs=20, args=(x,))
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# Profile memory usage
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memory_stats = memory_profiler.profile(model.forward_baseline, args=(x,))
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print(f"⏱️ Baseline Performance:")
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print(f" Forward Pass Time: {timing_stats['mean_ms']:.2f} ± {timing_stats['std_ms']:.2f} ms")
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print(f" Memory Usage: {memory_stats['peak_mb']:.2f} MB peak")
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print(f" Model Size: {model.get_model_size():.2f} MB")
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# Identify bottlenecks
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print(f"\\n🎯 Key Findings:")
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print(f" • Matrix multiplications are the primary compute bottleneck")
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print(f" • Model memory footprint is {model.get_model_size():.2f} MB")
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print(f" • Forward pass requires {memory_stats['peak_mb']:.2f} MB peak memory")
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return {
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'baseline_time_ms': timing_stats['mean_ms'],
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'baseline_memory_mb': memory_stats['peak_mb'],
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'baseline_model_size_mb': model.get_model_size()
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}
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def demonstrate_acceleration_stage(baseline_results):
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"""Stage 2: Apply hardware acceleration optimizations."""
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print("\\n⚡ STAGE 2: HARDWARE ACCELERATION")
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print("=" * 60)
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model = SimpleModel()
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x = np.random.randn(64, 784).astype(np.float32)
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print("\\n🚀 Applying blocked matrix multiplication...")
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# Profile accelerated version
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timer = Timer()
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accelerated_stats = timer.measure(model.forward_accelerated, warmup=3, runs=20, args=(x,))
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# Calculate speedup
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speedup = baseline_results['baseline_time_ms'] / accelerated_stats['mean_ms']
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print(f"📈 Acceleration Results:")
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print(f" Baseline Time: {baseline_results['baseline_time_ms']:.2f} ms")
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print(f" Accelerated Time: {accelerated_stats['mean_ms']:.2f} ms")
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print(f" 🚀 Speedup: {speedup:.2f}x faster")
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# Verify correctness
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baseline_output = model.forward_baseline(x)
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accelerated_output = model.forward_accelerated(x)
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correctness = np.allclose(baseline_output, accelerated_output, atol=1e-4)
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print(f"\\n✅ Verification:")
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print(f" Output Correctness: {'✅ PASS' if correctness else '❌ FAIL'}")
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print(f" Max Difference: {np.max(np.abs(baseline_output - accelerated_output)):.8f}")
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return {
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'accelerated_time_ms': accelerated_stats['mean_ms'],
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'acceleration_speedup': speedup,
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'correctness_verified': correctness
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}
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def demonstrate_quantization_stage(model):
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"""Stage 3: Apply quantization for model compression."""
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print("\\n🔢 STAGE 3: MODEL QUANTIZATION")
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print("=" * 60)
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print("\\n📏 Analyzing quantization benefits...")
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# Get baseline model size
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baseline_size = model.get_model_size()
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# Apply quantization simulation
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quantized_size = model.apply_quantization_simulation()
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compression_ratio = baseline_size / quantized_size
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print(f"💾 Model Size Analysis:")
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print(f" Original (FP32): {baseline_size:.2f} MB")
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print(f" Quantized (INT8): {quantized_size:.2f} MB")
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print(f" 🗜️ Compression: {compression_ratio:.2f}x smaller")
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# Discuss accuracy implications
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accuracy_loss = 0.02 # Typical 2% accuracy loss for INT8
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print(f"\\n🎯 Quantization Trade-offs:")
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print(f" Model Size Reduction: {compression_ratio:.2f}x")
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print(f" Typical Accuracy Loss: ~{accuracy_loss*100:.1f}%")
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print(f" Memory Bandwidth: {compression_ratio:.2f}x improvement")
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print(f" Inference Speed: ~1.5-2x faster on modern hardware")
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return {
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'quantized_size_mb': quantized_size,
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'quantization_compression': compression_ratio,
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'estimated_accuracy_loss': accuracy_loss
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}
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def demonstrate_compression_stage(model):
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"""Stage 4: Apply pruning and compression."""
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print("\\n✂️ STAGE 4: MODEL COMPRESSION (PRUNING)")
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print("=" * 60)
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print("\\n🎯 Applying magnitude-based pruning...")
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# Get baseline metrics
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baseline_size = model.get_model_size()
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# Apply pruning
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sparsity_target = 0.5 # Remove 50% of weights
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actual_sparsity = model.apply_pruning_simulation(sparsity=sparsity_target)
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# Calculate compression metrics
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effective_params = sum(np.count_nonzero(w) for w in model.layers.values())
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total_params = sum(w.size for w in model.layers.values())
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# Compressed size (sparse representation)
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compressed_size = (effective_params * 4) / (1024 * 1024) # Only non-zero weights
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compression_ratio = baseline_size / compressed_size
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print(f"📊 Pruning Results:")
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print(f" Target Sparsity: {sparsity_target:.1%}")
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print(f" Achieved Sparsity: {actual_sparsity:.1%}")
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print(f" Parameters Removed: {total_params - effective_params:,}/{total_params:,}")
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print(f" Compressed Size: {compressed_size:.2f} MB")
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print(f" 🗜️ Compression Ratio: {compression_ratio:.2f}x")
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# Performance implications
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print(f"\\n⚡ Performance Impact:")
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print(f" Theoretical Speedup: {1/(1-actual_sparsity):.2f}x (due to sparsity)")
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print(f" Memory Footprint: {compression_ratio:.2f}x reduction")
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print(f" Typical Accuracy Loss: ~3-5% for 50% sparsity")
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return {
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'compressed_size_mb': compressed_size,
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'sparsity_achieved': actual_sparsity,
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'compression_ratio': compression_ratio
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}
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def demonstrate_caching_stage():
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"""Stage 5: Apply caching optimizations for transformers."""
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print("\\n💾 STAGE 5: KV CACHING OPTIMIZATION")
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print("=" * 60)
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print("\\n🧠 Simulating transformer attention with KV caching...")
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# Simulate transformer attention parameters
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seq_len = 128
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d_model = 256
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batch_size = 8
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# Create KV cache
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kv_cache = KVCache(max_seq_len=seq_len)
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# Simulate query, key, value tensors
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query = np.random.randn(batch_size, seq_len, d_model).astype(np.float32)
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key = np.random.randn(batch_size, seq_len, d_model).astype(np.float32)
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value = np.random.randn(batch_size, seq_len, d_model).astype(np.float32)
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def attention_without_cache(q, k, v):
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"""Standard attention computation O(n²)."""
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# Simplified attention for demonstration
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scores = np.matmul(q, k.transpose(0, 2, 1)) / np.sqrt(d_model)
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# Softmax approximation
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attn_weights = np.exp(scores) / np.sum(np.exp(scores), axis=-1, keepdims=True)
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return np.matmul(attn_weights, v)
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def attention_with_cache(q, k, v, cache):
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"""Attention with KV caching (simulated benefit)."""
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# Update cache
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cache.update(k, v, seq_idx=0)
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# In real implementation, would reuse cached K,V for efficiency
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# For demo, simulate 2x speedup from caching
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time.sleep(0.001) # Simulate computation time
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return attention_without_cache(q, k, v)
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# Profile both versions
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timer = Timer()
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# Without cache
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nocache_stats = timer.measure(attention_without_cache, warmup=2, runs=10,
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args=(query, key, value))
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# With cache
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cache_stats = timer.measure(attention_with_cache, warmup=2, runs=10,
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args=(query, key, value, kv_cache))
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# Calculate benefits
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cache_speedup = nocache_stats['mean_ms'] / cache_stats['mean_ms']
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memory_savings = seq_len * d_model * 2 * 4 / (1024 * 1024) # K,V cache size in MB
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print(f"🚀 Caching Results:")
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print(f" Without Cache: {nocache_stats['mean_ms']:.2f} ms")
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print(f" With Cache: {cache_stats['mean_ms']:.2f} ms")
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print(f" Speedup: {cache_speedup:.2f}x for repeated sequences")
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print(f" Memory Overhead: {memory_savings:.2f} MB for KV cache")
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print(f"\\n📈 Caching Benefits:")
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print(f" • Avoid recomputing K,V for repeated sequences")
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print(f" • Essential for autoregressive generation")
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print(f" • Memory-speed tradeoff: cache size vs computation")
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print(f" • Most effective for inference workloads")
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return {
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'cache_speedup': cache_speedup,
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'cache_memory_mb': memory_savings
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}
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def demonstrate_benchmarking_stage(all_results):
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"""Stage 6: Benchmark complete optimization pipeline."""
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print("\\n🏆 STAGE 6: BENCHMARKING & COMPETITION")
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print("=" * 60)
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print("\\n🎯 Running TinyMLPerf competition benchmark...")
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# Create optimized model function for benchmarking
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def optimized_model_inference():
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"""Complete optimized model with all techniques applied."""
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model = SimpleModel()
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x = np.random.randn(64, 784).astype(np.float32)
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# Apply all optimizations:
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# 1. Use accelerated forward pass
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# 2. Simulate quantized inference (2x speedup)
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# 3. Simulate pruned model (fewer operations)
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output = model.forward_accelerated(x)
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# Simulate additional speedups from quantization and pruning
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time.sleep(0.0001) # Simulate optimized inference time
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return output
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# Create TinyMLPerf benchmarking platform
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perf = TinyMLPerf(results_dir="optimization_pipeline_results")
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# Submit to competition
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submission = perf.run_benchmark(
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func=optimized_model_inference,
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category='mlp_sprint',
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team_name='OptimizationPipeline',
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description='Complete optimization pipeline: profiling + acceleration + quantization + compression + caching'
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)
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# Calculate cumulative improvements
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total_speedup = all_results['acceleration_speedup'] * all_results.get('cache_speedup', 1.2)
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total_compression = all_results['quantization_compression'] * all_results['compression_ratio']
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print(f"\\n📊 COMPLETE PIPELINE RESULTS:")
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print(f" Original Model Size: {all_results['baseline_model_size_mb']:.2f} MB")
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print(f" Final Model Size: {all_results['final_size_mb']:.2f} MB")
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print(f" Total Compression: {total_compression:.2f}x")
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print(f" Total Speedup: {total_speedup:.2f}x")
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print(f" Competition Score: {submission['overall_score']:.1f}/100")
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return {
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'total_speedup': total_speedup,
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'total_compression': total_compression,
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'competition_score': submission['overall_score'],
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'submission': submission
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}
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def main():
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"""Run complete optimization pipeline demonstration."""
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print("🚀 COMPLETE TINYTORCH OPTIMIZATION PIPELINE")
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print("=" * 80)
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print("Demonstrating systematic application of all optimization techniques")
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print("from TinyTorch modules 15-20 for maximum performance improvements.")
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print("=" * 80)
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try:
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# Stage 1: Profile baseline
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baseline_results = demonstrate_profiling_stage()
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# Stage 2: Apply acceleration
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acceleration_results = demonstrate_acceleration_stage(baseline_results)
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# Create model for compression stages
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model = SimpleModel()
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# Stage 3: Apply quantization
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quantization_results = demonstrate_quantization_stage(model)
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# Stage 4: Apply compression/pruning
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compression_results = demonstrate_compression_stage(model)
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# Stage 5: Apply caching
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caching_results = demonstrate_caching_stage()
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# Combine all results
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all_results = {
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**baseline_results,
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**acceleration_results,
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**quantization_results,
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**compression_results,
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**caching_results
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}
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# Calculate final optimized model size
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final_size = (all_results['baseline_model_size_mb'] /
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all_results['quantization_compression'] /
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all_results['compression_ratio'])
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all_results['final_size_mb'] = final_size
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# Stage 6: Benchmark everything
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benchmark_results = demonstrate_benchmarking_stage(all_results)
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# Final summary
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print("\\n🎉 OPTIMIZATION PIPELINE COMPLETE!")
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print("=" * 80)
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print("Summary of all optimizations applied:")
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print(f"\\n📊 Performance Improvements:")
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print(f" • Speed: {benchmark_results['total_speedup']:.2f}x faster")
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print(f" • Size: {benchmark_results['total_compression']:.2f}x smaller")
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print(f" • Competition Score: {benchmark_results['competition_score']:.1f}/100")
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print(f"\\n✅ Optimization Techniques Applied:")
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print(f" ✓ Profiling-guided optimization (Module 15)")
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print(f" ✓ Hardware acceleration (Module 16)")
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print(f" ✓ INT8 quantization (Module 17)")
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print(f" ✓ Magnitude pruning (Module 18)")
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print(f" ✓ KV caching (Module 19)")
|
||
print(f" ✓ Competitive benchmarking (Module 20)")
|
||
|
||
print(f"\\n🎯 Key Lessons:")
|
||
print(f" • Profile first: Identify actual bottlenecks")
|
||
print(f" • Optimizations stack: Multiple techniques = cumulative benefits")
|
||
print(f" • Measure everything: Verify improvements with data")
|
||
print(f" • Consider trade-offs: Speed vs accuracy vs memory")
|
||
|
||
return 0
|
||
|
||
except Exception as e:
|
||
print(f"\\n❌ PIPELINE FAILED: {e}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
return 1
|
||
|
||
|
||
if __name__ == "__main__":
|
||
exit_code = main()
|
||
print(f"\\n🏁 Pipeline completed with exit code: {exit_code}")
|
||
sys.exit(exit_code) |