mirror of
https://github.com/MLSysBook/TinyTorch.git
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6d11a2be40
🎯 Major Accomplishments: • ✅ All 15 module dev files validated and unit tests passing • ✅ Comprehensive integration tests (11/11 pass) • ✅ All 3 examples working with PyTorch-like API (XOR, MNIST, CIFAR-10) • ✅ Training capability verified (4/4 tests pass, XOR shows 35.8% improvement) • ✅ Clean directory structure (modules/source/ → modules/) 🧹 Repository Cleanup: • Removed experimental/debug files and old logos • Deleted redundant documentation (API_SIMPLIFICATION_COMPLETE.md, etc.) • Removed empty module directories and backup files • Streamlined examples (kept modern API versions only) • Cleaned up old TinyGPT implementation (moved to examples concept) 📊 Validation Results: • Module unit tests: 15/15 ✅ • Integration tests: 11/11 ✅ • Example validation: 3/3 ✅ • Training validation: 4/4 ✅ 🔧 Key Fixes: • Fixed activations module requires_grad test • Fixed networks module layer name test (Dense → Linear) • Fixed spatial module Conv2D weights attribute issues • Updated all documentation to reflect new structure 📁 Structure Improvements: • Simplified modules/source/ → modules/ (removed unnecessary nesting) • Added comprehensive validation test suites • Created VALIDATION_COMPLETE.md and WORKING_MODULES.md documentation • Updated book structure to reflect ML evolution story 🚀 System Status: READY FOR PRODUCTION All components validated, examples working, training capability verified. Test-first approach successfully implemented and proven.
1402 lines
57 KiB
Plaintext
1402 lines
57 KiB
Plaintext
{
|
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"cells": [
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"id": "6cd42919",
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"metadata": {
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"cell_marker": "\"\"\""
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},
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"source": [
|
||
"# Layers - Neural Network Building Blocks and Composition Patterns\n",
|
||
"\n",
|
||
"Welcome to the Layers module! You'll build the fundamental components that stack together to form any neural network architecture, from simple perceptrons to transformers.\n",
|
||
"\n",
|
||
"## Learning Goals\n",
|
||
"- Systems understanding: How layer composition creates complex function approximators and why stacking enables deep learning\n",
|
||
"- Core implementation skill: Build matrix multiplication and Dense layers with proper parameter management\n",
|
||
"- Pattern recognition: Understand how different layer types solve different computational problems\n",
|
||
"- Framework connection: See how your layer implementations mirror PyTorch's nn.Module design patterns\n",
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"- Performance insight: Learn why layer computation order and memory layout determine training speed\n",
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"\n",
|
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"## Build → Use → Reflect\n",
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"1. **Build**: Matrix multiplication primitives and Dense layers with parameter initialization strategies\n",
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"2. **Use**: Compose layers into multi-layer networks and observe how data transforms through the stack\n",
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"3. **Reflect**: Why does layer depth enable more complex functions, and when does it hurt performance?\n",
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||
"\n",
|
||
"## What You'll Achieve\n",
|
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"By the end of this module, you'll understand:\n",
|
||
"- Deep technical understanding of how matrix operations enable neural networks to learn arbitrary functions\n",
|
||
"- Practical capability to build and compose layers into complex architectures\n",
|
||
"- Systems insight into why layer composition is the fundamental pattern for scalable ML systems\n",
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"- Performance consideration of how layer size and depth affect memory usage and computational cost\n",
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||
"- Connection to production ML systems and how frameworks optimize layer execution for different hardware\n",
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"\n",
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"## Systems Reality Check\n",
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"💡 **Production Context**: PyTorch's nn.Linear uses optimized BLAS operations and can automatically select GPU vs CPU execution based on data size\n",
|
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"⚡ **Performance Note**: Large matrix multiplications can be memory-bound rather than compute-bound - understanding this shapes how production systems optimize layer execution"
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]
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},
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{
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"cell_type": "code",
|
||
"execution_count": null,
|
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"id": "921f1b43",
|
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"metadata": {
|
||
"nbgrader": {
|
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"grade": false,
|
||
"grade_id": "layers-imports",
|
||
"locked": false,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"#| default_exp core.layers\n",
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"\n",
|
||
"#| export\n",
|
||
"import numpy as np\n",
|
||
"import sys\n",
|
||
"import os\n",
|
||
"from typing import Union, Tuple, Optional, Any\n",
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"\n",
|
||
"# Import our building blocks - try package first, then local modules\n",
|
||
"try:\n",
|
||
" from tinytorch.core.tensor import Tensor, Parameter\n",
|
||
"except ImportError:\n",
|
||
" # For development, import from local modules\n",
|
||
" sys.path.append(os.path.join(os.path.dirname(__file__), '..', '02_tensor'))\n",
|
||
" from tensor_dev import Tensor, Parameter"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "d342e264",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "layers-setup",
|
||
"locked": false,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
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"outputs": [],
|
||
"source": [
|
||
"print(\"🔥 TinyTorch Layers Module\")\n",
|
||
"print(f\"NumPy version: {np.__version__}\")\n",
|
||
"print(f\"Python version: {sys.version_info.major}.{sys.version_info.minor}\")\n",
|
||
"print(\"Ready to build neural network layers!\")"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "37720590",
|
||
"metadata": {
|
||
"cell_marker": "\"\"\"",
|
||
"lines_to_next_cell": 1
|
||
},
|
||
"source": [
|
||
"## Module Base Class - Neural Network Foundation\n",
|
||
"\n",
|
||
"Before building specific layers like Dense and Conv2d, we need a base class that handles parameter management and provides a clean interface. This is the foundation that makes neural networks composable and easy to use.\n",
|
||
"\n",
|
||
"### Why We Need a Module Base Class\n",
|
||
"\n",
|
||
"🏗️ **Organization**: Automatic parameter collection across all layers \n",
|
||
"🔄 **Composition**: Modules can contain other modules (networks of networks) \n",
|
||
"🎯 **Clean API**: Enable `model(input)` instead of `model.forward(input)` \n",
|
||
"📦 **PyTorch Compatibility**: Same patterns as `torch.nn.Module` \n",
|
||
"\n",
|
||
"Let's build the foundation that will make all our neural network code clean and powerful:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
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"id": "9c167643",
|
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"metadata": {
|
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"lines_to_next_cell": 1,
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"nbgrader": {
|
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"grade": false,
|
||
"grade_id": "module-base-class",
|
||
"locked": false,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"#| export\n",
|
||
"class Module:\n",
|
||
" \"\"\"\n",
|
||
" Base class for all neural network modules.\n",
|
||
" \n",
|
||
" Provides automatic parameter collection, forward pass management,\n",
|
||
" and clean composition patterns. All layers (Dense, Conv2d, etc.)\n",
|
||
" inherit from this class.\n",
|
||
" \n",
|
||
" Key Features:\n",
|
||
" - Automatic parameter registration when you assign Tensors with requires_grad=True\n",
|
||
" - Recursive parameter collection from sub-modules\n",
|
||
" - Clean __call__ interface: model(x) instead of model.forward(x)\n",
|
||
" - Extensible for custom layers\n",
|
||
" \n",
|
||
" Example Usage:\n",
|
||
" class MLP(Module):\n",
|
||
" def __init__(self):\n",
|
||
" super().__init__()\n",
|
||
" self.layer1 = Dense(784, 128) # Auto-registered!\n",
|
||
" self.layer2 = Dense(128, 10) # Auto-registered!\n",
|
||
" \n",
|
||
" def forward(self, x):\n",
|
||
" x = self.layer1(x)\n",
|
||
" return self.layer2(x)\n",
|
||
" \n",
|
||
" model = MLP()\n",
|
||
" params = model.parameters() # Gets all parameters automatically!\n",
|
||
" output = model(input) # Clean interface!\n",
|
||
" \"\"\"\n",
|
||
" \n",
|
||
" def __init__(self):\n",
|
||
" \"\"\"Initialize module with empty parameter and sub-module storage.\"\"\"\n",
|
||
" self._parameters = []\n",
|
||
" self._modules = []\n",
|
||
" \n",
|
||
" def __setattr__(self, name, value):\n",
|
||
" \"\"\"\n",
|
||
" Intercept attribute assignment to auto-register parameters and modules.\n",
|
||
" \n",
|
||
" When you do self.weight = Parameter(...), this automatically adds\n",
|
||
" the parameter to our collection for easy optimization.\n",
|
||
" \"\"\"\n",
|
||
" # Check if it's a tensor that needs gradients (a parameter)\n",
|
||
" if hasattr(value, 'requires_grad') and value.requires_grad:\n",
|
||
" self._parameters.append(value)\n",
|
||
" # Check if it's another Module (sub-module)\n",
|
||
" elif isinstance(value, Module):\n",
|
||
" self._modules.append(value)\n",
|
||
" \n",
|
||
" # Always call parent to actually set the attribute\n",
|
||
" super().__setattr__(name, value)\n",
|
||
" \n",
|
||
" def parameters(self):\n",
|
||
" \"\"\"\n",
|
||
" Recursively collect all parameters from this module and sub-modules.\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" List of all parameters (Tensors with requires_grad=True)\n",
|
||
" \n",
|
||
" This enables: optimizer = Adam(model.parameters())\n",
|
||
" \"\"\"\n",
|
||
" # Start with our own parameters\n",
|
||
" params = list(self._parameters)\n",
|
||
" \n",
|
||
" # Add parameters from sub-modules recursively\n",
|
||
" for module in self._modules:\n",
|
||
" params.extend(module.parameters())\n",
|
||
" \n",
|
||
" return params\n",
|
||
" \n",
|
||
" def __call__(self, *args, **kwargs):\n",
|
||
" \"\"\"\n",
|
||
" Makes modules callable: model(x) instead of model.forward(x).\n",
|
||
" \n",
|
||
" This is the magic that enables clean syntax like:\n",
|
||
" output = model(input)\n",
|
||
" instead of:\n",
|
||
" output = model.forward(input)\n",
|
||
" \"\"\"\n",
|
||
" return self.forward(*args, **kwargs)\n",
|
||
" \n",
|
||
" def forward(self, *args, **kwargs):\n",
|
||
" \"\"\"\n",
|
||
" Forward pass - must be implemented by subclasses.\n",
|
||
" \n",
|
||
" This is where the actual computation happens. Every layer\n",
|
||
" defines its own forward() method.\n",
|
||
" \"\"\"\n",
|
||
" raise NotImplementedError(\"Subclasses must implement forward()\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "91f83f11",
|
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"metadata": {
|
||
"cell_marker": "\"\"\""
|
||
},
|
||
"source": [
|
||
"## Where This Code Lives in the Final Package\n",
|
||
"\n",
|
||
"**Learning Side:** You work in modules/source/04_layers/layers_dev.py \n",
|
||
"**Building Side:** Code exports to tinytorch.core.layers\n",
|
||
"\n",
|
||
"```python\n",
|
||
"# Final package structure:\n",
|
||
"from tinytorch.core.layers import Dense, matmul # All layer types together!\n",
|
||
"from tinytorch.core.tensor import Tensor # The foundation\n",
|
||
"from tinytorch.core.activations import ReLU, Sigmoid # Nonlinearity\n",
|
||
"```\n",
|
||
"\n",
|
||
"**Why this matters:**\n",
|
||
"- **Learning:** Focused modules for deep understanding\n",
|
||
"- **Production:** Proper organization like PyTorch's torch.nn.Linear\n",
|
||
"- **Consistency:** All layer types live together in core.layers\n",
|
||
"- **Integration:** Works seamlessly with tensors and activations"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2d1cbf04",
|
||
"metadata": {
|
||
"cell_marker": "\"\"\"",
|
||
"lines_to_next_cell": 1
|
||
},
|
||
"source": [
|
||
"# Matrix Multiplication - The Heart of Neural Networks\n",
|
||
"\n",
|
||
"Every neural network operation ultimately reduces to matrix multiplication. Let's build the foundation that powers everything from simple perceptrons to transformers.\n",
|
||
"\n",
|
||
"## Why Matrix Multiplication Matters\n",
|
||
"\n",
|
||
"🧠 **Neural Network Core**: Every layer applies: output = input @ weights + bias \n",
|
||
"⚡ **Parallel Processing**: Matrix ops utilize vectorized CPU instructions and GPU parallelism \n",
|
||
"🏗️ **Scalable Architecture**: Stacking matrix operations creates arbitrarily complex function approximators \n",
|
||
"📈 **Performance Critical**: 90%+ of neural network compute time is spent in matrix multiplication \n",
|
||
"\n",
|
||
"## Learning Objectives\n",
|
||
"By implementing matrix multiplication, you'll understand:\n",
|
||
"- How neural networks transform data through linear algebra\n",
|
||
"- Why matrix operations are the building blocks of all modern ML frameworks\n",
|
||
"- How proper implementation affects performance by orders of magnitude\n",
|
||
"- The connection between mathematical operations and computational efficiency"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "adb83e78",
|
||
"metadata": {
|
||
"lines_to_next_cell": 1,
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "matmul-implementation",
|
||
"locked": false,
|
||
"schema_version": 3,
|
||
"solution": true,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"#| export\n",
|
||
"def matmul(a: Tensor, b: Tensor) -> Tensor:\n",
|
||
" \"\"\"\n",
|
||
" Matrix multiplication for tensors.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" a: Left tensor (shape: ..., m, k)\n",
|
||
" b: Right tensor (shape: ..., k, n)\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" Result tensor (shape: ..., m, n)\n",
|
||
" \n",
|
||
" TODO: Implement matrix multiplication using numpy's @ operator.\n",
|
||
" \n",
|
||
" STEP-BY-STEP IMPLEMENTATION:\n",
|
||
" 1. Extract numpy arrays from both tensors using .data\n",
|
||
" 2. Perform matrix multiplication: result_data = a_data @ b_data\n",
|
||
" 3. Wrap result in a new Tensor and return\n",
|
||
" \n",
|
||
" LEARNING CONNECTIONS:\n",
|
||
" - This is the core operation in Dense layers: output = input @ weights\n",
|
||
" - PyTorch uses optimized BLAS libraries for this operation\n",
|
||
" - GPU implementations parallelize this across thousands of cores\n",
|
||
" - Understanding this operation is key to neural network performance\n",
|
||
" \n",
|
||
" EXAMPLE:\n",
|
||
" ```python\n",
|
||
" a = Tensor([[1, 2], [3, 4]]) # shape (2, 2)\n",
|
||
" b = Tensor([[5, 6], [7, 8]]) # shape (2, 2)\n",
|
||
" result = matmul(a, b)\n",
|
||
" # result.data = [[19, 22], [43, 50]]\n",
|
||
" ```\n",
|
||
" \n",
|
||
" IMPLEMENTATION HINTS:\n",
|
||
" - Use the @ operator for clean matrix multiplication\n",
|
||
" - Ensure you return a Tensor, not a numpy array\n",
|
||
" - The operation should work for any compatible matrix shapes\n",
|
||
" \"\"\"\n",
|
||
" ### BEGIN SOLUTION\n",
|
||
" # Check if we're dealing with Variables (autograd) or plain Tensors\n",
|
||
" a_is_variable = hasattr(a, 'requires_grad') and hasattr(a, 'grad_fn')\n",
|
||
" b_is_variable = hasattr(b, 'requires_grad') and hasattr(b, 'grad_fn')\n",
|
||
" \n",
|
||
" # Extract numpy data appropriately\n",
|
||
" if a_is_variable:\n",
|
||
" a_data = a.data.data # Variable.data is a Tensor, so .data.data gets numpy array\n",
|
||
" else:\n",
|
||
" a_data = a.data # Tensor.data is numpy array directly\n",
|
||
" \n",
|
||
" if b_is_variable:\n",
|
||
" b_data = b.data.data\n",
|
||
" else:\n",
|
||
" b_data = b.data\n",
|
||
" \n",
|
||
" # Perform matrix multiplication\n",
|
||
" result_data = a_data @ b_data\n",
|
||
" \n",
|
||
" # If any input is a Variable, return Variable with gradient tracking\n",
|
||
" if a_is_variable or b_is_variable:\n",
|
||
" # Import Variable locally to avoid circular imports\n",
|
||
" if 'Variable' not in globals():\n",
|
||
" try:\n",
|
||
" from tinytorch.core.autograd import Variable\n",
|
||
" except ImportError:\n",
|
||
" from autograd_dev import Variable\n",
|
||
" \n",
|
||
" # Create gradient function for matrix multiplication\n",
|
||
" def grad_fn(grad_output):\n",
|
||
" # Matrix multiplication backward pass:\n",
|
||
" # If C = A @ B, then:\n",
|
||
" # dA = grad_output @ B^T\n",
|
||
" # dB = A^T @ grad_output\n",
|
||
" \n",
|
||
" if a_is_variable and a.requires_grad:\n",
|
||
" # Gradient w.r.t. A: grad_output @ B^T\n",
|
||
" grad_a_data = grad_output.data.data @ b_data.T\n",
|
||
" a.backward(Variable(grad_a_data))\n",
|
||
" \n",
|
||
" if b_is_variable and b.requires_grad:\n",
|
||
" # Gradient w.r.t. B: A^T @ grad_output \n",
|
||
" grad_b_data = a_data.T @ grad_output.data.data\n",
|
||
" b.backward(Variable(grad_b_data))\n",
|
||
" \n",
|
||
" # Determine if result should require gradients\n",
|
||
" requires_grad = (a_is_variable and a.requires_grad) or (b_is_variable and b.requires_grad)\n",
|
||
" \n",
|
||
" return Variable(result_data, requires_grad=requires_grad, grad_fn=grad_fn)\n",
|
||
" else:\n",
|
||
" # Both inputs are Tensors, return Tensor (backward compatible)\n",
|
||
" return Tensor(result_data)\n",
|
||
" ### END SOLUTION"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d7691910",
|
||
"metadata": {
|
||
"cell_marker": "\"\"\"",
|
||
"lines_to_next_cell": 1
|
||
},
|
||
"source": [
|
||
"## Testing Matrix Multiplication\n",
|
||
"\n",
|
||
"Let's verify our matrix multiplication works correctly with some test cases."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "d10bd1ed",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": true,
|
||
"grade_id": "test-matmul",
|
||
"locked": true,
|
||
"points": 2,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def test_matmul():\n",
|
||
" \"\"\"Test matrix multiplication implementation.\"\"\"\n",
|
||
" print(\"🧪 Testing Matrix Multiplication...\")\n",
|
||
" \n",
|
||
" # Test case 1: Simple 2x2 matrices\n",
|
||
" a = Tensor([[1, 2], [3, 4]])\n",
|
||
" b = Tensor([[5, 6], [7, 8]])\n",
|
||
" result = matmul(a, b)\n",
|
||
" expected = np.array([[19, 22], [43, 50]])\n",
|
||
" \n",
|
||
" assert np.allclose(result.data, expected), f\"Expected {expected}, got {result.data}\"\n",
|
||
" print(\"✅ 2x2 matrix multiplication\")\n",
|
||
" \n",
|
||
" # Test case 2: Non-square matrices\n",
|
||
" a = Tensor([[1, 2, 3], [4, 5, 6]]) # 2x3\n",
|
||
" b = Tensor([[7, 8], [9, 10], [11, 12]]) # 3x2\n",
|
||
" result = matmul(a, b)\n",
|
||
" expected = np.array([[58, 64], [139, 154]])\n",
|
||
" \n",
|
||
" assert np.allclose(result.data, expected), f\"Expected {expected}, got {result.data}\"\n",
|
||
" print(\"✅ Non-square matrix multiplication\")\n",
|
||
" \n",
|
||
" # Test case 3: Vector-matrix multiplication\n",
|
||
" a = Tensor([[1, 2, 3]]) # 1x3 (row vector)\n",
|
||
" b = Tensor([[4], [5], [6]]) # 3x1 (column vector)\n",
|
||
" result = matmul(a, b)\n",
|
||
" expected = np.array([[32]]) # 1*4 + 2*5 + 3*6 = 32\n",
|
||
" \n",
|
||
" assert np.allclose(result.data, expected), f\"Expected {expected}, got {result.data}\"\n",
|
||
" print(\"✅ Vector-matrix multiplication\")\n",
|
||
" \n",
|
||
" print(\"🎉 All matrix multiplication tests passed!\")\n",
|
||
"\n",
|
||
"test_matmul()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7f512ed2",
|
||
"metadata": {
|
||
"cell_marker": "\"\"\"",
|
||
"lines_to_next_cell": 1
|
||
},
|
||
"source": [
|
||
"# Dense Layer - The Fundamental Neural Network Component\n",
|
||
"\n",
|
||
"Dense layers (also called Linear or Fully Connected layers) are the building blocks of neural networks. They apply the transformation: **output = input @ weights + bias**\n",
|
||
"\n",
|
||
"## Why Dense Layers Matter\n",
|
||
"\n",
|
||
"🧠 **Universal Function Approximators**: Dense layers can approximate any continuous function when stacked \n",
|
||
"🔧 **Parameter Learning**: Weights and biases are learned through backpropagation \n",
|
||
"🏗️ **Modular Design**: Dense layers compose into complex architectures (MLPs, transformers, etc.) \n",
|
||
"⚡ **Computational Efficiency**: Matrix operations leverage optimized linear algebra libraries \n",
|
||
"\n",
|
||
"## Learning Objectives\n",
|
||
"By implementing Dense layers, you'll understand:\n",
|
||
"- How neural networks learn through adjustable parameters\n",
|
||
"- The mathematical foundation underlying all neural network layers\n",
|
||
"- Why proper parameter initialization is crucial for training success\n",
|
||
"- How layer composition enables complex function approximation"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "b5b4e929",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "dense-implementation",
|
||
"locked": false,
|
||
"schema_version": 3,
|
||
"solution": true,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"#| export\n",
|
||
"class Linear(Module):\n",
|
||
" \"\"\"\n",
|
||
" Linear (Fully Connected) Layer implementation.\n",
|
||
" \n",
|
||
" Applies the transformation: output = input @ weights + bias\n",
|
||
" \n",
|
||
" Inherits from Module for automatic parameter management and clean API.\n",
|
||
" This is PyTorch's nn.Linear equivalent with the same name for familiarity.\n",
|
||
" \n",
|
||
" Features:\n",
|
||
" - Automatic parameter registration (weights and bias)\n",
|
||
" - Clean call interface: layer(input) instead of layer.forward(input)\n",
|
||
" - Works with optimizers via model.parameters()\n",
|
||
" \"\"\"\n",
|
||
" \n",
|
||
" def __init__(self, input_size: int, output_size: int, use_bias: bool = True):\n",
|
||
" \"\"\"\n",
|
||
" Initialize Linear layer with random weights and optional bias.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" input_size: Number of input features\n",
|
||
" output_size: Number of output features \n",
|
||
" use_bias: Whether to include bias term\n",
|
||
" \n",
|
||
" TODO: Implement Linear layer initialization.\n",
|
||
" \n",
|
||
" STEP-BY-STEP IMPLEMENTATION:\n",
|
||
" 1. Store input_size and output_size as instance variables\n",
|
||
" 2. Initialize weights as Tensor with shape (input_size, output_size)\n",
|
||
" 3. Use small random values: np.random.randn(...) * 0.1\n",
|
||
" 4. Initialize bias as Tensor with shape (output_size,) if use_bias is True\n",
|
||
" 5. Set bias to None if use_bias is False\n",
|
||
" \n",
|
||
" LEARNING CONNECTIONS:\n",
|
||
" - Small random initialization prevents symmetry breaking\n",
|
||
" - Weight shape (input_size, output_size) enables matrix multiplication\n",
|
||
" - Bias allows shifting the output (like y-intercept in linear regression)\n",
|
||
" - PyTorch uses more sophisticated initialization (Xavier, Kaiming)\n",
|
||
" \n",
|
||
" IMPLEMENTATION HINTS:\n",
|
||
" - Use np.random.randn() for Gaussian random numbers\n",
|
||
" - Scale by 0.1 to keep initial values small\n",
|
||
" - Remember to wrap numpy arrays in Tensor()\n",
|
||
" - Store use_bias flag for forward pass logic\n",
|
||
" \"\"\"\n",
|
||
" ### BEGIN SOLUTION\n",
|
||
" super().__init__() # Initialize Module base class\n",
|
||
" \n",
|
||
" self.input_size = input_size\n",
|
||
" self.output_size = output_size\n",
|
||
" self.use_bias = use_bias\n",
|
||
" \n",
|
||
" # Initialize weights with small random values using Parameter\n",
|
||
" # Shape: (input_size, output_size) for matrix multiplication\n",
|
||
" weight_data = np.random.randn(input_size, output_size) * 0.1\n",
|
||
" self.weights = Parameter(weight_data) # Auto-registers for optimization!\n",
|
||
" \n",
|
||
" # Initialize bias if requested\n",
|
||
" if use_bias:\n",
|
||
" bias_data = np.random.randn(output_size) * 0.1\n",
|
||
" self.bias = Parameter(bias_data) # Auto-registers for optimization!\n",
|
||
" else:\n",
|
||
" self.bias = None\n",
|
||
" ### END SOLUTION\n",
|
||
" \n",
|
||
" def forward(self, x: Union[Tensor, 'Variable']) -> Union[Tensor, 'Variable']:\n",
|
||
" \"\"\"\n",
|
||
" Forward pass through the Linear layer.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" x: Input tensor or Variable (shape: ..., input_size)\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" Output tensor or Variable (shape: ..., output_size)\n",
|
||
" Preserves Variable type for gradient tracking in training\n",
|
||
" \n",
|
||
" TODO: Implement autograd-aware forward pass: output = input @ weights + bias\n",
|
||
" \n",
|
||
" STEP-BY-STEP IMPLEMENTATION:\n",
|
||
" 1. Perform matrix multiplication: output = matmul(x, self.weights)\n",
|
||
" 2. If bias exists, add it appropriately based on input type\n",
|
||
" 3. Preserve Variable type for gradient tracking if input is Variable\n",
|
||
" 4. Return result maintaining autograd capabilities\n",
|
||
" \n",
|
||
" AUTOGRAD CONSIDERATIONS:\n",
|
||
" - If x is Variable: weights and bias should also be Variables for training\n",
|
||
" - Preserve gradient tracking through the entire computation\n",
|
||
" - Enable backpropagation through this layer's parameters\n",
|
||
" - Handle mixed Tensor/Variable scenarios gracefully\n",
|
||
" \n",
|
||
" LEARNING CONNECTIONS:\n",
|
||
" - This is the core neural network transformation\n",
|
||
" - Matrix multiplication scales input features to output features \n",
|
||
" - Bias provides offset (like y-intercept in linear equations)\n",
|
||
" - Broadcasting handles different batch sizes automatically\n",
|
||
" - Autograd support enables automatic parameter optimization\n",
|
||
" \n",
|
||
" IMPLEMENTATION HINTS:\n",
|
||
" - Use the matmul function you implemented above (now autograd-aware)\n",
|
||
" - Handle bias addition based on input/output types\n",
|
||
" - Variables support + operator for gradient-tracked addition\n",
|
||
" - Check if self.bias is not None before adding\n",
|
||
" \"\"\"\n",
|
||
" ### BEGIN SOLUTION\n",
|
||
" # Matrix multiplication: input @ weights (now autograd-aware)\n",
|
||
" output = matmul(x, self.weights)\n",
|
||
" \n",
|
||
" # Add bias if it exists\n",
|
||
" # The addition will preserve Variable type if output is Variable\n",
|
||
" if self.bias is not None:\n",
|
||
" # Check if we need Variable-aware addition\n",
|
||
" if hasattr(output, 'requires_grad'):\n",
|
||
" # output is a Variable, use Variable addition\n",
|
||
" if hasattr(self.bias, 'requires_grad'):\n",
|
||
" # bias is also Variable, direct addition works\n",
|
||
" output = output + self.bias\n",
|
||
" else:\n",
|
||
" # bias is Tensor, convert to Variable for addition\n",
|
||
" # Import Variable if not already available\n",
|
||
" if 'Variable' not in globals():\n",
|
||
" try:\n",
|
||
" from tinytorch.core.autograd import Variable\n",
|
||
" except ImportError:\n",
|
||
" from autograd_dev import Variable\n",
|
||
" \n",
|
||
" bias_var = Variable(self.bias.data, requires_grad=False)\n",
|
||
" output = output + bias_var\n",
|
||
" else:\n",
|
||
" # output is Tensor, use regular addition\n",
|
||
" output = output + self.bias\n",
|
||
" \n",
|
||
" return output\n",
|
||
" ### END SOLUTION\n",
|
||
"\n",
|
||
"# Backward compatibility alias\n",
|
||
"#| export \n",
|
||
"Dense = Linear"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "df5cd843",
|
||
"metadata": {
|
||
"cell_marker": "\"\"\"",
|
||
"lines_to_next_cell": 1
|
||
},
|
||
"source": [
|
||
"## Testing Linear Layer\n",
|
||
"\n",
|
||
"Let's verify our Linear layer works correctly with comprehensive tests.\n",
|
||
"The tests use Dense for backward compatibility, but Dense is now an alias for Linear."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "385374fa",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": true,
|
||
"grade_id": "test-dense",
|
||
"locked": true,
|
||
"points": 3,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def test_dense_layer():\n",
|
||
" \"\"\"Test Dense layer implementation.\"\"\"\n",
|
||
" print(\"🧪 Testing Dense Layer...\")\n",
|
||
" \n",
|
||
" # Test case 1: Basic functionality\n",
|
||
" layer = Dense(input_size=3, output_size=2)\n",
|
||
" input_tensor = Tensor([[1.0, 2.0, 3.0]]) # Shape: (1, 3)\n",
|
||
" output = layer.forward(input_tensor)\n",
|
||
" \n",
|
||
" # Check output shape\n",
|
||
" assert output.shape == (1, 2), f\"Expected shape (1, 2), got {output.shape}\"\n",
|
||
" print(\"✅ Output shape correct\")\n",
|
||
" \n",
|
||
" # Test case 2: No bias\n",
|
||
" layer_no_bias = Dense(input_size=2, output_size=3, use_bias=False)\n",
|
||
" assert layer_no_bias.bias is None, \"Bias should be None when use_bias=False\"\n",
|
||
" print(\"✅ No bias option works\")\n",
|
||
" \n",
|
||
" # Test case 3: Multiple samples (batch processing)\n",
|
||
" batch_input = Tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]) # Shape: (3, 2)\n",
|
||
" layer_batch = Dense(input_size=2, output_size=2)\n",
|
||
" batch_output = layer_batch.forward(batch_input)\n",
|
||
" \n",
|
||
" assert batch_output.shape == (3, 2), f\"Expected shape (3, 2), got {batch_output.shape}\"\n",
|
||
" print(\"✅ Batch processing works\")\n",
|
||
" \n",
|
||
" # Test case 4: Callable interface\n",
|
||
" callable_output = layer_batch(batch_input)\n",
|
||
" assert np.allclose(callable_output.data, batch_output.data), \"Callable interface should match forward()\"\n",
|
||
" print(\"✅ Callable interface works\")\n",
|
||
" \n",
|
||
" # Test case 5: Parameter initialization\n",
|
||
" layer_init = Dense(input_size=10, output_size=5)\n",
|
||
" assert layer_init.weights.shape == (10, 5), f\"Expected weights shape (10, 5), got {layer_init.weights.shape}\"\n",
|
||
" assert layer_init.bias.shape == (5,), f\"Expected bias shape (5,), got {layer_init.bias.shape}\"\n",
|
||
" \n",
|
||
" # Check that weights are reasonably small (good initialization)\n",
|
||
" assert np.abs(layer_init.weights.data).mean() < 1.0, \"Weights should be small for good initialization\"\n",
|
||
" print(\"✅ Parameter initialization correct\")\n",
|
||
" \n",
|
||
" print(\"🎉 All Dense layer tests passed!\")\n",
|
||
"\n",
|
||
"test_dense_layer()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7f9bb46b",
|
||
"metadata": {
|
||
"cell_marker": "\"\"\"",
|
||
"lines_to_next_cell": 1
|
||
},
|
||
"source": [
|
||
"## Testing Autograd Integration\n",
|
||
"\n",
|
||
"Now let's test that our Dense layer works correctly with Variables for gradient tracking."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "df791018",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": true,
|
||
"grade_id": "test-dense-autograd",
|
||
"locked": true,
|
||
"points": 3,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def test_dense_layer_autograd():\n",
|
||
" \"\"\"Test Dense layer with autograd Variable support.\"\"\"\n",
|
||
" print(\"🧪 Testing Dense Layer Autograd Integration...\")\n",
|
||
" \n",
|
||
" try:\n",
|
||
" # Import Variable locally to handle import issues\n",
|
||
" try:\n",
|
||
" from tinytorch.core.autograd import Variable\n",
|
||
" except ImportError:\n",
|
||
" sys.path.append(os.path.join(os.path.dirname(__file__), '..', '09_autograd'))\n",
|
||
" from autograd_dev import Variable\n",
|
||
" \n",
|
||
" # Test case 1: Variable input with Tensor weights (inference mode)\n",
|
||
" layer = Dense(input_size=3, output_size=2)\n",
|
||
" variable_input = Variable([[1.0, 2.0, 3.0]], requires_grad=True)\n",
|
||
" output = layer.forward(variable_input)\n",
|
||
" \n",
|
||
" # Check that output is Variable and preserves gradient tracking\n",
|
||
" assert hasattr(output, 'requires_grad'), \"Output should be Variable with gradient tracking\"\n",
|
||
" assert output.shape == (1, 2), f\"Expected shape (1, 2), got {output.shape}\"\n",
|
||
" print(\"✅ Variable input preserves gradient tracking\")\n",
|
||
" \n",
|
||
" # Test case 2: Variable weights for training\n",
|
||
" # Convert weights and bias to Variables for training\n",
|
||
" layer_trainable = Dense(input_size=2, output_size=2)\n",
|
||
" layer_trainable.weights = Variable(layer_trainable.weights.data, requires_grad=True)\n",
|
||
" layer_trainable.bias = Variable(layer_trainable.bias.data, requires_grad=True)\n",
|
||
" \n",
|
||
" variable_input_2 = Variable([[1.0, 2.0]], requires_grad=True)\n",
|
||
" output_2 = layer_trainable.forward(variable_input_2)\n",
|
||
" \n",
|
||
" assert hasattr(output_2, 'requires_grad'), \"Output should support gradients\"\n",
|
||
" assert output_2.requires_grad, \"Output should require gradients when weights require gradients\"\n",
|
||
" print(\"✅ Variable weights enable training mode\")\n",
|
||
" \n",
|
||
" # Test case 3: Gradient flow through Dense layer\n",
|
||
" # Simple backward pass to check gradient computation\n",
|
||
" try:\n",
|
||
" # Create a simple loss (sum of outputs)\n",
|
||
" loss = Variable(np.sum(output_2.data.data))\n",
|
||
" loss.backward()\n",
|
||
" \n",
|
||
" # Check that gradients were computed\n",
|
||
" assert layer_trainable.weights.grad is not None, \"Weights should have gradients\"\n",
|
||
" assert layer_trainable.bias.grad is not None, \"Bias should have gradients\"\n",
|
||
" assert variable_input_2.grad is not None, \"Input should have gradients\"\n",
|
||
" print(\"✅ Gradient computation works\")\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"⚠️ Gradient computation test skipped: {e}\")\n",
|
||
" print(\" (This is expected if full autograd integration isn't complete yet)\")\n",
|
||
" \n",
|
||
" # Test case 4: Mixed Tensor/Variable scenarios\n",
|
||
" tensor_input = Tensor([[1.0, 2.0, 3.0]])\n",
|
||
" variable_layer = Dense(input_size=3, output_size=2)\n",
|
||
" mixed_output = variable_layer.forward(tensor_input)\n",
|
||
" \n",
|
||
" assert isinstance(mixed_output, Tensor), \"Tensor input should produce Tensor output\"\n",
|
||
" print(\"✅ Mixed Tensor/Variable handling works\")\n",
|
||
" \n",
|
||
" # Test case 5: Batch processing with Variables\n",
|
||
" batch_variable_input = Variable([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], requires_grad=True)\n",
|
||
" batch_layer = Dense(input_size=2, output_size=2)\n",
|
||
" batch_variable_output = batch_layer.forward(batch_variable_input)\n",
|
||
" \n",
|
||
" assert batch_variable_output.shape == (3, 2), f\"Expected batch shape (3, 2), got {batch_variable_output.shape}\"\n",
|
||
" assert hasattr(batch_variable_output, 'requires_grad'), \"Batch output should support gradients\"\n",
|
||
" print(\"✅ Batch processing with Variables works\")\n",
|
||
" \n",
|
||
" print(\"🎉 All Dense layer autograd tests passed!\")\n",
|
||
" \n",
|
||
" except ImportError as e:\n",
|
||
" print(f\"⚠️ Autograd tests skipped: {e}\")\n",
|
||
" print(\" (Variable class not available - this is expected during development)\")\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"❌ Autograd test failed: {e}\")\n",
|
||
" print(\" (This indicates an implementation issue that needs fixing)\")\n",
|
||
"\n",
|
||
"test_dense_layer_autograd()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f047fbc8",
|
||
"metadata": {
|
||
"cell_marker": "\"\"\"",
|
||
"lines_to_next_cell": 1
|
||
},
|
||
"source": [
|
||
"# Systems Analysis: Memory and Performance Characteristics\n",
|
||
"\n",
|
||
"Let's analyze the memory usage and computational complexity of our layer implementations.\n",
|
||
"\n",
|
||
"## Memory Analysis\n",
|
||
"- **Dense Layer Storage**: input_size × output_size weights + output_size bias terms\n",
|
||
"- **Forward Pass Memory**: Input tensor + weight tensor + output tensor (temporary storage)\n",
|
||
"- **Scaling Behavior**: Memory grows quadratically with layer size\n",
|
||
"\n",
|
||
"## Computational Complexity\n",
|
||
"- **Matrix Multiplication**: O(batch_size × input_size × output_size)\n",
|
||
"- **Bias Addition**: O(batch_size × output_size)\n",
|
||
"- **Total**: Dominated by matrix multiplication for large layers\n",
|
||
"\n",
|
||
"## Production Insights\n",
|
||
"In production ML systems:\n",
|
||
"- **Memory Management**: PyTorch uses memory pools to avoid frequent allocation/deallocation\n",
|
||
"- **Compute Optimization**: BLAS libraries (MKL, OpenBLAS) optimize matrix operations for specific hardware\n",
|
||
"- **GPU Acceleration**: CUDA kernels parallelize matrix operations across thousands of cores\n",
|
||
"- **Mixed Precision**: Using float16 instead of float32 can halve memory usage with minimal accuracy loss"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "b7825066",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "memory-analysis",
|
||
"locked": false,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def analyze_layer_memory():\n",
|
||
" \"\"\"Analyze memory usage of different layer sizes.\"\"\"\n",
|
||
" print(\"📊 Layer Memory Analysis\")\n",
|
||
" print(\"=\" * 40)\n",
|
||
" \n",
|
||
" layer_sizes = [(10, 10), (100, 100), (1000, 1000), (784, 128), (128, 10)]\n",
|
||
" \n",
|
||
" for input_size, output_size in layer_sizes:\n",
|
||
" # Calculate parameter count\n",
|
||
" weight_params = input_size * output_size\n",
|
||
" bias_params = output_size\n",
|
||
" total_params = weight_params + bias_params\n",
|
||
" \n",
|
||
" # Calculate memory usage (assuming float32 = 4 bytes)\n",
|
||
" memory_mb = total_params * 4 / (1024 * 1024)\n",
|
||
" \n",
|
||
" print(f\" {input_size:4d} → {output_size:4d}: {total_params:,} params, {memory_mb:.3f} MB\")\n",
|
||
" \n",
|
||
" print(\"\\n🔍 Key Insights:\")\n",
|
||
" print(\" • Memory grows quadratically with layer width\")\n",
|
||
" print(\" • Large layers (1000×1000) use significant memory\")\n",
|
||
" print(\" • Modern networks balance width vs depth for efficiency\")\n",
|
||
"\n",
|
||
"analyze_layer_memory()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8c04fb2c",
|
||
"metadata": {
|
||
"cell_marker": "\"\"\"",
|
||
"lines_to_next_cell": 1
|
||
},
|
||
"source": [
|
||
"# ML Systems Thinking: Interactive Questions\n",
|
||
"\n",
|
||
"Let's explore the deeper implications of our layer implementations."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "1ce66a00",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "systems-thinking",
|
||
"locked": false,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def explore_layer_scaling():\n",
|
||
" \"\"\"Explore how layer operations scale with size.\"\"\"\n",
|
||
" print(\"🤔 Scaling Analysis: Matrix Multiplication Performance\")\n",
|
||
" print(\"=\" * 55)\n",
|
||
" \n",
|
||
" sizes = [64, 128, 256, 512]\n",
|
||
" \n",
|
||
" for size in sizes:\n",
|
||
" # Estimate FLOPs for square matrix multiplication\n",
|
||
" flops = 2 * size * size * size # 2 operations per multiply-add\n",
|
||
" \n",
|
||
" # Estimate memory bandwidth (reading A, B, writing C)\n",
|
||
" memory_ops = 3 * size * size # Elements read/written\n",
|
||
" memory_mb = memory_ops * 4 / (1024 * 1024) # float32 = 4 bytes\n",
|
||
" \n",
|
||
" print(f\" Size {size:3d}×{size:3d}: {flops/1e6:.1f} MFLOPS, {memory_mb:.2f} MB transfers\")\n",
|
||
" \n",
|
||
" print(\"\\n💡 Performance Insights:\")\n",
|
||
" print(\" • FLOPs grow cubically (O(n³)) with matrix size\")\n",
|
||
" print(\" • Memory bandwidth grows quadratically (O(n²))\")\n",
|
||
" print(\" • Large matrices become memory-bound, not compute-bound\")\n",
|
||
" print(\" • This is why GPUs excel: high memory bandwidth + parallel compute\")\n",
|
||
"\n",
|
||
"explore_layer_scaling()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2de5338c",
|
||
"metadata": {
|
||
"cell_marker": "\"\"\""
|
||
},
|
||
"source": [
|
||
"## 🤔 ML Systems Thinking: Interactive Questions\n",
|
||
"\n",
|
||
"Now that you've implemented the core components, let's think about their implications for ML systems:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "b9dc3f4e",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "question-1",
|
||
"locked": false,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Question 1: Memory vs Computation Trade-offs\n",
|
||
"\"\"\"\n",
|
||
"🤔 **Question 1: Memory vs Computation Analysis**\n",
|
||
"\n",
|
||
"You're designing a neural network for deployment on a mobile device with limited memory (1GB RAM) but decent compute power.\n",
|
||
"\n",
|
||
"You have two architecture options:\n",
|
||
"A) Wide network: 784 → 2048 → 2048 → 10 (3 layers, wide)\n",
|
||
"B) Deep network: 784 → 256 → 256 → 256 → 256 → 10 (5 layers, narrow)\n",
|
||
"\n",
|
||
"Calculate the memory requirements for each option and explain which you'd choose for mobile deployment and why.\n",
|
||
"\n",
|
||
"Consider:\n",
|
||
"- Parameter storage requirements\n",
|
||
"- Intermediate activation storage during forward pass\n",
|
||
"- Training vs inference memory requirements\n",
|
||
"- How your choice affects model capacity and accuracy\n",
|
||
"\"\"\""
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "13d2171d",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "question-2",
|
||
"locked": false,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Question 2: Performance Optimization\n",
|
||
"\"\"\"\n",
|
||
"🤔 **Question 2: Production Performance Optimization**\n",
|
||
"\n",
|
||
"Your Dense layer implementation works correctly, but you notice it's slower than PyTorch's nn.Linear on the same hardware.\n",
|
||
"\n",
|
||
"Investigate and explain:\n",
|
||
"1. Why might our implementation be slower? (Hint: think about underlying linear algebra libraries)\n",
|
||
"2. What optimization techniques do production frameworks use?\n",
|
||
"3. How would you modify our implementation to approach production performance?\n",
|
||
"4. When might our simple implementation actually be preferable?\n",
|
||
"\n",
|
||
"Research areas to consider:\n",
|
||
"- BLAS (Basic Linear Algebra Subprograms) libraries\n",
|
||
"- Memory layout and cache efficiency\n",
|
||
"- Vectorization and SIMD instructions\n",
|
||
"- GPU kernel optimization\n",
|
||
"\"\"\""
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "fc136b4d",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "question-3",
|
||
"locked": false,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Question 3: Scaling and Architecture Design\n",
|
||
"\"\"\"\n",
|
||
"🤔 **Question 3: Systems Architecture Scaling**\n",
|
||
"\n",
|
||
"Modern transformer models like GPT-3 have billions of parameters, primarily in Dense layers.\n",
|
||
"\n",
|
||
"Analyze the scaling challenges:\n",
|
||
"1. How does memory requirement scale with model size? Calculate the memory needed for a 175B parameter model.\n",
|
||
"2. What are the computational bottlenecks during training vs inference?\n",
|
||
"3. How do systems like distributed training address these scaling challenges?\n",
|
||
"4. Why do large models use techniques like gradient checkpointing and model parallelism?\n",
|
||
"\n",
|
||
"Systems considerations:\n",
|
||
"- Memory hierarchy (L1/L2/L3 cache, RAM, storage)\n",
|
||
"- Network bandwidth for distributed training\n",
|
||
"- GPU memory constraints and model sharding\n",
|
||
"- Inference optimization for production serving\n",
|
||
"\"\"\""
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "264e2bd3",
|
||
"metadata": {
|
||
"cell_marker": "\"\"\"",
|
||
"lines_to_next_cell": 1
|
||
},
|
||
"source": [
|
||
"# Comprehensive Testing and Integration\n",
|
||
"\n",
|
||
"Let's run a comprehensive test suite to verify all our implementations work correctly together."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "e45d1bfe",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "comprehensive-tests",
|
||
"locked": false,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def run_comprehensive_tests():\n",
|
||
" \"\"\"Run comprehensive tests of all layer functionality.\"\"\"\n",
|
||
" print(\"🔬 Comprehensive Layer Testing Suite\")\n",
|
||
" print(\"=\" * 45)\n",
|
||
" \n",
|
||
" # Test 1: Matrix multiplication edge cases\n",
|
||
" print(\"\\n1. Matrix Multiplication Edge Cases:\")\n",
|
||
" \n",
|
||
" # Single element\n",
|
||
" a = Tensor([[5]])\n",
|
||
" b = Tensor([[3]])\n",
|
||
" result = matmul(a, b)\n",
|
||
" assert result.data[0, 0] == 15, \"Single element multiplication failed\"\n",
|
||
" print(\" ✅ Single element multiplication\")\n",
|
||
" \n",
|
||
" # Identity matrix\n",
|
||
" identity = Tensor([[1, 0], [0, 1]])\n",
|
||
" test_matrix = Tensor([[2, 3], [4, 5]])\n",
|
||
" result = matmul(test_matrix, identity)\n",
|
||
" assert np.allclose(result.data, test_matrix.data), \"Identity multiplication failed\"\n",
|
||
" print(\" ✅ Identity matrix multiplication\")\n",
|
||
" \n",
|
||
" # Test 2: Dense layer composition\n",
|
||
" print(\"\\n2. Dense Layer Composition:\")\n",
|
||
" \n",
|
||
" # Create a simple 2-layer network\n",
|
||
" layer1 = Dense(4, 3)\n",
|
||
" layer2 = Dense(3, 2)\n",
|
||
" \n",
|
||
" # Test data flow\n",
|
||
" input_data = Tensor([[1, 2, 3, 4]])\n",
|
||
" hidden = layer1(input_data)\n",
|
||
" output = layer2(hidden)\n",
|
||
" \n",
|
||
" assert output.shape == (1, 2), f\"Expected final output shape (1, 2), got {output.shape}\"\n",
|
||
" print(\" ✅ Multi-layer composition\")\n",
|
||
" \n",
|
||
" # Test 3: Batch processing\n",
|
||
" print(\"\\n3. Batch Processing:\")\n",
|
||
" \n",
|
||
" batch_size = 10\n",
|
||
" batch_input = Tensor(np.random.randn(batch_size, 4))\n",
|
||
" batch_hidden = layer1(batch_input)\n",
|
||
" batch_output = layer2(batch_hidden)\n",
|
||
" \n",
|
||
" assert batch_output.shape == (batch_size, 2), f\"Expected batch output shape ({batch_size}, 2), got {batch_output.shape}\"\n",
|
||
" print(\" ✅ Batch processing\")\n",
|
||
" \n",
|
||
" # Test 4: Parameter access and modification\n",
|
||
" print(\"\\n4. Parameter Management:\")\n",
|
||
" \n",
|
||
" layer = Dense(5, 3)\n",
|
||
" original_weights = layer.weights.data.copy()\n",
|
||
" \n",
|
||
" # Simulate parameter update\n",
|
||
" layer.weights = Tensor(original_weights + 0.1)\n",
|
||
" \n",
|
||
" assert not np.allclose(layer.weights.data, original_weights), \"Parameter update failed\"\n",
|
||
" print(\" ✅ Parameter modification\")\n",
|
||
" \n",
|
||
" print(\"\\n🎉 All comprehensive tests passed!\")\n",
|
||
" print(\" Your layer implementations are ready for neural network construction!\")\n",
|
||
"\n",
|
||
"run_comprehensive_tests()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6b9bc103",
|
||
"metadata": {
|
||
"cell_marker": "\"\"\"",
|
||
"lines_to_next_cell": 1
|
||
},
|
||
"source": [
|
||
"## Autograd Integration Demo\n",
|
||
"\n",
|
||
"Let's demonstrate how the Dense layer now works seamlessly with autograd Variables."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "f9d3d3c8",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "autograd-demo",
|
||
"locked": false,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def demonstrate_autograd_integration():\n",
|
||
" \"\"\"Demonstrate Dense layer working with autograd Variables.\"\"\"\n",
|
||
" print(\"🔥 Dense Layer Autograd Integration Demo\")\n",
|
||
" print(\"=\" * 50)\n",
|
||
" \n",
|
||
" try:\n",
|
||
" # Import Variable\n",
|
||
" try:\n",
|
||
" from tinytorch.core.autograd import Variable\n",
|
||
" except ImportError:\n",
|
||
" sys.path.append(os.path.join(os.path.dirname(__file__), '..', '09_autograd'))\n",
|
||
" from autograd_dev import Variable\n",
|
||
" \n",
|
||
" print(\"\\n1. Creating trainable Dense layer:\")\n",
|
||
" layer = Dense(input_size=3, output_size=2)\n",
|
||
" \n",
|
||
" # Convert to trainable parameters (Variables)\n",
|
||
" layer.weights = Variable(layer.weights.data, requires_grad=True)\n",
|
||
" layer.bias = Variable(layer.bias.data, requires_grad=True)\n",
|
||
" \n",
|
||
" print(f\" Weights shape: {layer.weights.shape}\")\n",
|
||
" print(f\" Weights require grad: {layer.weights.requires_grad}\")\n",
|
||
" print(f\" Bias shape: {layer.bias.shape}\")\n",
|
||
" print(f\" Bias require grad: {layer.bias.requires_grad}\")\n",
|
||
" \n",
|
||
" print(\"\\n2. Forward pass with Variable input:\")\n",
|
||
" x = Variable([[1.0, 2.0, 3.0]], requires_grad=True)\n",
|
||
" print(f\" Input: {x.data.data.tolist()}\")\n",
|
||
" \n",
|
||
" y = layer(x)\n",
|
||
" print(f\" Output shape: {y.shape}\")\n",
|
||
" print(f\" Output requires grad: {y.requires_grad}\")\n",
|
||
" print(f\" Output values: {y.data.data.tolist()}\")\n",
|
||
" \n",
|
||
" print(\"\\n3. Backward pass demonstration:\")\n",
|
||
" try:\n",
|
||
" # Simple loss: sum of all outputs\n",
|
||
" loss = Variable(np.sum(y.data.data))\n",
|
||
" print(f\" Loss: {loss.data.data}\")\n",
|
||
" \n",
|
||
" # Clear gradients\n",
|
||
" layer.weights.zero_grad()\n",
|
||
" layer.bias.zero_grad() \n",
|
||
" x.zero_grad()\n",
|
||
" \n",
|
||
" # Backward pass\n",
|
||
" loss.backward()\n",
|
||
" \n",
|
||
" print(f\" Weight gradients computed: {layer.weights.grad is not None}\")\n",
|
||
" print(f\" Bias gradients computed: {layer.bias.grad is not None}\")\n",
|
||
" print(f\" Input gradients computed: {x.grad is not None}\")\n",
|
||
" \n",
|
||
" if layer.weights.grad is not None:\n",
|
||
" print(f\" Weight gradient shape: {layer.weights.grad.shape}\")\n",
|
||
" if layer.bias.grad is not None:\n",
|
||
" print(f\" Bias gradient shape: {layer.bias.grad.shape}\")\n",
|
||
" \n",
|
||
" except Exception as e:\n",
|
||
" print(f\" ⚠️ Backward pass demo limited: {e}\")\n",
|
||
" \n",
|
||
" print(\"\\n4. Backward compatibility with Tensors:\")\n",
|
||
" tensor_input = Tensor([[1.0, 2.0, 3.0]])\n",
|
||
" tensor_layer = Dense(input_size=3, output_size=2)\n",
|
||
" tensor_output = tensor_layer(tensor_input)\n",
|
||
" \n",
|
||
" print(f\" Input type: {type(tensor_input).__name__}\")\n",
|
||
" print(f\" Output type: {type(tensor_output).__name__}\")\n",
|
||
" print(\" ✅ Tensor-only operations still work perfectly\")\n",
|
||
" \n",
|
||
" print(\"\\n🎉 Dense layer now supports both Tensors and Variables!\")\n",
|
||
" print(\" • Tensors: Fast inference without gradient tracking\")\n",
|
||
" print(\" • Variables: Full training with automatic differentiation\")\n",
|
||
" print(\" • Seamless interoperability for different use cases\")\n",
|
||
" \n",
|
||
" except ImportError as e:\n",
|
||
" print(f\"⚠️ Autograd demo skipped: {e}\")\n",
|
||
" print(\" (Variable class not available)\")\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"❌ Demo failed: {e}\")\n",
|
||
"\n",
|
||
"demonstrate_autograd_integration()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "deffd3a3",
|
||
"metadata": {
|
||
"cell_marker": "\"\"\""
|
||
},
|
||
"source": [
|
||
"# Module Summary\n",
|
||
"\n",
|
||
"## 🎯 What You've Accomplished\n",
|
||
"\n",
|
||
"You've successfully implemented the fundamental building blocks of neural networks:\n",
|
||
"\n",
|
||
"### ✅ **Core Implementations**\n",
|
||
"- **Matrix Multiplication**: The computational primitive underlying all neural network operations (now with autograd support)\n",
|
||
"- **Dense Layer**: Complete implementation with proper parameter initialization, forward propagation, and Variable support\n",
|
||
"- **Autograd Integration**: Seamless support for both Tensors (inference) and Variables (training with gradients)\n",
|
||
"- **Composition Patterns**: How layers stack together to form complex function approximators\n",
|
||
"\n",
|
||
"### ✅ **Systems Understanding**\n",
|
||
"- **Memory Analysis**: How layer size affects memory usage and why this matters for deployment\n",
|
||
"- **Performance Characteristics**: Understanding computational complexity and scaling behavior\n",
|
||
"- **Production Context**: Connection to real-world ML systems and optimization techniques\n",
|
||
"\n",
|
||
"### ✅ **ML Engineering Skills**\n",
|
||
"- **Parameter Management**: How neural networks store and update learnable parameters\n",
|
||
"- **Batch Processing**: Efficient handling of multiple data samples simultaneously\n",
|
||
"- **Architecture Design**: Trade-offs between network width, depth, and resource requirements\n",
|
||
"\n",
|
||
"## 🔗 **Connection to Production ML Systems**\n",
|
||
"\n",
|
||
"Your implementations mirror the core concepts used in:\n",
|
||
"- **PyTorch's nn.Linear**: Same mathematical operations with production optimizations\n",
|
||
"- **TensorFlow's Dense layers**: Identical parameter structure and forward pass logic\n",
|
||
"- **Transformer architectures**: Dense layers form the foundation of modern language models\n",
|
||
"- **Computer vision models**: ConvNets use similar principles with spatial structure\n",
|
||
"\n",
|
||
"## 🚀 **What's Next**\n",
|
||
"\n",
|
||
"With solid layer implementations, you're ready to:\n",
|
||
"- **Compose** these layers into complete neural networks\n",
|
||
"- **Add** nonlinear activations to enable complex function approximation\n",
|
||
"- **Implement** training algorithms to learn from data\n",
|
||
"- **Scale** to larger, more sophisticated architectures\n",
|
||
"\n",
|
||
"## 💡 **Key Systems Insights**\n",
|
||
"\n",
|
||
"1. **Matrix multiplication is the computational bottleneck** in neural networks\n",
|
||
"2. **Memory layout and access patterns** often matter more than raw compute power\n",
|
||
"3. **Layer composition** is the fundamental abstraction for building complex ML systems\n",
|
||
"4. **Parameter initialization and management** directly affects training success\n",
|
||
"\n",
|
||
"You now understand the mathematical and computational foundations that enable neural networks to learn complex patterns from data!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "e4d045ea",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "final-demo",
|
||
"locked": false,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"if __name__ == \"__main__\":\n",
|
||
" print(\"🔥 TinyTorch Layers Module - Final Demo\")\n",
|
||
" print(\"=\" * 50)\n",
|
||
" \n",
|
||
" # Create a simple neural network architecture\n",
|
||
" print(\"\\n🏗️ Building a 3-layer neural network:\")\n",
|
||
" layer1 = Dense(784, 128) # Input layer (like MNIST images)\n",
|
||
" layer2 = Dense(128, 64) # Hidden layer\n",
|
||
" layer3 = Dense(64, 10) # Output layer (10 classes)\n",
|
||
" \n",
|
||
" print(f\" Layer 1: {layer1.input_size} → {layer1.output_size} ({layer1.weights.data.size:,} parameters)\")\n",
|
||
" print(f\" Layer 2: {layer2.input_size} → {layer2.output_size} ({layer2.weights.data.size:,} parameters)\")\n",
|
||
" print(f\" Layer 3: {layer3.input_size} → {layer3.output_size} ({layer3.weights.data.size:,} parameters)\")\n",
|
||
" \n",
|
||
" # Simulate forward pass\n",
|
||
" print(\"\\n🚀 Forward pass through network:\")\n",
|
||
" batch_size = 32\n",
|
||
" input_data = Tensor(np.random.randn(batch_size, 784))\n",
|
||
" \n",
|
||
" print(f\" Input shape: {input_data.shape}\")\n",
|
||
" hidden1 = layer1(input_data)\n",
|
||
" print(f\" After layer 1: {hidden1.shape}\")\n",
|
||
" hidden2 = layer2(hidden1)\n",
|
||
" print(f\" After layer 2: {hidden2.shape}\")\n",
|
||
" output = layer3(hidden2)\n",
|
||
" print(f\" Final output: {output.shape}\")\n",
|
||
" \n",
|
||
" # Calculate total parameters\n",
|
||
" total_params = (layer1.weights.data.size + layer1.bias.data.size + \n",
|
||
" layer2.weights.data.size + layer2.bias.data.size +\n",
|
||
" layer3.weights.data.size + layer3.bias.data.size)\n",
|
||
" \n",
|
||
" print(f\"\\n📊 Network Statistics:\")\n",
|
||
" print(f\" Total parameters: {total_params:,}\")\n",
|
||
" print(f\" Memory usage: ~{total_params * 4 / 1024 / 1024:.2f} MB (float32)\")\n",
|
||
" print(f\" Forward pass: {batch_size} samples processed simultaneously\")\n",
|
||
" \n",
|
||
" print(\"\\n✅ Neural network construction complete!\")\n",
|
||
" print(\"Ready for activation functions and training algorithms!\")\n",
|
||
" \n",
|
||
" # Run autograd integration demo\n",
|
||
" print(\"\\n\" + \"=\"*60)\n",
|
||
" demonstrate_autograd_integration()"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"jupytext": {
|
||
"main_language": "python"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|