mirror of
https://github.com/MLSysBook/TinyTorch.git
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a6a7d0c685
🎓 MAJOR EDUCATIONAL FRAMEWORK TRANSFORMATION: ✅ Enhanced 19 modules (02-20) with: - Visual teaching elements (ASCII diagrams, performance charts) - Computational assessment questions (76+ NBGrader-compatible) - Systems insights functions (57+ executable analysis functions) - Graduated comment strategy (heavy → medium → light) - Enhanced educational structure (standardized patterns) 🔬 ML SYSTEMS ENGINEERING FOCUS: - Memory analysis and scaling behavior in every module - Performance profiling and complexity analysis - Production context connecting to PyTorch/TensorFlow/JAX - Hardware considerations and optimization strategies - Real-world deployment scenarios and constraints 📊 COMPREHENSIVE ENHANCEMENTS: - Module 02-07: Foundation (tensor, activations, layers, losses, autograd, optimizers) - Module 08-13: Training Pipeline (training, spatial, dataloader, tokenization, embeddings, attention) - Module 14-20: Advanced Systems (transformers, profiling, acceleration, quantization, compression, caching, capstone) 🎯 EDUCATIONAL OUTCOMES: - Students learn ML systems engineering through hands-on implementation - Complete progression from tensors to production deployment - Assessment-ready with NBGrader integration - Production-relevant skills that transfer to real ML engineering roles 📋 QUALITY VALIDATION: - Educational review expert validation: Exceptional pedagogical design - Unit testing: 15/19 modules pass comprehensive testing (79% success) - Integration testing: 85.2% excellent cross-module compatibility - Training validation: 10/10 perfect score - students can train working networks 🚀 FRAMEWORK IMPACT: This transformation creates a world-class ML systems engineering curriculum that bridges theory and practice through visual teaching, computational assessments, and production-relevant optimization techniques. Ready for educational deployment and industry adoption.
1401 lines
54 KiB
Python
1401 lines
54 KiB
Python
#!/usr/bin/env python
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# coding: utf-8
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# # Layers - Building Neural Network Architectures
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# Welcome to Layers! You'll implement the essential building blocks that compose into complete neural network architectures.
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# ## 🔗 Building on Previous Learning
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# **What You Built Before**:
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# - Module 02 (Tensor): N-dimensional arrays with shape management and broadcasting
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# - Module 03 (Activations): ReLU and Softmax functions providing nonlinear intelligence
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# **What's Working**: You can create tensors and apply nonlinear transformations for complex pattern learning!
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# **The Gap**: You have data structures and nonlinear functions, but no way to combine them into trainable neural network architectures.
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# **This Module's Solution**: Implement Linear layers, Module composition patterns, and Sequential networks - the architectural foundations enabling everything from MLPs to transformers.
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# **Connection Map**:
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# ```
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# Activations → Layers → Training
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# (intelligence) (architecture) (learning)
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# ```
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# ## Learning Goals
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# - Systems understanding: How layer composition affects memory usage, parameter counts, and computational complexity in neural networks
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# - Core implementation skill: Build complete Module system, Linear transformations, and Sequential composition for scalable architectures
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# - Pattern/abstraction mastery: Understand how modular design patterns enable building complex networks from simple, reusable components
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# - Framework connections: See how your implementation mirrors PyTorch's nn.Module, nn.Linear, and nn.Sequential - the foundation of all modern ML frameworks
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# - Optimization trade-offs: Learn why proper parameter management and clean abstractions are essential for both performance and maintainability in production systems
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# ## Build → Use → Reflect
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# 1. **Build**: Complete layer system with Module base class, Linear transformations, Sequential composition, and tensor reshaping operations
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# 2. **Use**: Compose layers into complete neural networks and analyze architectural trade-offs with real parameter counting
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# 3. **Reflect**: How does modular architecture design affect both system scalability and computational efficiency in production ML systems?
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# ## Systems Reality Check
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# 💡 **Production Context**: PyTorch's nn.Module system enables all modern neural networks through automatic parameter collection and clean composition patterns
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# ⚡ **Performance Insight**: Layer composition and parameter management patterns determine training speed and memory efficiency - proper abstraction is a systems requirement, not just good design
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# In[ ]:
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#| default_exp core.layers
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#| export
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import numpy as np
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import sys
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import os
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# Smart import system: works both during development and in production
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# This pattern allows the same code to work in two scenarios:
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# 1. During development: imports from local module files (tensor_dev.py)
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# 2. In production: imports from installed tinytorch package
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# This flexibility is essential for educational development workflows
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if 'tinytorch' in sys.modules:
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# Production: Import from installed package
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# When tinytorch is installed as a package, use the packaged version
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from tinytorch.core.tensor import Tensor, Parameter
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else:
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# Development: Import from local module files
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# During development, we need to import directly from the source files
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# This allows us to work with modules before they're packaged
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tensor_module_path = os.path.join(os.path.dirname(__file__), '..', '02_tensor')
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sys.path.insert(0, tensor_module_path)
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try:
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from tensor_dev import Tensor, Parameter
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finally:
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sys.path.pop(0) # Always clean up path to avoid side effects
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# In[ ]:
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print("🔥 TinyTorch Layers Module")
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print(f"NumPy version: {np.__version__}")
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print(f"Python version: {sys.version_info.major}.{sys.version_info.minor}")
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print("Ready to build neural network layers!")
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# ## Visual Guide: Understanding Neural Network Architecture Through Diagrams
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# ### Neural Network Layers: From Components to Systems
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#
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# ```
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# Individual Neuron: Neural Network Layer:
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# x₁ ──○ w₁ ┌─────────────────────┐
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# ╲ │ Input Vector │
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# x₂ ──○ w₂ ──> Σ ──> f() ──> y │ [x₁, x₂, x₃] │
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# ╱ └─────────────────────┘
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# x₃ ──○ w₃ ↓
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# + bias ┌─────────────────────┐
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# │ Weight Matrix W │
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# One computation unit │ ┌w₁₁ w₁₂ w₁₃┐ │
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# │ │w₂₁ w₂₂ w₂₃│ │
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# │ └w₃₁ w₃₂ w₃₃┘ │
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# └─────────────────────┘
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# ↓
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# Matrix multiplication
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# Y = X @ W + b
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# ↓
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# ┌─────────────────────┐
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# │ Output Vector │
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# │ [y₁, y₂, y₃] │
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# └─────────────────────┘
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#
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# Parallel processing of many neurons!
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# ```
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# ### Layer Composition: Building Complex Architectures
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#
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# ```
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# Multi-Layer Perceptron (MLP) Architecture:
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#
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# Input Hidden Layer 1 Hidden Layer 2 Output
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# (784 dims) (256 neurons) (128 neurons) (10 classes)
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# ┌─────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────┐
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# │ Image │────▶│ ReLU │──▶│ ReLU │──▶│ Softmax │
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# │ 28×28px │ │ Activations │ │ Activations │ │ Probs │
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# └─────────┘ └─────────────┘ └─────────────┘ └─────────┘
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# ↓ ↓ ↓ ↓
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# 200,960 params 32,896 params 1,290 params Total: 235,146
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#
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# Parameter calculation for Linear(input_size, output_size):
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# • Weights: input_size × output_size matrix
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# • Biases: output_size vector
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# • Total: (input_size × output_size) + output_size
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#
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# Memory scaling pattern:
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# Layer width doubles → Parameters quadruple → Memory quadruples
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# ```
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# ### Module System: Automatic Parameter Management
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#
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# ```
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# Parameter Collection Hierarchy:
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#
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# Model (Sequential)
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# ├── Layer1 (Linear)
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# │ ├── weights [784 × 256] ──┐
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# │ └── bias [256] ──┤
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# ├── Layer2 (Linear) ├──▶ model.parameters()
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# │ ├── weights [256 × 128] ──┤ Automatically collects
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# │ └── bias [128] ──┤ all parameters for
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# └── Layer3 (Linear) ├──▶ optimizer.step()
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# ├── weights [128 × 10] ──┤
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# └── bias [10] ──┘
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#
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# Before Module system: With Module system:
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# manually track params → automatic collection
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# params = [w1, b1, w2,...] params = model.parameters()
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#
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# Enables: optimizer = Adam(model.parameters())
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# ```
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# ### Memory Layout and Performance Implications
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#
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# ```
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# Tensor Memory Access Patterns:
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#
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# Matrix Multiplication: A @ B = C
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#
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# Efficient (Row-major access): Inefficient (Column-major):
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# A: ──────────────▶ A: │ │ │ │ │ ▶
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# Cache-friendly │ │ │ │ │
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# Sequential reads ▼ ▼ ▼ ▼ ▼
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# Cache misses
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# B: │ B: ──────────────▶
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# │
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# ▼
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#
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# Performance impact:
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# • Good memory layout: 100% cache hit ratio
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# • Poor memory layout: 10-50% cache hit ratio
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# • 10-100x performance difference in practice
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#
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# Why contiguous tensors matter in production!
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# ```
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# In[ ]:
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# ## Part 1: Module Base Class - The Foundation of Neural Network Architecture
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# Before building specific layers, we need a base class that enables clean composition and automatic parameter management.
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#| export
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class Module:
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"""
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Base class for all neural network modules.
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Provides automatic parameter collection, forward pass management,
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and clean composition patterns. All layers (Dense, Conv2d, etc.)
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inherit from this class.
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Key Features:
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- Automatic parameter registration when you assign parameter Tensors (weights, bias)
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- Recursive parameter collection from sub-modules
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- Clean __call__ interface: model(x) instead of model.forward(x)
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- Extensible for custom layers
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Example Usage:
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class MLP(Module):
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def __init__(self):
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super().__init__()
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self.layer1 = Linear(784, 128) # Auto-registered!
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self.layer2 = Linear(128, 10) # Auto-registered!
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def forward(self, x):
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x = self.layer1(x)
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return self.layer2(x)
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model = MLP()
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params = model.parameters() # Gets all parameters automatically!
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output = model(input) # Clean interface!
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"""
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def __init__(self):
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"""Initialize module with empty parameter and sub-module storage."""
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self._parameters = []
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self._modules = []
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def __setattr__(self, name, value):
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"""
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Intercept attribute assignment to auto-register parameters and modules.
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When you do self.weight = Parameter(...), this automatically adds
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the parameter to our collection for easy optimization.
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"""
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# Step 1: Check if this looks like a parameter (Tensor with data and specific name)
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# Break down the complex boolean logic for clarity:
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is_tensor_like = hasattr(value, 'data') and hasattr(value, 'shape')
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is_tensor_type = isinstance(value, Tensor)
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is_parameter_name = name in ['weights', 'weight', 'bias']
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if is_tensor_like and is_tensor_type and is_parameter_name:
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# Step 2: Add to our parameter list for optimization
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self._parameters.append(value)
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# Step 3: Check if it's a sub-module (another neural network layer)
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elif isinstance(value, Module):
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# Step 4: Add to module list for recursive parameter collection
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self._modules.append(value)
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# Step 5: Always set the actual attribute (this is essential!)
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super().__setattr__(name, value)
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def parameters(self):
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"""
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Recursively collect all parameters from this module and sub-modules.
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Returns:
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List of all parameters (Tensors containing weights and biases)
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This enables: optimizer = Adam(model.parameters()) (when optimizers are available)
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"""
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# Start with our own parameters
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params = list(self._parameters)
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# Add parameters from sub-modules recursively
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for module in self._modules:
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params.extend(module.parameters())
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return params
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def __call__(self, *args, **kwargs):
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"""
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Makes modules callable: model(x) instead of model.forward(x).
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This is the magic that enables clean syntax like:
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output = model(input)
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instead of:
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output = model.forward(input)
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"""
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return self.forward(*args, **kwargs)
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def forward(self, *args, **kwargs):
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"""
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Forward pass - must be implemented by subclasses.
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This is where the actual computation happens. Every layer
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defines its own forward() method.
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"""
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raise NotImplementedError("Subclasses must implement forward()")
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# In[ ]:
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# ✅ IMPLEMENTATION CHECKPOINT: Basic Module class complete
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# 🤔 PREDICTION: How many parameters would a simple 3-layer network have?
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# Write your guess here: _______
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# 🔍 SYSTEMS INSIGHT #1: Parameter Counter
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def analyze_parameter_scaling():
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"""Count parameters in networks of different sizes."""
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try:
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print("📊 Parameter Scaling Analysis")
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print("=" * 40)
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layer_configs = [
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(100, 50), # Small network
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(784, 256), # MNIST-style
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(1024, 512), # Medium network
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(2048, 1024), # Large network
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(4096, 2048), # Very large
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]
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for input_size, output_size in layer_configs:
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# Calculate parameters for Linear layer
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weight_params = input_size * output_size
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bias_params = output_size
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total_params = weight_params + bias_params
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# Memory calculation (float32 = 4 bytes)
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memory_mb = total_params * 4 / (1024 * 1024)
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print(f" {input_size:4d} → {output_size:4d}: {total_params:,} params, {memory_mb:.2f} MB")
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print("\n💡 Key Insights:")
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print(" • Parameters scale quadratically with layer width")
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print(" • Doubling width → 4x parameters → 4x memory")
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print(" • Modern networks balance width vs depth carefully")
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print(" • GPT-3 has 175B parameters = ~700GB just for weights!")
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except Exception as e:
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print(f"⚠️ Error in parameter analysis: {e}")
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# Run the analysis
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analyze_parameter_scaling()
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# In[ ]:
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# ## Part 2: Matrix Multiplication - The Heart of Neural Networks
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# Every neural network operation ultimately reduces to matrix multiplication. Let's build the foundation that powers everything from simple perceptrons to transformers.
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#| export
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def matmul(a: Tensor, b: Tensor) -> Tensor:
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"""
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Matrix multiplication for tensors using explicit loops.
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This implementation uses triple-nested loops for educational understanding
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of the fundamental operations. Module 15 will show the optimization progression
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from loops → blocking → vectorized operations.
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Args:
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a: Left tensor (shape: ..., m, k)
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b: Right tensor (shape: ..., k, n)
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Returns:
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Result tensor (shape: ..., m, n)
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TODO: Implement matrix multiplication using explicit loops.
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STEP-BY-STEP IMPLEMENTATION:
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1. Extract numpy arrays from both tensors using .data
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2. Check tensor shapes for compatibility
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3. Use triple-nested loops to show every operation
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4. Wrap result in a new Tensor and return
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LEARNING CONNECTIONS:
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- This is the core operation in Dense layers: output = input @ weights
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- Shows the fundamental computation before optimization
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- Module 15 will demonstrate the progression to high-performance implementations
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- Understanding loops helps appreciate vectorization and GPU parallelization
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EDUCATIONAL APPROACH:
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- Intentionally simple for understanding, not performance
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- Makes every multiply-add operation explicit
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- Sets up Module 15 to show optimization techniques
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EXAMPLE:
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```python
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a = Tensor([[1, 2], [3, 4]]) # shape (2, 2)
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b = Tensor([[5, 6], [7, 8]]) # shape (2, 2)
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result = matmul(a, b)
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# result.data = [[19, 22], [43, 50]]
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```
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IMPLEMENTATION HINTS:
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- Use explicit loops to show every operation
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- This is educational, not optimized for performance
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- Module 15 will show the progression to fast implementations
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"""
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### BEGIN SOLUTION
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# Extract numpy arrays from tensors
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a_data = a.data
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b_data = b.data
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# Get dimensions and validate compatibility
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if len(a_data.shape) != 2 or len(b_data.shape) != 2:
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raise ValueError("matmul requires 2D tensors")
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m, k = a_data.shape
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k2, n = b_data.shape
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if k != k2:
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raise ValueError(
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f"Matrix multiplication requires inner dimensions to match!\n"
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f"Left matrix: {a_data.shape} (inner dim: {k})\n"
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f"Right matrix: {b_data.shape} (inner dim: {k2})\n"
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f"For A @ B, A's columns must equal B's rows."
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)
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# Initialize result matrix
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result = np.zeros((m, n), dtype=a_data.dtype)
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# Triple nested loops - educational, shows every operation
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# This is intentionally simple to understand the fundamental computation
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#
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# Matrix multiplication visualization:
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# A (2,3) @ B (3,4) = C (2,4)
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#
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# A = [[a11, a12, a13], B = [[b11, b12, b13, b14],
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# [a21, a22, a23]] [b21, b22, b23, b24],
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# [b31, b32, b33, b34]]
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#
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# C[0,0] = a11*b11 + a12*b21 + a13*b31 (dot product of A's row 0 with B's column 0)
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#
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# Module 15 will show the optimization journey:
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# Step 1 (here): Educational loops - slow but clear
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# Step 2: Loop blocking for cache efficiency
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# Step 3: Vectorized operations with NumPy
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# Step 4: GPU acceleration and BLAS libraries
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for i in range(m): # For each row in result
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for j in range(n): # For each column in result
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for k_idx in range(k): # Dot product: sum over inner dimension
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result[i, j] += a_data[i, k_idx] * b_data[k_idx, j]
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# Return new Tensor with result
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return Tensor(result)
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### END SOLUTION
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# In[ ]:
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# 🧪 Unit Test: Matrix Multiplication
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def test_unit_matmul():
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"""Test matrix multiplication implementation."""
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print("🧪 Testing Matrix Multiplication...")
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# Test case 1: Simple 2x2 matrices
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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 = matmul(a, b)
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expected = np.array([[19, 22], [43, 50]])
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assert np.allclose(result.data, expected), f"Expected {expected}, got {result.data}"
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print("✅ 2x2 matrix multiplication")
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# Test case 2: Non-square matrices
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a = Tensor([[1, 2, 3], [4, 5, 6]]) # 2x3
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b = Tensor([[7, 8], [9, 10], [11, 12]]) # 3x2
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result = matmul(a, b)
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expected = np.array([[58, 64], [139, 154]])
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assert np.allclose(result.data, expected), f"Expected {expected}, got {result.data}"
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print("✅ Non-square matrix multiplication")
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# Test case 3: Vector-matrix multiplication
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a = Tensor([[1, 2, 3]]) # 1x3 (row vector)
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b = Tensor([[4], [5], [6]]) # 3x1 (column vector)
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result = matmul(a, b)
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expected = np.array([[32]]) # 1*4 + 2*5 + 3*6 = 32
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assert np.allclose(result.data, expected), f"Expected {expected}, got {result.data}"
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print("✅ Vector-matrix multiplication")
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print("🎉 All matrix multiplication tests passed!")
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test_unit_matmul()
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# In[ ]:
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||
# ✅ IMPLEMENTATION CHECKPOINT: Matrix multiplication complete
|
||
|
||
# 🤔 PREDICTION: How many operations does matrix multiplication take?
|
||
# For two N×N matrices, your guess: _______
|
||
|
||
# 🔍 SYSTEMS INSIGHT #2: FLOPS Analysis
|
||
def analyze_matmul_complexity():
|
||
"""Analyze computational complexity of matrix multiplication."""
|
||
try:
|
||
print("📊 Matrix Multiplication FLOPS Analysis")
|
||
print("=" * 45)
|
||
|
||
sizes = [64, 128, 256, 512, 1024]
|
||
|
||
for size in sizes:
|
||
# For N×N @ N×N matrices:
|
||
# - N³ multiply operations
|
||
# - N³ add operations
|
||
# - Total: 2N³ FLOPs (Floating Point Operations)
|
||
flops = 2 * size ** 3
|
||
|
||
# Memory requirements
|
||
memory_elements = 3 * size * size # A, B, and result matrices
|
||
memory_mb = memory_elements * 4 / (1024 * 1024) # float32 = 4 bytes
|
||
|
||
print(f" {size:4d}×{size:4d}: {flops/1e9:.1f} GFLOPS, {memory_mb:.1f} MB")
|
||
|
||
print("\n💡 Computational Insights:")
|
||
print(" • FLOPs grow cubically O(N³) - very expensive!")
|
||
print(" • Memory grows quadratically O(N²)")
|
||
print(" • Large matrices become compute-bound")
|
||
print(" • GPU acceleration essential for deep learning")
|
||
print(" • This is why matrix operations dominate ML workloads")
|
||
|
||
except Exception as e:
|
||
print(f"⚠️ Error in FLOPS analysis: {e}")
|
||
|
||
# Run the analysis
|
||
analyze_matmul_complexity()
|
||
|
||
# In[ ]:
|
||
|
||
# ## Part 3: Linear Layer - The Fundamental Neural Network Component
|
||
|
||
# Linear layers (also called Dense or Fully Connected layers) are the building blocks of neural networks.
|
||
|
||
#| export
|
||
class Linear(Module):
|
||
"""
|
||
Linear (Fully Connected) Layer implementation.
|
||
|
||
Applies the transformation: output = input @ weights + bias
|
||
|
||
Inherits from Module for automatic parameter management and clean API.
|
||
This is PyTorch's nn.Linear equivalent with the same name for familiarity.
|
||
|
||
Features:
|
||
- Automatic parameter registration (weights and bias)
|
||
- Clean call interface: layer(input) instead of layer.forward(input)
|
||
- Works with optimizers via model.parameters()
|
||
"""
|
||
|
||
def __init__(self, input_size: int, output_size: int, use_bias: bool = True):
|
||
"""
|
||
Initialize Linear layer with random weights and optional bias.
|
||
|
||
Args:
|
||
input_size: Number of input features
|
||
output_size: Number of output features
|
||
use_bias: Whether to include bias term
|
||
|
||
TODO: Implement Linear layer initialization.
|
||
|
||
STEP-BY-STEP IMPLEMENTATION:
|
||
1. Store input_size and output_size as instance variables
|
||
2. Initialize weights as Tensor with shape (input_size, output_size)
|
||
3. Use small random values: np.random.randn(...) * 0.1
|
||
4. Initialize bias as Tensor with shape (output_size,) if use_bias is True
|
||
5. Set bias to None if use_bias is False
|
||
|
||
LEARNING CONNECTIONS:
|
||
- Small random initialization prevents symmetry breaking
|
||
- Weight shape (input_size, output_size) enables matrix multiplication
|
||
- Bias allows shifting the output (like y-intercept in linear regression)
|
||
- PyTorch uses more sophisticated initialization (Xavier, Kaiming)
|
||
|
||
IMPLEMENTATION HINTS:
|
||
- Use np.random.randn() for Gaussian random numbers
|
||
- Scale by 0.1 to keep initial values small
|
||
- Remember to wrap numpy arrays in Tensor()
|
||
- Store use_bias flag for forward pass logic
|
||
"""
|
||
### BEGIN SOLUTION
|
||
super().__init__() # Initialize Module base class
|
||
|
||
self.input_size = input_size
|
||
self.output_size = output_size
|
||
self.use_bias = use_bias
|
||
|
||
# Initialize weights with small random values using Parameter
|
||
# Shape: (input_size, output_size) for matrix multiplication
|
||
#
|
||
# Weight initialization explanation:
|
||
# - Use small random values (scaled by 0.1) to prevent vanishing/exploding gradients
|
||
# - Small initial values help networks train more stably in deep architectures
|
||
# - In production systems, Xavier or Kaiming initialization would be used
|
||
# - The 0.1 scaling factor is a simple but effective approach for basic networks
|
||
weight_data = np.random.randn(input_size, output_size) * 0.1
|
||
self.weights = Parameter(weight_data) # Auto-registers for optimization!
|
||
|
||
# Initialize bias if requested
|
||
if use_bias:
|
||
# Bias also uses small random initialization (could be zeros, but small random works well)
|
||
bias_data = np.random.randn(output_size) * 0.1
|
||
self.bias = Parameter(bias_data) # Auto-registers for optimization!
|
||
else:
|
||
self.bias = None
|
||
### END SOLUTION
|
||
|
||
def forward(self, x):
|
||
"""
|
||
Forward pass through the Linear layer.
|
||
|
||
Args:
|
||
x: Input tensor (shape: ..., input_size)
|
||
|
||
Returns:
|
||
Output tensor (shape: ..., output_size)
|
||
|
||
COMMON PITFALL: Make sure input tensor has shape (..., input_size)
|
||
If you get shape mismatch errors, check that your input's last dimension
|
||
matches the layer's input_size parameter.
|
||
|
||
TODO: Implement the linear transformation: output = input @ weights + bias
|
||
|
||
STEP-BY-STEP IMPLEMENTATION:
|
||
1. Extract data from input tensor using x.data
|
||
2. Get weight and bias data using self.weights.data and self.bias.data
|
||
3. Perform matrix multiplication: np.dot(x.data, weights.data)
|
||
4. Add bias if it exists: result + bias.data
|
||
5. Return new Tensor with result
|
||
|
||
LEARNING CONNECTIONS:
|
||
- This is the core neural network operation: y = Wx + b
|
||
- Matrix multiplication handles batch processing automatically
|
||
- Each row in input produces one row in output
|
||
- This is pure linear algebra - no autograd complexity yet
|
||
|
||
IMPLEMENTATION HINTS:
|
||
- Use np.dot() for matrix multiplication
|
||
- Handle the case where bias is None
|
||
- Always return a new Tensor object
|
||
- Focus on the mathematical operation, not gradient tracking
|
||
"""
|
||
### BEGIN SOLUTION
|
||
# Extract data from input tensor
|
||
x_data = x.data
|
||
weights_data = self.weights.data
|
||
|
||
# Matrix multiplication: input @ weights
|
||
output_data = np.dot(x_data, weights_data)
|
||
|
||
# Add bias if it exists
|
||
if self.bias is not None:
|
||
bias_data = self.bias.data
|
||
output_data = output_data + bias_data
|
||
|
||
# Return new Tensor with result
|
||
return Tensor(output_data)
|
||
### END SOLUTION
|
||
|
||
# In[ ]:
|
||
|
||
# 🧪 Unit Test: Linear Layer
|
||
def test_unit_linear():
|
||
"""Test Linear layer implementation."""
|
||
print("🧪 Testing Linear Layer...")
|
||
|
||
# Test case 1: Basic functionality
|
||
layer = Linear(input_size=3, output_size=2)
|
||
input_tensor = Tensor([[1.0, 2.0, 3.0]]) # Shape: (1, 3)
|
||
output = layer.forward(input_tensor)
|
||
|
||
# Check output shape
|
||
assert output.shape == (1, 2), f"Expected shape (1, 2), got {output.shape}"
|
||
print("✅ Output shape correct")
|
||
|
||
# Test case 2: No bias
|
||
layer_no_bias = Linear(input_size=2, output_size=3, use_bias=False)
|
||
assert layer_no_bias.bias is None, "Bias should be None when use_bias=False"
|
||
print("✅ No bias option works")
|
||
|
||
# Test case 3: Multiple samples (batch processing)
|
||
batch_input = Tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]) # Shape: (3, 2)
|
||
layer_batch = Linear(input_size=2, output_size=2)
|
||
batch_output = layer_batch.forward(batch_input)
|
||
|
||
assert batch_output.shape == (3, 2), f"Expected shape (3, 2), got {batch_output.shape}"
|
||
print("✅ Batch processing works")
|
||
|
||
# Test case 4: Callable interface
|
||
callable_output = layer_batch(batch_input)
|
||
assert np.allclose(callable_output.data, batch_output.data), "Callable interface should match forward()"
|
||
print("✅ Callable interface works")
|
||
|
||
# Test case 5: Parameter initialization
|
||
layer_init = Linear(input_size=10, output_size=5)
|
||
assert layer_init.weights.shape == (10, 5), f"Expected weights shape (10, 5), got {layer_init.weights.shape}"
|
||
assert layer_init.bias.shape == (5,), f"Expected bias shape (5,), got {layer_init.bias.shape}"
|
||
|
||
# Check that weights are reasonably small (good initialization)
|
||
assert np.abs(layer_init.weights.data).mean() < 1.0, "Weights should be small for good initialization"
|
||
print("✅ Parameter initialization correct")
|
||
|
||
print("🎉 All Linear layer tests passed!")
|
||
|
||
test_unit_linear()
|
||
|
||
# In[ ]:
|
||
|
||
# 🧪 Unit Test: Parameter Management
|
||
def test_unit_parameter_management():
|
||
"""Test Linear layer parameter management and module composition."""
|
||
print("🧪 Testing Parameter Management...")
|
||
|
||
# Test case 1: Parameter registration
|
||
layer = Linear(input_size=3, output_size=2)
|
||
params = layer.parameters()
|
||
|
||
assert len(params) == 2, f"Expected 2 parameters (weights + bias), got {len(params)}"
|
||
assert layer.weights in params, "Weights should be in parameters list"
|
||
assert layer.bias in params, "Bias should be in parameters list"
|
||
print("✅ Parameter registration works")
|
||
|
||
# Test case 2: Module composition
|
||
class SimpleNetwork(Module):
|
||
def __init__(self):
|
||
super().__init__()
|
||
self.layer1 = Linear(4, 3)
|
||
self.layer2 = Linear(3, 2)
|
||
|
||
def forward(self, x):
|
||
x = self.layer1(x)
|
||
return self.layer2(x)
|
||
|
||
network = SimpleNetwork()
|
||
all_params = network.parameters()
|
||
|
||
# Should have 4 parameters: 2 from each layer (weights + bias)
|
||
assert len(all_params) == 4, f"Expected 4 parameters from network, got {len(all_params)}"
|
||
print("✅ Module composition and parameter collection works")
|
||
|
||
# Test case 3: Forward pass through composed network
|
||
input_tensor = Tensor([[1.0, 2.0, 3.0, 4.0]])
|
||
output = network(input_tensor)
|
||
|
||
assert output.shape == (1, 2), f"Expected output shape (1, 2), got {output.shape}"
|
||
print("✅ Network forward pass works")
|
||
|
||
# Test case 4: No bias option
|
||
layer_no_bias = Linear(input_size=3, output_size=2, use_bias=False)
|
||
params_no_bias = layer_no_bias.parameters()
|
||
|
||
assert len(params_no_bias) == 1, f"Expected 1 parameter (weights only), got {len(params_no_bias)}"
|
||
assert layer_no_bias.bias is None, "Bias should be None when use_bias=False"
|
||
print("✅ No bias option works")
|
||
|
||
print("🎉 All parameter management tests passed!")
|
||
|
||
test_unit_parameter_management()
|
||
|
||
# In[ ]:
|
||
|
||
# ✅ IMPLEMENTATION CHECKPOINT: Linear layer complete
|
||
|
||
# 🤔 PREDICTION: How does memory usage scale with network depth vs width?
|
||
# Deeper network (more layers): _______
|
||
# Wider network (more neurons per layer): _______
|
||
|
||
# 🔍 SYSTEMS INSIGHT #3: Architecture Memory Analysis
|
||
def analyze_architecture_scaling():
|
||
"""Compare memory usage of deep vs wide networks."""
|
||
try:
|
||
print("📊 Architecture Scaling: Deep vs Wide Networks")
|
||
print("=" * 50)
|
||
|
||
# Compare networks with similar parameter counts
|
||
print("\nDeep Network (8 layers, narrow):")
|
||
deep_layers = [128, 64, 64, 64, 64, 64, 64, 10]
|
||
deep_params = 0
|
||
deep_memory = 0
|
||
|
||
for i in range(len(deep_layers) - 1):
|
||
layer_params = deep_layers[i] * deep_layers[i+1] + deep_layers[i+1]
|
||
deep_params += layer_params
|
||
layer_memory = layer_params * 4 / (1024 * 1024) # MB
|
||
deep_memory += layer_memory
|
||
print(f" Layer {i+1}: {deep_layers[i]:3d} → {deep_layers[i+1]:3d} = {layer_params:,} params")
|
||
|
||
print(f" Total: {deep_params:,} params, {deep_memory:.2f} MB")
|
||
|
||
print("\nWide Network (3 layers, wide):")
|
||
wide_layers = [128, 256, 256, 10]
|
||
wide_params = 0
|
||
wide_memory = 0
|
||
|
||
for i in range(len(wide_layers) - 1):
|
||
layer_params = wide_layers[i] * wide_layers[i+1] + wide_layers[i+1]
|
||
wide_params += layer_params
|
||
layer_memory = layer_params * 4 / (1024 * 1024) # MB
|
||
wide_memory += layer_memory
|
||
print(f" Layer {i+1}: {wide_layers[i]:3d} → {wide_layers[i+1]:3d} = {layer_params:,} params")
|
||
|
||
print(f" Total: {wide_params:,} params, {wide_memory:.2f} MB")
|
||
|
||
print(f"\n💡 Architecture Insights:")
|
||
print(f" • Deep network: {len(deep_layers)-1} layers, {deep_params:,} params")
|
||
print(f" • Wide network: {len(wide_layers)-1} layers, {wide_params:,} params")
|
||
print(f" • Memory ratio: {wide_memory/deep_memory:.1f}x (wide uses more)")
|
||
print(f" • Deep networks: better feature hierarchies")
|
||
print(f" • Wide networks: more parallel computation")
|
||
print(f" • Modern trend: Balance depth + width for best performance")
|
||
|
||
except Exception as e:
|
||
print(f"⚠️ Error in architecture analysis: {e}")
|
||
|
||
# Run the analysis
|
||
analyze_architecture_scaling()
|
||
|
||
# In[ ]:
|
||
|
||
# ## Part 4: Sequential Network Composition
|
||
|
||
#| export
|
||
class Sequential(Module):
|
||
"""
|
||
Sequential Network: Composes layers in sequence.
|
||
|
||
The most fundamental network architecture that applies layers in order:
|
||
f(x) = layer_n(...layer_2(layer_1(x)))
|
||
|
||
Inherits from Module for automatic parameter collection from all sub-layers.
|
||
This enables optimizers to find all parameters automatically.
|
||
|
||
Example Usage:
|
||
# Create a 3-layer MLP
|
||
model = Sequential([
|
||
Linear(784, 128),
|
||
ReLU(),
|
||
Linear(128, 64),
|
||
ReLU(),
|
||
Linear(64, 10)
|
||
])
|
||
|
||
# Use the model
|
||
output = model(input_data) # Clean interface!
|
||
params = model.parameters() # All parameters from all layers!
|
||
"""
|
||
|
||
def __init__(self, layers=None):
|
||
"""
|
||
Initialize Sequential network with layers.
|
||
|
||
Args:
|
||
layers: List of layers to compose in order (optional)
|
||
"""
|
||
super().__init__() # Initialize Module base class
|
||
self.layers = layers if layers is not None else []
|
||
|
||
# Register all layers as sub-modules for parameter collection
|
||
for i, layer in enumerate(self.layers):
|
||
# This automatically adds each layer to self._modules
|
||
setattr(self, f'layer_{i}', layer)
|
||
|
||
def forward(self, x):
|
||
"""
|
||
Forward pass through all layers in sequence.
|
||
|
||
Args:
|
||
x: Input tensor
|
||
|
||
Returns:
|
||
Output tensor after passing through all layers
|
||
"""
|
||
for layer in self.layers:
|
||
x = layer(x)
|
||
return x
|
||
|
||
def add(self, layer):
|
||
"""Add a layer to the network."""
|
||
self.layers.append(layer)
|
||
# Register the new layer for parameter collection
|
||
setattr(self, f'layer_{len(self.layers)-1}', layer)
|
||
|
||
# In[ ]:
|
||
|
||
# 🧪 Unit Test: Sequential Networks
|
||
def test_unit_sequential():
|
||
"""Test Sequential network implementation."""
|
||
print("🧪 Testing Sequential Network...")
|
||
|
||
# Test case 1: Create empty network
|
||
empty_net = Sequential()
|
||
assert len(empty_net.layers) == 0, "Empty Sequential should have no layers"
|
||
print("✅ Empty Sequential network creation")
|
||
|
||
# Test case 2: Create network with layers
|
||
layers = [Linear(3, 4), Linear(4, 2)]
|
||
network = Sequential(layers)
|
||
assert len(network.layers) == 2, "Network should have 2 layers"
|
||
print("✅ Sequential network with layers")
|
||
|
||
# Test case 3: Forward pass through network
|
||
input_tensor = Tensor([[1.0, 2.0, 3.0]])
|
||
output = network(input_tensor)
|
||
assert output.shape == (1, 2), f"Expected output shape (1, 2), got {output.shape}"
|
||
print("✅ Forward pass through Sequential network")
|
||
|
||
# Test case 4: Parameter collection from all layers
|
||
all_params = network.parameters()
|
||
# Should have 4 parameters: 2 weights + 2 biases from 2 Linear layers
|
||
assert len(all_params) == 4, f"Expected 4 parameters from Sequential network, got {len(all_params)}"
|
||
print("✅ Parameter collection from all layers")
|
||
|
||
# Test case 5: Adding layers dynamically
|
||
network.add(Linear(2, 1))
|
||
assert len(network.layers) == 3, "Network should have 3 layers after adding one"
|
||
|
||
# Test forward pass after adding layer
|
||
final_output = network(input_tensor)
|
||
assert final_output.shape == (1, 1), f"Expected final output shape (1, 1), got {final_output.shape}"
|
||
print("✅ Dynamic layer addition")
|
||
|
||
print("🎉 All Sequential network tests passed!")
|
||
|
||
test_unit_sequential()
|
||
|
||
# In[ ]:
|
||
|
||
# ## Part 5: Flatten Operation - Connecting Different Layer Types
|
||
|
||
#| export
|
||
def flatten(x, start_dim=1):
|
||
"""
|
||
Flatten tensor starting from a given dimension.
|
||
|
||
This is essential for transitioning from convolutional layers
|
||
(which output 4D tensors) to linear layers (which expect 2D).
|
||
|
||
Args:
|
||
x: Input tensor (Tensor or any array-like)
|
||
start_dim: Dimension to start flattening from (default: 1 to preserve batch)
|
||
|
||
Returns:
|
||
Flattened tensor preserving batch dimension
|
||
|
||
Examples:
|
||
# Flatten CNN output for Linear layer
|
||
conv_output = Tensor(np.random.randn(32, 64, 8, 8)) # (batch, channels, height, width)
|
||
flat = flatten(conv_output) # (32, 4096) - ready for Linear layer!
|
||
|
||
# Flatten image for MLP
|
||
images = Tensor(np.random.randn(32, 3, 28, 28)) # CIFAR-10 batch
|
||
flat = flatten(images) # (32, 2352) - ready for MLP!
|
||
"""
|
||
# Get the data (handle both Tensor and numpy arrays)
|
||
if hasattr(x, 'data'):
|
||
data = x.data
|
||
else:
|
||
data = x
|
||
|
||
# Calculate new shape
|
||
batch_size = data.shape[0] if start_dim > 0 else 1
|
||
remaining_size = np.prod(data.shape[start_dim:])
|
||
new_shape = (batch_size, remaining_size) if start_dim > 0 else (remaining_size,)
|
||
|
||
# Reshape while preserving the original tensor type
|
||
if hasattr(x, 'data'):
|
||
# It's a Tensor - create a new Tensor with flattened data
|
||
flattened_data = data.reshape(new_shape)
|
||
# Use type(x) to preserve the exact Tensor type (Parameter vs regular Tensor)
|
||
# This ensures that if input was a Parameter, output is also a Parameter
|
||
return type(x)(flattened_data)
|
||
else:
|
||
# It's a numpy array - just reshape and return
|
||
return data.reshape(new_shape)
|
||
|
||
#| export
|
||
class Flatten(Module):
|
||
"""
|
||
Flatten layer that reshapes tensors from multi-dimensional to 2D.
|
||
|
||
Essential for connecting convolutional layers (which output 4D tensors)
|
||
to linear layers (which expect 2D tensors). Preserves the batch dimension.
|
||
|
||
Example Usage:
|
||
# In a CNN architecture
|
||
model = Sequential([
|
||
Conv2D(3, 16, kernel_size=3), # Output: (batch, 16, height, width)
|
||
ReLU(),
|
||
Flatten(), # Output: (batch, 16*height*width)
|
||
Linear(16*height*width, 10) # Now compatible!
|
||
])
|
||
"""
|
||
|
||
def __init__(self, start_dim=1):
|
||
"""
|
||
Initialize Flatten layer.
|
||
|
||
Args:
|
||
start_dim: Dimension to start flattening from (default: 1 to preserve batch)
|
||
"""
|
||
super().__init__()
|
||
self.start_dim = start_dim
|
||
|
||
def forward(self, x):
|
||
"""
|
||
Flatten tensor starting from start_dim.
|
||
|
||
Args:
|
||
x: Input tensor
|
||
|
||
Returns:
|
||
Flattened tensor with batch dimension preserved
|
||
"""
|
||
return flatten(x, start_dim=self.start_dim)
|
||
|
||
# In[ ]:
|
||
|
||
# 🧪 Unit Test: Flatten Operations
|
||
def test_unit_flatten():
|
||
"""Test Flatten layer and function implementation."""
|
||
print("🧪 Testing Flatten Operations...")
|
||
|
||
# Test case 1: Flatten function with 2D tensor
|
||
x_2d = Tensor([[1, 2], [3, 4]])
|
||
flattened_func = flatten(x_2d)
|
||
assert flattened_func.shape == (2, 2), f"Expected shape (2, 2), got {flattened_func.shape}"
|
||
print("✅ Flatten function with 2D tensor")
|
||
|
||
# Test case 2: Flatten function with 4D tensor (simulating CNN output)
|
||
x_4d = Tensor(np.random.randn(2, 3, 4, 4)) # (batch, channels, height, width)
|
||
flattened_4d = flatten(x_4d)
|
||
assert flattened_4d.shape == (2, 48), f"Expected shape (2, 48), got {flattened_4d.shape}" # 3*4*4 = 48
|
||
print("✅ Flatten function with 4D tensor")
|
||
|
||
# Test case 3: Flatten layer class
|
||
flatten_layer = Flatten()
|
||
layer_output = flatten_layer(x_4d)
|
||
assert layer_output.shape == (2, 48), f"Expected shape (2, 48), got {layer_output.shape}"
|
||
assert np.allclose(layer_output.data, flattened_4d.data), "Flatten layer should match flatten function"
|
||
print("✅ Flatten layer class")
|
||
|
||
# Test case 4: Different start dimensions
|
||
flatten_from_0 = Flatten(start_dim=0)
|
||
full_flat = flatten_from_0(x_2d)
|
||
assert len(full_flat.shape) <= 2, "Flattening from dim 0 should create vector"
|
||
print("✅ Different start dimensions")
|
||
|
||
# Test case 5: Integration with Sequential
|
||
network = Sequential([
|
||
Linear(8, 4),
|
||
Flatten()
|
||
])
|
||
test_input = Tensor(np.random.randn(2, 8))
|
||
output = network(test_input)
|
||
assert output.shape == (2, 4), f"Expected shape (2, 4), got {output.shape}"
|
||
print("✅ Flatten integration with Sequential")
|
||
|
||
print("🎉 All Flatten operations tests passed!")
|
||
|
||
test_unit_flatten()
|
||
|
||
# In[ ]:
|
||
|
||
# ## NBGrader Assessment Questions
|
||
|
||
# ⭐ QUESTION 1: Parameter Counting Challenge
|
||
"""
|
||
You're building a Multi-Layer Perceptron (MLP) for MNIST digit classification.
|
||
|
||
Network architecture:
|
||
- Input: 784 features (28×28 pixel images, flattened)
|
||
- Hidden layer 1: 256 neurons with ReLU activation
|
||
- Hidden layer 2: 128 neurons with ReLU activation
|
||
- Output layer: 10 neurons (one per digit class)
|
||
|
||
Calculate the total number of trainable parameters in this network.
|
||
|
||
Show your work:
|
||
- Layer 1 parameters: _____
|
||
- Layer 2 parameters: _____
|
||
- Layer 3 parameters: _____
|
||
- Total parameters: _____
|
||
|
||
Hint: Remember that each Linear layer has both weights and biases!
|
||
"""
|
||
|
||
# ### BEGIN SOLUTION
|
||
# Layer 1: Linear(784, 256)
|
||
# - Weights: 784 × 256 = 200,704
|
||
# - Biases: 256
|
||
# - Subtotal: 200,960
|
||
|
||
# Layer 2: Linear(256, 128)
|
||
# - Weights: 256 × 128 = 32,768
|
||
# - Biases: 128
|
||
# - Subtotal: 32,896
|
||
|
||
# Layer 3: Linear(128, 10)
|
||
# - Weights: 128 × 10 = 1,280
|
||
# - Biases: 10
|
||
# - Subtotal: 1,290
|
||
|
||
# Total: 200,960 + 32,896 + 1,290 = 235,146 parameters
|
||
# ### END SOLUTION
|
||
|
||
# ⭐ QUESTION 2: Memory Analysis Challenge
|
||
"""
|
||
Compare the memory requirements of two different MLP architectures for the same task:
|
||
|
||
Architecture A (Wide): 784 → 512 → 512 → 10
|
||
Architecture B (Deep): 784 → 128 → 128 → 128 → 128 → 10
|
||
|
||
For each architecture, calculate:
|
||
1. Total number of parameters
|
||
2. Memory usage for parameters (assume float32 = 4 bytes per parameter)
|
||
3. Which architecture would you choose for a mobile device with limited memory?
|
||
|
||
Architecture A calculations:
|
||
- Total parameters: _____
|
||
- Memory usage: _____ MB
|
||
|
||
Architecture B calculations:
|
||
- Total parameters: _____
|
||
- Memory usage: _____ MB
|
||
|
||
Mobile device choice and reasoning: _____
|
||
"""
|
||
|
||
# ### BEGIN SOLUTION
|
||
# Architecture A (Wide): 784 → 512 → 512 → 10
|
||
# - Layer 1: (784 × 512) + 512 = 401,920
|
||
# - Layer 2: (512 × 512) + 512 = 262,656
|
||
# - Layer 3: (512 × 10) + 10 = 5,130
|
||
# - Total: 669,706 parameters
|
||
# - Memory: 669,706 × 4 bytes = 2.68 MB
|
||
|
||
# Architecture B (Deep): 784 → 128 → 128 → 128 → 128 → 10
|
||
# - Layer 1: (784 × 128) + 128 = 100,480
|
||
# - Layer 2: (128 × 128) + 128 = 16,512
|
||
# - Layer 3: (128 × 128) + 128 = 16,512
|
||
# - Layer 4: (128 × 128) + 128 = 16,512
|
||
# - Layer 5: (128 × 10) + 10 = 1,290
|
||
# - Total: 151,306 parameters
|
||
# - Memory: 151,306 × 4 bytes = 0.61 MB
|
||
|
||
# Mobile choice: Architecture B (Deep)
|
||
# Reasoning: Uses 4.4x less memory while maintaining similar representational capacity through depth
|
||
# ### END SOLUTION
|
||
|
||
# ⭐ QUESTION 3: FLOPS Calculation Challenge
|
||
"""
|
||
Calculate the computational cost (in FLOPs) for a forward pass through this network:
|
||
|
||
Input batch: 32 samples × 784 features
|
||
Network: 784 → 256 → 128 → 10
|
||
|
||
For each layer, calculate:
|
||
- Matrix multiplication FLOPs: 2 × batch_size × input_size × output_size
|
||
- Bias addition FLOPs: batch_size × output_size
|
||
- Total FLOPs per layer
|
||
|
||
Layer 1 (784 → 256):
|
||
- MatMul FLOPs: _____
|
||
- Bias FLOPs: _____
|
||
- Layer total: _____
|
||
|
||
Layer 2 (256 → 128):
|
||
- MatMul FLOPs: _____
|
||
- Bias FLOPs: _____
|
||
- Layer total: _____
|
||
|
||
Layer 3 (128 → 10):
|
||
- MatMul FLOPs: _____
|
||
- Bias FLOPs: _____
|
||
- Layer total: _____
|
||
|
||
Network total FLOPs: _____
|
||
"""
|
||
|
||
# ### BEGIN SOLUTION
|
||
# Batch size = 32 samples
|
||
|
||
# Layer 1 (784 → 256):
|
||
# - MatMul FLOPs: 2 × 32 × 784 × 256 = 12,582,912
|
||
# - Bias FLOPs: 32 × 256 = 8,192
|
||
# - Layer total: 12,591,104
|
||
|
||
# Layer 2 (256 → 128):
|
||
# - MatMul FLOPs: 2 × 32 × 256 × 128 = 2,097,152
|
||
# - Bias FLOPs: 32 × 128 = 4,096
|
||
# - Layer total: 2,101,248
|
||
|
||
# Layer 3 (128 → 10):
|
||
# - MatMul FLOPs: 2 × 32 × 128 × 10 = 81,920
|
||
# - Bias FLOPs: 32 × 10 = 320
|
||
# - Layer total: 82,240
|
||
|
||
# Network total: 12,591,104 + 2,101,248 + 82,240 = 14,774,592 FLOPs (~14.8 MFLOPS)
|
||
# ### END SOLUTION
|
||
|
||
# In[ ]:
|
||
|
||
# ## Complete Neural Network Demo
|
||
|
||
def demonstrate_complete_networks():
|
||
"""Demonstrate complete neural networks using all implemented components."""
|
||
print("🔥 Complete Neural Network Demo")
|
||
print("=" * 50)
|
||
|
||
print("\n1. MLP for Classification (MNIST-style):")
|
||
# Multi-layer perceptron for image classification
|
||
mlp = Sequential([
|
||
Flatten(), # Flatten input images
|
||
Linear(784, 256), # First hidden layer
|
||
Linear(256, 128), # Second hidden layer
|
||
Linear(128, 10) # Output layer (10 classes)
|
||
])
|
||
|
||
# Test with batch of "images"
|
||
batch_images = Tensor(np.random.randn(32, 28, 28)) # 32 MNIST-like images
|
||
mlp_output = mlp(batch_images)
|
||
print(f" Input: {batch_images.shape} (batch of 28x28 images)")
|
||
print(f" Output: {mlp_output.shape} (class logits for 32 images)")
|
||
print(f" Parameters: {len(mlp.parameters())} tensors")
|
||
|
||
print("\n2. CNN-style Architecture (with Flatten):")
|
||
# Simulate CNN → Flatten → Dense pattern
|
||
cnn_style = Sequential([
|
||
# Simulate Conv2D output with random "features"
|
||
Flatten(), # Flatten spatial features
|
||
Linear(512, 256), # Dense layer after convolution
|
||
Linear(256, 10) # Classification head
|
||
])
|
||
|
||
# Test with simulated conv output
|
||
conv_features = Tensor(np.random.randn(16, 8, 8, 8)) # Simulated (B,C,H,W)
|
||
cnn_output = cnn_style(conv_features)
|
||
print(f" Input: {conv_features.shape} (simulated conv features)")
|
||
print(f" Output: {cnn_output.shape} (class predictions)")
|
||
|
||
print("\n3. Deep Network with Many Layers:")
|
||
# Demonstrate deep composition
|
||
deep_net = Sequential()
|
||
layer_sizes = [100, 80, 60, 40, 20, 10]
|
||
|
||
for i in range(len(layer_sizes) - 1):
|
||
deep_net.add(Linear(layer_sizes[i], layer_sizes[i+1]))
|
||
print(f" Added layer: {layer_sizes[i]} → {layer_sizes[i+1]}")
|
||
|
||
# Test deep network
|
||
deep_input = Tensor(np.random.randn(8, 100))
|
||
deep_output = deep_net(deep_input)
|
||
print(f" Deep network: {deep_input.shape} → {deep_output.shape}")
|
||
print(f" Total parameters: {len(deep_net.parameters())} tensors")
|
||
|
||
print("\n4. Parameter Management Across Networks:")
|
||
networks = {'MLP': mlp, 'CNN-style': cnn_style, 'Deep': deep_net}
|
||
|
||
for name, net in networks.items():
|
||
params = net.parameters()
|
||
total_params = sum(p.data.size for p in params)
|
||
memory_mb = total_params * 4 / (1024 * 1024) # float32 = 4 bytes
|
||
print(f" {name}: {len(params)} param tensors, {total_params:,} total params, {memory_mb:.2f} MB")
|
||
|
||
print("\n🎉 All components work together seamlessly!")
|
||
print(" • Module system enables automatic parameter collection")
|
||
print(" • Linear layers handle matrix transformations")
|
||
print(" • Sequential composes layers into complete architectures")
|
||
print(" • Flatten connects different layer types")
|
||
print(" • Everything integrates for production-ready neural networks!")
|
||
|
||
demonstrate_complete_networks()
|
||
|
||
# In[ ]:
|
||
|
||
# ## Testing Framework
|
||
|
||
def test_unit_all():
|
||
"""Run complete module validation."""
|
||
print("🧪 Running all unit tests...")
|
||
|
||
# Call every individual test function
|
||
test_unit_matmul()
|
||
test_unit_linear()
|
||
test_unit_parameter_management()
|
||
test_unit_sequential()
|
||
test_unit_flatten()
|
||
|
||
print("✅ All tests passed! Module ready for integration.")
|
||
|
||
# In[ ]:
|
||
|
||
if __name__ == "__main__":
|
||
print("🔥 TinyTorch Layers Module - Complete Foundation Demo")
|
||
print("=" * 60)
|
||
|
||
# Test all core components
|
||
print("\n🧪 Testing All Core Components:")
|
||
test_unit_all()
|
||
|
||
print("\n" + "="*60)
|
||
demonstrate_complete_networks()
|
||
|
||
print("\n🎉 Complete neural network foundation ready!")
|
||
print(" ✅ Module system for parameter management")
|
||
print(" ✅ Linear layers for transformations")
|
||
print(" ✅ Sequential networks for composition")
|
||
print(" ✅ Flatten operations for tensor reshaping")
|
||
print(" ✅ All components tested and integrated!")
|
||
|
||
# ## 🤔 ML Systems Thinking: Interactive Questions
|
||
|
||
# Now that you've implemented all the core neural network components, let's think about their implications for ML systems:
|
||
|
||
# ⭐ QUESTION: Memory vs Computation Trade-offs
|
||
"""
|
||
🤔 **Question 1: Memory vs Computation Analysis**
|
||
|
||
You're designing a neural network for deployment on a mobile device with limited memory (1GB RAM) but decent compute power.
|
||
|
||
You have two architecture options:
|
||
A) Wide network: 784 → 2048 → 2048 → 10 (3 layers, wide)
|
||
B) Deep network: 784 → 256 → 256 → 256 → 256 → 10 (5 layers, narrow)
|
||
|
||
Calculate the memory requirements for each option and explain which you'd choose for mobile deployment and why.
|
||
|
||
Consider:
|
||
- Parameter storage requirements
|
||
- Intermediate activation storage during forward pass
|
||
- Training vs inference memory requirements
|
||
- How your choice affects model capacity and accuracy
|
||
"""
|
||
|
||
# ⭐ QUESTION: Performance Optimization
|
||
"""
|
||
🤔 **Question 2: Production Performance Optimization**
|
||
|
||
Your Linear layer implementation works correctly, but you notice it's slower than PyTorch's nn.Linear on the same hardware.
|
||
|
||
Investigate and explain:
|
||
1. Why might our implementation be slower? (Hint: think about underlying linear algebra libraries)
|
||
2. What optimization techniques do production frameworks use?
|
||
3. How would you modify our implementation to approach production performance?
|
||
4. When might our simple implementation actually be preferable?
|
||
|
||
Research areas to consider:
|
||
- BLAS (Basic Linear Algebra Subprograms) libraries
|
||
- Memory layout and cache efficiency
|
||
- Vectorization and SIMD instructions
|
||
- GPU kernel optimization
|
||
"""
|
||
|
||
# ⭐ QUESTION: Scaling and Architecture Design
|
||
"""
|
||
🤔 **Question 3: Systems Architecture Scaling**
|
||
|
||
Modern transformer models like GPT-3 have billions of parameters, primarily in Linear layers.
|
||
|
||
Analyze the scaling challenges:
|
||
1. How does memory requirement scale with model size? Calculate the memory needed for a 175B parameter model.
|
||
2. What are the computational bottlenecks during training vs inference?
|
||
3. How do systems like distributed training address these scaling challenges?
|
||
4. Why do large models use techniques like gradient checkpointing and model parallelism?
|
||
|
||
Systems considerations:
|
||
- Memory hierarchy (L1/L2/L3 cache, RAM, storage)
|
||
- Network bandwidth for distributed training
|
||
- GPU memory constraints and model sharding
|
||
- Inference optimization for production serving
|
||
"""
|
||
|
||
# ## 🎯 MODULE SUMMARY: Layers - Complete Neural Network Foundation
|
||
|
||
# ## 🎯 What You've Accomplished
|
||
|
||
# You've successfully implemented the complete foundation for neural networks - all the essential components working together:
|
||
|
||
# ### ✅ **Complete Core System**
|
||
# - **Module Base Class**: Parameter management and composition patterns for all neural network components
|
||
# - **Matrix Multiplication**: The computational primitive underlying all neural network operations
|
||
# - **Linear (Dense) Layers**: Complete implementation with proper parameter initialization and forward propagation
|
||
# - **Sequential Networks**: Clean composition system for building complete neural network architectures
|
||
# - **Flatten Operations**: Tensor reshaping to connect different layer types (essential for CNN→MLP transitions)
|
||
|
||
# ### ✅ **Systems Understanding**
|
||
# - **Architectural Patterns**: How modular design enables everything from MLPs to complex deep networks
|
||
# - **Memory Analysis**: How layer composition affects memory usage and computational efficiency
|
||
# - **Performance Characteristics**: Understanding how tensor operations and layer composition affect performance
|
||
# - **Production Context**: Connection to real-world ML frameworks and their component organization
|
||
|
||
# ### ✅ **ML Engineering Skills**
|
||
# - **Complete Parameter Management**: How neural networks automatically collect parameters from all components
|
||
# - **Network Composition**: Building complex architectures from simple, reusable components
|
||
# - **Tensor Operations**: Essential reshaping and transformation operations for different network types
|
||
# - **Clean Abstraction**: Professional software design patterns that scale to production systems
|
||
|
||
# ## 🔗 **Connection to Production ML Systems**
|
||
|
||
# Your unified implementation mirrors the complete component systems used in:
|
||
# - **PyTorch's nn.Module system**: Same parameter management and composition patterns
|
||
# - **PyTorch's nn.Sequential**: Identical architecture composition approach
|
||
# - **All major frameworks**: The same modular design principles that power TensorFlow, JAX, and others
|
||
# - **Production ML systems**: Clean abstractions that enable complex models while maintaining manageable code
|
||
|
||
# ## 🚀 **What's Next**
|
||
|
||
# With your complete layer foundation, you're ready to:
|
||
# - **Module 05 (Dense)**: Build complete dense networks for classification tasks
|
||
# - **Module 06 (Spatial)**: Add convolutional layers for computer vision
|
||
# - **Module 09 (Autograd)**: Enable automatic differentiation for learning
|
||
# - **Module 10 (Optimizers)**: Implement sophisticated optimization algorithms
|
||
|
||
# ## 💡 **Key Systems Insights**
|
||
|
||
# 1. **Modular composition is the key to scalable ML systems** - clean interfaces enable complex behaviors
|
||
# 2. **Parameter management must be automatic** - manual parameter tracking doesn't scale to deep networks
|
||
# 3. **Tensor operations like flattening are architectural requirements** - different layer types need different tensor shapes
|
||
# 4. **Clean abstractions enable innovation** - good foundational design supports unlimited architectural experimentation
|
||
|
||
# You now understand how to build complete, production-ready neural network foundations that can scale to any architecture! |