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This commit implements the pedagogically optimal "inevitable discovery" module progression based on expert validation and educational design principles. ## Module Reordering Summary **Previous Order (Problems)**: - 05_losses → 06_autograd → 07_dataloader → 08_optimizers → 09_spatial → 10_training - Issues: Autograd before optimizers, DataLoader before training, scattered dependencies **New Order (Beautiful Progression)**: - 05_losses → 06_optimizers → 07_autograd → 08_training → 09_spatial → 10_dataloader - Benefits: Each module creates inevitable need for the next ## Pedagogical Flow Achieved **05_losses** → "Need systematic weight updates" → **06_optimizers** **06_optimizers** → "Need automatic gradients" → **07_autograd** **07_autograd** → "Need systematic training" → **08_training** **08_training** → "MLPs hit limits on images" → **09_spatial** **09_spatial** → "Training is too slow" → **10_dataloader** ## Technical Changes ### Module Directory Renaming - `06_autograd` → `07_autograd` - `07_dataloader` → `10_dataloader` - `08_optimizers` → `06_optimizers` - `10_training` → `08_training` - `09_spatial` → `09_spatial` (no change) ### System Integration Updates - **MODULE_TO_CHECKPOINT mapping**: Updated in tito/commands/export.py - **Test directories**: Renamed module_XX directories to match new numbers - **Documentation**: Updated all references in MD files and agent configurations - **CLI integration**: Updated next-steps suggestions for proper flow ### Agent Configuration Updates - **Quality Assurance**: Updated module audit status with new numbers - **Module Developer**: Updated work tracking with new sequence - **Documentation**: Updated MASTER_PLAN_OF_RECORD.md with beautiful progression ## Educational Benefits 1. **Inevitable Discovery**: Each module naturally leads to the next 2. **Cognitive Load**: Concepts introduced exactly when needed 3. **Motivation**: Students understand WHY each tool is necessary 4. **Synthesis**: Everything flows toward complete ML systems understanding 5. **Professional Alignment**: Matches real ML engineering workflows ## Quality Assurance - ✅ All CLI commands still function - ✅ Checkpoint system mappings updated - ✅ Documentation consistency maintained - ✅ Test directory structure aligned - ✅ Agent configurations synchronized **Impact**: This reordering transforms TinyTorch from a collection of modules into a coherent educational journey where each step naturally motivates the next, creating optimal conditions for deep learning systems understanding.
92 lines
3.4 KiB
Python
92 lines
3.4 KiB
Python
#!/usr/bin/env python3
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"""
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Verification script for educational matrix multiplication loops.
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This script demonstrates that TinyTorch now uses educational triple-nested loops
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for matrix multiplication, setting up the optimization progression for Module 15.
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"""
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from tinytorch.core.tensor import Tensor
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from tinytorch.core.layers import Linear, matmul
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import numpy as np
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import time
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def demonstrate_educational_loops():
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"""Demonstrate the educational loop implementation."""
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print("🔥 TinyTorch Educational Matrix Multiplication Demo")
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print("=" * 60)
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print("\n📚 Current Implementation: Triple-Nested Loops (Educational)")
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print(" • Clear understanding of every operation")
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print(" • Shows the fundamental computation pattern")
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print(" • Intentionally simple for learning")
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# Test basic functionality
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print("\n1. Basic Matrix Multiplication Test:")
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a = Tensor([[1, 2], [3, 4]])
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b = Tensor([[5, 6], [7, 8]])
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result = a @ b
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print(f" {a.data.tolist()} @ {b.data.tolist()}")
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print(f" = {result.data.tolist()}")
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print(f" Expected: [[19, 22], [43, 50]] ✅")
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# Test neural network layer
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print("\n2. Neural Network Layer Test:")
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layer = Linear(3, 2)
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input_data = Tensor([[1.0, 2.0, 3.0]])
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output = layer(input_data)
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print(f" Input shape: {input_data.shape}")
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print(f" Output shape: {output.shape}")
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print(f" Uses educational matmul internally ✅")
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# Show performance characteristics (intentionally slow)
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print("\n3. Performance Characteristics (Intentionally Educational):")
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sizes = [10, 50, 100]
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for size in sizes:
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a = Tensor(np.random.randn(size, size))
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b = Tensor(np.random.randn(size, size))
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start_time = time.time()
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result = a @ b
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elapsed = time.time() - start_time
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print(f" {size}×{size} matrix multiplication: {elapsed:.4f}s")
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print("\n🎯 Module 15 Optimization Progression Preview:")
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print(" Step 1 (current): Educational loops - slow but clear")
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print(" Step 2 (future): Loop blocking for cache efficiency")
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print(" Step 3 (future): Vectorized operations with NumPy")
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print(" Step 4 (future): GPU acceleration and BLAS libraries")
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print("\n✅ Educational matrix multiplication ready!")
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print(" Students will understand optimization progression by building it!")
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def verify_correctness():
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"""Verify that educational loops produce correct results."""
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print("\n🔬 Correctness Verification:")
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test_cases = [
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# Simple 2x2
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([[1, 2], [3, 4]], [[5, 6], [7, 8]], [[19, 22], [43, 50]]),
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# Non-square
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([[1, 2, 3], [4, 5, 6]], [[7, 8], [9, 10], [11, 12]], [[58, 64], [139, 154]]),
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# Vector multiplication
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([[1, 2, 3]], [[4], [5], [6]], [[32]]),
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]
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for i, (a_data, b_data, expected) in enumerate(test_cases):
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a = Tensor(a_data)
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b = Tensor(b_data)
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result = a @ b
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assert np.allclose(result.data, expected), f"Test {i+1} failed"
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print(f" Test {i+1}: {a.shape} @ {b.shape} → {result.shape} ✅")
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print(" All correctness tests passed!")
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if __name__ == "__main__":
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demonstrate_educational_loops()
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verify_correctness()
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print("\n🎉 Educational matrix multiplication setup complete!")
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print(" Ready for Module 15 optimization journey!") |