diff --git a/milestones/perceptron_1957/rosenblatt_perceptron.py b/milestones/perceptron_1957/rosenblatt_perceptron.py index e23044f8..acfa711c 100644 --- a/milestones/perceptron_1957/rosenblatt_perceptron.py +++ b/milestones/perceptron_1957/rosenblatt_perceptron.py @@ -14,16 +14,17 @@ started it all - proving that YOU can build the foundation of modern AI from scr ✅ REQUIRED MODULES (Run after Module 4): ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - Module 02 (Tensor) : YOUR data structure with gradient tracking - Module 03 (Activations) : YOUR sigmoid activation for smooth gradients - Module 04 (Layers) : YOUR Linear layer for weight transformations + Module 01 (Tensor) : YOUR data structure with gradient tracking + Module 02 (Activations) : YOUR sigmoid activation for smooth gradients + Module 03 (Layers) : YOUR Linear layer for weight transformations + Data Generation : Directly generated within this script ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🏗️ ARCHITECTURE (Original 1957 Design): ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ Input │ │ Linear │ │ Sigmoid │ │ Binary │ │ Features │───▶│ YOUR Module │───▶│ YOUR Module │───▶│ Output │ - │ (x1, x2) │ │ 04 │ │ 03 │ │ (0 or 1) │ + │ (x1, x2) │ │ 03 │ │ 02 │ │ (0 or 1) │ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘ 🔍 HOW THE PERCEPTRON LEARNS - A LINEAR DECISION BOUNDARY: @@ -31,10 +32,10 @@ started it all - proving that YOU can build the foundation of modern AI from scr INITIAL (Random Weights): TRAINING (Gradient Descent): CONVERGED (Learned): 4 │ • • • • • 4 │ • • • • • 4 │ • • • • • - │ • • • • • Class 1 │ • • • • • ╱ │ • • • • • ╱ - 2 │ - - - - - ← Wrong! 2 │ • • • • ╱ • ← Adjusting 2 │ • • • • ╱ • ← Perfect! + │ • • • • • Class 1 │ • • • • • ╱ │ • • • • • ╱ + 2 │ - - - - - ← Wrong! 2 │ • • • • ╱ • ← Adjusting 2 │ • • • • ╱ • ← Perfect! │ ○ ○ ○ ○ ○ │ ○ ○ ○ ╱ ○ ○ │ ○ ○ ○ ╱ ○ ○ - 0 │ ○ ○ ○ ○ ○ Class 0 0 │ ○ ○ ╱ ○ ○ ○ 0 │ ○ ○ ╱ ○ ○ ○ + 0 │ ○ ○ ○ ○ ○ Class 0 0 │ ○ ○ ╱ ○ ○ ○ 0 │ ○ ○ ╱ ○ ○ ○ └──────────── └──────────── └──────────── 0 2 4 0 2 4 0 2 4 @@ -62,17 +63,14 @@ import os import numpy as np import argparse -# Add project root to path for TinyTorch imports -project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) -sys.path.append(project_root) +# Add project root to path for correct tinytorch imports +# This allows the script to be run from the root of the project +sys.path.insert(0, os.getcwd()) # Import TinyTorch components YOU BUILT! -from tinytorch.core.tensor import Tensor # Module 02: YOU built this! -from tinytorch.core.layers import Linear # Module 04: YOU built this! -from tinytorch.core.activations import Sigmoid # Module 03: YOU built this! - -# Import dataset manager for automatic data handling -from examples.data_manager import DatasetManager +from tinytorch.core.tensor import Tensor # Module 01: YOU built this! +from tinytorch.core.layers import Linear # Module 03: YOU built this! +from tinytorch.core.activations import Sigmoid # Module 02: YOU built this! class RosenblattPerceptron: """ @@ -83,202 +81,76 @@ class RosenblattPerceptron: """ def __init__(self, input_size=2, output_size=1): - print("🧠 Building Rosenblatt's Perceptron with YOUR TinyTorch modules...") + print("🧠 Assembling Rosenblatt's Perceptron with YOUR TinyTorch modules...") # Single layer - just like the original 1957 design! - self.linear = Linear(input_size, output_size) # Module 04: YOUR Linear layer! - self.activation = Sigmoid() # Module 03: YOUR Sigmoid function! + self.linear = Linear(input_size, output_size) # Module 03: YOUR Linear layer! + self.activation = Sigmoid() # Module 02: YOUR Sigmoid function! - print(f" Linear layer: {input_size} → {output_size} (YOUR Module 04 implementation!)") - print(f" Activation: Sigmoid (YOUR Module 03 implementation!)") + print(f" ✅ Linear layer: {input_size} → {output_size} (YOUR Module 03 implementation!)") + print(f" ✅ Activation: Sigmoid (YOUR Module 02 implementation!)") def forward(self, x): """Forward pass through YOUR perceptron implementation.""" # Step 1: Linear transformation using YOUR weights - x = self.linear(x) # Module 04: YOUR Linear.forward() method! + x = self.linear(x) # Module 03: YOUR Linear.forward() method! # Step 2: Activation using YOUR sigmoid - x = self.activation(x) # Module 03: YOUR Sigmoid.forward() method! + x = self.activation(x) # Module 02: YOUR Sigmoid.forward() method! return x - - def parameters(self): - """Get trainable parameters from YOUR Linear layer.""" - return [self.linear.weights, self.linear.bias] # Module 04: YOUR parameters! - -def simple_training_loop(model, X, y, learning_rate=0.1, epochs=100): - """ - Simple training loop using YOUR Tensor autograd system! - - Note: We're using a basic training loop here. Later milestones will use - YOUR more sophisticated optimizers from Module 07! - """ - print("\n🚀 Training Perceptron with YOUR TinyTorch autograd system!") - print(f" Learning rate: {learning_rate}") - print(f" Epochs: {epochs}") - print(f" Using YOUR Tensor backward() method for gradients!") - - # Convert to YOUR Tensor format - X_tensor = Tensor(X) # Module 02: YOUR Tensor class! - y_tensor = Tensor(y.reshape(-1, 1)) # Module 02: YOUR data structure! - - for epoch in range(epochs): - # Forward pass using YOUR implementations - predictions = model.forward(X_tensor) # YOUR forward method! - - # For binary classification, we can use MSE as a differentiable loss - # that maintains the computational graph - # Note: Later you'll build proper loss functions in Module 05! - - # Use MSE loss: (predictions - targets)^2 - diff = predictions - y_tensor - squared_diff = diff * diff # Element-wise multiplication - - # We need to sum/average to get scalar loss - # Since our tensor operations are limited, we'll use backward directly - # with a gradient vector of ones to simulate the loss gradient - - # For display purposes, compute loss value - y_np = np.array(y_tensor.data.data if hasattr(y_tensor.data, 'data') else y_tensor.data) - pred_np = np.array(predictions.data.data if hasattr(predictions.data, 'data') else predictions.data) - loss_value = np.mean((pred_np - y_np) ** 2) - - # Backward pass - use the squared diff directly - # Provide gradient of ones to simulate scalar loss backward - n_samples = squared_diff.data.shape[0] - grad_output = Tensor(np.ones_like(squared_diff.data) / n_samples) - squared_diff.backward(grad_output) # Module 02: YOUR backward propagation! - - # Manual parameter updates (later you'll use YOUR optimizers!) - for param in model.parameters(): - if param.grad is not None: - # Extract gradient data properly - grad_data = param.grad.data if hasattr(param.grad, 'data') else param.grad - grad_np = np.array(grad_data.data if hasattr(grad_data, 'data') else grad_data) - param.data = param.data - learning_rate * grad_np # Simple gradient descent - param.grad = None # Clear gradients - - if epoch % 20 == 0 or epoch == epochs - 1: - print(f" Epoch {epoch:3d}: Loss = {loss_value:.4f} (YOUR training loop!)") - - return model - - -def test_model(model, X, y): - """Test YOUR perceptron on the data.""" - print("\n🧪 Testing YOUR Perceptron Implementation:") - - # Forward pass with YOUR components - X_tensor = Tensor(X) # Module 02: YOUR Tensor! - predictions = model.forward(X_tensor) # YOUR architecture! - - # Convert to binary predictions - pred_np = np.array(predictions.data.data if hasattr(predictions.data, 'data') else predictions.data) - binary_preds = (pred_np > 0.5).astype(int) - accuracy = np.mean(binary_preds.flatten() == y) * 100 - - print(f" Accuracy: {accuracy:.1f}% on linearly separable data") - print(f" YOUR perceptron correctly classified {accuracy:.1f}% of examples!") - - # Show some example predictions - print("\n Sample predictions (YOUR model's output):") - for i in range(min(5, len(X))): - x_val = X[i] - pred_prob = predictions.data[i, 0] - pred_class = binary_preds[i, 0] - true_class = y[i] - status = "✓" if pred_class == true_class else "✗" - print(f" {status} Input: [{x_val[0]:.2f}, {x_val[1]:.2f}] → " - f"Probability: {pred_prob:.3f} → Class: {pred_class} (True: {true_class})") - - return accuracy - -def analyze_perceptron_systems(model, X): - """Analyze YOUR perceptron from an ML systems perspective.""" - print("\n🔬 SYSTEMS ANALYSIS of YOUR Perceptron Implementation:") - - # Memory analysis using YOUR tensor system - import tracemalloc - tracemalloc.start() - - # Test forward pass with YOUR components - X_tensor = Tensor(X) # Module 02: YOUR Tensor! - output = model.forward(X_tensor) # Module 04 + 03: YOUR architecture! - - current, peak = tracemalloc.get_traced_memory() - tracemalloc.stop() - - # Parameter analysis - total_params = model.linear.weights.data.size + model.linear.bias.data.size - memory_per_param = 4 # bytes for float32 - - print(f" Memory usage: {peak / 1024:.1f} KB peak (YOUR Tensor operations)") - print(f" Parameters: {total_params} weights (YOUR Linear layer)") - print(f" Model size: {total_params * memory_per_param} bytes") - print(f" Computational complexity: O(n) per forward pass (linear scaling)") - print(f" YOUR implementation handles: Binary classification with linear decision boundary") - - # Historical context - print(f"\n 🏛️ Historical Context:") - print(f" • 1957: YOUR perceptron uses the SAME architecture as Rosenblatt's original") - print(f" • Limitation: Can only solve linearly separable problems") - print(f" • Innovation: First machine learning algorithm that could learn from data") - print(f" • Legacy: Foundation for all modern neural networks (including GPT!)") def main(): """Demonstrate Rosenblatt's Perceptron using YOUR TinyTorch system!""" - parser = argparse.ArgumentParser(description='Rosenblatt Perceptron 1957') - parser.add_argument('--test-only', action='store_true', - help='Test architecture without training') - parser.add_argument('--epochs', type=int, default=100, - help='Number of training epochs') - args = parser.parse_args() + print("🎯 MILESTONE: The Perceptron (1957)") + print(" Historical significance: The first trainable neural network.") + print(" YOUR achievement: Assembling it from YOUR own modules.") + print(" Components used: YOUR Tensor + YOUR Linear + YOUR Sigmoid.") + print("-" * 60) - print("🎯 PERCEPTRON 1957 - Proof of YOUR TinyTorch Mastery!") - print(" Historical significance: First trainable neural network") - print(" YOUR achievement: Recreated using YOUR own implementations") - print(" Components used: YOUR Tensor + YOUR Linear + YOUR Sigmoid") - print() - - # Step 1: Get linearly separable data - print("\n📊 Preparing linearly separable data...") - data_manager = DatasetManager() - X, y = data_manager.get_perceptron_data(num_samples=1000) - - # Step 2: Create perceptron with YOUR components + # Step 1: Prepare synthetic data + print("\n📊 Step 1: Preparing linearly separable data...") + np.random.seed(42) + cluster1 = np.random.normal([2, 2], 0.5, (5, 2)) # Just a few samples are needed + cluster2 = np.random.normal([-2, -2], 0.5, (5, 2)) + X = np.vstack([cluster1, cluster2]).astype(np.float32) + print(f" ✅ Data created successfully with shape: {X.shape}") + + # Step 2: Create the Perceptron model with YOUR components + print("\n🧠 Step 2: Instantiating the Perceptron model...") model = RosenblattPerceptron(input_size=2, output_size=1) + print(" ✅ Model assembled successfully!") + + # Step 3: Perform a forward pass + print("\n🔬 Step 3: Running a forward pass to test integration...") + # Convert data to YOUR Tensor format + input_tensor = Tensor(X) # Module 01: YOUR Tensor class! + print(f" - Input tensor created with shape: {input_tensor.shape}") + + # Run the forward pass through YOUR implementations + output_tensor = model.forward(input_tensor) + print(f" - Output tensor received with shape: {output_tensor.shape}") + + # --- Verification --- + print("\n" + "="*60) + print("✅ SUCCESS! Your components integrated perfectly.") + print(" You have successfully assembled the architecture of the first") + print(" trainable neural network using the modules YOU built.") + print("="*60) - if args.test_only: - print("\n🧪 ARCHITECTURE TEST MODE") - print("Testing YOUR components work together...") - - # Quick forward pass test - test_input = Tensor(X[:5]) # Module 02: YOUR Tensor! - test_output = model.forward(test_input) # YOUR architecture! - print(f"✅ Forward pass successful! Output shape: {test_output.data.shape}") - print("✅ YOUR TinyTorch modules integrate correctly!") - return - - # Step 3: Train using YOUR training system - model = simple_training_loop(model, X, y, epochs=args.epochs) - - # Step 4: Test YOUR implementation - accuracy = test_model(model, X, y) - - # Step 5: Analyze YOUR implementation - analyze_perceptron_systems(model, X) - - print("\n✅ SUCCESS! Perceptron Milestone Complete!") print("\n🎓 What YOU Accomplished:") - print(" • YOU built the first trainable neural network from scratch") - print(" • YOUR Linear layer performs the same math as Rosenblatt's original") - print(" • YOUR Sigmoid activation enables smooth gradient learning") - print(" • YOUR Tensor system handles automatic differentiation") + print(" • YOU assembled a neural network from scratch.") + print(" • YOUR Tensor class handled the data flow.") + print(" • YOUR Linear layer performed the mathematical transformation.") + print(" • YOUR Sigmoid activation processed the layer's output.") + print("\n🚀 Next Steps:") - print(" • Continue to XOR 1969 milestone after Module 06 (Autograd)") - print(" • YOUR foundation enables solving non-linear problems!") - print(f" • With {accuracy:.1f}% accuracy, YOUR perceptron works perfectly!") + print(" • In future modules, you will build the components needed to TRAIN this model:") + print(" - Module 04 (Losses): To measure how wrong the model's predictions are.") + print(" - Module 05 (Autograd): To calculate the gradients needed to improve.") + print(" - Module 06 (Optimizers): To update the model's weights automatically.") + print("\n For now, congratulations on this major milestone!") if __name__ == "__main__": main() \ No newline at end of file diff --git a/modules/source/01_tensor/tensor_dev.ipynb b/modules/source/01_tensor/tensor_dev.ipynb index 9d3820e0..c6d011b6 100644 --- a/modules/source/01_tensor/tensor_dev.ipynb +++ b/modules/source/01_tensor/tensor_dev.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "7e9f10f4", + "id": "6ca4b9f5", "metadata": { "cell_marker": "\"\"\"" }, @@ -38,7 +38,6 @@ "\n", "```python\n", "# Final package structure:\n", - "from tinytorch.core.tensor import Tensor # This module - foundation for everything\n", "# Future modules will import and extend this Tensor\n", "```\n", "\n", @@ -52,7 +51,7 @@ { "cell_type": "code", "execution_count": null, - "id": "76974680", + "id": "3dcaaffc", "metadata": { "nbgrader": { "grade": false, @@ -63,13 +62,14 @@ "outputs": [], "source": [ "#| default_exp core.tensor\n", + "#| export\n", "\n", "import numpy as np" ] }, { "cell_type": "markdown", - "id": "9885fe6c", + "id": "e70ae12a", "metadata": { "cell_marker": "\"\"\"" }, @@ -116,7 +116,7 @@ }, { "cell_type": "markdown", - "id": "c9ac7887", + "id": "7a1e48b5", "metadata": { "cell_marker": "\"\"\"" }, @@ -175,7 +175,7 @@ }, { "cell_type": "markdown", - "id": "4a901ed7", + "id": "42f2279e", "metadata": { "cell_marker": "\"\"\"" }, @@ -214,7 +214,7 @@ }, { "cell_type": "markdown", - "id": "f5325a29", + "id": "cb1e99f0", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -252,7 +252,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a532b1ec", + "id": "4a090be0", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -263,6 +263,7 @@ }, "outputs": [], "source": [ + "#| export\n", "class Tensor:\n", " \"\"\"Educational tensor that grows with student knowledge.\n", "\n", @@ -313,22 +314,13 @@ "\n", " def __str__(self):\n", " \"\"\"Human-readable string representation.\"\"\"\n", - " return f\"Tensor({self.data})\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dd2e63a9", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "addition-impl", - "solution": true - } - }, - "outputs": [], - "source": [ + " return f\"Tensor({self.data})\"\n", + "\n", + " def numpy(self):\n", + " \"\"\"Return the underlying NumPy array.\"\"\"\n", + " return self.data\n", + "\n", + " # nbgrader={\\\"grade\\\": false, \\\"grade_id\\\": \\\"addition-impl\\\", \\\"solution\\\": true}\n", " def __add__(self, other):\n", " \"\"\"\n", " Add two tensors element-wise with broadcasting support.\n", @@ -379,22 +371,9 @@ " result.requires_grad = self.requires_grad\n", "\n", " return result\n", - " ### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "223aaca8", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "more-arithmetic", - "solution": true - } - }, - "outputs": [], - "source": [ + " ### END SOLUTION\n", + "\n", + " # nbgrader={\"grade\": false, \"grade_id\": \"more-arithmetic\", \"solution\": true}\n", " def __sub__(self, other):\n", " \"\"\"\n", " Subtract two tensors element-wise.\n", @@ -427,22 +406,9 @@ " if isinstance(other, Tensor):\n", " return Tensor(self.data / other.data)\n", " else:\n", - " return Tensor(self.data / other)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "110326a6", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "matmul-impl", - "solution": true - } - }, - "outputs": [], - "source": [ + " return Tensor(self.data / other)\n", + "\n", + " # nbgrader={\"grade\": false, \"grade_id\": \"matmul-impl\", \"solution\": true}\n", " def matmul(self, other):\n", " \"\"\"\n", " Matrix multiplication of two tensors.\n", @@ -511,22 +477,9 @@ " # Perform optimized matrix multiplication\n", " result_data = np.dot(self.data, other.data)\n", " return Tensor(result_data)\n", - " ### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7307e0e8", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "shape-ops", - "solution": true - } - }, - "outputs": [], - "source": [ + " ### END SOLUTION\n", + "\n", + " # nbgrader={\"grade\": false, \"grade_id\": \"shape-ops\", \"solution\": true}\n", " def reshape(self, *shape):\n", " \"\"\"\n", " Reshape tensor to new dimensions.\n", @@ -661,22 +614,9 @@ " transposed_data = np.transpose(self.data, axes)\n", "\n", " return Tensor(transposed_data)\n", - " ### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7eb6e317", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "reduction-ops", - "solution": true - } - }, - "outputs": [], - "source": [ + " ### END SOLUTION\n", + "\n", + " # nbgrader={\"grade\": false, \"grade_id\": \"reduction-ops\", \"solution\": true}\n", " def sum(self, axis=None, keepdims=False):\n", " \"\"\"\n", " Sum tensor along specified axis.\n", @@ -736,22 +676,9 @@ " ### BEGIN SOLUTION\n", " result = np.max(self.data, axis=axis, keepdims=keepdims)\n", " return Tensor(result)\n", - " ### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "27313cc9", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "gradient-placeholder", - "solution": true - } - }, - "outputs": [], - "source": [ + " ### END SOLUTION\n", + "\n", + " # nbgrader={\"grade\": false, \"grade_id\": \"gradient-placeholder\", \"solution\": true}\n", " def backward(self):\n", " \"\"\"\n", " Compute gradients (implemented in Module 05: Autograd).\n", @@ -797,7 +724,7 @@ }, { "cell_type": "markdown", - "id": "581bca49", + "id": "a49cddfd", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -815,7 +742,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5fc1a793", + "id": "79195fe8", "metadata": { "nbgrader": { "grade": true, @@ -864,7 +791,7 @@ }, { "cell_type": "markdown", - "id": "1f8159c6", + "id": "7cbed527", "metadata": { "cell_marker": "\"\"\"" }, @@ -912,7 +839,7 @@ }, { "cell_type": "markdown", - "id": "6d13995a", + "id": "30f53e64", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 2 @@ -955,7 +882,7 @@ }, { "cell_type": "markdown", - "id": "4a34349e", + "id": "e13b5c91", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -973,7 +900,7 @@ { "cell_type": "code", "execution_count": null, - "id": "dd54a903", + "id": "26ab9e58", "metadata": { "nbgrader": { "grade": true, @@ -1030,7 +957,7 @@ }, { "cell_type": "markdown", - "id": "23242cc9", + "id": "8ab4eb75", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 2 @@ -1130,7 +1057,7 @@ }, { "cell_type": "markdown", - "id": "3d81481f", + "id": "75e72654", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -1148,7 +1075,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1564ad57", + "id": "434f6550", "metadata": { "nbgrader": { "grade": true, @@ -1205,7 +1132,7 @@ }, { "cell_type": "markdown", - "id": "78b99f8b", + "id": "de04fa2e", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 2 @@ -1308,7 +1235,7 @@ }, { "cell_type": "markdown", - "id": "2b16da4b", + "id": "2f4cd90a", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -1326,7 +1253,7 @@ { "cell_type": "code", "execution_count": null, - "id": "259f2769", + "id": "e497f3d1", "metadata": { "nbgrader": { "grade": true, @@ -1396,7 +1323,7 @@ }, { "cell_type": "markdown", - "id": "81696e32", + "id": "c944cd8b", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 2 @@ -1490,7 +1417,7 @@ }, { "cell_type": "markdown", - "id": "cd59ecd2", + "id": "e8312574", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -1508,7 +1435,7 @@ { "cell_type": "code", "execution_count": null, - "id": "84d2a40c", + "id": "66d6beb6", "metadata": { "nbgrader": { "grade": true, @@ -1581,7 +1508,7 @@ }, { "cell_type": "markdown", - "id": "3265a10f", + "id": "71042cd1", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 2 @@ -1656,7 +1583,7 @@ }, { "cell_type": "markdown", - "id": "f2929723", + "id": "9d5518b2", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 2 @@ -1717,21 +1644,29 @@ }, { "cell_type": "markdown", - "id": "ab817462", + "id": "23b79c43", "metadata": { - "cell_marker": "\"\"\"", "lines_to_next_cell": 1 }, "source": [ - "## 🧪 Module Integration Test\n", + "\"\"\"\n", + "# 🧪 Module Integration Test\n", "\n", - "Final validation that everything works together correctly before module completion." + "Final validation that everything works together correctly before module completion.\n", + "\"\"\"\n", + "\n", + "def import_previous_module(module_name: str, component_name: str):\n", + " import sys\n", + " import os\n", + " sys.path.append(os.path.join(os.path.dirname(__file__), '..', module_name))\n", + " module = __import__(f\"{module_name.split('_')[1]}_dev\")\n", + " return getattr(module, component_name)" ] }, { "cell_type": "code", "execution_count": null, - "id": "ae410a85", + "id": "c8fdde9c", "metadata": { "lines_to_next_cell": 2, "nbgrader": { @@ -1859,7 +1794,7 @@ }, { "cell_type": "markdown", - "id": "e0031c02", + "id": "f0f02362", "metadata": { "cell_marker": "\"\"\"" }, diff --git a/modules/source/01_tensor/tensor_dev.py b/modules/source/01_tensor/tensor_dev.py index 93902303..f44e28e5 100644 --- a/modules/source/01_tensor/tensor_dev.py +++ b/modules/source/01_tensor/tensor_dev.py @@ -276,7 +276,11 @@ class Tensor: """Human-readable string representation.""" return f"Tensor({self.data})" - # %% nbgrader={"grade": false, "grade_id": "addition-impl", "solution": true} + def numpy(self): + """Return the underlying NumPy array.""" + return self.data + + # nbgrader={\"grade\": false, \"grade_id\": \"addition-impl\", \"solution\": true} def __add__(self, other): """ Add two tensors element-wise with broadcasting support. @@ -329,7 +333,7 @@ class Tensor: return result ### END SOLUTION - # %% nbgrader={"grade": false, "grade_id": "more-arithmetic", "solution": true} + # nbgrader={"grade": false, "grade_id": "more-arithmetic", "solution": true} def __sub__(self, other): """ Subtract two tensors element-wise. @@ -364,7 +368,7 @@ class Tensor: else: return Tensor(self.data / other) - # %% nbgrader={"grade": false, "grade_id": "matmul-impl", "solution": true} + # nbgrader={"grade": false, "grade_id": "matmul-impl", "solution": true} def matmul(self, other): """ Matrix multiplication of two tensors. @@ -435,7 +439,7 @@ class Tensor: return Tensor(result_data) ### END SOLUTION - # %% nbgrader={"grade": false, "grade_id": "shape-ops", "solution": true} + # nbgrader={"grade": false, "grade_id": "shape-ops", "solution": true} def reshape(self, *shape): """ Reshape tensor to new dimensions. @@ -572,7 +576,7 @@ class Tensor: return Tensor(transposed_data) ### END SOLUTION - # %% nbgrader={"grade": false, "grade_id": "reduction-ops", "solution": true} + # nbgrader={"grade": false, "grade_id": "reduction-ops", "solution": true} def sum(self, axis=None, keepdims=False): """ Sum tensor along specified axis. @@ -634,7 +638,7 @@ class Tensor: return Tensor(result) ### END SOLUTION - # %% nbgrader={"grade": false, "grade_id": "gradient-placeholder", "solution": true} + # nbgrader={"grade": false, "grade_id": "gradient-placeholder", "solution": true} def backward(self): """ Compute gradients (implemented in Module 05: Autograd). diff --git a/modules/source/02_activations/activations_dev.ipynb b/modules/source/02_activations/activations_dev.ipynb index 6eb0700a..5c2a4375 100644 --- a/modules/source/02_activations/activations_dev.ipynb +++ b/modules/source/02_activations/activations_dev.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "26638093", + "id": "86f80cda", "metadata": { "cell_marker": "\"\"\"" }, @@ -34,7 +34,7 @@ }, { "cell_type": "markdown", - "id": "8fdad0cc", + "id": "34bcfef8", "metadata": { "cell_marker": "\"\"\"" }, @@ -57,10 +57,28 @@ "- **Integration:** Works seamlessly with Tensor for complete nonlinear transformations" ] }, + { + "cell_type": "markdown", + "id": "e0a91cc1", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 📋 Module Prerequisites & Setup\n", + "\n", + "This module builds on previous TinyTorch components. Here's what we need and why:\n", + "\n", + "**Required Components:**\n", + "- **Tensor** (Module 01): Foundation for all activation computations and data flow\n", + "\n", + "**Integration Helper:**\n", + "The `import_previous_module()` function below helps us cleanly import components from previous modules during development and testing." + ] + }, { "cell_type": "code", "execution_count": null, - "id": "b118828c", + "id": "c69cb91b", "metadata": { "nbgrader": { "grade": false, @@ -78,18 +96,13 @@ "import sys\n", "import os\n", "\n", - "# Import our Tensor class - try from package first, then from local module\n", - "try:\n", - " from tinytorch.core.tensor import Tensor\n", - "except ImportError:\n", - " # For development, import from local tensor module\n", - " sys.path.append(os.path.join(os.path.dirname(__file__), '..', '01_tensor'))\n", - " from tensor_dev import Tensor" + "\n", + "# Import will be in export cell" ] }, { "cell_type": "markdown", - "id": "f2ac3527", + "id": "b016be53", "metadata": { "cell_marker": "\"\"\"" }, @@ -131,7 +144,7 @@ }, { "cell_type": "markdown", - "id": "112ad140", + "id": "164dc8d6", "metadata": { "cell_marker": "\"\"\"" }, @@ -153,7 +166,7 @@ }, { "cell_type": "markdown", - "id": "1c6e3614", + "id": "2fc75140", "metadata": { "cell_marker": "\"\"\"" }, @@ -177,10 +190,9 @@ }, { "cell_type": "markdown", - "id": "6f4f2fac", + "id": "a3b53483", "metadata": { - "cell_marker": "\"\"\"", - "lines_to_next_cell": 1 + "cell_marker": "\"\"\"" }, "source": [ "## Sigmoid - The Probability Gatekeeper\n", @@ -216,7 +228,7 @@ { "cell_type": "code", "execution_count": null, - "id": "eb89cfed", + "id": "a7cab271", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -228,6 +240,8 @@ "outputs": [], "source": [ "#| export\n", + "from tinytorch.core.tensor import Tensor\n", + "\n", "class Sigmoid:\n", " \"\"\"\n", " Sigmoid activation: σ(x) = 1/(1 + e^(-x))\n", @@ -262,6 +276,10 @@ " return Tensor(result)\n", " ### END SOLUTION\n", "\n", + " def __call__(self, x: Tensor) -> Tensor:\n", + " \"\"\"Allows the activation to be called like a function.\"\"\"\n", + " return self.forward(x)\n", + "\n", " def backward(self, grad: Tensor) -> Tensor:\n", " \"\"\"Compute gradient (implemented in Module 05).\"\"\"\n", " pass # Will implement backward pass in Module 05" @@ -269,7 +287,7 @@ }, { "cell_type": "markdown", - "id": "35b7bc5b", + "id": "3f5ee438", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -285,7 +303,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a2f7b2fb", + "id": "7ff4646b", "metadata": { "nbgrader": { "grade": true, @@ -326,7 +344,7 @@ }, { "cell_type": "markdown", - "id": "10169736", + "id": "1c09c618", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -368,7 +386,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a7679bbe", + "id": "b5a1560f", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -420,7 +438,7 @@ }, { "cell_type": "markdown", - "id": "a9d8d19a", + "id": "24b611be", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -436,7 +454,7 @@ { "cell_type": "code", "execution_count": null, - "id": "12798838", + "id": "64fda5c1", "metadata": { "nbgrader": { "grade": true, @@ -483,7 +501,7 @@ }, { "cell_type": "markdown", - "id": "4c7a86fa", + "id": "443695ef", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -522,7 +540,7 @@ { "cell_type": "code", "execution_count": null, - "id": "61336eeb", + "id": "69d079ac", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -574,7 +592,7 @@ }, { "cell_type": "markdown", - "id": "2ea3362a", + "id": "6db0a7c8", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -590,7 +608,7 @@ { "cell_type": "code", "execution_count": null, - "id": "efa9866e", + "id": "1939ab03", "metadata": { "nbgrader": { "grade": true, @@ -638,7 +656,7 @@ }, { "cell_type": "markdown", - "id": "8a075f28", + "id": "078e18e1", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -681,7 +699,7 @@ { "cell_type": "code", "execution_count": null, - "id": "14cc2a59", + "id": "3759c49c", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -738,7 +756,7 @@ }, { "cell_type": "markdown", - "id": "25cbcb04", + "id": "b7172f9e", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -754,7 +772,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2ea81efa", + "id": "61c53a07", "metadata": { "nbgrader": { "grade": true, @@ -802,7 +820,7 @@ }, { "cell_type": "markdown", - "id": "8dd72698", + "id": "b35ea71b", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -840,7 +858,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f9cb33a7", + "id": "a15f0e81", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -908,7 +926,7 @@ }, { "cell_type": "markdown", - "id": "2be27bd4", + "id": "29fbd750", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -924,7 +942,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7434b6fd", + "id": "0ecab676", "metadata": { "nbgrader": { "grade": true, @@ -982,7 +1000,7 @@ }, { "cell_type": "markdown", - "id": "1724e759", + "id": "8931529d", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 2 @@ -995,7 +1013,7 @@ }, { "cell_type": "markdown", - "id": "8fadcbf4", + "id": "a3221ec3", "metadata": { "cell_marker": "\"\"\"" }, @@ -1015,7 +1033,7 @@ }, { "cell_type": "markdown", - "id": "ba765a81", + "id": "ecfea11b", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -1029,7 +1047,7 @@ { "cell_type": "code", "execution_count": null, - "id": "308d7856", + "id": "7d664752", "metadata": { "lines_to_next_cell": 2, "nbgrader": { @@ -1041,6 +1059,13 @@ }, "outputs": [], "source": [ + "def import_previous_module(module_name: str, component_name: str):\n", + " import sys\n", + " import os\n", + " sys.path.append(os.path.join(os.path.dirname(__file__), '..', module_name))\n", + " module = __import__(f\"{module_name.split('_')[1]}_dev\")\n", + " return getattr(module, component_name)\n", + "\n", "def test_module():\n", " \"\"\"\n", " Comprehensive test of entire module functionality.\n", @@ -1121,7 +1146,7 @@ }, { "cell_type": "markdown", - "id": "d5bd9de0", + "id": "f30b9a52", "metadata": { "cell_marker": "\"\"\"" }, diff --git a/modules/source/02_activations/activations_dev.py b/modules/source/02_activations/activations_dev.py index f4087ac3..63f7ffa1 100644 --- a/modules/source/02_activations/activations_dev.py +++ b/modules/source/02_activations/activations_dev.py @@ -74,21 +74,15 @@ The `import_previous_module()` function below helps us cleanly import components # %% nbgrader={"grade": false, "grade_id": "setup", "solution": true} #| default_exp core.activations +#| export import numpy as np from typing import Optional import sys import os -def import_previous_module(module_name: str, component_name: str): - import sys - import os - sys.path.append(os.path.join(os.path.dirname(__file__), '..', module_name)) - module = __import__(f"{module_name.split('_')[1]}_dev") - return getattr(module, component_name) -# Import from previous modules using our helper -Tensor = import_previous_module('01_tensor', 'Tensor') +# Import will be in export cell # %% [markdown] """ @@ -197,6 +191,8 @@ Sigmoid Curve: # %% nbgrader={"grade": false, "grade_id": "sigmoid-impl", "solution": true} #| export +from tinytorch.core.tensor import Tensor + class Sigmoid: """ Sigmoid activation: σ(x) = 1/(1 + e^(-x)) @@ -231,6 +227,10 @@ class Sigmoid: return Tensor(result) ### END SOLUTION + def __call__(self, x: Tensor) -> Tensor: + """Allows the activation to be called like a function.""" + return self.forward(x) + def backward(self, grad: Tensor) -> Tensor: """Compute gradient (implemented in Module 05).""" pass # Will implement backward pass in Module 05 diff --git a/modules/source/03_layers/layers_dev.ipynb b/modules/source/03_layers/layers_dev.ipynb index 2bcd45fc..76e3f822 100644 --- a/modules/source/03_layers/layers_dev.ipynb +++ b/modules/source/03_layers/layers_dev.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "f9274f85", + "id": "2cfa2aae", "metadata": { "cell_marker": "\"\"\"" }, @@ -53,7 +53,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5ee174f6", + "id": "c7a84c05", "metadata": { "nbgrader": { "grade": false, @@ -64,19 +64,20 @@ "outputs": [], "source": [ "#| default_exp core.layers\n", + "#| export\n", "\n", "import numpy as np\n", "import sys\n", "import os\n", "\n", - "# Import the proper Tensor class from Module 01\n", - "sys.path.append(os.path.join(os.path.dirname(__file__), '..', '01_tensor'))\n", - "from tensor_dev import Tensor" + "# Import dependencies from tinytorch package\n", + "from tinytorch.core.tensor import Tensor\n", + "from tinytorch.core.activations import ReLU, Sigmoid" ] }, { "cell_type": "markdown", - "id": "57f54e44", + "id": "e52c72c2", "metadata": { "cell_marker": "\"\"\"" }, @@ -100,7 +101,7 @@ }, { "cell_type": "markdown", - "id": "c655df09", + "id": "e05eee85", "metadata": { "cell_marker": "\"\"\"" }, @@ -138,7 +139,7 @@ }, { "cell_type": "markdown", - "id": "82f106c3", + "id": "f489f983", "metadata": { "cell_marker": "\"\"\"" }, @@ -159,7 +160,7 @@ }, { "cell_type": "markdown", - "id": "c960f336", + "id": "fff4865c", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -210,7 +211,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0e7a01cb", + "id": "da931144", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -221,6 +222,7 @@ }, "outputs": [], "source": [ + "#| export\n", "class Linear:\n", " \"\"\"\n", " Linear (fully connected) layer: y = xW + b\n", @@ -303,6 +305,10 @@ " return output\n", " ### END SOLUTION\n", "\n", + " def __call__(self, x):\n", + " \"\"\"Allows the layer to be called like a function.\"\"\"\n", + " return self.forward(x)\n", + "\n", " def parameters(self):\n", " \"\"\"\n", " Return list of trainable parameters.\n", @@ -329,7 +335,7 @@ }, { "cell_type": "markdown", - "id": "7005a1e9", + "id": "77988775", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -345,7 +351,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1e635c8e", + "id": "4c2e0b2e", "metadata": { "nbgrader": { "grade": true, @@ -405,7 +411,7 @@ }, { "cell_type": "markdown", - "id": "ef5efbc5", + "id": "d2fa31b7", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -467,7 +473,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ac9c2688", + "id": "88715659", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -478,6 +484,7 @@ }, "outputs": [], "source": [ + "#| export\n", "class Dropout:\n", " \"\"\"\n", " Dropout layer for regularization.\n", @@ -559,7 +566,7 @@ }, { "cell_type": "markdown", - "id": "a0524fdd", + "id": "0d33ff6f", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -575,7 +582,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f228eb90", + "id": "cb0b7ae8", "metadata": { "nbgrader": { "grade": true, @@ -651,7 +658,7 @@ }, { "cell_type": "markdown", - "id": "c92a7889", + "id": "4b4aac2f", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 2 @@ -711,7 +718,7 @@ }, { "cell_type": "markdown", - "id": "a41674af", + "id": "0f4f3b7d", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -770,7 +777,7 @@ { "cell_type": "code", "execution_count": null, - "id": "07d5bfe3", + "id": "99df0451", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -825,7 +832,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1f70ed2f", + "id": "5e82e08c", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -866,21 +873,29 @@ }, { "cell_type": "markdown", - "id": "ab500718", + "id": "85ea5db5", "metadata": { - "cell_marker": "\"\"\"", "lines_to_next_cell": 1 }, "source": [ - "## 🧪 Module Integration Test\n", + "\"\"\"\n", + "# 🧪 Module Integration Test\n", "\n", - "Final validation that everything works together correctly." + "Final validation that everything works together correctly.\n", + "\"\"\"\n", + "\n", + "def import_previous_module(module_name: str, component_name: str):\n", + " import sys\n", + " import os\n", + " sys.path.append(os.path.join(os.path.dirname(__file__), '..', module_name))\n", + " module = __import__(f\"{module_name.split('_')[1]}_dev\")\n", + " return getattr(module, component_name)" ] }, { "cell_type": "code", "execution_count": null, - "id": "58f4b2ab", + "id": "ebddc165", "metadata": { "lines_to_next_cell": 2, "nbgrader": { @@ -914,9 +929,8 @@ " # Test realistic neural network construction with manual composition\n", " print(\"🔬 Integration Test: Multi-layer Network...\")\n", "\n", - " # Import real activation from module 02\n", - " sys.path.append(os.path.join(os.path.dirname(__file__), '..', '02_activations'))\n", - " from activations_dev import ReLU\n", + " # Import real activation from module 02 using standardized helper\n", + " ReLU = import_previous_module('02_activations', 'ReLU')\n", "\n", " # Build individual layers for manual composition\n", " layer1 = Linear(784, 128)\n", @@ -972,7 +986,7 @@ }, { "cell_type": "markdown", - "id": "4c84f921", + "id": "31c54ee3", "metadata": { "cell_marker": "\"\"\"" }, diff --git a/modules/source/03_layers/layers_dev.py b/modules/source/03_layers/layers_dev.py index bbcd2d4b..32b2e7bd 100644 --- a/modules/source/03_layers/layers_dev.py +++ b/modules/source/03_layers/layers_dev.py @@ -65,12 +65,9 @@ import numpy as np import sys import os -# Import dependencies from other modules -sys.path.append(os.path.join(os.path.dirname(__file__), '..', '01_tensor')) -from tensor_dev import Tensor - -sys.path.append(os.path.join(os.path.dirname(__file__), '..', '02_activations')) -from activations_dev import ReLU, Sigmoid +# Import dependencies from tinytorch package +from tinytorch.core.tensor import Tensor +from tinytorch.core.activations import ReLU, Sigmoid # %% [markdown] """ @@ -268,6 +265,10 @@ class Linear: return output ### END SOLUTION + def __call__(self, x): + """Allows the layer to be called like a function.""" + return self.forward(x) + def parameters(self): """ Return list of trainable parameters. diff --git a/modules/source/04_losses/losses_dev.ipynb b/modules/source/04_losses/losses_dev.ipynb index 2248aaad..cc4e7cf7 100644 --- a/modules/source/04_losses/losses_dev.ipynb +++ b/modules/source/04_losses/losses_dev.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "799fd9d0", + "id": "fa79b67a", "metadata": { "cell_marker": "\"\"\"" }, @@ -35,7 +35,7 @@ }, { "cell_type": "markdown", - "id": "c9c24237", + "id": "6ed0eb62", "metadata": { "cell_marker": "\"\"\"" }, @@ -59,38 +59,63 @@ "- **Integration:** Works seamlessly with layers for complete prediction-to-error workflow" ] }, + { + "cell_type": "markdown", + "id": "db146ccc", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 📋 Module Prerequisites & Setup\n", + "\n", + "This module builds on previous TinyTorch components. Here's what we need and why:\n", + "\n", + "**Required Components:**\n", + "- **Tensor** (Module 01): Foundation for all loss computations\n", + "- **Linear** (Module 03): For testing loss functions with realistic predictions \n", + "- **ReLU** (Module 02): For building test networks that generate realistic outputs\n", + "\n", + "**Integration Helper:**\n", + "The `import_previous_module()` function below helps us cleanly import components from previous modules during development and testing." + ] + }, { "cell_type": "code", "execution_count": null, - "id": "3b801276", + "id": "0ede42b4", "metadata": { "nbgrader": { "grade": false, - "grade_id": "imports", + "grade_id": "setup", "solution": true } }, "outputs": [], "source": [ "#| default_exp core.losses\n", + "#| export\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import time\n", "from typing import Optional\n", "\n", - "# Import from previous modules\n", - "### BEGIN SOLUTION\n", - "import sys\n", - "import os\n", - "sys.path.append(os.path.join(os.path.dirname(__file__), '..', '01_tensor'))\n", - "from tensor_dev import Tensor\n", - "### END SOLUTION" + "def import_previous_module(module_name: str, component_name: str):\n", + " import sys\n", + " import os\n", + " sys.path.append(os.path.join(os.path.dirname(__file__), '..', module_name))\n", + " module = __import__(f\"{module_name.split('_')[1]}_dev\")\n", + " return getattr(module, component_name)\n", + "\n", + "# Import from tinytorch package\n", + "from tinytorch.core.tensor import Tensor\n", + "from tinytorch.core.layers import Linear\n", + "from tinytorch.core.activations import ReLU" ] }, { "cell_type": "markdown", - "id": "a731547c", + "id": "826b0d35", "metadata": { "cell_marker": "\"\"\"" }, @@ -166,7 +191,7 @@ }, { "cell_type": "markdown", - "id": "c52f716b", + "id": "43033510", "metadata": { "cell_marker": "\"\"\"" }, @@ -212,7 +237,7 @@ }, { "cell_type": "markdown", - "id": "ea0cf3d9", + "id": "498e7033", "metadata": { "cell_marker": "\"\"\"" }, @@ -224,7 +249,7 @@ }, { "cell_type": "markdown", - "id": "6f8b3c7d", + "id": "df8f99c9", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -274,7 +299,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3b8c8908", + "id": "9a20a55c", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -325,7 +350,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3c07efde", + "id": "06f1516d", "metadata": { "nbgrader": { "grade": true, @@ -366,7 +391,7 @@ }, { "cell_type": "markdown", - "id": "32214d29", + "id": "0608a0d1", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -436,7 +461,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ca7d1772", + "id": "e3cc908c", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -503,7 +528,7 @@ { "cell_type": "code", "execution_count": null, - "id": "38aa6b1d", + "id": "85788b7b", "metadata": { "nbgrader": { "grade": true, @@ -549,7 +574,7 @@ }, { "cell_type": "markdown", - "id": "ac7d3ea4", + "id": "b747c6b2", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -642,7 +667,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a8513163", + "id": "74ca765f", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -713,7 +738,7 @@ { "cell_type": "code", "execution_count": null, - "id": "291f993e", + "id": "8ccf459d", "metadata": { "nbgrader": { "grade": true, @@ -764,7 +789,7 @@ }, { "cell_type": "markdown", - "id": "03358d33", + "id": "b0f0f51f", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -873,7 +898,7 @@ { "cell_type": "code", "execution_count": null, - "id": "308d912c", + "id": "d6382f87", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -944,7 +969,7 @@ { "cell_type": "code", "execution_count": null, - "id": "eeb89891", + "id": "d9b9e257", "metadata": { "nbgrader": { "grade": true, @@ -995,7 +1020,7 @@ }, { "cell_type": "markdown", - "id": "c3b758ad", + "id": "e73d525c", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -1052,7 +1077,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f879b1eb", + "id": "cdff35c9", "metadata": { "nbgrader": { "grade": false, @@ -1108,7 +1133,7 @@ { "cell_type": "code", "execution_count": null, - "id": "df914f78", + "id": "45a39a16", "metadata": { "nbgrader": { "grade": false, @@ -1173,7 +1198,7 @@ }, { "cell_type": "markdown", - "id": "87da05ba", + "id": "5630cd6b", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -1248,7 +1273,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ff322026", + "id": "f12fdce6", "metadata": { "nbgrader": { "grade": false, @@ -1298,7 +1323,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1735f677", + "id": "5787be84", "metadata": { "nbgrader": { "grade": false, @@ -1355,7 +1380,7 @@ }, { "cell_type": "markdown", - "id": "c9752a0e", + "id": "7b760039", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -1419,7 +1444,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ae943732", + "id": "e1f75a85", "metadata": { "nbgrader": { "grade": false, @@ -1475,10 +1500,9 @@ }, { "cell_type": "markdown", - "id": "c9d38062", + "id": "5b0e2884", "metadata": { - "cell_marker": "\"\"\"", - "lines_to_next_cell": 1 + "cell_marker": "\"\"\"" }, "source": [ "## 🧪 Module Integration Test\n", @@ -1489,7 +1513,7 @@ { "cell_type": "code", "execution_count": null, - "id": "95e21f0f", + "id": "2b5815dd", "metadata": { "nbgrader": { "grade": true, @@ -1521,7 +1545,7 @@ "\n", " print(\"\\nRunning integration scenarios...\")\n", "\n", - " # Test realistic end-to-end scenario\n", + " # Test realistic end-to-end scenario with previous modules\n", " print(\"🔬 Integration Test: Realistic training scenario...\")\n", "\n", " # Simulate a complete prediction -> loss computation pipeline\n", @@ -1569,7 +1593,7 @@ { "cell_type": "code", "execution_count": null, - "id": "271b0171", + "id": "8cefa198", "metadata": { "lines_to_next_cell": 2 }, @@ -1582,7 +1606,7 @@ }, { "cell_type": "markdown", - "id": "f1382c7a", + "id": "bdb71661", "metadata": { "cell_marker": "\"\"\"" }, diff --git a/modules/source/04_losses/losses_dev.py b/modules/source/04_losses/losses_dev.py index 1a5120e0..b9ace1bd 100644 --- a/modules/source/04_losses/losses_dev.py +++ b/modules/source/04_losses/losses_dev.py @@ -78,6 +78,7 @@ The `import_previous_module()` function below helps us cleanly import components # %% nbgrader={"grade": false, "grade_id": "setup", "solution": true} #| default_exp core.losses +#| export import numpy as np import matplotlib.pyplot as plt @@ -91,8 +92,10 @@ def import_previous_module(module_name: str, component_name: str): module = __import__(f"{module_name.split('_')[1]}_dev") return getattr(module, component_name) -# Import from previous modules using our helper -Tensor = import_previous_module('01_tensor', 'Tensor') +# Import from tinytorch package +from tinytorch.core.tensor import Tensor +from tinytorch.core.layers import Linear +from tinytorch.core.activations import ReLU # %% [markdown] """ @@ -257,7 +260,6 @@ Both give the same result, but the stable version never overflows! """ # %% nbgrader={"grade": false, "grade_id": "log_softmax", "solution": true} -#| export def log_softmax(x: Tensor, dim: int = -1) -> Tensor: """ Compute log-softmax with numerical stability. @@ -1287,11 +1289,6 @@ def test_module(): # Test realistic end-to-end scenario with previous modules print("🔬 Integration Test: Realistic training scenario...") - # Import components from previous modules using standardized helper - Tensor = import_previous_module('01_tensor', 'Tensor') - Linear = import_previous_module('03_layers', 'Linear') - ReLU = import_previous_module('02_activations', 'ReLU') - # Simulate a complete prediction -> loss computation pipeline # 1. MSE for regression (house price prediction) diff --git a/modules/source/05_autograd/autograd_dev.ipynb b/modules/source/05_autograd/autograd_dev.ipynb index 770f7543..16175170 100644 --- a/modules/source/05_autograd/autograd_dev.ipynb +++ b/modules/source/05_autograd/autograd_dev.ipynb @@ -2,18 +2,18 @@ "cells": [ { "cell_type": "markdown", - "id": "70e293d5", + "id": "b3c084bd", "metadata": { "cell_marker": "\"\"\"" }, "source": [ - "# Module 05: Autograd - Awakening the Gradient Engine\n", + "# Module 05: Autograd ⚡ - The Gradient Engine\n", "\n", - "Welcome to Module 05! Today you'll bring gradients to life and unlock automatic differentiation.\n", + "Welcome to Module 05! Today you'll awaken the gradient engine and unlock automatic differentiation.\n", "\n", "## 🔗 Prerequisites & Progress\n", - "**You've Built**: Tensor operations, activations, layers, and loss functions\n", - "**You'll Build**: The autograd system that computes gradients automatically\n", + "**You've Built**: Tensor operations, activations, layers, and loss functions \n", + "**You'll Build**: The autograd system that computes gradients automatically \n", "**You'll Enable**: Learning! Training! The ability to optimize neural networks!\n", "\n", "**Connection Map**:\n", @@ -22,39 +22,39 @@ "(forward pass) (backward pass) (learning loops)\n", "```\n", "\n", - "## Learning Objectives\n", + "## Learning Objectives ⭐⭐\n", "By the end of this module, you will:\n", - "1. Implement the backward() method for Tensor to enable gradient computation\n", - "2. Create a Function base class for operation tracking\n", - "3. Build computation graphs for automatic differentiation\n", - "4. Test gradient correctness and chain rule implementation\n", + "1. **Enhance Tensor** with automatic differentiation capabilities\n", + "2. **Build computation graphs** that track operations for gradient flow\n", + "3. **Implement backward()** method for reverse-mode differentiation\n", + "4. **Create Function classes** for operation-specific gradient rules\n", + "5. **Test gradient correctness** with mathematical validation\n", "\n", - "**CRITICAL**: This module enhances the existing Tensor class by implementing its dormant gradient features!\n", - "\n", - "Let's awaken the gradient engine!\n", + "**CRITICAL**: This module enhances the existing Tensor class - no new wrapper classes needed!\n", "\n", "## 📦 Where This Code Lives in the Final Package\n", "\n", - "**Learning Side:** You work in modules/05_autograd/autograd_dev.py\n", - "**Building Side:** Code exports to tinytorch.core.autograd\n", + "**Learning Side:** You work in `modules/05_autograd/autograd_dev.py` \n", + "**Building Side:** Code exports to `tinytorch.core.autograd`\n", "\n", "```python\n", - "# Final package structure:\n", - "from tinytorch.core.autograd import Function # This module - gradient computation\n", - "from tinytorch.core.tensor import Tensor # Enhanced with gradients from this module\n", + "# How to use this module:\n", + "from tinytorch.core.autograd import Function, enable_autograd\n", "```\n", "\n", "**Why this matters:**\n", "- **Learning:** Complete autograd system enabling automatic differentiation\n", "- **Production:** PyTorch-style computational graph and backward pass\n", "- **Consistency:** All gradient operations in core.autograd\n", - "- **Integration:** Enhances existing Tensor without breaking anything" + "- **Integration:** Enhances existing Tensor without breaking anything\n", + "\n", + "Let's build the gradient engine that makes neural networks learn! 🚀" ] }, { "cell_type": "code", "execution_count": null, - "id": "9d8ef1a7", + "id": "cf8d7835", "metadata": { "nbgrader": { "grade": false, @@ -65,20 +65,19 @@ "outputs": [], "source": [ "#| default_exp core.autograd\n", + "#| export\n", "\n", "import numpy as np\n", - "from typing import List, Optional, Callable\n", + "from typing import Optional, List, Tuple\n", "import sys\n", "import os\n", "\n", - "# Import the modern Tensor class\n", - "sys.path.append(os.path.join(os.path.dirname(__file__), '..', '01_tensor'))\n", - "from tensor_dev import Tensor" + "from tinytorch.core.tensor import Tensor" ] }, { "cell_type": "markdown", - "id": "b56abee5", + "id": "e9ac3956", "metadata": { "cell_marker": "\"\"\"" }, @@ -88,7 +87,7 @@ "Automatic differentiation (autograd) is the magic that makes neural networks learn. Instead of manually computing gradients for every parameter, autograd tracks operations and automatically computes gradients via the chain rule.\n", "\n", "### The Challenge\n", - "In Module 04, you implemented a loss function. To train a model, you need:\n", + "In previous modules, you implemented layers and loss functions. To train a model, you need:\n", "```\n", "Loss = f(W₃, f(W₂, f(W₁, x)))\n", "∂Loss/∂W₁ = ? ∂Loss/∂W₂ = ? ∂Loss/∂W₃ = ?\n", @@ -132,7 +131,7 @@ }, { "cell_type": "markdown", - "id": "7e4c7c87", + "id": "df3696be", "metadata": { "cell_marker": "\"\"\"" }, @@ -191,7 +190,7 @@ }, { "cell_type": "markdown", - "id": "cca81534", + "id": "d785654a", "metadata": { "cell_marker": "\"\"\"" }, @@ -211,16 +210,15 @@ "┌─────────────────────────────────────┐\n", "│ Function (Base Class) │\n", "├─────────────────────────────────────┤\n", - "│ • save_for_backward() ← Store data │\n", - "│ • forward() ← Compute │\n", - "│ • backward() ← Gradients │\n", + "│ • saved_tensors ← Store data │\n", + "│ • apply() ← Compute grads │\n", "└─────────────────────────────────────┘\n", " ↑\n", " ┌─────┴─────┬─────────┬──────────┐\n", " │ │ │ │\n", "┌───▼────┐ ┌────▼───┐ ┌───▼────┐ ┌───▼────┐\n", "│ Add │ │ Mul │ │ Matmul │ │ Sum │\n", - "│Function│ │Function│ │Function│ │Function│\n", + "│Backward│ │Backward│ │Backward│ │Backward│\n", "└────────┘ └────────┘ └────────┘ └────────┘\n", "```\n", "\n", @@ -229,7 +227,7 @@ }, { "cell_type": "markdown", - "id": "a2374a63", + "id": "808b70ee", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -241,14 +239,14 @@ "\n", "**Why Functions Matter:**\n", "- They remember inputs needed for backward pass\n", - "- They implement forward computation\n", - "- They implement gradient computation via backward()\n", + "- They implement gradient computation via apply()\n", "- They connect to form computation graphs\n", + "- They enable the chain rule to flow gradients\n", "\n", "**The Pattern:**\n", "```\n", "Forward: inputs → Function.forward() → output\n", - "Backward: grad_output → Function.backward() → grad_inputs\n", + "Backward: grad_output → Function.apply() → grad_inputs\n", "```\n", "\n", "This pattern enables the chain rule to flow gradients through complex computations." @@ -257,7 +255,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c4e83fb5", + "id": "5f66e075", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -273,107 +271,56 @@ " Base class for differentiable operations.\n", "\n", " Every operation that needs gradients (add, multiply, matmul, etc.)\n", - " will inherit from this class.\n", + " will inherit from this class and implement the apply() method.\n", + " \n", + " **Key Concepts:**\n", + " - **saved_tensors**: Store inputs needed for backward pass\n", + " - **apply()**: Compute gradients using chain rule\n", + " - **next_functions**: Track computation graph connections\n", + " \n", + " **Example Usage:**\n", + " ```python\n", + " class AddBackward(Function):\n", + " def apply(self, grad_output):\n", + " # Addition distributes gradients equally\n", + " return grad_output, grad_output\n", + " ```\n", " \"\"\"\n", "\n", - " def __init__(self):\n", - " \"\"\"Initialize function with empty input tracking.\"\"\"\n", - " self.inputs = []\n", - " self.saved_tensors = []\n", - "\n", - " def save_for_backward(self, *tensors):\n", + " def __init__(self, *tensors):\n", " \"\"\"\n", - " Save tensors needed for backward pass.\n", - "\n", - " TODO: Store tensors that backward() will need\n", - "\n", - " EXAMPLE:\n", - " In multiplication: y = a * b\n", - " We need to save 'a' and 'b' because:\n", - " ∂y/∂a = b and ∂y/∂b = a\n", + " Initialize function with input tensors.\n", + " \n", + " Args:\n", + " *tensors: Input tensors that will be saved for backward pass\n", " \"\"\"\n", - " ### BEGIN SOLUTION\n", " self.saved_tensors = tensors\n", - " ### END SOLUTION\n", + " self.next_functions = []\n", "\n", - " def forward(self, *inputs):\n", + " # Build computation graph connections\n", + " for t in tensors:\n", + " if isinstance(t, Tensor) and t.requires_grad:\n", + " if hasattr(t, '_grad_fn'):\n", + " self.next_functions.append(t._grad_fn)\n", + "\n", + " def apply(self, grad_output):\n", " \"\"\"\n", - " Compute forward pass.\n", - "\n", - " TODO: Implement in subclasses\n", - " This should be overridden by each specific operation.\n", + " Compute gradients for inputs.\n", + " \n", + " Args:\n", + " grad_output: Gradient flowing backward from the output\n", + " \n", + " Returns:\n", + " Tuple of gradients for each input tensor\n", + " \n", + " **Must be implemented by subclasses**\n", " \"\"\"\n", - " raise NotImplementedError(\"Forward pass must be implemented by subclasses\")\n", - "\n", - " def backward(self, grad_output):\n", - " \"\"\"\n", - " Compute backward pass.\n", - "\n", - " TODO: Implement in subclasses\n", - "\n", - " APPROACH:\n", - " 1. Take gradient flowing backward (grad_output)\n", - " 2. Apply chain rule with local gradients\n", - " 3. Return gradients for inputs\n", - " \"\"\"\n", - " raise NotImplementedError(\"Backward pass must be implemented by subclasses\")" + " raise NotImplementedError(\"Each Function must implement apply() method\")" ] }, { "cell_type": "markdown", - "id": "3d390955", - "metadata": { - "cell_marker": "\"\"\"", - "lines_to_next_cell": 1 - }, - "source": [ - "### 🔬 Unit Test: Function Base Class\n", - "This test validates our Function base class works correctly.\n", - "**What we're testing**: Function initialization and interface\n", - "**Why it matters**: Foundation for all differentiable operations\n", - "**Expected**: Proper initialization and save_for_backward functionality" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1d2df72b", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "test-function-base", - "locked": true, - "points": 10 - } - }, - "outputs": [], - "source": [ - "def test_unit_function_base():\n", - " \"\"\"🔬 Test Function base class.\"\"\"\n", - " print(\"🔬 Unit Test: Function Base Class...\")\n", - "\n", - " # Test initialization\n", - " func = Function()\n", - " assert func.inputs == []\n", - " assert func.saved_tensors == []\n", - "\n", - " # Test save_for_backward\n", - " tensor1 = Tensor([1, 2, 3])\n", - " tensor2 = Tensor([4, 5, 6])\n", - " func.save_for_backward(tensor1, tensor2)\n", - " assert len(func.saved_tensors) == 2\n", - " assert func.saved_tensors[0] is tensor1\n", - " assert func.saved_tensors[1] is tensor2\n", - "\n", - " print(\"✅ Function base class works correctly!\")\n", - "\n", - "if __name__ == \"__main__\":\n", - " test_unit_function_base()" - ] - }, - { - "cell_type": "markdown", - "id": "66e62bea", + "id": "1102d2c2", "metadata": { "cell_marker": "\"\"\"" }, @@ -412,13 +359,13 @@ }, { "cell_type": "markdown", - "id": "659f0192", + "id": "fad4deeb", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 }, "source": [ - "### AddFunction - Gradient Rules for Addition\n", + "### AddBackward - Gradient Rules for Addition\n", "\n", "Addition is the simplest gradient operation: gradients flow unchanged to both inputs.\n", "\n", @@ -441,87 +388,67 @@ { "cell_type": "code", "execution_count": null, - "id": "db506b35", + "id": "1e908f9a", "metadata": { "lines_to_next_cell": 1, "nbgrader": { "grade": false, - "grade_id": "operation-functions", + "grade_id": "add-backward", "solution": true } }, "outputs": [], "source": [ - "class AddFunction(Function):\n", - " \"\"\"Gradient computation for tensor addition.\"\"\"\n", + "class AddBackward(Function):\n", + " \"\"\"\n", + " Gradient computation for tensor addition.\n", + " \n", + " **Mathematical Rule:** If z = a + b, then ∂z/∂a = 1 and ∂z/∂b = 1\n", + " \n", + " **Key Insight:** Addition distributes gradients equally to both inputs.\n", + " The gradient flowing backward is passed unchanged to each input.\n", + " \n", + " **Broadcasting Handling:** When input shapes differ due to broadcasting,\n", + " we sum gradients appropriately to match original tensor shapes.\n", + " \"\"\"\n", "\n", - " def forward(self, a, b):\n", + " def apply(self, grad_output):\n", " \"\"\"\n", - " Forward pass: compute a + b\n", - "\n", - " TODO: Implement addition forward pass\n", + " Compute gradients for addition.\n", + " \n", + " Args:\n", + " grad_output: Gradient flowing backward from output\n", + " \n", + " Returns:\n", + " Tuple of (grad_a, grad_b) for the two inputs\n", + " \n", + " **Mathematical Foundation:**\n", + " - ∂(a+b)/∂a = 1 → grad_a = grad_output\n", + " - ∂(a+b)/∂b = 1 → grad_b = grad_output\n", " \"\"\"\n", - " ### BEGIN SOLUTION\n", - " # Save inputs for backward pass (shapes might be needed)\n", - " self.save_for_backward(a, b)\n", - "\n", - " # Compute addition\n", - " if isinstance(b, Tensor):\n", - " result = a.data + b.data\n", - " else:\n", - " result = a.data + b\n", - "\n", - " return result\n", - " ### END SOLUTION\n", - "\n", - " def backward(self, grad_output):\n", - " \"\"\"\n", - " Backward pass: compute gradients for addition\n", - "\n", - " TODO: Implement addition backward pass\n", - "\n", - " MATH: If z = a + b, then ∂z/∂a = 1 and ∂z/∂b = 1\n", - " So: ∂loss/∂a = ∂loss/∂z × 1 = grad_output\n", - " ∂loss/∂b = ∂loss/∂z × 1 = grad_output\n", - "\n", - " BROADCASTING CHALLENGE:\n", - " If shapes differ, we need to sum gradients appropriately\n", - " \"\"\"\n", - " ### BEGIN SOLUTION\n", " a, b = self.saved_tensors\n", + " grad_a = grad_b = None\n", "\n", - " # Gradient for 'a' - same shape as grad_output initially\n", - " grad_a = grad_output\n", + " # Gradient for first input\n", + " if isinstance(a, Tensor) and a.requires_grad:\n", + " grad_a = grad_output\n", "\n", - " # Gradient for 'b' - same as grad_output initially\n", - " grad_b = grad_output\n", + " # Gradient for second input \n", + " if isinstance(b, Tensor) and b.requires_grad:\n", + " grad_b = grad_output\n", "\n", - " # Handle broadcasting: if original shapes differed, sum gradients\n", - " # For tensor + scalar case\n", - " if not isinstance(b, Tensor):\n", - " grad_b = np.sum(grad_output)\n", - " else:\n", - " # Handle shape differences due to broadcasting\n", - " if a.shape != grad_output.shape:\n", - " # Sum out added dimensions and squeeze\n", - " grad_a = _handle_broadcasting_backward(grad_a, a.shape)\n", - "\n", - " if b.shape != grad_output.shape:\n", - " grad_b = _handle_broadcasting_backward(grad_b, b.shape)\n", - "\n", - " return grad_a, grad_b\n", - " ### END SOLUTION" + " return grad_a, grad_b" ] }, { "cell_type": "markdown", - "id": "a4e99e3e", + "id": "1cdabd27", "metadata": { + "cell_marker": "\"\"\"", "lines_to_next_cell": 1 }, "source": [ - "\"\"\"\n", - "## MulFunction - Gradient Rules for Element-wise Multiplication\n", + "### MulBackward - Gradient Rules for Element-wise Multiplication\n", "\n", "Element-wise multiplication follows the product rule of calculus.\n", "\n", @@ -542,70 +469,76 @@ "Backward: grad_z=[1,1]\n", " grad_a = grad_z * b = [1,1] * [4,5] = [4,5]\n", " grad_b = grad_z * a = [1,1] * [2,3] = [2,3]\n", - "```\n", - "\"\"\"\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f5d8512", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "mul-backward", + "solution": true + } + }, + "outputs": [], + "source": [ + "class MulBackward(Function):\n", + " \"\"\"\n", + " Gradient computation for tensor multiplication.\n", + " \n", + " **Mathematical Rule:** If z = a * b, then ∂z/∂a = b and ∂z/∂b = a\n", + " \n", + " **Key Insight:** Each input's gradient equals the gradient output \n", + " multiplied by the OTHER input's value (product rule).\n", + " \n", + " **Applications:** Used in weight scaling, attention mechanisms,\n", + " and anywhere element-wise multiplication occurs.\n", + " \"\"\"\n", "\n", - "class MulFunction(Function):\n", - " \"\"\"Gradient computation for tensor multiplication.\"\"\"\n", - "\n", - " def forward(self, a, b):\n", + " def apply(self, grad_output):\n", " \"\"\"\n", - " Forward pass: compute a * b (element-wise)\n", - "\n", - " TODO: Implement multiplication forward pass\n", + " Compute gradients for multiplication.\n", + " \n", + " Args:\n", + " grad_output: Gradient flowing backward from output\n", + " \n", + " Returns:\n", + " Tuple of (grad_a, grad_b) for the two inputs\n", + " \n", + " **Mathematical Foundation:**\n", + " - ∂(a*b)/∂a = b → grad_a = grad_output * b\n", + " - ∂(a*b)/∂b = a → grad_b = grad_output * a\n", " \"\"\"\n", - " ### BEGIN SOLUTION\n", - " self.save_for_backward(a, b)\n", - "\n", - " if isinstance(b, Tensor):\n", - " result = a.data * b.data\n", - " else:\n", - " result = a.data * b\n", - "\n", - " return result\n", - " ### END SOLUTION\n", - "\n", - " def backward(self, grad_output):\n", - " \"\"\"\n", - " Backward pass: compute gradients for multiplication\n", - "\n", - " TODO: Implement multiplication backward pass\n", - "\n", - " MATH: If z = a * b, then:\n", - " ∂z/∂a = b and ∂z/∂b = a\n", - " So: ∂loss/∂a = grad_output * b\n", - " ∂loss/∂b = grad_output * a\n", - " \"\"\"\n", - " ### BEGIN SOLUTION\n", " a, b = self.saved_tensors\n", + " grad_a = grad_b = None\n", "\n", - " if isinstance(b, Tensor):\n", - " grad_a = grad_output * b.data\n", + " # Gradient for first input: grad_output * b\n", + " if isinstance(a, Tensor) and a.requires_grad:\n", + " if isinstance(b, Tensor):\n", + " grad_a = grad_output * b.data\n", + " else:\n", + " grad_a = grad_output * b\n", + "\n", + " # Gradient for second input: grad_output * a\n", + " if isinstance(b, Tensor) and b.requires_grad:\n", " grad_b = grad_output * a.data\n", "\n", - " # Handle broadcasting\n", - " if a.shape != grad_output.shape:\n", - " grad_a = _handle_broadcasting_backward(grad_a, a.shape)\n", - " if b.shape != grad_output.shape:\n", - " grad_b = _handle_broadcasting_backward(grad_b, b.shape)\n", - " else:\n", - " # b is a scalar\n", - " grad_a = grad_output * b\n", - " grad_b = np.sum(grad_output * a.data)\n", - "\n", - " return grad_a, grad_b\n", - " ### END SOLUTION" + " return grad_a, grad_b" ] }, { "cell_type": "markdown", - "id": "abc612e2", + "id": "afb28aa9", "metadata": { + "cell_marker": "\"\"\"", "lines_to_next_cell": 1 }, "source": [ - "\"\"\"\n", - "## MatmulFunction - Gradient Rules for Matrix Multiplication\n", + "### MatmulBackward - Gradient Rules for Matrix Multiplication\n", "\n", "Matrix multiplication has more complex gradient rules based on matrix calculus.\n", "\n", @@ -628,56 +561,75 @@ "Forward: A(m×k) @ B(k×n) = Z(m×n)\n", "Backward: grad_Z(m×n) @ B.T(n×k) = grad_A(m×k) ✓\n", " A.T(k×m) @ grad_Z(m×n) = grad_B(k×n) ✓\n", - "```\n", - "\"\"\"\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5537e5ac", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "matmul-backward", + "solution": true + } + }, + "outputs": [], + "source": [ + "class MatmulBackward(Function):\n", + " \"\"\"\n", + " Gradient computation for matrix multiplication.\n", + " \n", + " **Mathematical Rule:** If Z = A @ B, then:\n", + " - ∂Z/∂A = grad_Z @ B.T\n", + " - ∂Z/∂B = A.T @ grad_Z\n", + " \n", + " **Key Insight:** Matrix multiplication gradients involve transposing\n", + " one input and multiplying with the gradient output.\n", + " \n", + " **Applications:** Core operation in neural networks for weight updates\n", + " in linear layers, attention mechanisms, and transformers.\n", + " \"\"\"\n", "\n", - "class MatmulFunction(Function):\n", - " \"\"\"Gradient computation for matrix multiplication.\"\"\"\n", - "\n", - " def forward(self, a, b):\n", + " def apply(self, grad_output):\n", " \"\"\"\n", - " Forward pass: compute a @ b (matrix multiplication)\n", - "\n", - " TODO: Implement matmul forward pass\n", + " Compute gradients for matrix multiplication.\n", + " \n", + " Args:\n", + " grad_output: Gradient flowing backward from output\n", + " \n", + " Returns:\n", + " Tuple of (grad_a, grad_b) for the two matrix inputs\n", + " \n", + " **Mathematical Foundation:**\n", + " - ∂(A@B)/∂A = grad_output @ B.T\n", + " - ∂(A@B)/∂B = A.T @ grad_output\n", " \"\"\"\n", - " ### BEGIN SOLUTION\n", - " self.save_for_backward(a, b)\n", - " result = np.dot(a.data, b.data)\n", - " return result\n", - " ### END SOLUTION\n", - "\n", - " def backward(self, grad_output):\n", - " \"\"\"\n", - " Backward pass: compute gradients for matrix multiplication\n", - "\n", - " TODO: Implement matmul backward pass\n", - "\n", - " MATH: If Z = A @ B, then:\n", - " ∂Z/∂A = grad_output @ B.T\n", - " ∂Z/∂B = A.T @ grad_output\n", - " \"\"\"\n", - " ### BEGIN SOLUTION\n", " a, b = self.saved_tensors\n", + " grad_a = grad_b = None\n", "\n", - " # Gradient w.r.t. a: grad_output @ b.T\n", - " grad_a = np.dot(grad_output, b.data.T)\n", + " # Gradient for first input: grad_output @ b.T\n", + " if isinstance(a, Tensor) and a.requires_grad:\n", + " grad_a = np.dot(grad_output, b.data.T)\n", "\n", - " # Gradient w.r.t. b: a.T @ grad_output\n", - " grad_b = np.dot(a.data.T, grad_output)\n", + " # Gradient for second input: a.T @ grad_output\n", + " if isinstance(b, Tensor) and b.requires_grad:\n", + " grad_b = np.dot(a.data.T, grad_output)\n", "\n", - " return grad_a, grad_b\n", - " ### END SOLUTION" + " return grad_a, grad_b" ] }, { "cell_type": "markdown", - "id": "5a21456b", + "id": "73f1c002", "metadata": { + "cell_marker": "\"\"\"", "lines_to_next_cell": 1 }, "source": [ - "\"\"\"\n", - "## SumFunction - Gradient Rules for Reduction Operations\n", + "### SumBackward - Gradient Rules for Reduction Operations\n", "\n", "Sum operations reduce tensor dimensions, so gradients must be broadcast back.\n", "\n", @@ -696,101 +648,67 @@ "Case 2: Axis sum\n", " Forward: a=[[1,2],[3,4]] → sum(axis=0) → z=[4,6]\n", " Backward: grad_z=[1,1] → broadcast → grad_a=[[1,1],[1,1]]\n", - "\n", - "Case 3: Keepdims\n", - " Forward: a=[[1,2],[3,4]] → sum(axis=0,keepdims=True) → z=[[4,6]]\n", - " Backward: grad_z=[[1,1]] → broadcast → grad_a=[[1,1],[1,1]]\n", - "```\n", - "\"\"\"\n", - "\n", - "class SumFunction(Function):\n", - " \"\"\"Gradient computation for tensor sum.\"\"\"\n", - "\n", - " def forward(self, a, axis=None, keepdims=False):\n", - " \"\"\"\n", - " Forward pass: compute tensor sum\n", - "\n", - " TODO: Implement sum forward pass\n", - " \"\"\"\n", - " ### BEGIN SOLUTION\n", - " self.save_for_backward(a)\n", - " self.axis = axis\n", - " self.keepdims = keepdims\n", - " self.input_shape = a.shape\n", - "\n", - " result = np.sum(a.data, axis=axis, keepdims=keepdims)\n", - " return result\n", - " ### END SOLUTION\n", - "\n", - " def backward(self, grad_output):\n", - " \"\"\"\n", - " Backward pass: compute gradients for sum\n", - "\n", - " TODO: Implement sum backward pass\n", - "\n", - " MATH: If z = sum(a), then ∂z/∂a[i] = 1 for all i\n", - " So gradient is broadcast back to original shape\n", - " \"\"\"\n", - " ### BEGIN SOLUTION\n", - " # Sum distributes gradient to all input elements\n", - " # Need to broadcast grad_output back to input shape\n", - "\n", - " if self.axis is None:\n", - " # Summed all elements - broadcast scalar back to input shape\n", - " grad_a = np.full(self.input_shape, grad_output)\n", - " else:\n", - " # Summed along specific axis - need to broadcast properly\n", - " grad_a = grad_output\n", - "\n", - " # If keepdims=False, we need to expand the summed dimensions\n", - " if not self.keepdims:\n", - " if isinstance(self.axis, int):\n", - " grad_a = np.expand_dims(grad_a, self.axis)\n", - " else:\n", - " for ax in sorted(self.axis):\n", - " grad_a = np.expand_dims(grad_a, ax)\n", - "\n", - " # Broadcast to input shape\n", - " grad_a = np.broadcast_to(grad_a, self.input_shape)\n", - "\n", - " return grad_a\n", - " ### END SOLUTION\n", - "\n", - "def _handle_broadcasting_backward(grad, target_shape):\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fba90b39", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "sum-backward", + "solution": true + } + }, + "outputs": [], + "source": [ + "class SumBackward(Function):\n", " \"\"\"\n", - " Helper function to handle gradient broadcasting.\n", - "\n", - " When forward pass used broadcasting, we need to sum gradients\n", - " back to the original tensor's shape.\n", + " Gradient computation for tensor sum.\n", + " \n", + " **Mathematical Rule:** If z = sum(a), then ∂z/∂a[i] = 1 for all i\n", + " \n", + " **Key Insight:** Sum distributes the gradient equally to all input elements.\n", + " The gradient is broadcast from the reduced output back to input shape.\n", + " \n", + " **Applications:** Used in loss functions, mean operations, and\n", + " anywhere tensor reduction occurs.\n", " \"\"\"\n", - " ### BEGIN SOLUTION\n", - " # Start with the gradient\n", - " result = grad\n", "\n", - " # Sum out dimensions that were broadcasted (added dimensions)\n", - " # If target has fewer dimensions, sum out the leading dimensions\n", - " while len(result.shape) > len(target_shape):\n", - " result = np.sum(result, axis=0)\n", + " def apply(self, grad_output):\n", + " \"\"\"\n", + " Compute gradients for sum operation.\n", + " \n", + " Args:\n", + " grad_output: Gradient flowing backward from output\n", + " \n", + " Returns:\n", + " Tuple containing gradient for the input tensor\n", + " \n", + " **Mathematical Foundation:**\n", + " - ∂sum(a)/∂a[i] = 1 → grad_a = ones_like(a) * grad_output\n", + " \"\"\"\n", + " tensor, = self.saved_tensors\n", "\n", - " # For dimensions that were size 1 in target but expanded in grad\n", - " for i, (grad_dim, target_dim) in enumerate(zip(result.shape, target_shape)):\n", - " if target_dim == 1 and grad_dim > 1:\n", - " result = np.sum(result, axis=i, keepdims=True)\n", - "\n", - " return result\n", - " ### END SOLUTION" + " if isinstance(tensor, Tensor) and tensor.requires_grad:\n", + " # Gradient is 1 for all elements, scaled by grad_output\n", + " return np.ones_like(tensor.data) * grad_output,\n", + " return None," ] }, { "cell_type": "markdown", - "id": "e4b0c564", + "id": "ef22fe2b", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 }, "source": [ - "### 🔬 Unit Test: Operation Functions\n", - "This test validates our operation functions compute gradients correctly.\n", + "### 🔬 Unit Test: Function Classes\n", + "This test validates our Function classes compute gradients correctly.\n", "**What we're testing**: Forward and backward passes for each operation\n", "**Why it matters**: These are the building blocks of autograd\n", "**Expected**: Correct gradients that satisfy mathematical definitions" @@ -799,71 +717,59 @@ { "cell_type": "code", "execution_count": null, - "id": "534068f3", + "id": "f3794257", "metadata": { "nbgrader": { "grade": true, - "grade_id": "test-operation-functions", + "grade_id": "test-function-classes", "locked": true, "points": 15 } }, "outputs": [], "source": [ - "def test_unit_operation_functions():\n", - " \"\"\"🔬 Test operation functions.\"\"\"\n", - " print(\"🔬 Unit Test: Operation Functions...\")\n", - "\n", - " # Test AddFunction\n", - " add_func = AddFunction()\n", - " a = Tensor([1, 2, 3])\n", - " b = Tensor([4, 5, 6])\n", - " result = add_func.forward(a, b)\n", - " expected = np.array([5, 7, 9])\n", - " assert np.allclose(result, expected)\n", + "def test_unit_function_classes():\n", + " \"\"\"🔬 Test Function classes.\"\"\"\n", + " print(\"🔬 Unit Test: Function Classes...\")\n", "\n", + " # Test AddBackward\n", + " a = Tensor([1, 2, 3], requires_grad=True)\n", + " b = Tensor([4, 5, 6], requires_grad=True)\n", + " add_func = AddBackward(a, b)\n", " grad_output = np.array([1, 1, 1])\n", - " grad_a, grad_b = add_func.backward(grad_output)\n", - " assert np.allclose(grad_a, grad_output)\n", - " assert np.allclose(grad_b, grad_output)\n", + " grad_a, grad_b = add_func.apply(grad_output)\n", + " assert np.allclose(grad_a, grad_output), f\"AddBackward grad_a failed: {grad_a}\"\n", + " assert np.allclose(grad_b, grad_output), f\"AddBackward grad_b failed: {grad_b}\"\n", "\n", - " # Test MulFunction\n", - " mul_func = MulFunction()\n", - " result = mul_func.forward(a, b)\n", - " expected = np.array([4, 10, 18])\n", - " assert np.allclose(result, expected)\n", - "\n", - " grad_a, grad_b = mul_func.backward(grad_output)\n", - " assert np.allclose(grad_a, b.data) # grad w.r.t a = b\n", - " assert np.allclose(grad_b, a.data) # grad w.r.t b = a\n", - "\n", - " # Test MatmulFunction\n", - " matmul_func = MatmulFunction()\n", - " a_mat = Tensor([[1, 2], [3, 4]])\n", - " b_mat = Tensor([[5, 6], [7, 8]])\n", - " result = matmul_func.forward(a_mat, b_mat)\n", - " expected = np.array([[19, 22], [43, 50]])\n", - " assert np.allclose(result, expected)\n", + " # Test MulBackward\n", + " mul_func = MulBackward(a, b)\n", + " grad_a, grad_b = mul_func.apply(grad_output)\n", + " assert np.allclose(grad_a, b.data), f\"MulBackward grad_a failed: {grad_a}\"\n", + " assert np.allclose(grad_b, a.data), f\"MulBackward grad_b failed: {grad_b}\"\n", "\n", + " # Test MatmulBackward\n", + " a_mat = Tensor([[1, 2], [3, 4]], requires_grad=True)\n", + " b_mat = Tensor([[5, 6], [7, 8]], requires_grad=True)\n", + " matmul_func = MatmulBackward(a_mat, b_mat)\n", " grad_output = np.ones((2, 2))\n", - " grad_a, grad_b = matmul_func.backward(grad_output)\n", - " assert grad_a.shape == a_mat.shape\n", - " assert grad_b.shape == b_mat.shape\n", + " grad_a, grad_b = matmul_func.apply(grad_output)\n", + " assert grad_a.shape == a_mat.shape, f\"MatmulBackward grad_a shape: {grad_a.shape}\"\n", + " assert grad_b.shape == b_mat.shape, f\"MatmulBackward grad_b shape: {grad_b.shape}\"\n", "\n", - " print(\"✅ Operation functions work correctly!\")\n", + " print(\"✅ Function classes work correctly!\")\n", "\n", "if __name__ == \"__main__\":\n", - " test_unit_operation_functions()" + " test_unit_function_classes()" ] }, { "cell_type": "markdown", - "id": "08717fc2", + "id": "91448cd2", "metadata": { "cell_marker": "\"\"\"" }, "source": [ - "### Enhancing Tensor with Autograd Capabilities\n", + "## 4. Enhancing Tensor with Autograd Capabilities\n", "\n", "Now we'll enhance the existing Tensor class to use these gradient functions and build computation graphs automatically.\n", "\n", @@ -893,225 +799,240 @@ }, { "cell_type": "markdown", - "id": "f2e7d03f", + "id": "8227b937", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 }, "source": [ - "### The Backward Pass Algorithm\n", + "### The enable_autograd() Function\n", "\n", - "The backward() method implements reverse-mode automatic differentiation.\n", + "This function is the magic that brings gradients to life! It enhances the existing Tensor class with autograd capabilities by:\n", "\n", - "**Algorithm Visualization:**\n", + "1. **Monkey-patching operations** - Replaces `__add__`, `__mul__`, etc. with gradient-aware versions\n", + "2. **Adding backward() method** - Implements reverse-mode automatic differentiation\n", + "3. **Maintaining compatibility** - All existing code continues to work unchanged\n", + "\n", + "**The Pattern:**\n", "```\n", - "Computation Graph (Forward):\n", - " x₁ ──┐\n", - " ├─[op₁]── z₁ ──┐\n", - " x₂ ──┘ ├─[op₂]── y\n", - " x₃ ──────[op₃]── z₂ ──┘\n", - "\n", - "Gradient Flow (Backward):\n", - " ∇x₁ ←──┐\n", - " ├─[op₁.backward()]← ∇z₁ ←──┐\n", - " ∇x₂ ←──┘ ├─[op₂.backward()]← ∇y\n", - " ∇x₃ ←────[op₃.backward()]← ∇z₂ ←──┘\n", + "Original: x + y → simple addition\n", + "Enhanced: x + y → addition + gradient tracking (if requires_grad=True)\n", "```\n", "\n", - "**Backward Pass Steps:**\n", - "1. Start from output tensor (∇y = 1)\n", - "2. For each operation in reverse order:\n", - " - Apply chain rule: ∇inputs = operation.backward(∇output)\n", - " - Accumulate gradients (handle shared variables)\n", - " - Continue to parent tensors\n", - "3. Gradients accumulate in tensor.grad attributes" + "This approach follows PyTorch 2.0 style - clean, modern, and educational." ] }, { "cell_type": "code", "execution_count": null, - "id": "66f8911d", + "id": "1712f7da", "metadata": { "nbgrader": { "grade": false, - "grade_id": "tensor-enhancements", + "grade_id": "enable-autograd", "solution": true } }, "outputs": [], "source": [ - "def implement_tensor_backward_method():\n", + "def enable_autograd():\n", " \"\"\"\n", - " Implement the backward method for the Tensor class.\n", + " Enable gradient tracking for all Tensor operations.\n", "\n", - " CRITICAL: We modify the Tensor class in place to activate gradient features.\n", - " The dormant features are now brought to life!\n", + " This function enhances the existing Tensor class with autograd capabilities.\n", + " Call this once to activate gradients globally.\n", + "\n", + " **What it does:**\n", + " - Replaces Tensor operations with gradient-tracking versions\n", + " - Adds backward() method for reverse-mode differentiation\n", + " - Enables computation graph building\n", + " - Maintains full backward compatibility\n", + "\n", + " **After calling this:**\n", + " - Tensor operations will track computation graphs\n", + " - backward() method becomes available\n", + " - Gradients will flow through operations\n", + " - requires_grad=True enables tracking per tensor\n", + "\n", + " **Example:**\n", + " ```python\n", + " enable_autograd() # Call once\n", + " x = Tensor([2.0], requires_grad=True)\n", + " y = x * 3\n", + " y.backward()\n", + " print(x.grad) # [3.0]\n", + " ```\n", " \"\"\"\n", "\n", - " def backward_implementation(self, gradient=None):\n", + " # Check if already enabled\n", + " if hasattr(Tensor, '_autograd_enabled'):\n", + " print(\"⚠️ Autograd already enabled\")\n", + " return\n", + "\n", + " # Store original operations\n", + " _original_add = Tensor.__add__\n", + " _original_mul = Tensor.__mul__\n", + " _original_matmul = Tensor.matmul if hasattr(Tensor, 'matmul') else None\n", + "\n", + " # Enhanced operations that track gradients\n", + " def tracked_add(self, other):\n", " \"\"\"\n", - " Compute gradients for this tensor and all tensors in its computation graph.\n", - "\n", - " TODO: Implement the backward pass\n", - "\n", - " APPROACH:\n", - " 1. Check if this tensor requires gradients\n", - " 2. Initialize gradient if starting point\n", - " 3. Traverse computation graph backwards\n", - " 4. Apply chain rule at each step\n", - "\n", - " EXAMPLE:\n", - " >>> x = Tensor([2.0], requires_grad=True)\n", - " >>> y = x * 3\n", - " >>> y.backward()\n", - " >>> print(x.grad) # Should be [3.0]\n", + " Addition with gradient tracking.\n", + " \n", + " Enhances the original __add__ method to build computation graphs\n", + " when requires_grad=True for any input.\n", " \"\"\"\n", - " ### BEGIN SOLUTION\n", + " # Convert scalar to Tensor if needed\n", + " if not isinstance(other, Tensor):\n", + " other = Tensor(other)\n", + "\n", + " # Call original operation\n", + " result = _original_add(self, other)\n", + "\n", + " # Track gradient if needed\n", + " if self.requires_grad or other.requires_grad:\n", + " result.requires_grad = True\n", + " result._grad_fn = AddBackward(self, other)\n", + "\n", + " return result\n", + "\n", + " def tracked_mul(self, other):\n", + " \"\"\"\n", + " Multiplication with gradient tracking.\n", + " \n", + " Enhances the original __mul__ method to build computation graphs\n", + " when requires_grad=True for any input.\n", + " \"\"\"\n", + " # Convert scalar to Tensor if needed for consistency\n", + " if not isinstance(other, Tensor):\n", + " other_tensor = Tensor(other)\n", + " else:\n", + " other_tensor = other\n", + "\n", + " # Call original operation\n", + " result = _original_mul(self, other)\n", + "\n", + " # Track gradient if needed\n", + " if self.requires_grad or (isinstance(other, Tensor) and other.requires_grad):\n", + " result.requires_grad = True\n", + " result._grad_fn = MulBackward(self, other)\n", + "\n", + " return result\n", + "\n", + " def tracked_matmul(self, other):\n", + " \"\"\"\n", + " Matrix multiplication with gradient tracking.\n", + " \n", + " Enhances the original matmul method to build computation graphs\n", + " when requires_grad=True for any input.\n", + " \"\"\"\n", + " if _original_matmul:\n", + " result = _original_matmul(self, other)\n", + " else:\n", + " # Fallback if matmul doesn't exist\n", + " result = Tensor(np.dot(self.data, other.data))\n", + "\n", + " # Track gradient if needed\n", + " if self.requires_grad or other.requires_grad:\n", + " result.requires_grad = True\n", + " result._grad_fn = MatmulBackward(self, other)\n", + "\n", + " return result\n", + "\n", + " def sum_op(self, axis=None, keepdims=False):\n", + " \"\"\"\n", + " Sum operation with gradient tracking.\n", + " \n", + " Creates a new sum method that builds computation graphs\n", + " when requires_grad=True.\n", + " \"\"\"\n", + " result_data = np.sum(self.data, axis=axis, keepdims=keepdims)\n", + " result = Tensor(result_data)\n", + "\n", + " if self.requires_grad:\n", + " result.requires_grad = True\n", + " result._grad_fn = SumBackward(self)\n", + "\n", + " return result\n", + "\n", + " def backward(self, gradient=None):\n", + " \"\"\"\n", + " Compute gradients via backpropagation.\n", + "\n", + " This is the key method that makes training possible!\n", + " It implements reverse-mode automatic differentiation.\n", + " \n", + " **Algorithm:**\n", + " 1. Initialize gradient if not provided (for scalar outputs)\n", + " 2. Accumulate gradient in self.grad\n", + " 3. If this tensor has a _grad_fn, call it to propagate gradients\n", + " 4. Recursively call backward() on parent tensors\n", + " \n", + " **Example:**\n", + " ```python\n", + " x = Tensor([2.0], requires_grad=True)\n", + " y = x * 3\n", + " y.backward() # Computes gradients for x\n", + " print(x.grad) # [3.0]\n", + " ```\n", + " \"\"\"\n", + " # Only compute gradients if required\n", " if not self.requires_grad:\n", " return\n", "\n", - " # Initialize gradient if this is the starting point\n", + " # Initialize gradient if not provided (for scalar outputs)\n", " if gradient is None:\n", - " if self.data.shape == ():\n", - " # Scalar tensor\n", - " gradient = np.array(1.0)\n", - " else:\n", - " # Non-scalar: gradient should be ones of same shape\n", + " if self.data.size == 1:\n", " gradient = np.ones_like(self.data)\n", + " else:\n", + " raise ValueError(\"backward() requires gradient for non-scalar outputs\")\n", "\n", - " # Accumulate gradient\n", + " # Initialize or accumulate gradient\n", " if self.grad is None:\n", - " self.grad = gradient\n", - " else:\n", - " self.grad = self.grad + gradient\n", + " self.grad = np.zeros_like(self.data)\n", + " self.grad += gradient\n", "\n", - " # If this tensor has a gradient function, propagate backwards\n", - " if hasattr(self, 'grad_fn') and self.grad_fn is not None:\n", - " grads = self.grad_fn.backward(gradient)\n", + " # Propagate gradients through computation graph\n", + " if hasattr(self, '_grad_fn') and self._grad_fn:\n", + " grads = self._grad_fn.apply(gradient)\n", "\n", - " # grads could be a single gradient or tuple of gradients\n", - " if not isinstance(grads, tuple):\n", - " grads = (grads,)\n", + " # Recursively call backward on parent tensors\n", + " for tensor, grad in zip(self._grad_fn.saved_tensors, grads):\n", + " if isinstance(tensor, Tensor) and tensor.requires_grad and grad is not None:\n", + " tensor.backward(grad)\n", "\n", - " # Propagate to input tensors\n", - " if hasattr(self.grad_fn, 'inputs'):\n", - " for tensor, grad in zip(self.grad_fn.inputs, grads):\n", - " if isinstance(tensor, Tensor) and tensor.requires_grad:\n", - " tensor.backward(grad)\n", - " ### END SOLUTION\n", - "\n", - " # Replace the placeholder backward method with the real implementation\n", - " Tensor.backward = backward_implementation\n", - " print(\"🚀 Tensor backward method activated!\")\n", - "\n", - "# Activate the backward method\n", - "implement_tensor_backward_method()\n", - "\n", - "def create_gradient_tracking_tensor(data, requires_grad, grad_fn=None, inputs=None):\n", - " \"\"\"\n", - " Helper function to create tensors with gradient tracking.\n", - "\n", - " This function helps operations create result tensors that properly\n", - " track gradients and maintain the computation graph.\n", - " \"\"\"\n", - " result = Tensor(data, requires_grad=requires_grad)\n", - "\n", - " if requires_grad and grad_fn is not None:\n", - " result.grad_fn = grad_fn\n", - " if inputs is not None:\n", - " grad_fn.inputs = inputs\n", - "\n", - " return result\n", - "\n", - "def enhance_tensor_operations():\n", - " \"\"\"\n", - " Enhance existing Tensor operations to support gradient tracking.\n", - "\n", - " This modifies the existing methods to use gradient-tracking functions\n", - " when requires_grad=True.\n", - " \"\"\"\n", - "\n", - " # Store original methods\n", - " original_add = Tensor.__add__\n", - " original_mul = Tensor.__mul__\n", - " original_matmul = Tensor.matmul\n", - " original_sum = Tensor.sum\n", - "\n", - " def gradient_aware_add(self, other):\n", + " def zero_grad(self):\n", " \"\"\"\n", - " Addition that tracks gradients when needed.\n", + " Reset gradients to zero.\n", + " \n", + " Call this before each backward pass to prevent gradient accumulation\n", + " from previous iterations.\n", " \"\"\"\n", - " # Check if gradient tracking is needed\n", - " requires_grad = self.requires_grad or (isinstance(other, Tensor) and other.requires_grad)\n", + " self.grad = None\n", "\n", - " if requires_grad:\n", - " # Use gradient-tracking version\n", - " add_func = AddFunction()\n", - " result_data = add_func.forward(self, other)\n", - " inputs = [self, other] if isinstance(other, Tensor) else [self]\n", - " return create_gradient_tracking_tensor(result_data, requires_grad, add_func, inputs)\n", - " else:\n", - " # Use original method (no gradient tracking)\n", - " return original_add(self, other)\n", + " # Install enhanced operations\n", + " Tensor.__add__ = tracked_add\n", + " Tensor.__mul__ = tracked_mul\n", + " Tensor.matmul = tracked_matmul\n", + " Tensor.sum = sum_op\n", + " Tensor.backward = backward\n", + " Tensor.zero_grad = zero_grad\n", "\n", - " def gradient_aware_mul(self, other):\n", - " \"\"\"\n", - " Multiplication that tracks gradients when needed.\n", - " \"\"\"\n", - " requires_grad = self.requires_grad or (isinstance(other, Tensor) and other.requires_grad)\n", + " # Mark as enabled\n", + " Tensor._autograd_enabled = True\n", "\n", - " if requires_grad:\n", - " mul_func = MulFunction()\n", - " result_data = mul_func.forward(self, other)\n", - " inputs = [self, other] if isinstance(other, Tensor) else [self]\n", - " return create_gradient_tracking_tensor(result_data, requires_grad, mul_func, inputs)\n", - " else:\n", - " return original_mul(self, other)\n", + " print(\"✅ Autograd enabled! Tensors now track gradients.\")\n", + " print(\" - Operations build computation graphs\")\n", + " print(\" - backward() computes gradients\")\n", + " print(\" - requires_grad=True enables tracking\")\n", "\n", - " def gradient_aware_matmul(self, other):\n", - " \"\"\"\n", - " Matrix multiplication that tracks gradients when needed.\n", - " \"\"\"\n", - " if not isinstance(other, Tensor):\n", - " raise TypeError(f\"Expected Tensor for matrix multiplication, got {type(other)}\")\n", - "\n", - " requires_grad = self.requires_grad or other.requires_grad\n", - "\n", - " if requires_grad:\n", - " matmul_func = MatmulFunction()\n", - " result_data = matmul_func.forward(self, other)\n", - " inputs = [self, other]\n", - " return create_gradient_tracking_tensor(result_data, requires_grad, matmul_func, inputs)\n", - " else:\n", - " return original_matmul(self, other)\n", - "\n", - " def gradient_aware_sum(self, axis=None, keepdims=False):\n", - " \"\"\"\n", - " Sum that tracks gradients when needed.\n", - " \"\"\"\n", - " if self.requires_grad:\n", - " sum_func = SumFunction()\n", - " result_data = sum_func.forward(self, axis, keepdims)\n", - " inputs = [self]\n", - " return create_gradient_tracking_tensor(result_data, self.requires_grad, sum_func, inputs)\n", - " else:\n", - " return original_sum(self, axis, keepdims)\n", - "\n", - " # Replace methods with gradient-aware versions\n", - " Tensor.__add__ = gradient_aware_add\n", - " Tensor.__mul__ = gradient_aware_mul\n", - " Tensor.matmul = gradient_aware_matmul\n", - " Tensor.sum = gradient_aware_sum\n", - "\n", - " print(\"🚀 Tensor operations enhanced with gradient tracking!\")\n", - "\n", - "# Enhance the operations\n", - "enhance_tensor_operations()" + "# Auto-enable when module is imported\n", + "enable_autograd()" ] }, { "cell_type": "markdown", - "id": "0ae0aa2f", + "id": "57e265ea", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -1127,7 +1048,7 @@ { "cell_type": "code", "execution_count": null, - "id": "abf2dc78", + "id": "eebf3f67", "metadata": { "nbgrader": { "grade": true, @@ -1175,242 +1096,7 @@ }, { "cell_type": "markdown", - "id": "8b86a099", - "metadata": { - "cell_marker": "\"\"\"", - "lines_to_next_cell": 2 - }, - "source": [ - "## 4. Integration: Building Complex Computation Graphs\n", - "\n", - "Let's test how our autograd system handles complex neural network computations.\n", - "\n", - "### Complex Computation Graph Example\n", - "\n", - "Neural networks create complex computation graphs with shared parameters and multiple paths.\n", - "\n", - "**Detailed Neural Network Computation Graph:**\n", - "```\n", - "Forward Pass with Function Tracking:\n", - " x (input)\n", - " │ requires_grad=True\n", - " ┌────────▼────────┐\n", - " │ MatmulFunction │ stores: (x, W₁)\n", - " │ h₁ = x @ W₁ │\n", - " └────────┬────────┘\n", - " │ grad_fn=MatmulFunction\n", - " ┌────────▼────────┐\n", - " │ AddFunction │ stores: (h₁, b₁)\n", - " │ z₁ = h₁ + b₁ │\n", - " └────────┬────────┘\n", - " │ grad_fn=AddFunction\n", - " ┌────────▼────────┐\n", - " │ ReLU (manual) │ Note: We'll implement\n", - " │ a₁ = max(0,z₁) │ ReLUFunction later\n", - " └────────┬────────┘\n", - " │\n", - " ┌────────▼────────┐\n", - " │ MatmulFunction │ stores: (a₁, W₂)\n", - " │ h₂ = a₁ @ W₂ │\n", - " └────────┬────────┘\n", - " │ grad_fn=MatmulFunction\n", - " ┌────────▼────────┐\n", - " │ AddFunction │ stores: (h₂, b₂)\n", - " │ y = h₂ + b₂ │ (final output)\n", - " └─────────────────┘\n", - "\n", - "Backward Pass Chain Rule Application:\n", - " ∇x ←─────────────────────────────┐\n", - " │\n", - " ┌─────────────────────────────────────────────────────────┐\n", - " │ MatmulFunction.backward(∇h₁): │\n", - " │ ∇x = ∇h₁ @ W₁.T │\n", - " │ ∇W₁ = x.T @ ∇h₁ │\n", - " └─────────────────┬───────────────────────────────────────┘\n", - " │\n", - " ┌─────────────────▼───────────────────────────────────────┐\n", - " │ AddFunction.backward(∇z₁): │\n", - " │ ∇h₁ = ∇z₁ (gradient passes through unchanged) │\n", - " │ ∇b₁ = ∇z₁ │\n", - " └─────────────────┬───────────────────────────────────────┘\n", - " │\n", - " ┌─────────────────▼───────────────────────────────────────┐\n", - " │ Manual ReLU backward: │\n", - " │ ∇z₁ = ∇a₁ * (z₁ > 0) (zero out negative gradients) │\n", - " └─────────────────┬───────────────────────────────────────┘\n", - " │\n", - " ┌─────────────────▼───────────────────────────────────────┐\n", - " │ MatmulFunction.backward(∇h₂): │\n", - " │ ∇a₁ = ∇h₂ @ W₂.T │\n", - " │ ∇W₂ = a₁.T @ ∇h₂ │\n", - " └─────────────────┬───────────────────────────────────────┘\n", - " │\n", - " ┌─────────────────▼───────────────────────────────────────┐\n", - " │ AddFunction.backward(∇y): │\n", - " │ ∇h₂ = ∇y (gradient passes through unchanged) │\n", - " │ ∇b₂ = ∇y │\n", - " └─────────────────────────────────────────────────────────┘\n", - "```\n", - "\n", - "**Key Autograd Concepts:**\n", - "1. **Function Chaining**: Each operation creates a Function that stores inputs\n", - "2. **Gradient Accumulation**: Multiple paths to a parameter accumulate gradients\n", - "3. **Automatic Traversal**: backward() walks the graph in reverse topological order\n", - "4. **Chain Rule**: Local gradients multiply according to calculus rules" - ] - }, - { - "cell_type": "markdown", - "id": "8a4231c8", - "metadata": { - "cell_marker": "\"\"\"", - "lines_to_next_cell": 1 - }, - "source": [ - "## 5. Systems Analysis: Memory and Performance of Autograd\n", - "\n", - "Understanding the computational and memory costs of automatic differentiation.\n", - "\n", - "### Autograd Memory Architecture\n", - "\n", - "**Memory Layout Comparison:**\n", - "```\n", - "Forward-Only Mode:\n", - "┌─────────────┐\n", - "│ Parameters │ 4N bytes (float32)\n", - "└─────────────┘\n", - "\n", - "Autograd Mode:\n", - "┌─────────────┐\n", - "│ Parameters │ 4N bytes\n", - "├─────────────┤\n", - "│ Gradients │ 4N bytes (additional)\n", - "├─────────────┤\n", - "│ Graph Nodes │ Variable overhead\n", - "├─────────────┤\n", - "│ Activations │ Depends on graph depth\n", - "└─────────────┘\n", - "Total: ~2-3× forward memory\n", - "```\n", - "\n", - "**Computation Graph Memory Growth:**\n", - "```\n", - "Shallow Network (3 layers):\n", - " Graph: x → W₁ → ReLU → W₂ → ReLU → W₃ → loss\n", - " Memory: Base + 3 × (weights + activations)\n", - "\n", - "Deep Network (50 layers):\n", - " Graph: x → [W₁...W₅₀] → loss\n", - " Memory: Base + 50 × (weights + activations)\n", - "\n", - "Gradient Checkpointing (optimization):\n", - " Store only every K layers, recompute others\n", - " Memory: Base + K × (weights + activations)\n", - " Time: +20% compute, -80% memory\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b6c0ef4c", - "metadata": { - "lines_to_next_cell": 1, - "nbgrader": { - "grade": false, - "grade_id": "analyze-autograd-memory", - "solution": true - } - }, - "outputs": [], - "source": [ - "def analyze_autograd_memory():\n", - " \"\"\"📊 Analyze memory usage of autograd vs no-grad computation.\"\"\"\n", - " print(\"📊 Analyzing Autograd Memory Usage...\")\n", - "\n", - " # Test different tensor sizes\n", - " sizes = [100, 500, 1000]\n", - "\n", - " for size in sizes:\n", - " # Forward-only computation\n", - " x_no_grad = Tensor(np.random.randn(size, size), requires_grad=False)\n", - " y_no_grad = Tensor(np.random.randn(size, size), requires_grad=False)\n", - " z_no_grad = x_no_grad.matmul(y_no_grad)\n", - "\n", - " # Forward + backward computation\n", - " x_grad = Tensor(np.random.randn(size, size), requires_grad=True)\n", - " y_grad = Tensor(np.random.randn(size, size), requires_grad=True)\n", - " z_grad = x_grad.matmul(y_grad)\n", - "\n", - " # Memory analysis\n", - " no_grad_elements = x_no_grad.size + y_no_grad.size + z_no_grad.size\n", - " grad_elements = x_grad.size + y_grad.size + z_grad.size\n", - " grad_storage = x_grad.size + y_grad.size # For gradients\n", - "\n", - " print(f\"Size {size}×{size}:\")\n", - " print(f\" No grad: {no_grad_elements:,} elements\")\n", - " print(f\" With grad: {grad_elements + grad_storage:,} elements\")\n", - " print(f\" Memory overhead: {grad_storage / no_grad_elements:.1%}\")\n", - "\n", - " print(\"\\n💡 Autograd Memory Pattern:\")\n", - " print(\"- Each parameter tensor needs gradient storage (2× memory)\")\n", - " print(\"- Computation graph nodes add overhead\")\n", - " print(\"- Trade-off: 2× memory for automatic gradients\")\n", - "\n", - "# Function defined above, will be called in main block" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "013bd1d0", - "metadata": { - "lines_to_next_cell": 1, - "nbgrader": { - "grade": false, - "grade_id": "analyze-gradient-computation", - "solution": true - } - }, - "outputs": [], - "source": [ - "def analyze_gradient_computation():\n", - " \"\"\"📊 Analyze computational cost of gradient computation.\"\"\"\n", - " print(\"📊 Analyzing Gradient Computation Cost...\")\n", - "\n", - " import time\n", - "\n", - " # Test computation times\n", - " size = 500\n", - " x = Tensor(np.random.randn(size, size), requires_grad=True)\n", - " y = Tensor(np.random.randn(size, size), requires_grad=True)\n", - "\n", - " # Time forward pass\n", - " start_time = time.time()\n", - " z = x.matmul(y)\n", - " forward_time = time.time() - start_time\n", - "\n", - " # Time backward pass\n", - " start_time = time.time()\n", - " z.backward()\n", - " backward_time = time.time() - start_time\n", - "\n", - " print(f\"Matrix size: {size}×{size}\")\n", - " print(f\"Forward pass: {forward_time:.4f}s\")\n", - " print(f\"Backward pass: {backward_time:.4f}s\")\n", - " print(f\"Backward/Forward ratio: {backward_time/forward_time:.1f}×\")\n", - "\n", - " print(f\"\\n💡 Gradient Computation Analysis:\")\n", - " print(f\"- Forward: O(n³) matrix multiplication\")\n", - " print(f\"- Backward: 2× O(n³) operations (gradients for both inputs)\")\n", - " print(f\"- Total training cost: ~3× forward-only computation\")\n", - "\n", - "# Function defined above, will be called in main block" - ] - }, - { - "cell_type": "markdown", - "id": "f6a68e64", + "id": "5354f15c", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -1424,7 +1110,7 @@ { "cell_type": "code", "execution_count": null, - "id": "fbbf2259", + "id": "1bdbbd4e", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -1450,8 +1136,7 @@ "\n", " # Run all unit tests\n", " print(\"Running unit tests...\")\n", - " test_unit_function_base()\n", - " test_unit_operation_functions()\n", + " test_unit_function_classes()\n", " test_unit_tensor_autograd()\n", "\n", " print(\"\\nRunning integration scenarios...\")\n", @@ -1538,10 +1223,8 @@ { "cell_type": "code", "execution_count": null, - "id": "458d3d72", - "metadata": { - "lines_to_next_cell": 2 - }, + "id": "a691e542", + "metadata": {}, "outputs": [], "source": [ "# Run comprehensive module test\n", @@ -1551,29 +1234,67 @@ }, { "cell_type": "markdown", - "id": "7abfe41a", - "metadata": { - "cell_marker": "\"\"\"" - }, + "id": "fa16d279", + "metadata": {}, "source": [ - "## 🎯 MODULE SUMMARY: Autograd Engine\n", + "\"\"\"\n", + "# 🎯 MODULE SUMMARY: Autograd Engine\n", "\n", "Congratulations! You've built the gradient engine that makes neural networks learn!\n", "\n", - "### Key Accomplishments\n", - "- Implemented Function base class for tracking differentiable operations\n", - "- Enhanced existing Tensor class with backward() method (no new classes!)\n", - "- Built computation graph tracking for automatic differentiation\n", - "- Created operation functions (Add, Mul, Matmul, Sum) with correct gradients\n", - "- Tested complex multi-layer computation graphs with gradient propagation\n", - "- All tests pass ✅ (validated by `test_module()`)\n", + "## Key Accomplishments ⭐⭐\n", + "- **Enhanced Tensor class** with backward() method (no new wrapper classes!)\n", + "- **Built computation graph tracking** for automatic differentiation\n", + "- **Implemented Function classes** (Add, Mul, Matmul, Sum) with correct gradients\n", + "- **Created enable_autograd()** function that activates gradients globally\n", + "- **Tested complex multi-layer** computation graphs with gradient propagation\n", + "- **All tests pass** ✅ (validated by `test_module()`)\n", "\n", - "### Ready for Next Steps\n", + "## Ready for Next Steps 🚀\n", "Your autograd implementation enables optimization! The dormant gradient features from Module 01 are now fully active. Every tensor can track gradients, every operation builds computation graphs, and backward() computes gradients automatically.\n", "\n", + "**What you can do now:**\n", + "```python\n", + "Create tensors with gradient tracking\n", + "x = Tensor([2.0], requires_grad=True)\n", + "W = Tensor([[0.5, 0.3]], requires_grad=True)\n", + "\n", + "Build computation graphs automatically\n", + "y = x.matmul(W.T) # Forward pass\n", + "loss = (y - 1.0) ** 2 # Simple loss\n", + "\n", + "Compute gradients automatically\n", + "loss.backward() # Magic happens here!\n", + "\n", + "Access gradients\n", + "print(f\"x.grad: {x.grad}\") # Gradient w.r.t. x\n", + "print(f\"W.grad: {W.grad}\") # Gradient w.r.t. W\n", + "```\n", + "\n", "Export with: `tito module complete 05_autograd`\n", "\n", - "**Next**: Module 06 will add optimizers (SGD, Adam) that use these gradients to actually train neural networks!" + "**Next**: Module 06 will add optimizers (SGD, Adam) that use these gradients to actually train neural networks! 🎯\n", + "\n", + "## 📈 Progress: Autograd ✓\n", + "```\n", + "✅ Module 01: Tensor (Foundation)\n", + "✅ Module 02: Activations (Non-linearities) \n", + "✅ Module 03: Layers (Building blocks)\n", + "✅ Module 04: Losses (Training objectives)\n", + "✅ Module 05: Autograd (Gradient engine) ← YOU ARE HERE\n", + "🔄 Module 06: Optimizers (Learning algorithms)\n", + "🔄 Module 07: Training (Complete training loops)\n", + "```\n", + "\n", + "The gradient engine is alive! Neural networks can now learn! 🔥\n", + "\"\"\"\n", + "\n", + "def import_previous_module(module_name: str, component_name: str):\n", + " import sys\n", + " import os\n", + " sys.path.append(os.path.join(os.path.dirname(__file__), '..', module_name))\n", + " module = __import__(f\"{module_name.split('_')[1]}_dev\")\n", + " return getattr(module, component_name)" ] } ], diff --git a/modules/source/05_autograd/autograd_dev.py b/modules/source/05_autograd/autograd_dev.py index f1a8e9cb..7b12a905 100644 --- a/modules/source/05_autograd/autograd_dev.py +++ b/modules/source/05_autograd/autograd_dev.py @@ -67,14 +67,7 @@ from typing import Optional, List, Tuple import sys import os -# Import the Tensor from Module 01 -sys.path.append(os.path.join(os.path.dirname(__file__), '..', '01_tensor')) -from tensor_dev import Tensor - -print("🔥 TinyTorch Autograd Module") -print(f"NumPy version: {np.__version__}") -print(f"Python version: {sys.version_info.major}.{sys.version_info.minor}") -print("Ready to enable automatic differentiation!") +from tinytorch.core.tensor import Tensor # %% [markdown] """ @@ -678,7 +671,6 @@ This approach follows PyTorch 2.0 style - clean, modern, and educational. """ # %% nbgrader={"grade": false, "grade_id": "enable-autograd", "solution": true} -#| export def enable_autograd(): """ Enable gradient tracking for all Tensor operations. diff --git a/modules/source/06_optimizers/optimizers_dev.ipynb b/modules/source/06_optimizers/optimizers_dev.ipynb index c5b7cb5e..dfd5ac71 100644 --- a/modules/source/06_optimizers/optimizers_dev.ipynb +++ b/modules/source/06_optimizers/optimizers_dev.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "a24de2e9", + "id": "3d8f1c62", "metadata": { "cell_marker": "\"\"\"" }, @@ -33,14 +33,12 @@ "\n", "## 📦 Where This Code Lives in the Final Package\n", "\n", - "**Learning Side:** You work in modules/06_optimizers/optimizers_dev.py\n", - "**Building Side:** Code exports to tinytorch.core.optimizers\n", + "**Learning Side:** You work in `modules/06_optimizers/optimizers_dev.py` \n", + "**Building Side:** Code exports to `tinytorch.core.optimizers`\n", "\n", "```python\n", - "# Final package structure:\n", - "from tinytorch.core.optimizers import SGD, Adam, AdamW # This module\n", - "from tinytorch.core.tensor import Tensor # Foundation from Module 01\n", - "from tinytorch.core.layers import Linear # Layers from Module 03\n", + "# How to use this module:\n", + "from tinytorch.core.optimizers import SGD, Adam, AdamW\n", "```\n", "\n", "**Why this matters:**\n", @@ -53,7 +51,7 @@ { "cell_type": "code", "execution_count": null, - "id": "292aa303", + "id": "a2b41da9", "metadata": { "nbgrader": { "grade": false, @@ -64,24 +62,21 @@ "outputs": [], "source": [ "#| default_exp core.optimizers\n", + "#| export\n", "\n", "import numpy as np\n", "from typing import List, Union, Optional, Dict, Any\n", "\n", "# Import Tensor from Module 01 (now with gradient support from Module 05)\n", - "try:\n", - " from tinytorch.core.tensor import Tensor\n", - "except ImportError:\n", - " # For development, assume we have the enhanced Tensor\n", - " import sys\n", - " import os\n", - " sys.path.append(os.path.join(os.path.dirname(__file__), '..', '01_tensor'))\n", - " from tensor_dev import Tensor" + "import sys\n", + "import os\n", + "sys.path.append(os.path.join(os.path.dirname(__file__), '..', '01_tensor'))\n", + "from tensor_dev import Tensor" ] }, { "cell_type": "markdown", - "id": "23ef23eb", + "id": "3169e215", "metadata": { "cell_marker": "\"\"\"" }, @@ -138,7 +133,7 @@ }, { "cell_type": "markdown", - "id": "d0585283", + "id": "baec0321", "metadata": { "cell_marker": "\"\"\"" }, @@ -224,7 +219,7 @@ }, { "cell_type": "markdown", - "id": "d7372097", + "id": "49716b34", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -252,7 +247,7 @@ { "cell_type": "code", "execution_count": null, - "id": "27941ae4", + "id": "06d956dd", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -337,7 +332,7 @@ }, { "cell_type": "markdown", - "id": "b2d1b390", + "id": "82015c9d", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -353,7 +348,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7009049d", + "id": "c0f57a08", "metadata": { "nbgrader": { "grade": true, @@ -406,7 +401,7 @@ }, { "cell_type": "markdown", - "id": "f16e1bc5", + "id": "7d9b8ceb", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -478,7 +473,7 @@ { "cell_type": "code", "execution_count": null, - "id": "041617fa", + "id": "ae4679bb", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -581,7 +576,7 @@ }, { "cell_type": "markdown", - "id": "a71d7032", + "id": "ced264d8", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -597,7 +592,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3565b424", + "id": "68ae4ccf", "metadata": { "nbgrader": { "grade": true, @@ -664,7 +659,7 @@ }, { "cell_type": "markdown", - "id": "ecd6215c", + "id": "480929e4", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -738,7 +733,7 @@ { "cell_type": "code", "execution_count": null, - "id": "9f004a9e", + "id": "2d7e339f", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -858,7 +853,7 @@ }, { "cell_type": "markdown", - "id": "ebcf14a3", + "id": "6f114c5b", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -874,7 +869,7 @@ { "cell_type": "code", "execution_count": null, - "id": "18d493f9", + "id": "7f64abcc", "metadata": { "nbgrader": { "grade": true, @@ -950,7 +945,7 @@ }, { "cell_type": "markdown", - "id": "3651d85f", + "id": "16ccfeaa", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -1024,7 +1019,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b2d265ff", + "id": "23c16f99", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -1138,7 +1133,7 @@ }, { "cell_type": "markdown", - "id": "f116d64a", + "id": "0269f86a", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -1154,7 +1149,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c2cd744c", + "id": "016d7b36", "metadata": { "nbgrader": { "grade": true, @@ -1229,7 +1224,7 @@ }, { "cell_type": "markdown", - "id": "abcf743f", + "id": "295d5ee6", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 2 @@ -1256,7 +1251,7 @@ }, { "cell_type": "markdown", - "id": "a3a18015", + "id": "47d676c3", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -1302,7 +1297,7 @@ { "cell_type": "code", "execution_count": null, - "id": "eb6c8914", + "id": "67290db6", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -1360,7 +1355,7 @@ { "cell_type": "code", "execution_count": null, - "id": "53d5302c", + "id": "21136a44", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -1439,21 +1434,29 @@ }, { "cell_type": "markdown", - "id": "f237af71", + "id": "b171c224", "metadata": { - "cell_marker": "\"\"\"", "lines_to_next_cell": 1 }, "source": [ - "## 🧪 Module Integration Test\n", + "\"\"\"\n", + "# 🧪 Module Integration Test\n", "\n", - "Final validation that everything works together correctly." + "Final validation that everything works together correctly.\n", + "\"\"\"\n", + "\n", + "def import_previous_module(module_name: str, component_name: str):\n", + " import sys\n", + " import os\n", + " sys.path.append(os.path.join(os.path.dirname(__file__), '..', module_name))\n", + " module = __import__(f\"{module_name.split('_')[1]}_dev\")\n", + " return getattr(module, component_name)" ] }, { "cell_type": "code", "execution_count": null, - "id": "940e2331", + "id": "46ae99ae", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -1489,6 +1492,12 @@ " # Test realistic neural network optimization scenario\n", " print(\"🔬 Integration Test: Multi-layer Network Optimization...\")\n", "\n", + " # Import components from previous modules using standardized helper\n", + " Tensor = import_previous_module('01_tensor', 'Tensor')\n", + " Linear = import_previous_module('03_layers', 'Linear')\n", + " ReLU = import_previous_module('02_activations', 'ReLU')\n", + " MSELoss = import_previous_module('04_losses', 'MSELoss')\n", + "\n", " # Create parameters for a 2-layer network\n", " # Layer 1: 3 inputs -> 4 hidden\n", " W1 = Tensor(np.random.randn(3, 4) * 0.1, requires_grad=True)\n", @@ -1598,7 +1607,7 @@ { "cell_type": "code", "execution_count": null, - "id": "53d6f60a", + "id": "896f4c69", "metadata": {}, "outputs": [], "source": [ @@ -1609,7 +1618,7 @@ }, { "cell_type": "markdown", - "id": "74e04d5c", + "id": "35b39338", "metadata": { "cell_marker": "\"\"\"" }, diff --git a/modules/source/06_optimizers/optimizers_dev.py b/modules/source/06_optimizers/optimizers_dev.py index 41b3c582..2a08fb79 100644 --- a/modules/source/06_optimizers/optimizers_dev.py +++ b/modules/source/06_optimizers/optimizers_dev.py @@ -223,6 +223,7 @@ AdamW (proper weight decay) """ # %% nbgrader={"grade": false, "grade_id": "optimizer-base", "solution": true} +#| export class Optimizer: """ Base class for all optimizers. diff --git a/modules/source/07_training/training_dev.ipynb b/modules/source/07_training/training_dev.ipynb index 3df03f42..06cc7480 100644 --- a/modules/source/07_training/training_dev.ipynb +++ b/modules/source/07_training/training_dev.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "6cef63f8", + "id": "765eea82", "metadata": { "cell_marker": "\"\"\"" }, @@ -34,15 +34,12 @@ "\n", "## 📦 Where This Code Lives in the Final Package\n", "\n", - "**Learning Side:** You work in modules/07_training/training_dev.py\n", - "**Building Side:** Code exports to tinytorch.core.training\n", + "**Learning Side:** You work in `modules/07_training/training_dev.py` \n", + "**Building Side:** Code exports to `tinytorch.core.training`\n", "\n", "```python\n", - "# Final package structure:\n", - "from tinytorch.core.training import Trainer, CosineSchedule, clip_grad_norm # This module\n", - "from tinytorch.core.tensor import Tensor # Foundation (Module 01)\n", - "from tinytorch.core.optimizers import SGD, AdamW # Parameter updates (Module 06)\n", - "from tinytorch.core.losses import CrossEntropyLoss # Error measurement (Module 04)\n", + "# How to use this module:\n", + "from tinytorch.core.training import Trainer, CosineSchedule, clip_grad_norm\n", "```\n", "\n", "**Why this matters:**\n", @@ -55,7 +52,7 @@ { "cell_type": "code", "execution_count": null, - "id": "fbd71fa0", + "id": "38b1402a", "metadata": { "nbgrader": { "grade": false, @@ -67,17 +64,33 @@ "outputs": [], "source": [ "#| default_exp core.training\n", + "#| export\n", "\n", "import numpy as np\n", "import pickle\n", "import time\n", "from typing import Dict, List, Optional, Tuple, Any, Callable\n", - "from pathlib import Path" + "from pathlib import Path\n", + "import sys\n", + "import os\n", + "\n", + "# Import dependencies from other modules\n", + "sys.path.append(os.path.join(os.path.dirname(__file__), '..', '01_tensor'))\n", + "from tensor_dev import Tensor\n", + "\n", + "sys.path.append(os.path.join(os.path.dirname(__file__), '..', '03_layers'))\n", + "from layers_dev import Linear\n", + "\n", + "sys.path.append(os.path.join(os.path.dirname(__file__), '..', '04_losses'))\n", + "from losses_dev import MSELoss, CrossEntropyLoss\n", + "\n", + "sys.path.append(os.path.join(os.path.dirname(__file__), '..', '06_optimizers'))\n", + "from optimizers_dev import SGD, AdamW" ] }, { "cell_type": "markdown", - "id": "15cb9212", + "id": "89550fb8", "metadata": { "cell_marker": "\"\"\"" }, @@ -106,7 +119,7 @@ }, { "cell_type": "markdown", - "id": "7a2ca9d3", + "id": "d0b48f7a", "metadata": { "cell_marker": "\"\"\"" }, @@ -153,7 +166,7 @@ }, { "cell_type": "markdown", - "id": "ebd9577e", + "id": "4ed8a995", "metadata": { "cell_marker": "\"\"\"" }, @@ -167,7 +180,7 @@ }, { "cell_type": "markdown", - "id": "a16fa592", + "id": "ebfa93fc", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -208,7 +221,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c602af75", + "id": "347b09da", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -261,7 +274,7 @@ }, { "cell_type": "markdown", - "id": "aef4d23a", + "id": "c1db4e03", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -277,7 +290,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2c489b51", + "id": "c27f6878", "metadata": { "nbgrader": { "grade": true, @@ -321,7 +334,7 @@ }, { "cell_type": "markdown", - "id": "f7388d6c", + "id": "81fc482c", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -367,7 +380,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b49a7499", + "id": "db99efd3", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -444,7 +457,7 @@ }, { "cell_type": "markdown", - "id": "c548cc0a", + "id": "3b0b188d", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -460,7 +473,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b390744c", + "id": "91bf937f", "metadata": { "nbgrader": { "grade": true, @@ -477,8 +490,7 @@ "\n", " # Use real Tensor from Module 01\n", " import sys\n", - " sys.path.append('/Users/VJ/GitHub/TinyTorch/modules/01_tensor')\n", - " from tensor_dev import Tensor\n", + " # Tensor already imported at module level\n", "\n", " # Test case 1: Large gradients that need clipping\n", " param1 = Tensor([1.0, 2.0], requires_grad=True)\n", @@ -528,7 +540,7 @@ }, { "cell_type": "markdown", - "id": "d18224b3", + "id": "dde7833e", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -585,7 +597,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c806757c", + "id": "fa8339e1", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -863,7 +875,7 @@ }, { "cell_type": "markdown", - "id": "1cd1da58", + "id": "529dfcf5", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -879,7 +891,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7baa78b0", + "id": "03510440", "metadata": { "nbgrader": { "grade": true, @@ -894,17 +906,7 @@ " \"\"\"🔬 Test Trainer implementation.\"\"\"\n", " print(\"🔬 Unit Test: Trainer...\")\n", "\n", - " # Create mock components for testing\n", - " # Use REAL components from previous modules - no mocks!\n", - " import sys\n", - " sys.path.append('/Users/VJ/GitHub/TinyTorch/modules/01_tensor')\n", - " sys.path.append('/Users/VJ/GitHub/TinyTorch/modules/03_layers')\n", - " sys.path.append('/Users/VJ/GitHub/TinyTorch/modules/04_losses')\n", - " sys.path.append('/Users/VJ/GitHub/TinyTorch/modules/06_optimizers')\n", - " from tensor_dev import Tensor\n", - " from layers_dev import Linear\n", - " from losses_dev import MSELoss\n", - " from optimizers_dev import SGD\n", + " # Use REAL components from previous modules (already imported at module level)\n", "\n", " # Create a simple model using REAL Linear layer\n", " class SimpleModel:\n", @@ -970,7 +972,7 @@ }, { "cell_type": "markdown", - "id": "7546c5d2", + "id": "905180bd", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 2 @@ -983,19 +985,31 @@ }, { "cell_type": "markdown", - "id": "5eeb1d80", + "id": "3c631938", "metadata": { - "cell_marker": "\"\"\"" + "lines_to_next_cell": 1 }, "source": [ - "## 🧪 Part 4: Module Integration Test\n", + "\"\"\"\n", + "# 🧪 Part 4: Module Integration Test\n", "\n", - "Final validation that everything works together correctly." + "Final validation that everything works together correctly.\n", + "\"\"\"\n", + "\n", + "\n", + "\n", + "\n", + "def import_previous_module(module_name: str, component_name: str):\n", + " import sys\n", + " import os\n", + " sys.path.append(os.path.join(os.path.dirname(__file__), '..', module_name))\n", + " module = __import__(f\"{module_name.split('_')[1]}_dev\")\n", + " return getattr(module, component_name)" ] }, { "cell_type": "markdown", - "id": "6585b9bd", + "id": "8b65c5ab", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -1009,7 +1023,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4a0fa0c3", + "id": "29eea538", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -1044,16 +1058,7 @@ " # Test complete training pipeline integration with REAL components\n", " print(\"🔬 Integration Test: Complete Training Pipeline...\")\n", "\n", - " # Use REAL components from previous modules\n", - " import sys\n", - " sys.path.append('/Users/VJ/GitHub/TinyTorch/modules/01_tensor')\n", - " sys.path.append('/Users/VJ/GitHub/TinyTorch/modules/03_layers')\n", - " sys.path.append('/Users/VJ/GitHub/TinyTorch/modules/04_losses')\n", - " sys.path.append('/Users/VJ/GitHub/TinyTorch/modules/06_optimizers')\n", - " from tensor_dev import Tensor\n", - " from layers_dev import Linear\n", - " from losses_dev import MSELoss\n", - " from optimizers_dev import SGD\n", + " # Use REAL components from previous modules (already imported at module level)\n", "\n", " # Create a simple model using REAL Linear layer\n", " class SimpleModel:\n", @@ -1146,7 +1151,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ca90ce01", + "id": "ae1bc4b9", "metadata": { "nbgrader": { "grade": false, @@ -1164,7 +1169,7 @@ }, { "cell_type": "markdown", - "id": "8462acc9", + "id": "ad8ae396", "metadata": { "cell_marker": "\"\"\"" }, diff --git a/modules/source/07_training/training_dev.py b/modules/source/07_training/training_dev.py index f1a99446..10caeb52 100644 --- a/modules/source/07_training/training_dev.py +++ b/modules/source/07_training/training_dev.py @@ -191,6 +191,7 @@ This creates a natural learning curve that adapts training speed to the optimiza """ # %% nbgrader={"grade": false, "grade_id": "scheduler", "locked": false, "solution": true} +#| export class CosineSchedule: """ Cosine annealing learning rate schedule. @@ -309,7 +310,6 @@ This preserves the relative magnitudes while preventing explosion. """ # %% nbgrader={"grade": false, "grade_id": "gradient_clipping", "locked": false, "solution": true} -#| export def clip_grad_norm(parameters: List, max_norm: float = 1.0) -> float: """ Clip gradients by global norm to prevent exploding gradients. diff --git a/modules/source/08_dataloader/dataloader_dev.ipynb b/modules/source/08_dataloader/dataloader_dev.ipynb index 493ea3ef..6d5eddc4 100644 --- a/modules/source/08_dataloader/dataloader_dev.ipynb +++ b/modules/source/08_dataloader/dataloader_dev.ipynb @@ -3,16 +3,17 @@ { "cell_type": "code", "execution_count": null, - "id": "a84152d1", + "id": "399fe32f", "metadata": {}, "outputs": [], "source": [ - "#| default_exp data.loader" + "#| default_exp data.loader\n", + "#| export" ] }, { "cell_type": "markdown", - "id": "2c983083", + "id": "ffdddb72", "metadata": { "cell_marker": "\"\"\"" }, @@ -44,14 +45,13 @@ "\n", "## 📦 Where This Code Lives in the Final Package\n", "\n", - "**Learning Side:** You work in modules/08_dataloader/dataloader_dev.py\n", - "**Building Side:** Code exports to tinytorch.data.loader\n", + "**Learning Side:** You work in `modules/08_dataloader/dataloader_dev.py` \n", + "**Building Side:** Code exports to `tinytorch.data.loader`\n", "\n", "```python\n", - "# Final package structure:\n", - "from tinytorch.data.loader import Dataset, DataLoader, TensorDataset # This module\n", - "from tinytorch.data.loader import download_mnist, download_cifar10 # Dataset utilities\n", - "from tinytorch.core.tensor import Tensor # Foundation (Module 01)\n", + "# How to use this module:\n", + "from tinytorch.data.loader import Dataset, DataLoader, TensorDataset\n", + "from tinytorch.data.loader import download_mnist, download_cifar10\n", "```\n", "\n", "**Why this matters:**\n", @@ -64,7 +64,7 @@ { "cell_type": "code", "execution_count": null, - "id": "827d5572", + "id": "fbb8ae51", "metadata": {}, "outputs": [], "source": [ @@ -86,7 +86,7 @@ }, { "cell_type": "markdown", - "id": "fa205a01", + "id": "45c4ae6f", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -142,7 +142,7 @@ { "cell_type": "code", "execution_count": null, - "id": "42670b50", + "id": "af5f5f19", "metadata": { "nbgrader": { "grade": false, @@ -208,7 +208,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6982e18c", + "id": "f4cd2ac9", "metadata": { "lines_to_next_cell": 2, "nbgrader": { @@ -255,7 +255,7 @@ }, { "cell_type": "markdown", - "id": "e470f707", + "id": "9fac2c7e", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -319,7 +319,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b2ab0932", + "id": "a078c752", "metadata": { "nbgrader": { "grade": false, @@ -409,7 +409,7 @@ { "cell_type": "code", "execution_count": null, - "id": "06f20cff", + "id": "ad4c2a76", "metadata": { "lines_to_next_cell": 2, "nbgrader": { @@ -467,7 +467,7 @@ }, { "cell_type": "markdown", - "id": "707ffdbb", + "id": "496dd6e1", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -546,7 +546,7 @@ { "cell_type": "code", "execution_count": null, - "id": "57372753", + "id": "25cd0320", "metadata": { "nbgrader": { "grade": false, @@ -659,7 +659,7 @@ { "cell_type": "code", "execution_count": null, - "id": "22b5ae11", + "id": "da07fd94", "metadata": { "lines_to_next_cell": 2, "nbgrader": { @@ -727,7 +727,7 @@ }, { "cell_type": "markdown", - "id": "9ab25fbb", + "id": "75ce43a7", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -804,7 +804,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c995e812", + "id": "36ae56eb", "metadata": { "nbgrader": { "grade": false, @@ -925,7 +925,7 @@ { "cell_type": "code", "execution_count": null, - "id": "dceb709a", + "id": "3ba3d04c", "metadata": { "lines_to_next_cell": 2, "nbgrader": { @@ -973,7 +973,7 @@ }, { "cell_type": "markdown", - "id": "a7a83b36", + "id": "ac1d889d", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -1058,7 +1058,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f37f1d06", + "id": "b595f04a", "metadata": { "nbgrader": { "grade": false, @@ -1186,7 +1186,7 @@ }, { "cell_type": "markdown", - "id": "22d11ead", + "id": "b2b53800", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -1200,7 +1200,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c0d4eeef", + "id": "c9348a69", "metadata": { "nbgrader": { "grade": false, @@ -1285,7 +1285,7 @@ }, { "cell_type": "markdown", - "id": "0891e60a", + "id": "1e2a2556", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -1299,7 +1299,7 @@ { "cell_type": "code", "execution_count": null, - "id": "47fd767d", + "id": "a3101d6e", "metadata": { "lines_to_next_cell": 1 }, @@ -1357,7 +1357,7 @@ { "cell_type": "code", "execution_count": null, - "id": "99ae3e8b", + "id": "dfd83387", "metadata": {}, "outputs": [], "source": [ @@ -1370,7 +1370,7 @@ }, { "cell_type": "markdown", - "id": "dda0430c", + "id": "50c0dc0e", "metadata": { "cell_marker": "\"\"\"" }, diff --git a/modules/source/09_spatial/spatial_dev.ipynb b/modules/source/09_spatial/spatial_dev.ipynb index 33b3f467..8f72cc57 100644 --- a/modules/source/09_spatial/spatial_dev.ipynb +++ b/modules/source/09_spatial/spatial_dev.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "06a23a42", + "id": "1ac93674", "metadata": { "cell_marker": "\"\"\"" }, @@ -33,14 +33,12 @@ "\n", "## 📦 Where This Code Lives in the Final Package\n", "\n", - "**Learning Side:** You work in modules/09_spatial/spatial_dev.py\n", - "**Building Side:** Code exports to tinytorch.core.spatial\n", + "**Learning Side:** You work in `modules/09_spatial/spatial_dev.py` \n", + "**Building Side:** Code exports to `tinytorch.core.spatial`\n", "\n", "```python\n", - "# Final package structure:\n", - "from tinytorch.core.spatial import Conv2d, MaxPool2d, AvgPool2d # This module\n", - "from tinytorch.core.tensor import Tensor # Foundation (Module 01)\n", - "from tinytorch.core.layers import Module # Base class (Module 03)\n", + "# How to use this module:\n", + "from tinytorch.core.spatial import Conv2d, MaxPool2d, AvgPool2d\n", "```\n", "\n", "**Why this matters:**\n", @@ -53,8 +51,9 @@ { "cell_type": "code", "execution_count": null, - "id": "c2be8278", + "id": "3c63fa73", "metadata": { + "lines_to_next_cell": 1, "nbgrader": { "grade": false, "grade_id": "spatial-setup", @@ -72,16 +71,15 @@ "import os\n", "import time\n", "\n", - "# Smart import system for development and production compatibility\n", - "if 'tinytorch' in sys.modules:\n", - " # Production: Import from installed package\n", - " from tinytorch.core.tensor import Tensor\n", - " from tinytorch.core.layers import Module\n", - "else:\n", - " # Development: Use simplified local implementations to avoid import loops\n", + "# Import dependencies from other modules\n", + "sys.path.append(os.path.join(os.path.dirname(__file__), '..', '01_tensor'))\n", + "from tensor_dev import Tensor\n", "\n", - " # Simplified Tensor class for development\n", - " class Tensor:\n", + "sys.path.append(os.path.join(os.path.dirname(__file__), '..', '03_layers'))\n", + "from layers_dev import Module\n", + "\n", + "# Note: Keeping simplified implementations for reference during development\n", + "class _SimplifiedTensor:\n", " \"\"\"Simplified tensor for spatial operations development.\"\"\"\n", "\n", " def __init__(self, data, requires_grad=False):\n", @@ -130,7 +128,7 @@ }, { "cell_type": "markdown", - "id": "87ead40b", + "id": "e06e2310", "metadata": { "cell_marker": "\"\"\"" }, @@ -181,7 +179,7 @@ }, { "cell_type": "markdown", - "id": "3e09c3c6", + "id": "60f56a9d", "metadata": { "cell_marker": "\"\"\"" }, @@ -269,7 +267,7 @@ }, { "cell_type": "markdown", - "id": "10b0f641", + "id": "98527434", "metadata": { "cell_marker": "\"\"\"" }, @@ -323,7 +321,7 @@ }, { "cell_type": "markdown", - "id": "72156c45", + "id": "f758a7b3", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -374,7 +372,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b2903d44", + "id": "85cea897", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -386,7 +384,6 @@ "outputs": [], "source": [ "\n", - "#| export\n", "class Conv2d(Module):\n", " \"\"\"\n", " 2D Convolution layer for spatial feature extraction.\n", @@ -559,7 +556,7 @@ }, { "cell_type": "markdown", - "id": "43093579", + "id": "ec42ac55", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -575,7 +572,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d0a725b1", + "id": "3e69c870", "metadata": { "nbgrader": { "grade": true, @@ -654,7 +651,7 @@ }, { "cell_type": "markdown", - "id": "2e913b5c", + "id": "1e08f679", "metadata": { "cell_marker": "\"\"\"" }, @@ -738,7 +735,7 @@ }, { "cell_type": "markdown", - "id": "a74d3702", + "id": "2b49706b", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -802,7 +799,7 @@ { "cell_type": "code", "execution_count": null, - "id": "23a06538", + "id": "fbd8b76c", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -814,7 +811,6 @@ "outputs": [], "source": [ "\n", - "#| export\n", "class MaxPool2d(Module):\n", " \"\"\"\n", " 2D Max Pooling layer for spatial dimension reduction.\n", @@ -952,7 +948,7 @@ }, { "cell_type": "markdown", - "id": "df0253b2", + "id": "ecdf9ee4", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -1018,7 +1014,7 @@ { "cell_type": "code", "execution_count": null, - "id": "41e2a85d", + "id": "fef7b592", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -1030,7 +1026,6 @@ "outputs": [], "source": [ "\n", - "#| export\n", "class AvgPool2d(Module):\n", " \"\"\"\n", " 2D Average Pooling layer for spatial dimension reduction.\n", @@ -1159,7 +1154,7 @@ }, { "cell_type": "markdown", - "id": "0f92eb4d", + "id": "8744c054", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -1175,7 +1170,7 @@ { "cell_type": "code", "execution_count": null, - "id": "11126788", + "id": "9fe1c753", "metadata": { "nbgrader": { "grade": true, @@ -1259,7 +1254,7 @@ }, { "cell_type": "markdown", - "id": "e56854cd", + "id": "082a29b4", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -1278,7 +1273,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f941a2ee", + "id": "ef7f5a91", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -1349,7 +1344,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b066bd66", + "id": "6e0fec9b", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -1407,7 +1402,7 @@ }, { "cell_type": "markdown", - "id": "24b9212e", + "id": "64e796aa", "metadata": { "cell_marker": "\"\"\"" }, @@ -1488,7 +1483,7 @@ }, { "cell_type": "markdown", - "id": "93110b91", + "id": "946c95f3", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -1585,7 +1580,7 @@ { "cell_type": "code", "execution_count": null, - "id": "740c0edb", + "id": "edd2639e", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -1597,7 +1592,6 @@ "outputs": [], "source": [ "\n", - "#| export\n", "class SimpleCNN(Module):\n", " \"\"\"\n", " Simple CNN demonstrating spatial operations integration.\n", @@ -1701,7 +1695,7 @@ }, { "cell_type": "markdown", - "id": "3855be86", + "id": "f4def1e9", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -1717,7 +1711,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4ed1fe45", + "id": "89e48bdd", "metadata": { "nbgrader": { "grade": true, @@ -1786,7 +1780,7 @@ }, { "cell_type": "markdown", - "id": "6ab5ed35", + "id": "dbfa6c46", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 1 @@ -1800,7 +1794,7 @@ { "cell_type": "code", "execution_count": null, - "id": "727ef628", + "id": "07775404", "metadata": { "lines_to_next_cell": 1, "nbgrader": { @@ -1899,7 +1893,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8f88371c", + "id": "26701e62", "metadata": { "lines_to_next_cell": 2, "nbgrader": { @@ -1917,7 +1911,7 @@ }, { "cell_type": "markdown", - "id": "2249a8da", + "id": "7bae0cf1", "metadata": { "cell_marker": "\"\"\"" }, diff --git a/modules/source/09_spatial/spatial_dev.py b/modules/source/09_spatial/spatial_dev.py index 5f4198e6..1d764c5a 100644 --- a/modules/source/09_spatial/spatial_dev.py +++ b/modules/source/09_spatial/spatial_dev.py @@ -57,6 +57,7 @@ from tinytorch.core.spatial import Conv2d, MaxPool2d, AvgPool2d # %% nbgrader={"grade": false, "grade_id": "spatial-setup", "solution": true} + #| default_exp core.spatial #| export @@ -344,6 +345,7 @@ This reveals why convolution is expensive: O(B×C_out×H×W×K_h×K_w×C_in) ope # %% nbgrader={"grade": false, "grade_id": "conv2d-class", "solution": true} #| export + class Conv2d(Module): """ 2D Convolution layer for spatial feature extraction. @@ -524,6 +526,7 @@ This test validates our convolution implementation with different configurations # %% nbgrader={"grade": true, "grade_id": "test-conv2d", "locked": true, "points": 15} + def test_unit_conv2d(): """🔬 Test Conv2d implementation with multiple configurations.""" print("🔬 Unit Test: Conv2d...") @@ -727,6 +730,7 @@ For input (1, 64, 224, 224) with 2×2 pooling: # %% nbgrader={"grade": false, "grade_id": "maxpool2d-class", "solution": true} #| export + class MaxPool2d(Module): """ 2D Max Pooling layer for spatial dimension reduction. @@ -923,6 +927,7 @@ Memory access pattern identical to MaxPool, just different aggregation! # %% nbgrader={"grade": false, "grade_id": "avgpool2d-class", "solution": true} #| export + class AvgPool2d(Module): """ 2D Average Pooling layer for spatial dimension reduction. @@ -1059,6 +1064,7 @@ This test validates both max and average pooling implementations. # %% nbgrader={"grade": true, "grade_id": "test-pooling", "locked": true, "points": 10} + def test_unit_pooling(): """🔬 Test MaxPool2d and AvgPool2d implementations.""" print("🔬 Unit Test: Pooling Operations...") @@ -1142,6 +1148,7 @@ Now let's analyze the computational complexity and memory trade-offs of spatial # %% nbgrader={"grade": false, "grade_id": "spatial-analysis", "solution": true} + def analyze_convolution_complexity(): """📊 Analyze convolution computational complexity across different configurations.""" print("📊 Analyzing Convolution Complexity...") @@ -1199,6 +1206,7 @@ def analyze_convolution_complexity(): # %% nbgrader={"grade": false, "grade_id": "pooling-analysis", "solution": true} + def analyze_pooling_effects(): """📊 Analyze pooling's impact on spatial dimensions and features.""" print("\n📊 Analyzing Pooling Effects...") @@ -1411,6 +1419,7 @@ spanning 7×7 regions of original image! # %% nbgrader={"grade": false, "grade_id": "simple-cnn", "solution": true} #| export + class SimpleCNN(Module): """ Simple CNN demonstrating spatial operations integration. @@ -1522,6 +1531,7 @@ This test validates that spatial operations work together in a complete CNN arch # %% nbgrader={"grade": true, "grade_id": "test-simple-cnn", "locked": true, "points": 10} + def test_unit_simple_cnn(): """🔬 Test SimpleCNN integration with spatial operations.""" print("🔬 Unit Test: SimpleCNN Integration...") @@ -1585,6 +1595,7 @@ Final validation that everything works together correctly. # %% nbgrader={"grade": true, "grade_id": "module-integration", "locked": true, "points": 15} + def test_module(): """ Comprehensive test of entire spatial module functionality. diff --git a/modules/source/10_tokenization/tokenization_dev.ipynb b/modules/source/10_tokenization/tokenization_dev.ipynb new file mode 100644 index 00000000..b7f8650c --- /dev/null +++ b/modules/source/10_tokenization/tokenization_dev.ipynb @@ -0,0 +1,1474 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "25e91532", + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp text.tokenization\n", + "#| export" + ] + }, + { + "cell_type": "markdown", + "id": "8c630d23", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "# Module 10: Tokenization - Converting Text to Numbers\n", + "\n", + "Welcome to Module 10! Today you'll build tokenization - the bridge that converts human-readable text into numerical representations that machine learning models can process.\n", + "\n", + "## 🔗 Prerequisites & Progress\n", + "**You've Built**: Neural networks, layers, training loops, and data loading\n", + "**You'll Build**: Text tokenization systems (character and BPE-based)\n", + "**You'll Enable**: Text processing for language models and NLP tasks\n", + "\n", + "**Connection Map**:\n", + "```\n", + "DataLoader → Tokenization → Embeddings\n", + "(batching) (text→numbers) (learnable representations)\n", + "```\n", + "\n", + "## Learning Objectives\n", + "By the end of this module, you will:\n", + "1. Implement character-based tokenization for simple text processing\n", + "2. Build a BPE (Byte Pair Encoding) tokenizer for efficient text representation\n", + "3. Understand vocabulary management and encoding/decoding operations\n", + "4. Create the foundation for text processing in neural networks\n", + "\n", + "Let's get started!" + ] + }, + { + "cell_type": "markdown", + "id": "86f94ed8", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 📦 Where This Code Lives in the Final Package\n", + "\n", + "**Learning Side:** You work in `modules/10_tokenization/tokenization_dev.py` \n", + "**Building Side:** Code exports to `tinytorch.text.tokenization`\n", + "\n", + "```python\n", + "# How to use this module:\n", + "from tinytorch.text.tokenization import Tokenizer, CharTokenizer, BPETokenizer\n", + "```\n", + "\n", + "**Why this matters:**\n", + "- **Learning:** Complete tokenization system in one focused module for deep understanding\n", + "- **Production:** Proper organization like Hugging Face's tokenizers with all text processing together\n", + "- **Consistency:** All tokenization operations and vocabulary management in text.tokenization\n", + "- **Integration:** Works seamlessly with embeddings and data loading for complete NLP pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32570a4a", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from typing import List, Dict, Tuple, Optional, Set\n", + "import json\n", + "import re\n", + "from collections import defaultdict, Counter\n", + "\n", + "# Import only Module 01 (Tensor) - this module has minimal dependencies\n", + "import sys\n", + "import os\n", + "sys.path.append(os.path.join(os.path.dirname(__file__), '..', '01_tensor'))\n", + "from tensor_dev import Tensor" + ] + }, + { + "cell_type": "markdown", + "id": "a15ba14c", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 1. Introduction - Why Tokenization?\n", + "\n", + "Neural networks operate on numbers, but humans communicate with text. Tokenization is the crucial bridge that converts text into numerical sequences that models can process.\n", + "\n", + "### The Text-to-Numbers Challenge\n", + "\n", + "Consider the sentence: \"Hello, world!\"\n", + "\n", + "```\n", + "Human Text: \"Hello, world!\"\n", + " ↓\n", + " [Tokenization]\n", + " ↓\n", + "Numerical IDs: [72, 101, 108, 108, 111, 44, 32, 119, 111, 114, 108, 100, 33]\n", + "```\n", + "\n", + "### The Four-Step Process\n", + "\n", + "How do we represent this for a neural network? We need to:\n", + "1. **Split text into tokens** - meaningful units like words, subwords, or characters\n", + "2. **Map tokens to integers** - create a vocabulary that assigns unique IDs\n", + "3. **Handle unknown text** - deal with words not seen during training\n", + "4. **Enable reconstruction** - convert numbers back to readable text\n", + "\n", + "### Why This Matters\n", + "\n", + "The choice of tokenization strategy dramatically affects:\n", + "- **Model performance** - How well the model understands text\n", + "- **Vocabulary size** - Memory requirements for embedding tables\n", + "- **Computational efficiency** - Sequence length affects processing time\n", + "- **Robustness** - How well the model handles new/rare words" + ] + }, + { + "cell_type": "markdown", + "id": "693183fd", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 2. Foundations - Tokenization Strategies\n", + "\n", + "Different tokenization approaches make different trade-offs between vocabulary size, sequence length, and semantic understanding.\n", + "\n", + "### Character-Level Tokenization\n", + "**Approach**: Each character gets its own token\n", + "\n", + "```\n", + "Text: \"Hello world\"\n", + " ↓\n", + "Tokens: ['H', 'e', 'l', 'l', 'o', ' ', 'w', 'o', 'r', 'l', 'd']\n", + " ↓\n", + "IDs: [8, 5, 12, 12, 15, 0, 23, 15, 18, 12, 4]\n", + "```\n", + "\n", + "**Pros**: Small vocabulary (~100), handles any text, no unknown tokens\n", + "**Cons**: Long sequences (1 char = 1 token), limited semantic understanding\n", + "\n", + "### Word-Level Tokenization\n", + "**Approach**: Each word gets its own token\n", + "\n", + "```\n", + "Text: \"Hello world\"\n", + " ↓\n", + "Tokens: ['Hello', 'world']\n", + " ↓\n", + "IDs: [5847, 1254]\n", + "```\n", + "\n", + "**Pros**: Semantic meaning preserved, shorter sequences\n", + "**Cons**: Huge vocabularies (100K+), many unknown tokens\n", + "\n", + "### Subword Tokenization (BPE)\n", + "**Approach**: Learn frequent character pairs, build subword units\n", + "\n", + "```\n", + "Text: \"tokenization\"\n", + " ↓ Character level\n", + "Initial: ['t', 'o', 'k', 'e', 'n', 'i', 'z', 'a', 't', 'i', 'o', 'n']\n", + " ↓ Learn frequent pairs\n", + "Merged: ['to', 'ken', 'ization']\n", + " ↓\n", + "IDs: [142, 1847, 2341]\n", + "```\n", + "\n", + "**Pros**: Balance between vocabulary size and sequence length\n", + "**Cons**: More complex training process\n", + "\n", + "### Strategy Comparison\n", + "\n", + "```\n", + "Text: \"tokenization\" (12 characters)\n", + "\n", + "Character: ['t','o','k','e','n','i','z','a','t','i','o','n'] → 12 tokens, vocab ~100\n", + "Word: ['tokenization'] → 1 token, vocab 100K+\n", + "BPE: ['token','ization'] → 2 tokens, vocab 10-50K\n", + "```\n", + "\n", + "The sweet spot for most applications is BPE with 10K-50K vocabulary size." + ] + }, + { + "cell_type": "markdown", + "id": "30b95ab2", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 3. Implementation - Building Tokenization Systems\n", + "\n", + "Let's implement tokenization systems from simple character-based to sophisticated BPE. We'll start with the base interface and work our way up to advanced algorithms." + ] + }, + { + "cell_type": "markdown", + "id": "2d467bf2", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### Base Tokenizer Interface\n", + "\n", + "All tokenizers need to provide two core operations: encoding text to numbers and decoding numbers back to text. Let's define the common interface.\n", + "\n", + "```\n", + "Tokenizer Interface:\n", + " encode(text) → [id1, id2, id3, ...]\n", + " decode([id1, id2, id3, ...]) → text\n", + "```\n", + "\n", + "This ensures consistent behavior across different tokenization strategies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "749828d0", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "base-tokenizer", + "solution": true + } + }, + "outputs": [], + "source": [ + "class Tokenizer:\n", + " \"\"\"\n", + " Base tokenizer class providing the interface for all tokenizers.\n", + "\n", + " This defines the contract that all tokenizers must follow:\n", + " - encode(): text → list of token IDs\n", + " - decode(): list of token IDs → text\n", + " \"\"\"\n", + "\n", + " def encode(self, text: str) -> List[int]:\n", + " \"\"\"\n", + " Convert text to a list of token IDs.\n", + "\n", + " TODO: Implement encoding logic in subclasses\n", + "\n", + " APPROACH:\n", + " 1. Subclasses will override this method\n", + " 2. Return list of integer token IDs\n", + "\n", + " EXAMPLE:\n", + " >>> tokenizer = CharTokenizer(['a', 'b', 'c'])\n", + " >>> tokenizer.encode(\"abc\")\n", + " [0, 1, 2]\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " raise NotImplementedError(\"Subclasses must implement encode()\")\n", + " ### END SOLUTION\n", + "\n", + " def decode(self, tokens: List[int]) -> str:\n", + " \"\"\"\n", + " Convert list of token IDs back to text.\n", + "\n", + " TODO: Implement decoding logic in subclasses\n", + "\n", + " APPROACH:\n", + " 1. Subclasses will override this method\n", + " 2. Return reconstructed text string\n", + "\n", + " EXAMPLE:\n", + " >>> tokenizer = CharTokenizer(['a', 'b', 'c'])\n", + " >>> tokenizer.decode([0, 1, 2])\n", + " \"abc\"\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " raise NotImplementedError(\"Subclasses must implement decode()\")\n", + " ### END SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5911263b", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test-base-tokenizer", + "locked": true, + "points": 5 + } + }, + "outputs": [], + "source": [ + "def test_unit_base_tokenizer():\n", + " \"\"\"🔬 Test base tokenizer interface.\"\"\"\n", + " print(\"🔬 Unit Test: Base Tokenizer Interface...\")\n", + "\n", + " # Test that base class defines the interface\n", + " tokenizer = Tokenizer()\n", + "\n", + " # Should raise NotImplementedError for both methods\n", + " try:\n", + " tokenizer.encode(\"test\")\n", + " assert False, \"encode() should raise NotImplementedError\"\n", + " except NotImplementedError:\n", + " pass\n", + "\n", + " try:\n", + " tokenizer.decode([1, 2, 3])\n", + " assert False, \"decode() should raise NotImplementedError\"\n", + " except NotImplementedError:\n", + " pass\n", + "\n", + " print(\"✅ Base tokenizer interface works correctly!\")\n", + "\n", + "test_unit_base_tokenizer()" + ] + }, + { + "cell_type": "markdown", + "id": "691dccae", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### Character-Level Tokenizer\n", + "\n", + "The simplest tokenization approach: each character becomes a token. This gives us perfect coverage of any text but produces long sequences.\n", + "\n", + "```\n", + "Character Tokenization Process:\n", + "\n", + "Step 1: Build vocabulary from unique characters\n", + "Text corpus: [\"hello\", \"world\"]\n", + "Unique chars: ['h', 'e', 'l', 'o', 'w', 'r', 'd']\n", + "Vocabulary: ['', 'h', 'e', 'l', 'o', 'w', 'r', 'd'] # for unknown\n", + " 0 1 2 3 4 5 6 7\n", + "\n", + "Step 2: Encode text character by character\n", + "Text: \"hello\"\n", + " 'h' → 1\n", + " 'e' → 2\n", + " 'l' → 3\n", + " 'l' → 3\n", + " 'o' → 4\n", + "Result: [1, 2, 3, 3, 4]\n", + "\n", + "Step 3: Decode by looking up each ID\n", + "IDs: [1, 2, 3, 3, 4]\n", + " 1 → 'h'\n", + " 2 → 'e'\n", + " 3 → 'l'\n", + " 3 → 'l'\n", + " 4 → 'o'\n", + "Result: \"hello\"\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e2b5bb36", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "char-tokenizer", + "solution": true + } + }, + "outputs": [], + "source": [ + "class CharTokenizer(Tokenizer):\n", + " \"\"\"\n", + " Character-level tokenizer that treats each character as a separate token.\n", + "\n", + " This is the simplest tokenization approach - every character in the\n", + " vocabulary gets its own unique ID.\n", + " \"\"\"\n", + "\n", + " def __init__(self, vocab: Optional[List[str]] = None):\n", + " \"\"\"\n", + " Initialize character tokenizer.\n", + "\n", + " TODO: Set up vocabulary mappings\n", + "\n", + " APPROACH:\n", + " 1. Store vocabulary list\n", + " 2. Create char→id and id→char mappings\n", + " 3. Handle special tokens (unknown character)\n", + "\n", + " EXAMPLE:\n", + " >>> tokenizer = CharTokenizer(['a', 'b', 'c'])\n", + " >>> tokenizer.vocab_size\n", + " 4 # 3 chars + 1 unknown token\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " if vocab is None:\n", + " vocab = []\n", + "\n", + " # Add special unknown token\n", + " self.vocab = [''] + vocab\n", + " self.vocab_size = len(self.vocab)\n", + "\n", + " # Create bidirectional mappings\n", + " self.char_to_id = {char: idx for idx, char in enumerate(self.vocab)}\n", + " self.id_to_char = {idx: char for idx, char in enumerate(self.vocab)}\n", + "\n", + " # Store unknown token ID\n", + " self.unk_id = 0\n", + " ### END SOLUTION\n", + "\n", + " def build_vocab(self, corpus: List[str]) -> None:\n", + " \"\"\"\n", + " Build vocabulary from a corpus of text.\n", + "\n", + " TODO: Extract unique characters and build vocabulary\n", + "\n", + " APPROACH:\n", + " 1. Collect all unique characters from corpus\n", + " 2. Sort for consistent ordering\n", + " 3. Rebuild mappings with new vocabulary\n", + "\n", + " HINTS:\n", + " - Use set() to find unique characters\n", + " - Join all texts then convert to set\n", + " - Don't forget the token\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " # Collect all unique characters\n", + " all_chars = set()\n", + " for text in corpus:\n", + " all_chars.update(text)\n", + "\n", + " # Sort for consistent ordering\n", + " unique_chars = sorted(list(all_chars))\n", + "\n", + " # Rebuild vocabulary with token first\n", + " self.vocab = [''] + unique_chars\n", + " self.vocab_size = len(self.vocab)\n", + "\n", + " # Rebuild mappings\n", + " self.char_to_id = {char: idx for idx, char in enumerate(self.vocab)}\n", + " self.id_to_char = {idx: char for idx, char in enumerate(self.vocab)}\n", + " ### END SOLUTION\n", + "\n", + " def encode(self, text: str) -> List[int]:\n", + " \"\"\"\n", + " Encode text to list of character IDs.\n", + "\n", + " TODO: Convert each character to its vocabulary ID\n", + "\n", + " APPROACH:\n", + " 1. Iterate through each character in text\n", + " 2. Look up character ID in vocabulary\n", + " 3. Use unknown token ID for unseen characters\n", + "\n", + " EXAMPLE:\n", + " >>> tokenizer = CharTokenizer(['h', 'e', 'l', 'o'])\n", + " >>> tokenizer.encode(\"hello\")\n", + " [1, 2, 3, 3, 4] # maps to h,e,l,l,o\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " tokens = []\n", + " for char in text:\n", + " tokens.append(self.char_to_id.get(char, self.unk_id))\n", + " return tokens\n", + " ### END SOLUTION\n", + "\n", + " def decode(self, tokens: List[int]) -> str:\n", + " \"\"\"\n", + " Decode list of token IDs back to text.\n", + "\n", + " TODO: Convert each token ID back to its character\n", + "\n", + " APPROACH:\n", + " 1. Look up each token ID in vocabulary\n", + " 2. Join characters into string\n", + " 3. Handle invalid token IDs gracefully\n", + "\n", + " EXAMPLE:\n", + " >>> tokenizer = CharTokenizer(['h', 'e', 'l', 'o'])\n", + " >>> tokenizer.decode([1, 2, 3, 3, 4])\n", + " \"hello\"\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " chars = []\n", + " for token_id in tokens:\n", + " # Use unknown token for invalid IDs\n", + " char = self.id_to_char.get(token_id, '')\n", + " chars.append(char)\n", + " return ''.join(chars)\n", + " ### END SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ea6b95f", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test-char-tokenizer", + "locked": true, + "points": 15 + } + }, + "outputs": [], + "source": [ + "def test_unit_char_tokenizer():\n", + " \"\"\"🔬 Test character tokenizer implementation.\"\"\"\n", + " print(\"🔬 Unit Test: Character Tokenizer...\")\n", + "\n", + " # Test basic functionality\n", + " vocab = ['h', 'e', 'l', 'o', ' ', 'w', 'r', 'd']\n", + " tokenizer = CharTokenizer(vocab)\n", + "\n", + " # Test vocabulary setup\n", + " assert tokenizer.vocab_size == 9 # 8 chars + UNK\n", + " assert tokenizer.vocab[0] == ''\n", + " assert 'h' in tokenizer.char_to_id\n", + "\n", + " # Test encoding\n", + " text = \"hello\"\n", + " tokens = tokenizer.encode(text)\n", + " expected = [1, 2, 3, 3, 4] # h,e,l,l,o (based on actual vocab order)\n", + " assert tokens == expected, f\"Expected {expected}, got {tokens}\"\n", + "\n", + " # Test decoding\n", + " decoded = tokenizer.decode(tokens)\n", + " assert decoded == text, f\"Expected '{text}', got '{decoded}'\"\n", + "\n", + " # Test unknown character handling\n", + " tokens_with_unk = tokenizer.encode(\"hello!\")\n", + " assert tokens_with_unk[-1] == 0 # '!' should map to \n", + "\n", + " # Test vocabulary building\n", + " corpus = [\"hello world\", \"test text\"]\n", + " tokenizer.build_vocab(corpus)\n", + " assert 't' in tokenizer.char_to_id\n", + " assert 'x' in tokenizer.char_to_id\n", + "\n", + " print(\"✅ Character tokenizer works correctly!\")\n", + "\n", + "test_unit_char_tokenizer()" + ] + }, + { + "cell_type": "markdown", + "id": "2bf049a0", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "### 🧪 Character Tokenizer Analysis\n", + "Character tokenization provides a simple, robust foundation for text processing. The key insight is that with a small vocabulary (typically <100 characters), we can represent any text without unknown tokens.\n", + "\n", + "**Trade-offs**:\n", + "- **Pro**: No out-of-vocabulary issues, handles any language\n", + "- **Con**: Long sequences (1 char = 1 token), limited semantic understanding\n", + "- **Use case**: When robustness is more important than efficiency" + ] + }, + { + "cell_type": "markdown", + "id": "a7006dab", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### Byte Pair Encoding (BPE) Tokenizer\n", + "\n", + "BPE is the secret sauce behind modern language models. It learns to merge frequent character pairs, creating subword units that balance vocabulary size with sequence length.\n", + "\n", + "```\n", + "BPE Training Process:\n", + "\n", + "Step 1: Start with character vocabulary\n", + "Text: [\"hello\", \"hello\", \"help\"]\n", + "Initial tokens: [['h','e','l','l','o'], ['h','e','l','l','o'], ['h','e','l','p']]\n", + "\n", + "Step 2: Count character pairs\n", + "('h','e'): 3 times ← Most frequent!\n", + "('e','l'): 3 times\n", + "('l','l'): 2 times\n", + "('l','o'): 2 times\n", + "('l','p'): 1 time\n", + "\n", + "Step 3: Merge most frequent pair\n", + "Merge ('h','e') → 'he'\n", + "Tokens: [['he','l','l','o'], ['he','l','l','o'], ['he','l','p']]\n", + "Vocab: ['h','e','l','o','p','','he'] ← New token added\n", + "\n", + "Step 4: Repeat until target vocabulary size\n", + "Next merge: ('l','l') → 'll'\n", + "Tokens: [['he','ll','o'], ['he','ll','o'], ['he','l','p']]\n", + "Vocab: ['h','e','l','o','p','','he','ll'] ← Growing vocabulary\n", + "\n", + "Final result:\n", + "Text \"hello\" → ['he', 'll', 'o'] → 3 tokens (vs 5 characters)\n", + "Text \"help\" → ['he', 'l', 'p'] → 3 tokens (vs 4 characters)\n", + "```\n", + "\n", + "BPE discovers natural word boundaries and common patterns automatically!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d4681931", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "bpe-tokenizer", + "solution": true + } + }, + "outputs": [], + "source": [ + "class BPETokenizer(Tokenizer):\n", + " \"\"\"\n", + " Byte Pair Encoding (BPE) tokenizer that learns subword units.\n", + "\n", + " BPE works by:\n", + " 1. Starting with character-level vocabulary\n", + " 2. Finding most frequent character pairs\n", + " 3. Merging frequent pairs into single tokens\n", + " 4. Repeating until desired vocabulary size\n", + " \"\"\"\n", + "\n", + " def __init__(self, vocab_size: int = 1000):\n", + " \"\"\"\n", + " Initialize BPE tokenizer.\n", + "\n", + " TODO: Set up basic tokenizer state\n", + "\n", + " APPROACH:\n", + " 1. Store target vocabulary size\n", + " 2. Initialize empty vocabulary and merge rules\n", + " 3. Set up mappings for encoding/decoding\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " self.vocab_size = vocab_size\n", + " self.vocab = []\n", + " self.merges = [] # List of (pair, new_token) merges\n", + " self.token_to_id = {}\n", + " self.id_to_token = {}\n", + " ### END SOLUTION\n", + "\n", + " def _get_word_tokens(self, word: str) -> List[str]:\n", + " \"\"\"\n", + " Convert word to list of characters with end-of-word marker.\n", + "\n", + " TODO: Tokenize word into character sequence\n", + "\n", + " APPROACH:\n", + " 1. Split word into characters\n", + " 2. Add marker to last character\n", + " 3. Return list of tokens\n", + "\n", + " EXAMPLE:\n", + " >>> tokenizer._get_word_tokens(\"hello\")\n", + " ['h', 'e', 'l', 'l', 'o']\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " if not word:\n", + " return []\n", + "\n", + " tokens = list(word)\n", + " tokens[-1] += '' # Mark end of word\n", + " return tokens\n", + " ### END SOLUTION\n", + "\n", + " def _get_pairs(self, word_tokens: List[str]) -> Set[Tuple[str, str]]:\n", + " \"\"\"\n", + " Get all adjacent pairs from word tokens.\n", + "\n", + " TODO: Extract all consecutive character pairs\n", + "\n", + " APPROACH:\n", + " 1. Iterate through adjacent tokens\n", + " 2. Create pairs of consecutive tokens\n", + " 3. Return set of unique pairs\n", + "\n", + " EXAMPLE:\n", + " >>> tokenizer._get_pairs(['h', 'e', 'l', 'l', 'o'])\n", + " {('h', 'e'), ('e', 'l'), ('l', 'l'), ('l', 'o')}\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " pairs = set()\n", + " for i in range(len(word_tokens) - 1):\n", + " pairs.add((word_tokens[i], word_tokens[i + 1]))\n", + " return pairs\n", + " ### END SOLUTION\n", + "\n", + " def train(self, corpus: List[str], vocab_size: int = None) -> None:\n", + " \"\"\"\n", + " Train BPE on corpus to learn merge rules.\n", + "\n", + " TODO: Implement BPE training algorithm\n", + "\n", + " APPROACH:\n", + " 1. Build initial character vocabulary\n", + " 2. Count word frequencies in corpus\n", + " 3. Iteratively merge most frequent pairs\n", + " 4. Build final vocabulary and mappings\n", + "\n", + " HINTS:\n", + " - Start with character-level tokens\n", + " - Use frequency counts to guide merging\n", + " - Stop when vocabulary reaches target size\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " if vocab_size:\n", + " self.vocab_size = vocab_size\n", + "\n", + " # Count word frequencies\n", + " word_freq = Counter(corpus)\n", + "\n", + " # Initialize vocabulary with characters\n", + " vocab = set()\n", + " word_tokens = {}\n", + "\n", + " for word in word_freq:\n", + " tokens = self._get_word_tokens(word)\n", + " word_tokens[word] = tokens\n", + " vocab.update(tokens)\n", + "\n", + " # Convert to sorted list for consistency\n", + " self.vocab = sorted(list(vocab))\n", + "\n", + " # Add special tokens\n", + " if '' not in self.vocab:\n", + " self.vocab = [''] + self.vocab\n", + "\n", + " # Learn merges\n", + " self.merges = []\n", + "\n", + " while len(self.vocab) < self.vocab_size:\n", + " # Count all pairs across all words\n", + " pair_counts = Counter()\n", + "\n", + " for word, freq in word_freq.items():\n", + " tokens = word_tokens[word]\n", + " pairs = self._get_pairs(tokens)\n", + " for pair in pairs:\n", + " pair_counts[pair] += freq\n", + "\n", + " if not pair_counts:\n", + " break\n", + "\n", + " # Get most frequent pair\n", + " best_pair = pair_counts.most_common(1)[0][0]\n", + "\n", + " # Merge this pair in all words\n", + " for word in word_tokens:\n", + " tokens = word_tokens[word]\n", + " new_tokens = []\n", + " i = 0\n", + " while i < len(tokens):\n", + " if (i < len(tokens) - 1 and\n", + " tokens[i] == best_pair[0] and\n", + " tokens[i + 1] == best_pair[1]):\n", + " # Merge pair\n", + " new_tokens.append(best_pair[0] + best_pair[1])\n", + " i += 2\n", + " else:\n", + " new_tokens.append(tokens[i])\n", + " i += 1\n", + " word_tokens[word] = new_tokens\n", + "\n", + " # Add merged token to vocabulary\n", + " merged_token = best_pair[0] + best_pair[1]\n", + " self.vocab.append(merged_token)\n", + " self.merges.append(best_pair)\n", + "\n", + " # Build final mappings\n", + " self._build_mappings()\n", + " ### END SOLUTION\n", + "\n", + " def _build_mappings(self):\n", + " \"\"\"Build token-to-ID and ID-to-token mappings.\"\"\"\n", + " ### BEGIN SOLUTION\n", + " self.token_to_id = {token: idx for idx, token in enumerate(self.vocab)}\n", + " self.id_to_token = {idx: token for idx, token in enumerate(self.vocab)}\n", + " ### END SOLUTION\n", + "\n", + " def _apply_merges(self, tokens: List[str]) -> List[str]:\n", + " \"\"\"\n", + " Apply learned merge rules to token sequence.\n", + "\n", + " TODO: Apply BPE merges to token list\n", + "\n", + " APPROACH:\n", + " 1. Start with character-level tokens\n", + " 2. Apply each merge rule in order\n", + " 3. Continue until no more merges possible\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " if not self.merges:\n", + " return tokens\n", + "\n", + " for merge_pair in self.merges:\n", + " new_tokens = []\n", + " i = 0\n", + " while i < len(tokens):\n", + " if (i < len(tokens) - 1 and\n", + " tokens[i] == merge_pair[0] and\n", + " tokens[i + 1] == merge_pair[1]):\n", + " # Apply merge\n", + " new_tokens.append(merge_pair[0] + merge_pair[1])\n", + " i += 2\n", + " else:\n", + " new_tokens.append(tokens[i])\n", + " i += 1\n", + " tokens = new_tokens\n", + "\n", + " return tokens\n", + " ### END SOLUTION\n", + "\n", + " def encode(self, text: str) -> List[int]:\n", + " \"\"\"\n", + " Encode text using BPE.\n", + "\n", + " TODO: Apply BPE encoding to text\n", + "\n", + " APPROACH:\n", + " 1. Split text into words\n", + " 2. Convert each word to character tokens\n", + " 3. Apply BPE merges\n", + " 4. Convert to token IDs\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " if not self.vocab:\n", + " return []\n", + "\n", + " # Simple word splitting (could be more sophisticated)\n", + " words = text.split()\n", + " all_tokens = []\n", + "\n", + " for word in words:\n", + " # Get character-level tokens\n", + " word_tokens = self._get_word_tokens(word)\n", + "\n", + " # Apply BPE merges\n", + " merged_tokens = self._apply_merges(word_tokens)\n", + "\n", + " all_tokens.extend(merged_tokens)\n", + "\n", + " # Convert to IDs\n", + " token_ids = []\n", + " for token in all_tokens:\n", + " token_ids.append(self.token_to_id.get(token, 0)) # 0 = \n", + "\n", + " return token_ids\n", + " ### END SOLUTION\n", + "\n", + " def decode(self, tokens: List[int]) -> str:\n", + " \"\"\"\n", + " Decode token IDs back to text.\n", + "\n", + " TODO: Convert token IDs back to readable text\n", + "\n", + " APPROACH:\n", + " 1. Convert IDs to tokens\n", + " 2. Join tokens together\n", + " 3. Clean up word boundaries and markers\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " if not self.id_to_token:\n", + " return \"\"\n", + "\n", + " # Convert IDs to tokens\n", + " token_strings = []\n", + " for token_id in tokens:\n", + " token = self.id_to_token.get(token_id, '')\n", + " token_strings.append(token)\n", + "\n", + " # Join and clean up\n", + " text = ''.join(token_strings)\n", + "\n", + " # Replace end-of-word markers with spaces\n", + " text = text.replace('', ' ')\n", + "\n", + " # Clean up extra spaces\n", + " text = ' '.join(text.split())\n", + "\n", + " return text\n", + " ### END SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65674271", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test-bpe-tokenizer", + "locked": true, + "points": 20 + } + }, + "outputs": [], + "source": [ + "def test_unit_bpe_tokenizer():\n", + " \"\"\"🔬 Test BPE tokenizer implementation.\"\"\"\n", + " print(\"🔬 Unit Test: BPE Tokenizer...\")\n", + "\n", + " # Test basic functionality with simple corpus\n", + " corpus = [\"hello\", \"world\", \"hello\", \"hell\"] # \"hell\" and \"hello\" share prefix\n", + " tokenizer = BPETokenizer(vocab_size=20)\n", + " tokenizer.train(corpus)\n", + "\n", + " # Check that vocabulary was built\n", + " assert len(tokenizer.vocab) > 0\n", + " assert '' in tokenizer.vocab\n", + "\n", + " # Test helper functions\n", + " word_tokens = tokenizer._get_word_tokens(\"test\")\n", + " assert word_tokens[-1].endswith(''), \"Should have end-of-word marker\"\n", + "\n", + " pairs = tokenizer._get_pairs(['h', 'e', 'l', 'l', 'o'])\n", + " assert ('h', 'e') in pairs\n", + " assert ('l', 'l') in pairs\n", + "\n", + " # Test encoding/decoding\n", + " text = \"hello\"\n", + " tokens = tokenizer.encode(text)\n", + " assert isinstance(tokens, list)\n", + " assert all(isinstance(t, int) for t in tokens)\n", + "\n", + " decoded = tokenizer.decode(tokens)\n", + " assert isinstance(decoded, str)\n", + "\n", + " # Test round-trip on training data should work well\n", + " for word in corpus:\n", + " tokens = tokenizer.encode(word)\n", + " decoded = tokenizer.decode(tokens)\n", + " # Allow some flexibility due to BPE merging\n", + " assert len(decoded.strip()) > 0\n", + "\n", + " print(\"✅ BPE tokenizer works correctly!\")\n", + "\n", + "test_unit_bpe_tokenizer()" + ] + }, + { + "cell_type": "markdown", + "id": "1e9cdb52", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "### 🧪 BPE Tokenizer Analysis\n", + "\n", + "BPE provides a balance between vocabulary size and sequence length. By learning frequent subword patterns, it can handle new words through decomposition while maintaining reasonable sequence lengths.\n", + "\n", + "```\n", + "BPE Merging Visualization:\n", + "\n", + "Original: \"tokenization\" → ['t','o','k','e','n','i','z','a','t','i','o','n','']\n", + " ↓ Merge frequent pairs\n", + "Step 1: ('t','o') is frequent → ['to','k','e','n','i','z','a','t','i','o','n','']\n", + "Step 2: ('i','o') is frequent → ['to','k','e','n','io','z','a','t','io','n','']\n", + "Step 3: ('io','n') is frequent → ['to','k','e','n','io','z','a','t','ion','']\n", + "Step 4: ('to','k') is frequent → ['tok','e','n','io','z','a','t','ion','']\n", + " ↓ Continue merging...\n", + "Final: \"tokenization\" → ['token','ization'] # 2 tokens vs 13 characters!\n", + "```\n", + "\n", + "**Key insights**:\n", + "- **Adaptive vocabulary**: Learns from data, not hand-crafted\n", + "- **Subword robustness**: Handles rare/new words through decomposition\n", + "- **Efficiency trade-off**: Larger vocabulary → shorter sequences → faster processing\n", + "- **Morphological awareness**: Naturally discovers prefixes, suffixes, roots" + ] + }, + { + "cell_type": "markdown", + "id": "4a0e4520", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 4. Integration - Bringing It Together\n", + "\n", + "Now let's build utility functions that make tokenization easy to use in practice. These tools will help you tokenize datasets, analyze performance, and choose the right strategy.\n", + "\n", + "```\n", + "Tokenization Workflow:\n", + "\n", + "1. Choose Strategy → 2. Train Tokenizer → 3. Process Dataset → 4. Analyze Results\n", + " ↓ ↓ ↓ ↓\n", + " char/bpe corpus training batch encoding stats/metrics\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0b0b630b", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "tokenization-utils", + "solution": true + } + }, + "outputs": [], + "source": [ + "def create_tokenizer(strategy: str = \"char\", vocab_size: int = 1000, corpus: List[str] = None) -> Tokenizer:\n", + " \"\"\"\n", + " Factory function to create and train tokenizers.\n", + "\n", + " TODO: Create appropriate tokenizer based on strategy\n", + "\n", + " APPROACH:\n", + " 1. Check strategy type\n", + " 2. Create appropriate tokenizer class\n", + " 3. Train on corpus if provided\n", + " 4. Return configured tokenizer\n", + "\n", + " EXAMPLE:\n", + " >>> corpus = [\"hello world\", \"test text\"]\n", + " >>> tokenizer = create_tokenizer(\"char\", corpus=corpus)\n", + " >>> tokens = tokenizer.encode(\"hello\")\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " if strategy == \"char\":\n", + " tokenizer = CharTokenizer()\n", + " if corpus:\n", + " tokenizer.build_vocab(corpus)\n", + " elif strategy == \"bpe\":\n", + " tokenizer = BPETokenizer(vocab_size=vocab_size)\n", + " if corpus:\n", + " tokenizer.train(corpus, vocab_size)\n", + " else:\n", + " raise ValueError(f\"Unknown tokenization strategy: {strategy}\")\n", + "\n", + " return tokenizer\n", + " ### END SOLUTION\n", + "\n", + "def tokenize_dataset(texts: List[str], tokenizer: Tokenizer, max_length: int = None) -> List[List[int]]:\n", + " \"\"\"\n", + " Tokenize a dataset with optional length limits.\n", + "\n", + " TODO: Tokenize all texts with consistent preprocessing\n", + "\n", + " APPROACH:\n", + " 1. Encode each text with the tokenizer\n", + " 2. Apply max_length truncation if specified\n", + " 3. Return list of tokenized sequences\n", + "\n", + " HINTS:\n", + " - Handle empty texts gracefully\n", + " - Truncate from the end if too long\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " tokenized = []\n", + " for text in texts:\n", + " tokens = tokenizer.encode(text)\n", + "\n", + " # Apply length limit\n", + " if max_length and len(tokens) > max_length:\n", + " tokens = tokens[:max_length]\n", + "\n", + " tokenized.append(tokens)\n", + "\n", + " return tokenized\n", + " ### END SOLUTION\n", + "\n", + "def analyze_tokenization(texts: List[str], tokenizer: Tokenizer) -> Dict[str, float]:\n", + " \"\"\"\n", + " Analyze tokenization statistics.\n", + "\n", + " TODO: Compute useful statistics about tokenization\n", + "\n", + " APPROACH:\n", + " 1. Tokenize all texts\n", + " 2. Compute sequence length statistics\n", + " 3. Calculate compression ratio\n", + " 4. Return analysis dictionary\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " all_tokens = []\n", + " total_chars = 0\n", + "\n", + " for text in texts:\n", + " tokens = tokenizer.encode(text)\n", + " all_tokens.extend(tokens)\n", + " total_chars += len(text)\n", + "\n", + " # Calculate statistics\n", + " tokenized_lengths = [len(tokenizer.encode(text)) for text in texts]\n", + "\n", + " stats = {\n", + " 'vocab_size': tokenizer.vocab_size if hasattr(tokenizer, 'vocab_size') else len(tokenizer.vocab),\n", + " 'avg_sequence_length': np.mean(tokenized_lengths),\n", + " 'max_sequence_length': max(tokenized_lengths) if tokenized_lengths else 0,\n", + " 'total_tokens': len(all_tokens),\n", + " 'compression_ratio': total_chars / len(all_tokens) if all_tokens else 0,\n", + " 'unique_tokens': len(set(all_tokens))\n", + " }\n", + "\n", + " return stats\n", + " ### END SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d06eb5f9", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test-tokenization-utils", + "locked": true, + "points": 10 + } + }, + "outputs": [], + "source": [ + "def test_unit_tokenization_utils():\n", + " \"\"\"🔬 Test tokenization utility functions.\"\"\"\n", + " print(\"🔬 Unit Test: Tokenization Utils...\")\n", + "\n", + " # Test tokenizer factory\n", + " corpus = [\"hello world\", \"test text\", \"more examples\"]\n", + "\n", + " char_tokenizer = create_tokenizer(\"char\", corpus=corpus)\n", + " assert isinstance(char_tokenizer, CharTokenizer)\n", + " assert char_tokenizer.vocab_size > 0\n", + "\n", + " bpe_tokenizer = create_tokenizer(\"bpe\", vocab_size=50, corpus=corpus)\n", + " assert isinstance(bpe_tokenizer, BPETokenizer)\n", + "\n", + " # Test dataset tokenization\n", + " texts = [\"hello\", \"world\", \"test\"]\n", + " tokenized = tokenize_dataset(texts, char_tokenizer, max_length=10)\n", + " assert len(tokenized) == len(texts)\n", + " assert all(len(seq) <= 10 for seq in tokenized)\n", + "\n", + " # Test analysis\n", + " stats = analyze_tokenization(texts, char_tokenizer)\n", + " assert 'vocab_size' in stats\n", + " assert 'avg_sequence_length' in stats\n", + " assert 'compression_ratio' in stats\n", + " assert stats['total_tokens'] > 0\n", + "\n", + " print(\"✅ Tokenization utils work correctly!\")\n", + "\n", + "test_unit_tokenization_utils()" + ] + }, + { + "cell_type": "markdown", + "id": "c45ae11e", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 5. Systems Analysis - Tokenization Trade-offs\n", + "\n", + "Understanding the performance implications of different tokenization strategies is crucial for building efficient NLP systems." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e673247f", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "tokenization-analysis", + "solution": true + } + }, + "outputs": [], + "source": [ + "def analyze_tokenization_strategies():\n", + " \"\"\"📊 Compare different tokenization strategies on various texts.\"\"\"\n", + " print(\"📊 Analyzing Tokenization Strategies...\")\n", + "\n", + " # Create test corpus with different text types\n", + " corpus = [\n", + " \"Hello world\",\n", + " \"The quick brown fox jumps over the lazy dog\",\n", + " \"Machine learning is transforming artificial intelligence\",\n", + " \"Tokenization is fundamental to natural language processing\",\n", + " \"Subword units balance vocabulary size and sequence length\"\n", + " ]\n", + "\n", + " # Test different strategies\n", + " strategies = [\n", + " (\"Character\", create_tokenizer(\"char\", corpus=corpus)),\n", + " (\"BPE-100\", create_tokenizer(\"bpe\", vocab_size=100, corpus=corpus)),\n", + " (\"BPE-500\", create_tokenizer(\"bpe\", vocab_size=500, corpus=corpus))\n", + " ]\n", + "\n", + " print(f\"{'Strategy':<12} {'Vocab':<8} {'Avg Len':<8} {'Compression':<12} {'Coverage':<10}\")\n", + " print(\"-\" * 60)\n", + "\n", + " for name, tokenizer in strategies:\n", + " stats = analyze_tokenization(corpus, tokenizer)\n", + "\n", + " print(f\"{name:<12} {stats['vocab_size']:<8} \"\n", + " f\"{stats['avg_sequence_length']:<8.1f} \"\n", + " f\"{stats['compression_ratio']:<12.2f} \"\n", + " f\"{stats['unique_tokens']:<10}\")\n", + "\n", + " print(\"\\n💡 Key Insights:\")\n", + " print(\"- Character tokenization: Small vocab, long sequences, perfect coverage\")\n", + " print(\"- BPE: Larger vocab trades off with shorter sequences\")\n", + " print(\"- Higher compression ratio = more characters per token = efficiency\")\n", + "\n", + "analyze_tokenization_strategies()" + ] + }, + { + "cell_type": "markdown", + "id": "aa77ec6d", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "### 📊 Performance Analysis: Vocabulary Size vs Sequence Length\n", + "\n", + "The fundamental trade-off in tokenization creates a classic systems engineering challenge:\n", + "\n", + "```\n", + "Tokenization Trade-off Spectrum:\n", + "\n", + "Character BPE-Small BPE-Large Word-Level\n", + "vocab: ~100 → vocab: ~1K → vocab: ~50K → vocab: ~100K+\n", + "seq: very long → seq: long → seq: medium → seq: short\n", + "memory: low → memory: med → memory: high → memory: very high\n", + "compute: high → compute: med → compute: low → compute: very low\n", + "coverage: 100% → coverage: 99% → coverage: 95% → coverage: <80%\n", + "```\n", + "\n", + "**Character tokenization (vocab ~100)**:\n", + "- Pro: Universal coverage, simple implementation, small embedding table\n", + "- Con: Long sequences (high compute), limited semantic units\n", + "- Use case: Morphologically rich languages, robust preprocessing\n", + "\n", + "**BPE tokenization (vocab 10K-50K)**:\n", + "- Pro: Balanced efficiency, handles morphology, good coverage\n", + "- Con: Training complexity, domain-specific vocabularies\n", + "- Use case: Most modern language models (GPT, BERT family)\n", + "\n", + "**Real-world scaling examples**:\n", + "```\n", + "GPT-3/4: ~50K BPE tokens, avg 3-4 chars/token\n", + "BERT: ~30K WordPiece tokens, avg 4-5 chars/token\n", + "T5: ~32K SentencePiece tokens, handles 100+ languages\n", + "ChatGPT: ~100K tokens with extended vocabulary\n", + "```\n", + "\n", + "**Memory implications for embedding tables**:\n", + "```\n", + "Tokenizer Vocab Size Embed Dim Parameters Memory (fp32)\n", + "Character 100 512 51K 204 KB\n", + "BPE-1K 1,000 512 512K 2.0 MB\n", + "BPE-50K 50,000 512 25.6M 102.4 MB\n", + "Word-100K 100,000 512 51.2M 204.8 MB\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "86ec17b3", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 6. Module Integration Test\n", + "\n", + "Let's test our complete tokenization system to ensure everything works together." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6fe1bf5a", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test-module", + "locked": true, + "points": 20 + } + }, + "outputs": [], + "source": [ + "def test_module():\n", + " \"\"\"\n", + " Comprehensive test of entire tokenization module.\n", + "\n", + " This final test runs before module summary to ensure:\n", + " - All unit tests pass\n", + " - Functions work together correctly\n", + " - Module is ready for integration with TinyTorch\n", + " \"\"\"\n", + " print(\"🧪 RUNNING MODULE INTEGRATION TEST\")\n", + " print(\"=\" * 50)\n", + "\n", + " # Run all unit tests\n", + " print(\"Running unit tests...\")\n", + " test_unit_base_tokenizer()\n", + " test_unit_char_tokenizer()\n", + " test_unit_bpe_tokenizer()\n", + " test_unit_tokenization_utils()\n", + "\n", + " print(\"\\nRunning integration scenarios...\")\n", + "\n", + " # Test realistic tokenization workflow\n", + " print(\"🔬 Integration Test: Complete tokenization pipeline...\")\n", + "\n", + " # Create training corpus\n", + " training_corpus = [\n", + " \"Natural language processing\",\n", + " \"Machine learning models\",\n", + " \"Neural networks learn\",\n", + " \"Tokenization enables text processing\",\n", + " \"Embeddings represent meaning\"\n", + " ]\n", + "\n", + " # Train different tokenizers\n", + " char_tokenizer = create_tokenizer(\"char\", corpus=training_corpus)\n", + " bpe_tokenizer = create_tokenizer(\"bpe\", vocab_size=200, corpus=training_corpus)\n", + "\n", + " # Test on new text\n", + " test_text = \"Neural language models\"\n", + "\n", + " # Test character tokenization\n", + " char_tokens = char_tokenizer.encode(test_text)\n", + " char_decoded = char_tokenizer.decode(char_tokens)\n", + " assert char_decoded == test_text, \"Character round-trip failed\"\n", + "\n", + " # Test BPE tokenization (may not be exact due to subword splits)\n", + " bpe_tokens = bpe_tokenizer.encode(test_text)\n", + " bpe_decoded = bpe_tokenizer.decode(bpe_tokens)\n", + " assert len(bpe_decoded.strip()) > 0, \"BPE decoding failed\"\n", + "\n", + " # Test dataset processing\n", + " test_dataset = [\"hello world\", \"tokenize this\", \"neural networks\"]\n", + " char_dataset = tokenize_dataset(test_dataset, char_tokenizer, max_length=20)\n", + " bpe_dataset = tokenize_dataset(test_dataset, bpe_tokenizer, max_length=10)\n", + "\n", + " assert len(char_dataset) == len(test_dataset)\n", + " assert len(bpe_dataset) == len(test_dataset)\n", + " assert all(len(seq) <= 20 for seq in char_dataset)\n", + " assert all(len(seq) <= 10 for seq in bpe_dataset)\n", + "\n", + " # Test analysis functions\n", + " char_stats = analyze_tokenization(test_dataset, char_tokenizer)\n", + " bpe_stats = analyze_tokenization(test_dataset, bpe_tokenizer)\n", + "\n", + " assert char_stats['vocab_size'] > 0\n", + " assert bpe_stats['vocab_size'] > 0\n", + " assert char_stats['compression_ratio'] < bpe_stats['compression_ratio'] # BPE should compress better\n", + "\n", + " print(\"✅ End-to-end tokenization pipeline works!\")\n", + "\n", + " print(\"\\n\" + \"=\" * 50)\n", + " print(\"🎉 ALL TESTS PASSED! Module ready for export.\")\n", + " print(\"Run: tito module complete 10\")\n", + "\n", + "# Call the comprehensive test\n", + "test_module()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "069cfff2", + "metadata": {}, + "outputs": [], + "source": [ + "if __name__ == \"__main__\":\n", + " print(\"🚀 Running Tokenization module...\")\n", + " test_module()\n", + " print(\"✅ Module validation complete!\")" + ] + }, + { + "cell_type": "markdown", + "id": "2baaec3b", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🤔 ML Systems Thinking: Text Processing Foundations\n", + "\n", + "### Question 1: Vocabulary Size vs Memory\n", + "You implemented tokenizers with different vocabulary sizes.\n", + "If you have a BPE tokenizer with vocab_size=50,000 and embed_dim=512:\n", + "- How many parameters are in the embedding table? _____ million\n", + "- If using float32, how much memory does this embedding table require? _____ MB\n", + "\n", + "### Question 2: Sequence Length Trade-offs\n", + "Your character tokenizer produces longer sequences than BPE.\n", + "For the text \"machine learning\" (16 characters):\n", + "- Character tokenizer produces ~16 tokens\n", + "- BPE tokenizer might produce ~3-4 tokens\n", + "If processing batch_size=32 with max_length=512:\n", + "- Character model needs _____ total tokens per batch\n", + "- BPE model needs _____ total tokens per batch\n", + "- Which requires more memory during training? _____\n", + "\n", + "### Question 3: Tokenization Coverage\n", + "Your BPE tokenizer handles unknown words by decomposing into subwords.\n", + "- Why is this better than word-level tokenization for real applications? _____\n", + "- What happens to model performance when many tokens map to ? _____\n", + "- How does vocabulary size affect the number of unknown decompositions? _____" + ] + }, + { + "cell_type": "markdown", + "id": "33c9fd6d", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🎯 MODULE SUMMARY: Tokenization\n", + "\n", + "Congratulations! You've built a complete tokenization system for converting text to numerical representations!\n", + "\n", + "### Key Accomplishments\n", + "- Built character-level tokenizer with perfect text coverage\n", + "- Implemented BPE tokenizer that learns efficient subword representations\n", + "- Created vocabulary management and encoding/decoding systems\n", + "- Discovered the vocabulary size vs sequence length trade-off\n", + "- All tests pass ✅ (validated by `test_module()`)\n", + "\n", + "### Ready for Next Steps\n", + "Your tokenization implementation enables text processing for language models.\n", + "Export with: `tito module complete 10`\n", + "\n", + "**Next**: Module 11 will add learnable embeddings that convert your token IDs into rich vector representations!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/modules/source/11_embeddings/embeddings_dev.ipynb b/modules/source/11_embeddings/embeddings_dev.ipynb new file mode 100644 index 00000000..654484dc --- /dev/null +++ b/modules/source/11_embeddings/embeddings_dev.ipynb @@ -0,0 +1,1639 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "602a5ff8", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "# Module 11: Embeddings - Converting Tokens to Learnable Representations\n", + "\n", + "Welcome to Module 11! You're about to build embedding layers that convert discrete tokens into dense, learnable vectors - the foundation of all modern NLP models.\n", + "\n", + "## 🔗 Prerequisites & Progress\n", + "**You've Built**: Tensors, layers, tokenization (discrete text processing)\n", + "**You'll Build**: Embedding lookups and positional encodings for sequence modeling\n", + "**You'll Enable**: Foundation for attention mechanisms and transformer architectures\n", + "\n", + "**Connection Map**:\n", + "```\n", + "Tokenization → Embeddings → Positional Encoding → Attention (Module 12)\n", + "(discrete) (dense) (position-aware) (context-aware)\n", + "```\n", + "\n", + "## Learning Objectives\n", + "By the end of this module, you will:\n", + "1. Implement embedding layers for token-to-vector conversion\n", + "2. Understand learnable vs fixed positional encodings\n", + "3. Build both sinusoidal and learned position encodings\n", + "4. Analyze embedding memory requirements and lookup performance\n", + "\n", + "Let's transform tokens into intelligence!\n", + "\n", + "## 📦 Where This Code Lives in the Final Package\n", + "\n", + "**Learning Side:** You work in `modules/11_embeddings/embeddings_dev.py` \n", + "**Building Side:** Code exports to `tinytorch.text.embeddings`\n", + "\n", + "```python\n", + "# How to use this module:\n", + "from tinytorch.text.embeddings import Embedding, PositionalEncoding, create_sinusoidal_embeddings\n", + "```\n", + "\n", + "**Why this matters:**\n", + "- **Learning:** Complete embedding system for converting discrete tokens to continuous representations\n", + "- **Production:** Essential component matching PyTorch's torch.nn.Embedding with positional encoding patterns\n", + "- **Consistency:** All embedding operations and positional encodings in text.embeddings\n", + "- **Integration:** Works seamlessly with tokenizers for complete text processing pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa08bf69", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "imports", + "solution": true + } + }, + "outputs": [], + "source": [ + "\"\"\"\n", + "## 1. Essential Imports and Setup\n", + "\n", + "Setting up our embedding toolkit with tensor operations and mathematical functions.\n", + "\"\"\"\n", + "\n", + "#| default_exp text.embeddings\n", + "#| export\n", + "\n", + "import numpy as np\n", + "import math\n", + "from typing import List, Optional, Tuple\n", + "\n", + "# Core tensor operations - our foundation\n", + "### BEGIN SOLUTION\n", + "# For this educational implementation, we'll create a simple Tensor class\n", + "# In practice, this would import from tinytorch.core.tensor\n", + "\n", + "class Tensor:\n", + " \"\"\"Educational tensor for embeddings module.\"\"\"\n", + "\n", + " def __init__(self, data, requires_grad=False):\n", + " self.data = np.array(data)\n", + " self.shape = self.data.shape\n", + " self.requires_grad = requires_grad\n", + " self.grad = None\n", + "\n", + " def __repr__(self):\n", + " return f\"Tensor({self.data})\"\n", + "\n", + " def __getitem__(self, idx):\n", + " return Tensor(self.data[idx])\n", + "\n", + " def __add__(self, other):\n", + " if isinstance(other, Tensor):\n", + " return Tensor(self.data + other.data)\n", + " return Tensor(self.data + other)\n", + "\n", + " def size(self, dim=None):\n", + " if dim is None:\n", + " return self.shape\n", + " return self.shape[dim]\n", + "\n", + " def reshape(self, *shape):\n", + " return Tensor(self.data.reshape(shape))\n", + "\n", + " def expand(self, *shape):\n", + " return Tensor(np.broadcast_to(self.data, shape))\n", + "\n", + " def parameters(self):\n", + " return [self] if self.requires_grad else []\n", + "\n", + "# Simple Linear layer for this module\n", + "class Linear:\n", + " \"\"\"Educational linear layer.\"\"\"\n", + "\n", + " def __init__(self, in_features, out_features, bias=True):\n", + " # Xavier initialization\n", + " limit = math.sqrt(6.0 / (in_features + out_features))\n", + " self.weight = Tensor(\n", + " np.random.uniform(-limit, limit, (in_features, out_features)),\n", + " requires_grad=True\n", + " )\n", + " self.bias = Tensor(np.zeros(out_features), requires_grad=True) if bias else None\n", + "\n", + " def forward(self, x):\n", + " result = Tensor(np.dot(x.data, self.weight.data))\n", + " if self.bias is not None:\n", + " result = result + self.bias\n", + " return result\n", + "\n", + " def parameters(self):\n", + " params = [self.weight]\n", + " if self.bias is not None:\n", + " params.append(self.bias)\n", + " return params\n", + "### END SOLUTION" + ] + }, + { + "cell_type": "markdown", + "id": "deba8ac1", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 2. Understanding Token Embeddings - From Discrete to Dense\n", + "\n", + "Before we implement embeddings, let's understand what problem they solve and how the lookup process works.\n", + "\n", + "### The Fundamental Challenge\n", + "\n", + "When dealing with text, we start with discrete symbols (words, characters, tokens) but neural networks need continuous numbers. Embeddings bridge this gap by creating a learned mapping from discrete tokens to dense vector representations.\n", + "\n", + "### Token-to-Vector Transformation Visualization\n", + "\n", + "```\n", + "Traditional One-Hot Encoding (Sparse):\n", + "Token \"cat\" (index 42) → [0, 0, ..., 1, ..., 0] (50,000 elements, mostly zeros)\n", + " position 42\n", + "\n", + "Modern Embedding Lookup (Dense):\n", + "Token \"cat\" (index 42) → [0.1, -0.3, 0.7, 0.2, ...] (512 dense, meaningful values)\n", + "```\n", + "\n", + "### How Embedding Lookup Works\n", + "\n", + "```\n", + "Embedding Table (vocab_size × embed_dim):\n", + " Token ID Embedding Vector\n", + " ┌─────┐ ┌─────────────────────────┐\n", + " 0 │ 0 │ → │ [0.2, -0.1, 0.3, ...] │ \"the\"\n", + " 1 │ 1 │ → │ [0.1, 0.4, -0.2, ...] │ \"cat\"\n", + " 2 │ 2 │ → │ [-0.3, 0.1, 0.5, ...] │ \"sat\"\n", + "... │ ... │ │ ... │ ...\n", + "42 │ 42 │ → │ [0.7, -0.2, 0.1, ...] │ \"dog\"\n", + "... │ ... │ │ ... │ ...\n", + " └─────┘ └─────────────────────────┘\n", + "\n", + "Lookup Process:\n", + "Input tokens: [1, 2, 42] → Output: Matrix (3 × embed_dim)\n", + "Row 0: embedding[1] → [0.1, 0.4, -0.2, ...] \"cat\"\n", + "Row 1: embedding[2] → [-0.3, 0.1, 0.5, ...] \"sat\"\n", + "Row 2: embedding[42] → [0.7, -0.2, 0.1, ...] \"dog\"\n", + "```\n", + "\n", + "### Why Embeddings Are Powerful\n", + "\n", + "1. **Dense Representation**: Every dimension can contribute meaningful information\n", + "2. **Learnable**: Vectors adjust during training to capture semantic relationships\n", + "3. **Efficient**: O(1) lookup time regardless of vocabulary size\n", + "4. **Semantic**: Similar words learn similar vector representations\n", + "\n", + "### Memory Implications\n", + "\n", + "For a vocabulary of 50,000 tokens with 512-dimensional embeddings:\n", + "- **Storage**: 50,000 × 512 × 4 bytes = ~100MB (in FP32)\n", + "- **Scaling**: Memory grows linearly with vocab_size × embed_dim\n", + "- **Trade-off**: Larger embeddings capture more nuance but require more memory\n", + "\n", + "This is why embedding tables often dominate memory usage in large language models!" + ] + }, + { + "cell_type": "markdown", + "id": "081e21ef", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 3. Implementing Token Embeddings\n", + "\n", + "Now let's build the core embedding layer that performs efficient token-to-vector lookups." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45893623", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "embedding-class", + "solution": true + } + }, + "outputs": [], + "source": [ + "class Embedding:\n", + " \"\"\"\n", + " Learnable embedding layer that maps token indices to dense vectors.\n", + "\n", + " This is the fundamental building block for converting discrete tokens\n", + " into continuous representations that neural networks can process.\n", + "\n", + " TODO: Implement the Embedding class\n", + "\n", + " APPROACH:\n", + " 1. Initialize embedding matrix with random weights (vocab_size, embed_dim)\n", + " 2. Implement forward pass as matrix lookup using numpy indexing\n", + " 3. Handle batch dimensions correctly\n", + " 4. Return parameters for optimization\n", + "\n", + " EXAMPLE:\n", + " >>> embed = Embedding(vocab_size=100, embed_dim=64)\n", + " >>> tokens = Tensor([[1, 2, 3], [4, 5, 6]]) # batch_size=2, seq_len=3\n", + " >>> output = embed.forward(tokens)\n", + " >>> print(output.shape)\n", + " (2, 3, 64)\n", + "\n", + " HINTS:\n", + " - Use numpy advanced indexing for lookup: weight[indices]\n", + " - Embedding matrix shape: (vocab_size, embed_dim)\n", + " - Initialize with Xavier/Glorot uniform for stable gradients\n", + " - Handle multi-dimensional indices correctly\n", + " \"\"\"\n", + "\n", + " ### BEGIN SOLUTION\n", + " def __init__(self, vocab_size: int, embed_dim: int):\n", + " \"\"\"\n", + " Initialize embedding layer.\n", + "\n", + " Args:\n", + " vocab_size: Size of vocabulary (number of unique tokens)\n", + " embed_dim: Dimension of embedding vectors\n", + " \"\"\"\n", + " self.vocab_size = vocab_size\n", + " self.embed_dim = embed_dim\n", + "\n", + " # Xavier initialization for better gradient flow\n", + " limit = math.sqrt(6.0 / (vocab_size + embed_dim))\n", + " self.weight = Tensor(\n", + " np.random.uniform(-limit, limit, (vocab_size, embed_dim)),\n", + " requires_grad=True\n", + " )\n", + "\n", + " def forward(self, indices: Tensor) -> Tensor:\n", + " \"\"\"\n", + " Forward pass: lookup embeddings for given indices.\n", + "\n", + " Args:\n", + " indices: Token indices of shape (batch_size, seq_len) or (seq_len,)\n", + "\n", + " Returns:\n", + " Embedded vectors of shape (*indices.shape, embed_dim)\n", + " \"\"\"\n", + " # Handle input validation\n", + " if np.any(indices.data >= self.vocab_size) or np.any(indices.data < 0):\n", + " raise ValueError(\n", + " f\"Index out of range. Expected 0 <= indices < {self.vocab_size}, \"\n", + " f\"got min={np.min(indices.data)}, max={np.max(indices.data)}\"\n", + " )\n", + "\n", + " # Perform embedding lookup using advanced indexing\n", + " # This is equivalent to one-hot multiplication but much more efficient\n", + " embedded = self.weight.data[indices.data.astype(int)]\n", + "\n", + " return Tensor(embedded)\n", + "\n", + " def parameters(self) -> List[Tensor]:\n", + " \"\"\"Return trainable parameters.\"\"\"\n", + " return [self.weight]\n", + "\n", + " def __repr__(self):\n", + " return f\"Embedding(vocab_size={self.vocab_size}, embed_dim={self.embed_dim})\"\n", + " ### END SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "188a22f9", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test-embedding", + "locked": true, + "points": 10 + } + }, + "outputs": [], + "source": [ + "def test_unit_embedding():\n", + " \"\"\"🔬 Unit Test: Embedding Layer Implementation\"\"\"\n", + " print(\"🔬 Unit Test: Embedding Layer...\")\n", + "\n", + " # Test 1: Basic embedding creation and forward pass\n", + " embed = Embedding(vocab_size=100, embed_dim=64)\n", + "\n", + " # Single sequence\n", + " tokens = Tensor([1, 2, 3])\n", + " output = embed.forward(tokens)\n", + "\n", + " assert output.shape == (3, 64), f\"Expected shape (3, 64), got {output.shape}\"\n", + " assert len(embed.parameters()) == 1, \"Should have 1 parameter (weight matrix)\"\n", + " assert embed.parameters()[0].shape == (100, 64), \"Weight matrix has wrong shape\"\n", + "\n", + " # Test 2: Batch processing\n", + " batch_tokens = Tensor([[1, 2, 3], [4, 5, 6]])\n", + " batch_output = embed.forward(batch_tokens)\n", + "\n", + " assert batch_output.shape == (2, 3, 64), f\"Expected batch shape (2, 3, 64), got {batch_output.shape}\"\n", + "\n", + " # Test 3: Embedding lookup consistency\n", + " single_lookup = embed.forward(Tensor([1]))\n", + " batch_lookup = embed.forward(Tensor([[1]]))\n", + "\n", + " # Should get same embedding for same token\n", + " assert np.allclose(single_lookup.data[0], batch_lookup.data[0, 0]), \"Inconsistent embedding lookup\"\n", + "\n", + " # Test 4: Parameter access\n", + " params = embed.parameters()\n", + " assert all(p.requires_grad for p in params), \"All parameters should require gradients\"\n", + "\n", + " print(\"✅ Embedding layer works correctly!\")\n", + "\n", + "test_unit_embedding()" + ] + }, + { + "cell_type": "markdown", + "id": "b7ada430", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 4. Understanding Positional Encoding - Teaching Models About Order\n", + "\n", + "Sequences have inherent order, but embeddings by themselves are orderless. We need to explicitly encode positional information so the model understands that \"cat chased dog\" is different from \"dog chased cat\".\n", + "\n", + "### Why Position Matters in Sequences\n", + "\n", + "Unlike images where spatial relationships are built into the 2D structure, text sequences need explicit position encoding:\n", + "\n", + "```\n", + "Word Order Changes Meaning:\n", + "\"The cat chased the dog\" ≠ \"The dog chased the cat\"\n", + "\"Not good\" ≠ \"Good not\"\n", + "\"She told him\" ≠ \"Him told she\"\n", + "```\n", + "\n", + "### Two Approaches to Position Encoding\n", + "\n", + "```\n", + "1. Learned Positional Embeddings:\n", + " ┌─────────────────────────────────────┐\n", + " │ Position │ Learned Vector │\n", + " ├─────────────────────────────────────┤\n", + " │ 0 │ [0.1, -0.2, 0.4, ...] │ (trained)\n", + " │ 1 │ [0.3, 0.1, -0.1, ...] │ (trained)\n", + " │ 2 │ [-0.1, 0.5, 0.2, ...] │ (trained)\n", + " │ ... │ ... │\n", + " │ 511 │ [0.4, -0.3, 0.1, ...] │ (trained)\n", + " └─────────────────────────────────────┘\n", + " ✓ Can learn task-specific patterns\n", + " ✗ Fixed maximum sequence length\n", + " ✗ Requires additional parameters\n", + "\n", + "2. Sinusoidal Position Encodings:\n", + " ┌─────────────────────────────────────┐\n", + " │ Position │ Mathematical Pattern │\n", + " ├─────────────────────────────────────┤\n", + " │ 0 │ [0.0, 1.0, 0.0, ...] │ (computed)\n", + " │ 1 │ [sin1, cos1, sin2, ...] │ (computed)\n", + " │ 2 │ [sin2, cos2, sin4, ...] │ (computed)\n", + " │ ... │ ... │\n", + " │ N │ [sinN, cosN, sin2N,...] │ (computed)\n", + " └─────────────────────────────────────┘\n", + " ✓ No additional parameters\n", + " ✓ Can extrapolate to longer sequences\n", + " ✗ Cannot adapt to specific patterns\n", + "```\n", + "\n", + "### How Positional Information Gets Added\n", + "\n", + "```\n", + "Token Embeddings + Positional Encodings = Position-Aware Representations\n", + "\n", + "Input Sequence: [\"The\", \"cat\", \"sat\"]\n", + "Token IDs: [ 1, 42, 7 ]\n", + "\n", + "Step 1: Token Embeddings\n", + "[1] → [0.1, 0.4, -0.2, ...]\n", + "[42]→ [0.7, -0.2, 0.1, ...]\n", + "[7] → [-0.3, 0.1, 0.5, ...]\n", + "\n", + "Step 2: Position Encodings\n", + "pos 0 → [0.0, 1.0, 0.0, ...]\n", + "pos 1 → [0.8, 0.6, 0.1, ...]\n", + "pos 2 → [0.9, -0.4, 0.2, ...]\n", + "\n", + "Step 3: Addition (element-wise)\n", + "Result:\n", + "[0.1+0.0, 0.4+1.0, -0.2+0.0, ...] = [0.1, 1.4, -0.2, ...] \"The\" at position 0\n", + "[0.7+0.8, -0.2+0.6, 0.1+0.1, ...] = [1.5, 0.4, 0.2, ...] \"cat\" at position 1\n", + "[-0.3+0.9, 0.1-0.4, 0.5+0.2, ...] = [0.6, -0.3, 0.7, ...] \"sat\" at position 2\n", + "```\n", + "\n", + "This way, the same word gets different representations based on its position in the sentence!" + ] + }, + { + "cell_type": "markdown", + "id": "1e0ad59c", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 5. Implementing Learned Positional Encoding\n", + "\n", + "Let's build trainable positional embeddings that can learn position-specific patterns for our specific task." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "621f7e1e", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "positional-encoding", + "solution": true + } + }, + "outputs": [], + "source": [ + "class PositionalEncoding:\n", + " \"\"\"\n", + " Learnable positional encoding layer.\n", + "\n", + " Adds trainable position-specific vectors to token embeddings,\n", + " allowing the model to learn positional patterns specific to the task.\n", + "\n", + " TODO: Implement learnable positional encoding\n", + "\n", + " APPROACH:\n", + " 1. Create embedding matrix for positions: (max_seq_len, embed_dim)\n", + " 2. Forward pass: lookup position embeddings and add to input\n", + " 3. Handle different sequence lengths gracefully\n", + " 4. Return parameters for training\n", + "\n", + " EXAMPLE:\n", + " >>> pos_enc = PositionalEncoding(max_seq_len=512, embed_dim=64)\n", + " >>> embeddings = Tensor(np.random.randn(2, 10, 64)) # (batch, seq, embed)\n", + " >>> output = pos_enc.forward(embeddings)\n", + " >>> print(output.shape)\n", + " (2, 10, 64) # Same shape, but now position-aware\n", + "\n", + " HINTS:\n", + " - Position embeddings shape: (max_seq_len, embed_dim)\n", + " - Use slice [:seq_len] to handle variable lengths\n", + " - Add position encodings to input embeddings element-wise\n", + " - Initialize with smaller values than token embeddings (they're additive)\n", + " \"\"\"\n", + "\n", + " ### BEGIN SOLUTION\n", + " def __init__(self, max_seq_len: int, embed_dim: int):\n", + " \"\"\"\n", + " Initialize learnable positional encoding.\n", + "\n", + " Args:\n", + " max_seq_len: Maximum sequence length to support\n", + " embed_dim: Embedding dimension (must match token embeddings)\n", + " \"\"\"\n", + " self.max_seq_len = max_seq_len\n", + " self.embed_dim = embed_dim\n", + "\n", + " # Initialize position embedding matrix\n", + " # Smaller initialization than token embeddings since these are additive\n", + " limit = math.sqrt(2.0 / embed_dim)\n", + " self.position_embeddings = Tensor(\n", + " np.random.uniform(-limit, limit, (max_seq_len, embed_dim)),\n", + " requires_grad=True\n", + " )\n", + "\n", + " def forward(self, x: Tensor) -> Tensor:\n", + " \"\"\"\n", + " Add positional encodings to input embeddings.\n", + "\n", + " Args:\n", + " x: Input embeddings of shape (batch_size, seq_len, embed_dim)\n", + "\n", + " Returns:\n", + " Position-encoded embeddings of same shape\n", + " \"\"\"\n", + " if len(x.shape) != 3:\n", + " raise ValueError(f\"Expected 3D input (batch, seq, embed), got shape {x.shape}\")\n", + "\n", + " batch_size, seq_len, embed_dim = x.shape\n", + "\n", + " if seq_len > self.max_seq_len:\n", + " raise ValueError(\n", + " f\"Sequence length {seq_len} exceeds maximum {self.max_seq_len}\"\n", + " )\n", + "\n", + " if embed_dim != self.embed_dim:\n", + " raise ValueError(\n", + " f\"Embedding dimension mismatch: expected {self.embed_dim}, got {embed_dim}\"\n", + " )\n", + "\n", + " # Get position embeddings for this sequence length\n", + " pos_embeddings = self.position_embeddings.data[:seq_len] # (seq_len, embed_dim)\n", + "\n", + " # Broadcast to match batch dimension: (1, seq_len, embed_dim)\n", + " pos_embeddings = pos_embeddings[np.newaxis, :, :]\n", + "\n", + " # Add positional information to input embeddings\n", + " result = x.data + pos_embeddings\n", + "\n", + " return Tensor(result)\n", + "\n", + " def parameters(self) -> List[Tensor]:\n", + " \"\"\"Return trainable parameters.\"\"\"\n", + " return [self.position_embeddings]\n", + "\n", + " def __repr__(self):\n", + " return f\"PositionalEncoding(max_seq_len={self.max_seq_len}, embed_dim={self.embed_dim})\"\n", + " ### END SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51dd828a", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test-positional", + "locked": true, + "points": 10 + } + }, + "outputs": [], + "source": [ + "def test_unit_positional_encoding():\n", + " \"\"\"🔬 Unit Test: Positional Encoding Implementation\"\"\"\n", + " print(\"🔬 Unit Test: Positional Encoding...\")\n", + "\n", + " # Test 1: Basic functionality\n", + " pos_enc = PositionalEncoding(max_seq_len=512, embed_dim=64)\n", + "\n", + " # Create sample embeddings\n", + " embeddings = Tensor(np.random.randn(2, 10, 64))\n", + " output = pos_enc.forward(embeddings)\n", + "\n", + " assert output.shape == (2, 10, 64), f\"Expected shape (2, 10, 64), got {output.shape}\"\n", + "\n", + " # Test 2: Position consistency\n", + " # Same position should always get same encoding\n", + " emb1 = Tensor(np.zeros((1, 5, 64)))\n", + " emb2 = Tensor(np.zeros((1, 5, 64)))\n", + "\n", + " out1 = pos_enc.forward(emb1)\n", + " out2 = pos_enc.forward(emb2)\n", + "\n", + " assert np.allclose(out1.data, out2.data), \"Position encodings should be consistent\"\n", + "\n", + " # Test 3: Different positions get different encodings\n", + " short_emb = Tensor(np.zeros((1, 3, 64)))\n", + " long_emb = Tensor(np.zeros((1, 5, 64)))\n", + "\n", + " short_out = pos_enc.forward(short_emb)\n", + " long_out = pos_enc.forward(long_emb)\n", + "\n", + " # First 3 positions should match\n", + " assert np.allclose(short_out.data, long_out.data[:, :3, :]), \"Position encoding prefix should match\"\n", + "\n", + " # Test 4: Parameters\n", + " params = pos_enc.parameters()\n", + " assert len(params) == 1, \"Should have 1 parameter (position embeddings)\"\n", + " assert params[0].shape == (512, 64), \"Position embedding matrix has wrong shape\"\n", + "\n", + " print(\"✅ Positional encoding works correctly!\")\n", + "\n", + "test_unit_positional_encoding()" + ] + }, + { + "cell_type": "markdown", + "id": "17d6953f", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 6. Understanding Sinusoidal Position Encodings\n", + "\n", + "Now let's explore the elegant mathematical approach to position encoding used in the original Transformer paper. Instead of learning position patterns, we'll use trigonometric functions to create unique, continuous position signatures.\n", + "\n", + "### The Mathematical Intuition\n", + "\n", + "Sinusoidal encodings use sine and cosine functions at different frequencies to create unique position signatures:\n", + "\n", + "```\n", + "PE(pos, 2i) = sin(pos / 10000^(2i/d_model)) # Even dimensions\n", + "PE(pos, 2i+1) = cos(pos / 10000^(2i/d_model)) # Odd dimensions\n", + "```\n", + "\n", + "### Why This Works - Frequency Visualization\n", + "\n", + "```\n", + "Position Encoding Pattern (embed_dim=8, showing 4 positions):\n", + "\n", + "Dimension: 0 1 2 3 4 5 6 7\n", + "Frequency: High High Med Med Low Low VLow VLow\n", + "Function: sin cos sin cos sin cos sin cos\n", + "\n", + "pos=0: [0.00, 1.00, 0.00, 1.00, 0.00, 1.00, 0.00, 1.00]\n", + "pos=1: [0.84, 0.54, 0.01, 1.00, 0.00, 1.00, 0.00, 1.00]\n", + "pos=2: [0.91, -0.42, 0.02, 1.00, 0.00, 1.00, 0.00, 1.00]\n", + "pos=3: [0.14, -0.99, 0.03, 1.00, 0.00, 1.00, 0.00, 1.00]\n", + "\n", + "Notice how:\n", + "- High frequency dimensions (0,1) change quickly between positions\n", + "- Low frequency dimensions (6,7) change slowly\n", + "- Each position gets a unique \"fingerprint\"\n", + "```\n", + "\n", + "### Visual Pattern of Sinusoidal Encodings\n", + "\n", + "```\n", + "Frequency Spectrum Across Dimensions:\n", + "High Freq ← - - - - - - - - - - - - - - - - - - - - - → Low Freq\n", + "Dim: 0 1 2 3 4 5 6 7 8 9 ... 510 511\n", + "\n", + "Wave Pattern for Position Progression:\n", + "Dim 0: ∿∿∿∿∿∿∿∿∿∿∿∿∿∿∿∿∿∿∿∿ (rapid oscillation)\n", + "Dim 2: ∿---∿---∿---∿---∿---∿ (medium frequency)\n", + "Dim 4: ∿-----∿-----∿-----∿-- (low frequency)\n", + "Dim 6: ∿----------∿---------- (very slow changes)\n", + "\n", + "This creates a unique \"barcode\" for each position!\n", + "```\n", + "\n", + "### Advantages of Sinusoidal Encodings\n", + "\n", + "1. **No Parameters**: Zero additional memory overhead\n", + "2. **Extrapolation**: Can handle sequences longer than training data\n", + "3. **Unique Signatures**: Each position gets a distinct encoding\n", + "4. **Smooth Transitions**: Similar positions have similar encodings\n", + "5. **Mathematical Elegance**: Clean, interpretable patterns" + ] + }, + { + "cell_type": "markdown", + "id": "c587b2ff", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 7. Implementing Sinusoidal Positional Encodings\n", + "\n", + "Let's implement the mathematical position encoding that creates unique signatures for each position using trigonometric functions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec27cdcd", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "sinusoidal-function", + "solution": true + } + }, + "outputs": [], + "source": [ + "def create_sinusoidal_embeddings(max_seq_len: int, embed_dim: int) -> Tensor:\n", + " \"\"\"\n", + " Create sinusoidal positional encodings as used in \"Attention Is All You Need\".\n", + "\n", + " These fixed encodings use sine and cosine functions to create unique\n", + " positional patterns that don't require training and can extrapolate\n", + " to longer sequences than seen during training.\n", + "\n", + " TODO: Implement sinusoidal positional encoding generation\n", + "\n", + " APPROACH:\n", + " 1. Create position indices: [0, 1, 2, ..., max_seq_len-1]\n", + " 2. Create dimension indices for frequency calculation\n", + " 3. Apply sine to even dimensions, cosine to odd dimensions\n", + " 4. Use the transformer paper formula with 10000 base\n", + "\n", + " MATHEMATICAL FORMULA:\n", + " PE(pos, 2i) = sin(pos / 10000^(2i/embed_dim))\n", + " PE(pos, 2i+1) = cos(pos / 10000^(2i/embed_dim))\n", + "\n", + " EXAMPLE:\n", + " >>> pe = create_sinusoidal_embeddings(512, 64)\n", + " >>> print(pe.shape)\n", + " (512, 64)\n", + " >>> # Position 0: [0, 1, 0, 1, 0, 1, ...] (sin(0)=0, cos(0)=1)\n", + " >>> # Each position gets unique trigonometric signature\n", + "\n", + " HINTS:\n", + " - Use np.arange to create position and dimension arrays\n", + " - Calculate div_term using exponential for frequency scaling\n", + " - Apply different formulas to even/odd dimensions\n", + " - The 10000 base creates different frequencies for different dimensions\n", + " \"\"\"\n", + "\n", + " ### BEGIN SOLUTION\n", + " # Create position indices [0, 1, 2, ..., max_seq_len-1]\n", + " position = np.arange(max_seq_len, dtype=np.float32)[:, np.newaxis] # (max_seq_len, 1)\n", + "\n", + " # Create dimension indices for calculating frequencies\n", + " div_term = np.exp(\n", + " np.arange(0, embed_dim, 2, dtype=np.float32) *\n", + " -(math.log(10000.0) / embed_dim)\n", + " ) # (embed_dim//2,)\n", + "\n", + " # Initialize the positional encoding matrix\n", + " pe = np.zeros((max_seq_len, embed_dim), dtype=np.float32)\n", + "\n", + " # Apply sine to even indices (0, 2, 4, ...)\n", + " pe[:, 0::2] = np.sin(position * div_term)\n", + "\n", + " # Apply cosine to odd indices (1, 3, 5, ...)\n", + " if embed_dim % 2 == 1:\n", + " # Handle odd embed_dim by only filling available positions\n", + " pe[:, 1::2] = np.cos(position * div_term[:-1])\n", + " else:\n", + " pe[:, 1::2] = np.cos(position * div_term)\n", + "\n", + " return Tensor(pe)\n", + " ### END SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8cc1a33b", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test-sinusoidal", + "locked": true, + "points": 10 + } + }, + "outputs": [], + "source": [ + "def test_unit_sinusoidal_embeddings():\n", + " \"\"\"🔬 Unit Test: Sinusoidal Positional Embeddings\"\"\"\n", + " print(\"🔬 Unit Test: Sinusoidal Embeddings...\")\n", + "\n", + " # Test 1: Basic shape and properties\n", + " pe = create_sinusoidal_embeddings(512, 64)\n", + "\n", + " assert pe.shape == (512, 64), f\"Expected shape (512, 64), got {pe.shape}\"\n", + "\n", + " # Test 2: Position 0 should be mostly zeros and ones\n", + " pos_0 = pe.data[0]\n", + "\n", + " # Even indices should be sin(0) = 0\n", + " assert np.allclose(pos_0[0::2], 0, atol=1e-6), \"Even indices at position 0 should be ~0\"\n", + "\n", + " # Odd indices should be cos(0) = 1\n", + " assert np.allclose(pos_0[1::2], 1, atol=1e-6), \"Odd indices at position 0 should be ~1\"\n", + "\n", + " # Test 3: Different positions should have different encodings\n", + " pe_small = create_sinusoidal_embeddings(10, 8)\n", + "\n", + " # Check that consecutive positions are different\n", + " for i in range(9):\n", + " assert not np.allclose(pe_small.data[i], pe_small.data[i+1]), f\"Positions {i} and {i+1} are too similar\"\n", + "\n", + " # Test 4: Frequency properties\n", + " # Higher dimensions should have lower frequencies (change more slowly)\n", + " pe_test = create_sinusoidal_embeddings(100, 16)\n", + "\n", + " # First dimension should change faster than last dimension\n", + " first_dim_changes = np.sum(np.abs(np.diff(pe_test.data[:10, 0])))\n", + " last_dim_changes = np.sum(np.abs(np.diff(pe_test.data[:10, -1])))\n", + "\n", + " assert first_dim_changes > last_dim_changes, \"Lower dimensions should change faster than higher dimensions\"\n", + "\n", + " # Test 5: Odd embed_dim handling\n", + " pe_odd = create_sinusoidal_embeddings(10, 7)\n", + " assert pe_odd.shape == (10, 7), \"Should handle odd embedding dimensions\"\n", + "\n", + " print(\"✅ Sinusoidal embeddings work correctly!\")\n", + "\n", + "test_unit_sinusoidal_embeddings()" + ] + }, + { + "cell_type": "markdown", + "id": "c4badc9e", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 8. Building the Complete Embedding System\n", + "\n", + "Now let's integrate everything into a production-ready embedding system that handles both token and positional embeddings, supports multiple encoding types, and manages the full embedding pipeline used in modern NLP models.\n", + "\n", + "### Complete Embedding Pipeline Visualization\n", + "\n", + "```\n", + "Complete Embedding System Architecture:\n", + "\n", + "Input: Token IDs [1, 42, 7, 99]\n", + " ↓\n", + " ┌─────────────────────┐\n", + " │ Token Embedding │ vocab_size × embed_dim table\n", + " │ Lookup Table │\n", + " └─────────────────────┘\n", + " ↓\n", + " Token Vectors (4 × embed_dim)\n", + " [0.1, 0.4, -0.2, ...] ← token 1\n", + " [0.7, -0.2, 0.1, ...] ← token 42\n", + " [-0.3, 0.1, 0.5, ...] ← token 7\n", + " [0.9, -0.1, 0.3, ...] ← token 99\n", + " ↓\n", + " ┌─────────────────────┐\n", + " │ Positional Encoding │ Choose: Learned, Sinusoidal, or None\n", + " │ (Add position info) │\n", + " └─────────────────────┘\n", + " ↓\n", + " Position-Aware Embeddings (4 × embed_dim)\n", + " [0.1+pos0, 0.4+pos0, ...] ← token 1 at position 0\n", + " [0.7+pos1, -0.2+pos1, ...] ← token 42 at position 1\n", + " [-0.3+pos2, 0.1+pos2, ...] ← token 7 at position 2\n", + " [0.9+pos3, -0.1+pos3, ...] ← token 99 at position 3\n", + " ↓\n", + " Optional: Scale by √embed_dim (Transformer convention)\n", + " ↓\n", + " Ready for Attention Mechanisms!\n", + "```\n", + "\n", + "### Integration Features\n", + "\n", + "- **Flexible Position Encoding**: Support learned, sinusoidal, or no positional encoding\n", + "- **Batch Processing**: Handle variable-length sequences with padding\n", + "- **Memory Efficiency**: Reuse position encodings across batches\n", + "- **Production Ready**: Matches PyTorch patterns and conventions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e075f93", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "complete-system", + "solution": true + } + }, + "outputs": [], + "source": [ + "class EmbeddingLayer:\n", + " \"\"\"\n", + " Complete embedding system combining token and positional embeddings.\n", + "\n", + " This is the production-ready component that handles the full embedding\n", + " pipeline used in transformers and other sequence models.\n", + "\n", + " TODO: Implement complete embedding system\n", + "\n", + " APPROACH:\n", + " 1. Combine token embedding + positional encoding\n", + " 2. Support both learned and sinusoidal position encodings\n", + " 3. Handle variable sequence lengths gracefully\n", + " 4. Add optional embedding scaling (Transformer convention)\n", + "\n", + " EXAMPLE:\n", + " >>> embed_layer = EmbeddingLayer(\n", + " ... vocab_size=50000,\n", + " ... embed_dim=512,\n", + " ... max_seq_len=2048,\n", + " ... pos_encoding='learned'\n", + " ... )\n", + " >>> tokens = Tensor([[1, 2, 3], [4, 5, 6]])\n", + " >>> output = embed_layer.forward(tokens)\n", + " >>> print(output.shape)\n", + " (2, 3, 512)\n", + "\n", + " HINTS:\n", + " - First apply token embedding, then add positional encoding\n", + " - Support 'learned', 'sinusoidal', or None for pos_encoding\n", + " - Handle both 2D (batch, seq) and 1D (seq) inputs gracefully\n", + " - Scale embeddings by sqrt(embed_dim) if requested (transformer convention)\n", + " \"\"\"\n", + "\n", + " ### BEGIN SOLUTION\n", + " def __init__(\n", + " self,\n", + " vocab_size: int,\n", + " embed_dim: int,\n", + " max_seq_len: int = 512,\n", + " pos_encoding: str = 'learned',\n", + " scale_embeddings: bool = False\n", + " ):\n", + " \"\"\"\n", + " Initialize complete embedding system.\n", + "\n", + " Args:\n", + " vocab_size: Size of vocabulary\n", + " embed_dim: Embedding dimension\n", + " max_seq_len: Maximum sequence length for positional encoding\n", + " pos_encoding: Type of positional encoding ('learned', 'sinusoidal', or None)\n", + " scale_embeddings: Whether to scale embeddings by sqrt(embed_dim)\n", + " \"\"\"\n", + " self.vocab_size = vocab_size\n", + " self.embed_dim = embed_dim\n", + " self.max_seq_len = max_seq_len\n", + " self.pos_encoding_type = pos_encoding\n", + " self.scale_embeddings = scale_embeddings\n", + "\n", + " # Token embedding layer\n", + " self.token_embedding = Embedding(vocab_size, embed_dim)\n", + "\n", + " # Positional encoding\n", + " if pos_encoding == 'learned':\n", + " self.pos_encoding = PositionalEncoding(max_seq_len, embed_dim)\n", + " elif pos_encoding == 'sinusoidal':\n", + " # Create fixed sinusoidal encodings (no parameters)\n", + " self.pos_encoding = create_sinusoidal_embeddings(max_seq_len, embed_dim)\n", + " elif pos_encoding is None:\n", + " self.pos_encoding = None\n", + " else:\n", + " raise ValueError(f\"Unknown pos_encoding: {pos_encoding}. Use 'learned', 'sinusoidal', or None\")\n", + "\n", + " def forward(self, tokens: Tensor) -> Tensor:\n", + " \"\"\"\n", + " Forward pass through complete embedding system.\n", + "\n", + " Args:\n", + " tokens: Token indices of shape (batch_size, seq_len) or (seq_len,)\n", + "\n", + " Returns:\n", + " Embedded tokens with positional information\n", + " \"\"\"\n", + " # Handle 1D input by adding batch dimension\n", + " if len(tokens.shape) == 1:\n", + " tokens = Tensor(tokens.data[np.newaxis, :]) # (1, seq_len)\n", + " squeeze_batch = True\n", + " else:\n", + " squeeze_batch = False\n", + "\n", + " # Get token embeddings\n", + " token_embeds = self.token_embedding.forward(tokens) # (batch, seq, embed)\n", + "\n", + " # Scale embeddings if requested (transformer convention)\n", + " if self.scale_embeddings:\n", + " token_embeds = Tensor(token_embeds.data * math.sqrt(self.embed_dim))\n", + "\n", + " # Add positional encoding\n", + " if self.pos_encoding_type == 'learned':\n", + " # Use learnable positional encoding\n", + " output = self.pos_encoding.forward(token_embeds)\n", + " elif self.pos_encoding_type == 'sinusoidal':\n", + " # Use fixed sinusoidal encoding\n", + " batch_size, seq_len, embed_dim = token_embeds.shape\n", + " pos_embeddings = self.pos_encoding.data[:seq_len] # (seq_len, embed_dim)\n", + " pos_embeddings = pos_embeddings[np.newaxis, :, :] # (1, seq_len, embed_dim)\n", + " output = Tensor(token_embeds.data + pos_embeddings)\n", + " else:\n", + " # No positional encoding\n", + " output = token_embeds\n", + "\n", + " # Remove batch dimension if it was added\n", + " if squeeze_batch:\n", + " output = Tensor(output.data[0]) # (seq_len, embed_dim)\n", + "\n", + " return output\n", + "\n", + " def parameters(self) -> List[Tensor]:\n", + " \"\"\"Return all trainable parameters.\"\"\"\n", + " params = self.token_embedding.parameters()\n", + "\n", + " if self.pos_encoding_type == 'learned':\n", + " params.extend(self.pos_encoding.parameters())\n", + "\n", + " return params\n", + "\n", + " def __repr__(self):\n", + " return (f\"EmbeddingLayer(vocab_size={self.vocab_size}, \"\n", + " f\"embed_dim={self.embed_dim}, \"\n", + " f\"pos_encoding='{self.pos_encoding_type}')\")\n", + " ### END SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "628747e8", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test-complete-system", + "locked": true, + "points": 15 + } + }, + "outputs": [], + "source": [ + "def test_unit_complete_embedding_system():\n", + " \"\"\"🔬 Unit Test: Complete Embedding System\"\"\"\n", + " print(\"🔬 Unit Test: Complete Embedding System...\")\n", + "\n", + " # Test 1: Learned positional encoding\n", + " embed_learned = EmbeddingLayer(\n", + " vocab_size=100,\n", + " embed_dim=64,\n", + " max_seq_len=128,\n", + " pos_encoding='learned'\n", + " )\n", + "\n", + " tokens = Tensor([[1, 2, 3], [4, 5, 6]])\n", + " output_learned = embed_learned.forward(tokens)\n", + "\n", + " assert output_learned.shape == (2, 3, 64), f\"Expected shape (2, 3, 64), got {output_learned.shape}\"\n", + "\n", + " # Test 2: Sinusoidal positional encoding\n", + " embed_sin = EmbeddingLayer(\n", + " vocab_size=100,\n", + " embed_dim=64,\n", + " pos_encoding='sinusoidal'\n", + " )\n", + "\n", + " output_sin = embed_sin.forward(tokens)\n", + " assert output_sin.shape == (2, 3, 64), \"Sinusoidal embedding should have same shape\"\n", + "\n", + " # Test 3: No positional encoding\n", + " embed_none = EmbeddingLayer(\n", + " vocab_size=100,\n", + " embed_dim=64,\n", + " pos_encoding=None\n", + " )\n", + "\n", + " output_none = embed_none.forward(tokens)\n", + " assert output_none.shape == (2, 3, 64), \"No pos encoding should have same shape\"\n", + "\n", + " # Test 4: 1D input handling\n", + " tokens_1d = Tensor([1, 2, 3])\n", + " output_1d = embed_learned.forward(tokens_1d)\n", + "\n", + " assert output_1d.shape == (3, 64), f\"Expected shape (3, 64) for 1D input, got {output_1d.shape}\"\n", + "\n", + " # Test 5: Embedding scaling\n", + " embed_scaled = EmbeddingLayer(\n", + " vocab_size=100,\n", + " embed_dim=64,\n", + " pos_encoding=None,\n", + " scale_embeddings=True\n", + " )\n", + "\n", + " # Use same weights to ensure fair comparison\n", + " embed_scaled.token_embedding.weight = embed_none.token_embedding.weight\n", + "\n", + " output_scaled = embed_scaled.forward(tokens)\n", + " output_unscaled = embed_none.forward(tokens)\n", + "\n", + " # Scaled version should be sqrt(64) times larger\n", + " scale_factor = math.sqrt(64)\n", + " expected_scaled = output_unscaled.data * scale_factor\n", + " assert np.allclose(output_scaled.data, expected_scaled, rtol=1e-5), \"Embedding scaling not working correctly\"\n", + "\n", + " # Test 6: Parameter counting\n", + " params_learned = embed_learned.parameters()\n", + " params_sin = embed_sin.parameters()\n", + " params_none = embed_none.parameters()\n", + "\n", + " assert len(params_learned) == 2, \"Learned encoding should have 2 parameter tensors\"\n", + " assert len(params_sin) == 1, \"Sinusoidal encoding should have 1 parameter tensor\"\n", + " assert len(params_none) == 1, \"No pos encoding should have 1 parameter tensor\"\n", + "\n", + " print(\"✅ Complete embedding system works correctly!\")\n", + "\n", + "test_unit_complete_embedding_system()" + ] + }, + { + "cell_type": "markdown", + "id": "0eb96ac1", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 9. Systems Analysis - Embedding Memory and Performance\n", + "\n", + "Understanding the systems implications of embedding layers is crucial for building scalable NLP models. Let's analyze memory usage, lookup performance, and trade-offs between different approaches.\n", + "\n", + "### Memory Usage Analysis\n", + "\n", + "```\n", + "Embedding Memory Scaling:\n", + "Vocabulary Size vs Memory Usage (embed_dim=512, FP32):\n", + "\n", + " 10K vocab: 10,000 × 512 × 4 bytes = 20 MB\n", + " 50K vocab: 50,000 × 512 × 4 bytes = 100 MB\n", + "100K vocab: 100,000 × 512 × 4 bytes = 200 MB\n", + " 1M vocab: 1,000,000 × 512 × 4 bytes = 2 GB\n", + "\n", + "GPT-3 Scale: 50,257 × 12,288 × 4 bytes ≈ 2.4 GB just for embeddings!\n", + "\n", + "Memory Formula: vocab_size × embed_dim × 4 bytes (FP32)\n", + "```\n", + "\n", + "### Performance Characteristics\n", + "\n", + "```\n", + "Embedding Lookup Performance:\n", + "- Time Complexity: O(1) per token (hash table lookup)\n", + "- Memory Access: Random access pattern\n", + "- Bottleneck: Memory bandwidth, not computation\n", + "- Batching: Improves throughput via vectorization\n", + "\n", + "Cache Efficiency:\n", + "Repeated tokens → Cache hits → Faster access\n", + "Diverse vocab → Cache misses → Slower access\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "013ea8d0", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "memory-analysis", + "solution": true + } + }, + "outputs": [], + "source": [ + "def analyze_embedding_memory():\n", + " \"\"\"📊 Analyze embedding memory requirements and scaling behavior.\"\"\"\n", + " print(\"📊 Analyzing Embedding Memory Requirements...\")\n", + "\n", + " # Vocabulary and embedding dimension scenarios\n", + " scenarios = [\n", + " (\"Small Model\", 10_000, 256),\n", + " (\"Medium Model\", 50_000, 512),\n", + " (\"Large Model\", 100_000, 1024),\n", + " (\"GPT-3 Scale\", 50_257, 12_288),\n", + " ]\n", + "\n", + " print(f\"{'Model':<15} {'Vocab Size':<12} {'Embed Dim':<12} {'Memory (MB)':<15} {'Parameters (M)':<15}\")\n", + " print(\"-\" * 80)\n", + "\n", + " for name, vocab_size, embed_dim in scenarios:\n", + " # Calculate memory for FP32 (4 bytes per parameter)\n", + " params = vocab_size * embed_dim\n", + " memory_mb = params * 4 / (1024 * 1024)\n", + " params_m = params / 1_000_000\n", + "\n", + " print(f\"{name:<15} {vocab_size:<12,} {embed_dim:<12} {memory_mb:<15.1f} {params_m:<15.2f}\")\n", + "\n", + " print(\"\\n💡 Key Insights:\")\n", + " print(\"• Embedding tables often dominate model memory (especially for large vocabularies)\")\n", + " print(\"• Memory scales linearly with vocab_size × embed_dim\")\n", + " print(\"• Consider vocabulary pruning for memory-constrained environments\")\n", + "\n", + " # Positional encoding memory comparison\n", + " print(f\"\\n📊 Positional Encoding Memory Comparison (embed_dim=512, max_seq_len=2048):\")\n", + "\n", + " learned_params = 2048 * 512\n", + " learned_memory = learned_params * 4 / (1024 * 1024)\n", + "\n", + " print(f\"Learned PE: {learned_memory:.1f} MB ({learned_params:,} parameters)\")\n", + " print(f\"Sinusoidal PE: 0.0 MB (0 parameters - computed on-the-fly)\")\n", + " print(f\"No PE: 0.0 MB (0 parameters)\")\n", + "\n", + " print(\"\\n🚀 Production Implications:\")\n", + " print(\"• GPT-3's embedding table: ~2.4GB (50K vocab × 12K dims)\")\n", + " print(\"• Learned PE adds memory but may improve task-specific performance\")\n", + " print(\"• Sinusoidal PE saves memory and allows longer sequences\")\n", + "\n", + "analyze_embedding_memory()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24e1dccb", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "lookup-performance", + "solution": true + } + }, + "outputs": [], + "source": [ + "def analyze_lookup_performance():\n", + " \"\"\"📊 Analyze embedding lookup performance characteristics.\"\"\"\n", + " print(\"\\n📊 Analyzing Embedding Lookup Performance...\")\n", + "\n", + " import time\n", + "\n", + " # Test different vocabulary sizes and batch configurations\n", + " vocab_sizes = [1_000, 10_000, 100_000]\n", + " embed_dim = 512\n", + " seq_len = 128\n", + " batch_sizes = [1, 16, 64, 256]\n", + "\n", + " print(f\"{'Vocab Size':<12} {'Batch Size':<12} {'Lookup Time (ms)':<18} {'Throughput (tokens/s)':<20}\")\n", + " print(\"-\" * 70)\n", + "\n", + " for vocab_size in vocab_sizes:\n", + " # Create embedding layer\n", + " embed = Embedding(vocab_size, embed_dim)\n", + "\n", + " for batch_size in batch_sizes:\n", + " # Create random token batch\n", + " tokens = Tensor(np.random.randint(0, vocab_size, (batch_size, seq_len)))\n", + "\n", + " # Warmup\n", + " for _ in range(5):\n", + " _ = embed.forward(tokens)\n", + "\n", + " # Time the lookup\n", + " start_time = time.time()\n", + " iterations = 100\n", + "\n", + " for _ in range(iterations):\n", + " output = embed.forward(tokens)\n", + "\n", + " end_time = time.time()\n", + "\n", + " # Calculate metrics\n", + " total_time = end_time - start_time\n", + " avg_time_ms = (total_time / iterations) * 1000\n", + " total_tokens = batch_size * seq_len * iterations\n", + " throughput = total_tokens / total_time\n", + "\n", + " print(f\"{vocab_size:<12,} {batch_size:<12} {avg_time_ms:<18.2f} {throughput:<20,.0f}\")\n", + "\n", + " print(\"\\n💡 Performance Insights:\")\n", + " print(\"• Lookup time is O(1) per token - vocabulary size doesn't affect individual lookups\")\n", + " print(\"• Larger batches improve throughput due to vectorization\")\n", + " print(\"• Memory bandwidth becomes bottleneck for large embedding dimensions\")\n", + " print(\"• Cache locality important for repeated token patterns\")\n", + "\n", + "analyze_lookup_performance()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9f3a8e19", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "position-encoding-comparison", + "solution": true + } + }, + "outputs": [], + "source": [ + "def analyze_positional_encoding_trade_offs():\n", + " \"\"\"📊 Compare learned vs sinusoidal positional encodings.\"\"\"\n", + " print(\"\\n📊 Analyzing Positional Encoding Trade-offs...\")\n", + "\n", + " max_seq_len = 512\n", + " embed_dim = 256\n", + "\n", + " # Create both types of positional encodings\n", + " learned_pe = PositionalEncoding(max_seq_len, embed_dim)\n", + " sinusoidal_pe = create_sinusoidal_embeddings(max_seq_len, embed_dim)\n", + "\n", + " # Analyze memory footprint\n", + " learned_params = max_seq_len * embed_dim\n", + " learned_memory = learned_params * 4 / (1024 * 1024) # MB\n", + "\n", + " print(f\"📈 Memory Comparison:\")\n", + " print(f\"Learned PE: {learned_memory:.2f} MB ({learned_params:,} parameters)\")\n", + " print(f\"Sinusoidal PE: 0.00 MB (0 parameters)\")\n", + "\n", + " # Analyze encoding patterns\n", + " print(f\"\\n📈 Encoding Pattern Analysis:\")\n", + "\n", + " # Test sample sequences\n", + " test_input = Tensor(np.random.randn(1, 10, embed_dim))\n", + "\n", + " learned_output = learned_pe.forward(test_input)\n", + "\n", + " # For sinusoidal, manually add to match learned interface\n", + " sin_encodings = sinusoidal_pe.data[:10][np.newaxis, :, :] # (1, 10, embed_dim)\n", + " sinusoidal_output = Tensor(test_input.data + sin_encodings)\n", + "\n", + " # Analyze variance across positions\n", + " learned_var = np.var(learned_output.data, axis=1).mean() # Variance across positions\n", + " sin_var = np.var(sinusoidal_output.data, axis=1).mean()\n", + "\n", + " print(f\"Position variance (learned): {learned_var:.4f}\")\n", + " print(f\"Position variance (sinusoidal): {sin_var:.4f}\")\n", + "\n", + " # Check extrapolation capability\n", + " print(f\"\\n📈 Extrapolation Analysis:\")\n", + " extended_length = max_seq_len + 100\n", + "\n", + " try:\n", + " # Learned PE cannot handle longer sequences\n", + " extended_learned = PositionalEncoding(extended_length, embed_dim)\n", + " print(f\"Learned PE: Requires retraining for sequences > {max_seq_len}\")\n", + " except:\n", + " print(f\"Learned PE: Cannot handle sequences > {max_seq_len}\")\n", + "\n", + " # Sinusoidal can extrapolate\n", + " extended_sin = create_sinusoidal_embeddings(extended_length, embed_dim)\n", + " print(f\"Sinusoidal PE: Can extrapolate to length {extended_length} (smooth continuation)\")\n", + "\n", + " print(f\"\\n🚀 Production Trade-offs:\")\n", + " print(f\"Learned PE:\")\n", + " print(f\" + Can learn task-specific positional patterns\")\n", + " print(f\" + May perform better for tasks with specific position dependencies\")\n", + " print(f\" - Requires additional memory and parameters\")\n", + " print(f\" - Fixed maximum sequence length\")\n", + " print(f\" - Needs training data for longer sequences\")\n", + "\n", + " print(f\"\\nSinusoidal PE:\")\n", + " print(f\" + Zero additional parameters\")\n", + " print(f\" + Can extrapolate to any sequence length\")\n", + " print(f\" + Provides rich, mathematically grounded position signals\")\n", + " print(f\" - Cannot adapt to task-specific position patterns\")\n", + " print(f\" - May be suboptimal for highly position-dependent tasks\")\n", + "\n", + "analyze_positional_encoding_trade_offs()" + ] + }, + { + "cell_type": "markdown", + "id": "ec702eff", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 10. Module Integration Test\n", + "\n", + "Final validation that our complete embedding system works correctly and integrates with the TinyTorch ecosystem." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9919660b", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": true, + "grade_id": "module-test", + "locked": true, + "points": 20 + } + }, + "outputs": [], + "source": [ + "def test_module():\n", + " \"\"\"\n", + " Comprehensive test of entire embeddings module functionality.\n", + "\n", + " This final test ensures all components work together and the module\n", + " is ready for integration with attention mechanisms and transformers.\n", + " \"\"\"\n", + " print(\"🧪 RUNNING MODULE INTEGRATION TEST\")\n", + " print(\"=\" * 50)\n", + "\n", + " # Run all unit tests\n", + " print(\"Running unit tests...\")\n", + " test_unit_embedding()\n", + " test_unit_positional_encoding()\n", + " test_unit_sinusoidal_embeddings()\n", + " test_unit_complete_embedding_system()\n", + "\n", + " print(\"\\nRunning integration scenarios...\")\n", + "\n", + " # Integration Test 1: Realistic NLP pipeline\n", + " print(\"🔬 Integration Test: NLP Pipeline Simulation...\")\n", + "\n", + " # Simulate a small transformer setup\n", + " vocab_size = 1000\n", + " embed_dim = 128\n", + " max_seq_len = 64\n", + "\n", + " # Create embedding layer\n", + " embed_layer = EmbeddingLayer(\n", + " vocab_size=vocab_size,\n", + " embed_dim=embed_dim,\n", + " max_seq_len=max_seq_len,\n", + " pos_encoding='learned',\n", + " scale_embeddings=True\n", + " )\n", + "\n", + " # Simulate tokenized sentences\n", + " sentences = [\n", + " [1, 15, 42, 7, 99], # \"the cat sat on mat\"\n", + " [23, 7, 15, 88], # \"dog chased the ball\"\n", + " [1, 67, 15, 42, 7, 99, 34] # \"the big cat sat on mat here\"\n", + " ]\n", + "\n", + " # Process each sentence\n", + " outputs = []\n", + " for sentence in sentences:\n", + " tokens = Tensor(sentence)\n", + " embedded = embed_layer.forward(tokens)\n", + " outputs.append(embedded)\n", + "\n", + " # Verify output shape\n", + " expected_shape = (len(sentence), embed_dim)\n", + " assert embedded.shape == expected_shape, f\"Wrong shape for sentence: {embedded.shape} != {expected_shape}\"\n", + "\n", + " print(\"✅ Variable length sentence processing works!\")\n", + "\n", + " # Integration Test 2: Batch processing with padding\n", + " print(\"🔬 Integration Test: Batched Processing...\")\n", + "\n", + " # Create padded batch (real-world scenario)\n", + " max_len = max(len(s) for s in sentences)\n", + " batch_tokens = []\n", + "\n", + " for sentence in sentences:\n", + " # Pad with zeros (assuming 0 is padding token)\n", + " padded = sentence + [0] * (max_len - len(sentence))\n", + " batch_tokens.append(padded)\n", + "\n", + " batch_tensor = Tensor(batch_tokens) # (3, 7)\n", + " batch_output = embed_layer.forward(batch_tensor)\n", + "\n", + " assert batch_output.shape == (3, max_len, embed_dim), f\"Batch output shape incorrect: {batch_output.shape}\"\n", + "\n", + " print(\"✅ Batch processing with padding works!\")\n", + "\n", + " # Integration Test 3: Different positional encoding types\n", + " print(\"🔬 Integration Test: Position Encoding Variants...\")\n", + "\n", + " test_tokens = Tensor([[1, 2, 3, 4, 5]])\n", + "\n", + " # Test all position encoding types\n", + " for pe_type in ['learned', 'sinusoidal', None]:\n", + " embed_test = EmbeddingLayer(\n", + " vocab_size=100,\n", + " embed_dim=64,\n", + " pos_encoding=pe_type\n", + " )\n", + "\n", + " output = embed_test.forward(test_tokens)\n", + " assert output.shape == (1, 5, 64), f\"PE type {pe_type} failed shape test\"\n", + "\n", + " # Check parameter counts\n", + " if pe_type == 'learned':\n", + " assert len(embed_test.parameters()) == 2, f\"Learned PE should have 2 param tensors\"\n", + " else:\n", + " assert len(embed_test.parameters()) == 1, f\"PE type {pe_type} should have 1 param tensor\"\n", + "\n", + " print(\"✅ All positional encoding variants work!\")\n", + "\n", + " # Integration Test 4: Memory efficiency check\n", + " print(\"🔬 Integration Test: Memory Efficiency...\")\n", + "\n", + " # Test that we're not creating unnecessary copies\n", + " large_embed = EmbeddingLayer(vocab_size=10000, embed_dim=512)\n", + " test_batch = Tensor(np.random.randint(0, 10000, (32, 128)))\n", + "\n", + " # Multiple forward passes should not accumulate memory (in production)\n", + " for _ in range(5):\n", + " output = large_embed.forward(test_batch)\n", + " assert output.shape == (32, 128, 512), \"Large batch processing failed\"\n", + "\n", + " print(\"✅ Memory efficiency check passed!\")\n", + "\n", + " print(\"\\n\" + \"=\" * 50)\n", + " print(\"🎉 ALL TESTS PASSED! Module ready for export.\")\n", + " print(\"📚 Summary of capabilities built:\")\n", + " print(\" • Token embedding with trainable lookup tables\")\n", + " print(\" • Learned positional encodings for position awareness\")\n", + " print(\" • Sinusoidal positional encodings for extrapolation\")\n", + " print(\" • Complete embedding system for NLP pipelines\")\n", + " print(\" • Efficient batch processing and memory management\")\n", + " print(\"\\n🚀 Ready for: Attention mechanisms, transformers, and language models!\")\n", + " print(\"Export with: tito module complete 11\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "60fe818f", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "main-execution", + "solution": true + } + }, + "outputs": [], + "source": [ + "if __name__ == \"__main__\":\n", + " \"\"\"Main execution block for module validation.\"\"\"\n", + " print(\"🚀 Running Embeddings module...\")\n", + " test_module()\n", + " print(\"✅ Module validation complete!\")" + ] + }, + { + "cell_type": "markdown", + "id": "fb9dc663", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🤔 ML Systems Thinking: Embedding Foundations\n", + "\n", + "### Question 1: Memory Scaling\n", + "You implemented an embedding layer with vocab_size=50,000 and embed_dim=512.\n", + "- How many parameters does this embedding table contain? _____ million\n", + "- If using FP32 (4 bytes per parameter), how much memory does this use? _____ MB\n", + "- If you double the embedding dimension to 1024, what happens to memory usage? _____ MB\n", + "\n", + "### Question 2: Lookup Complexity\n", + "Your embedding layer performs table lookups for token indices.\n", + "- What is the time complexity of looking up a single token? O(_____)\n", + "- For a batch of 32 sequences, each of length 128, how many lookup operations? _____\n", + "- Why doesn't vocabulary size affect individual lookup performance? _____\n", + "\n", + "### Question 3: Positional Encoding Trade-offs\n", + "You implemented both learned and sinusoidal positional encodings.\n", + "- Learned PE for max_seq_len=2048, embed_dim=512 adds how many parameters? _____\n", + "- What happens if you try to process a sequence longer than max_seq_len with learned PE? _____\n", + "- Which type of PE can handle sequences longer than seen during training? _____\n", + "\n", + "### Question 4: Production Implications\n", + "Your complete EmbeddingLayer combines token and positional embeddings.\n", + "- In GPT-3 (vocab_size≈50K, embed_dim≈12K), approximately what percentage of total parameters are in the embedding table? _____%\n", + "- If you wanted to reduce memory usage by 50%, which would be more effective: halving vocab_size or halving embed_dim? _____\n", + "- Why might sinusoidal PE be preferred for models that need to handle variable sequence lengths? _____" + ] + }, + { + "cell_type": "markdown", + "id": "5009ffd5", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🎯 MODULE SUMMARY: Embeddings\n", + "\n", + "Congratulations! You've built a complete embedding system that transforms discrete tokens into learnable representations!\n", + "\n", + "### Key Accomplishments\n", + "- Built `Embedding` class with efficient token-to-vector lookup (10M+ token support)\n", + "- Implemented `PositionalEncoding` for learnable position awareness (unlimited sequence patterns)\n", + "- Created `create_sinusoidal_embeddings` with mathematical position encoding (extrapolates beyond training)\n", + "- Developed `EmbeddingLayer` integrating both token and positional embeddings (production-ready)\n", + "- Analyzed embedding memory scaling and lookup performance trade-offs\n", + "- All tests pass ✅ (validated by `test_module()`)\n", + "\n", + "### Technical Achievements\n", + "- **Memory Efficiency**: Optimized embedding table storage and lookup patterns\n", + "- **Flexible Architecture**: Support for learned, sinusoidal, and no positional encoding\n", + "- **Batch Processing**: Efficient handling of variable-length sequences with padding\n", + "- **Systems Analysis**: Deep understanding of memory vs performance trade-offs\n", + "\n", + "### Ready for Next Steps\n", + "Your embeddings implementation enables attention mechanisms and transformer architectures!\n", + "The combination of token and positional embeddings provides the foundation for sequence-to-sequence models.\n", + "\n", + "**Next**: Module 12 will add attention mechanisms for context-aware representations!\n", + "\n", + "### Production Context\n", + "You've built the exact embedding patterns used in:\n", + "- **GPT models**: Token embeddings + learned positional encoding\n", + "- **BERT models**: Token embeddings + sinusoidal positional encoding\n", + "- **T5 models**: Relative positional embeddings (variant of your implementations)\n", + "\n", + "Export with: `tito module complete 11`" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/modules/source/11_embeddings/embeddings_dev.py b/modules/source/11_embeddings/embeddings_dev.py index e2740848..11bc5d2f 100644 --- a/modules/source/11_embeddings/embeddings_dev.py +++ b/modules/source/11_embeddings/embeddings_dev.py @@ -761,6 +761,7 @@ Input: Token IDs [1, 42, 7, 99] """ # %% nbgrader={"grade": false, "grade_id": "complete-system", "solution": true} +#| export class EmbeddingLayer: """ Complete embedding system combining token and positional embeddings. diff --git a/modules/source/12_attention/attention_dev.ipynb b/modules/source/12_attention/attention_dev.ipynb new file mode 100644 index 00000000..d6995f70 --- /dev/null +++ b/modules/source/12_attention/attention_dev.ipynb @@ -0,0 +1,1369 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "a2138437", + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp core.attention\n", + "#| export" + ] + }, + { + "cell_type": "markdown", + "id": "2e26d6f6", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "# Module 12: Attention - Learning to Focus\n", + "\n", + "Welcome to Module 12! You're about to build the attention mechanism that revolutionized deep learning and powers GPT, BERT, and modern transformers.\n", + "\n", + "## 🔗 Prerequisites & Progress\n", + "**You've Built**: Tensor, activations, layers, losses, autograd, optimizers, training, dataloaders, spatial layers, tokenization, and embeddings\n", + "**You'll Build**: Scaled dot-product attention and multi-head attention mechanisms\n", + "**You'll Enable**: Transformer architectures, GPT-style language models, and sequence-to-sequence processing\n", + "\n", + "**Connection Map**:\n", + "```\n", + "Embeddings → Attention → Transformers → Language Models\n", + "(representations) (focus mechanism) (complete architecture) (text generation)\n", + "```\n", + "\n", + "## Learning Objectives\n", + "By the end of this module, you will:\n", + "1. Implement scaled dot-product attention with explicit O(n²) complexity\n", + "2. Build multi-head attention for parallel processing streams\n", + "3. Understand attention weight computation and interpretation\n", + "4. Experience attention's quadratic memory scaling firsthand\n", + "5. Test attention mechanisms with masking and sequence processing\n", + "\n", + "Let's get started!\n", + "\n", + "## 📦 Where This Code Lives in the Final Package\n", + "\n", + "**Learning Side:** You work in `modules/12_attention/attention_dev.py` \n", + "**Building Side:** Code exports to `tinytorch.core.attention`\n", + "\n", + "```python\n", + "# How to use this module:\n", + "from tinytorch.core.attention import scaled_dot_product_attention, MultiHeadAttention\n", + "```\n", + "\n", + "**Why this matters:**\n", + "- **Learning:** Complete attention system in one focused module for deep understanding\n", + "- **Production:** Proper organization like PyTorch's torch.nn.functional and torch.nn with attention operations\n", + "- **Consistency:** All attention computations and multi-head mechanics in core.attention\n", + "- **Integration:** Works seamlessly with embeddings for complete sequence processing pipelines" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15910289", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "imports", + "locked": false, + "solution": true + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import math\n", + "import time\n", + "import sys\n", + "import os\n", + "from typing import Optional, Tuple, List\n", + "\n", + "# Import dependencies from other modules\n", + "sys.path.append(os.path.join(os.path.dirname(__file__), '..', '01_tensor'))\n", + "from tinytorch.core.tensor import Tensor\n", + "\n", + "sys.path.append(os.path.join(os.path.dirname(__file__), '..', '03_layers'))\n", + "from tinytorch.core.layers import Linear\n", + "\n", + "# Note: Keeping simplified implementations for reference during development\n", + "class _SimplifiedTensor:\n", + " \"\"\"Simplified tensor for attention operations development.\"\"\"\n", + "\n", + " def __init__(self, data, requires_grad=False):\n", + " self.data = np.array(data, dtype=np.float32)\n", + " self.shape = self.data.shape\n", + " self.requires_grad = requires_grad\n", + " self.grad = None\n", + "\n", + " def __repr__(self):\n", + " return f\"Tensor(shape={self.shape}, data=\\n{self.data})\"\n", + "\n", + " def __add__(self, other):\n", + " if isinstance(other, Tensor):\n", + " return Tensor(self.data + other.data)\n", + " return Tensor(self.data + other)\n", + "\n", + " def __mul__(self, other):\n", + " if isinstance(other, Tensor):\n", + " return Tensor(self.data * other.data)\n", + " return Tensor(self.data * other)\n", + "\n", + " def sum(self, axis=None):\n", + " return Tensor(np.sum(self.data, axis=axis))\n", + "\n", + " def mean(self, axis=None):\n", + " return Tensor(np.mean(self.data, axis=axis))\n", + "\n", + " def matmul(self, other):\n", + " return Tensor(np.matmul(self.data, other.data))\n", + "\n", + " def softmax(self, axis=-1):\n", + " \"\"\"Apply softmax along specified axis.\"\"\"\n", + " # Subtract max for numerical stability\n", + " shifted = self.data - np.max(self.data, axis=axis, keepdims=True)\n", + " exp_values = np.exp(shifted)\n", + " return Tensor(exp_values / np.sum(exp_values, axis=axis, keepdims=True))\n", + "\n", + " # Simplified Linear layer for development\n", + " class Linear:\n", + " \"\"\"Simplified linear layer for attention projections.\"\"\"\n", + "\n", + " def __init__(self, in_features, out_features):\n", + " self.in_features = in_features\n", + " self.out_features = out_features\n", + " # Initialize weights and bias (simplified Xavier initialization)\n", + " self.weight = Tensor(np.random.randn(in_features, out_features) * np.sqrt(2.0 / in_features))\n", + " self.bias = Tensor(np.zeros(out_features))\n", + "\n", + " def forward(self, x):\n", + " \"\"\"Forward pass: y = xW + b\"\"\"\n", + " output = x.matmul(self.weight)\n", + " # Add bias (broadcast across batch and sequence dimensions)\n", + " return Tensor(output.data + self.bias.data)\n", + "\n", + " def parameters(self):\n", + " \"\"\"Return list of parameters for this layer.\"\"\"\n", + " return [self.weight, self.bias]" + ] + }, + { + "cell_type": "markdown", + "id": "b8ca28ff", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## Part 1: Introduction - What is Attention?\n", + "\n", + "Attention is the mechanism that allows models to focus on relevant parts of the input when processing sequences. Think of it as a search engine inside your neural network - given a query, attention finds the most relevant keys and retrieves their associated values.\n", + "\n", + "### The Attention Intuition\n", + "\n", + "When you read \"The cat sat on the ___\", your brain automatically focuses on \"cat\" and \"sat\" to predict \"mat\". This selective focus is exactly what attention mechanisms provide to neural networks.\n", + "\n", + "Imagine attention as a library research system:\n", + "- **Query (Q)**: \"I need information about machine learning\"\n", + "- **Keys (K)**: Index cards describing each book's content\n", + "- **Values (V)**: The actual books on the shelves\n", + "- **Attention Process**: Find books whose descriptions match your query, then retrieve those books\n", + "\n", + "### Why Attention Changed Everything\n", + "\n", + "Before attention, RNNs processed sequences step-by-step, creating an information bottleneck:\n", + "\n", + "```\n", + "RNN Processing (Sequential):\n", + "Token 1 → Hidden → Token 2 → Hidden → ... → Final Hidden\n", + " ↓ ↓ ↓\n", + " Limited Info Compressed State All Information Lost\n", + "```\n", + "\n", + "Attention allows direct connections between any two positions:\n", + "\n", + "```\n", + "Attention Processing (Parallel):\n", + "Token 1 ←─────────→ Token 2 ←─────────→ Token 3 ←─────────→ Token 4\n", + " ↑ ↑ ↑ ↑\n", + " └─────────────── Direct Connections ──────────────────────┘\n", + "```\n", + "\n", + "This enables:\n", + "- **Long-range dependencies**: Connecting words far apart\n", + "- **Parallel computation**: No sequential dependencies\n", + "- **Interpretable focus patterns**: We can see what the model attends to\n", + "\n", + "### The Mathematical Foundation\n", + "\n", + "Attention computes a weighted sum of values, where weights are determined by the similarity between queries and keys:\n", + "\n", + "```\n", + "Attention(Q, K, V) = softmax(QK^T / √d_k) V\n", + "```\n", + "\n", + "This simple formula powers GPT, BERT, and virtually every modern language model." + ] + }, + { + "cell_type": "markdown", + "id": "a85b79df", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## Part 2: Foundations - Attention Mathematics\n", + "\n", + "### The Three Components Visualized\n", + "\n", + "Think of attention like a sophisticated address book lookup:\n", + "\n", + "```\n", + "Query: \"What information do I need?\"\n", + "┌─────────────────────────────────────┐\n", + "│ Q: [0.1, 0.8, 0.3, 0.2] │ ← Query vector (what we're looking for)\n", + "└─────────────────────────────────────┘\n", + "\n", + "Keys: \"What information is available at each position?\"\n", + "┌─────────────────────────────────────┐\n", + "│ K₁: [0.2, 0.7, 0.1, 0.4] │ ← Key 1 (description of position 1)\n", + "│ K₂: [0.1, 0.9, 0.2, 0.1] │ ← Key 2 (description of position 2)\n", + "│ K₃: [0.3, 0.1, 0.8, 0.3] │ ← Key 3 (description of position 3)\n", + "│ K₄: [0.4, 0.2, 0.1, 0.9] │ ← Key 4 (description of position 4)\n", + "└─────────────────────────────────────┘\n", + "\n", + "Values: \"What actual content can I retrieve?\"\n", + "┌─────────────────────────────────────┐\n", + "│ V₁: [content from position 1] │ ← Value 1 (actual information)\n", + "│ V₂: [content from position 2] │ ← Value 2 (actual information)\n", + "│ V₃: [content from position 3] │ ← Value 3 (actual information)\n", + "│ V₄: [content from position 4] │ ← Value 4 (actual information)\n", + "└─────────────────────────────────────┘\n", + "```\n", + "\n", + "### The Attention Process Step by Step\n", + "\n", + "```\n", + "Step 1: Compute Similarity Scores\n", + "Q · K₁ = 0.64 Q · K₂ = 0.81 Q · K₃ = 0.35 Q · K₄ = 0.42\n", + " ↓ ↓ ↓ ↓\n", + "Raw similarity scores (higher = more relevant)\n", + "\n", + "Step 2: Scale and Normalize\n", + "Scores / √d_k = [0.32, 0.41, 0.18, 0.21] ← Scale for stability\n", + " ↓\n", + "Softmax = [0.20, 0.45, 0.15, 0.20] ← Convert to probabilities\n", + "\n", + "Step 3: Weighted Combination\n", + "Output = 0.20×V₁ + 0.45×V₂ + 0.15×V₃ + 0.20×V₄\n", + "```\n", + "\n", + "### Dimensions and Shapes\n", + "\n", + "```\n", + "Input Shapes:\n", + "Q: (batch_size, seq_len, d_model) ← Each position has a query\n", + "K: (batch_size, seq_len, d_model) ← Each position has a key\n", + "V: (batch_size, seq_len, d_model) ← Each position has a value\n", + "\n", + "Intermediate Shapes:\n", + "QK^T: (batch_size, seq_len, seq_len) ← Attention matrix (the O(n²) part!)\n", + "Weights: (batch_size, seq_len, seq_len) ← After softmax\n", + "Output: (batch_size, seq_len, d_model) ← Weighted combination of values\n", + "```\n", + "\n", + "### Why O(n²) Complexity?\n", + "\n", + "For sequence length n, we compute:\n", + "1. **QK^T**: n queries × n keys = n² similarity scores\n", + "2. **Softmax**: n² weights to normalize\n", + "3. **Weights×V**: n² weights × n values = n² operations for aggregation\n", + "\n", + "This quadratic scaling is attention's blessing (global connectivity) and curse (memory/compute limits).\n", + "\n", + "### The Attention Matrix Visualization\n", + "\n", + "For a 4-token sequence \"The cat sat down\":\n", + "\n", + "```\n", + "Attention Matrix (after softmax):\n", + " The cat sat down\n", + "The [0.30 0.20 0.15 0.35] ← \"The\" attends mostly to \"down\"\n", + "cat [0.10 0.60 0.25 0.05] ← \"cat\" focuses on itself and \"sat\"\n", + "sat [0.05 0.40 0.50 0.05] ← \"sat\" attends to \"cat\" and itself\n", + "down [0.25 0.15 0.10 0.50] ← \"down\" focuses on itself and \"The\"\n", + "\n", + "Each row sums to 1.0 (probability distribution)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "396fac34", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## Part 3: Implementation - Building Scaled Dot-Product Attention\n", + "\n", + "Now let's implement the core attention mechanism that powers all transformer models. We'll use explicit loops first to make the O(n²) complexity visible and educational.\n", + "\n", + "### Understanding the Algorithm Visually\n", + "\n", + "```\n", + "Step-by-Step Attention Computation:\n", + "\n", + "1. Score Computation (Q @ K^T):\n", + " For each query position i and key position j:\n", + " score[i,j] = Σ(Q[i,d] × K[j,d]) for d in embedding_dims\n", + "\n", + " Query i Key j Dot Product\n", + " [0.1,0.8] · [0.2,0.7] = 0.1×0.2 + 0.8×0.7 = 0.58\n", + "\n", + "2. Scaling (÷ √d_k):\n", + " scaled_scores = scores / √embedding_dim\n", + " (Prevents softmax saturation for large dimensions)\n", + "\n", + "3. Masking (optional):\n", + " For causal attention: scores[i,j] = -∞ if j > i\n", + "\n", + " Causal Mask (lower triangular):\n", + " [ OK -∞ -∞ -∞ ]\n", + " [ OK OK -∞ -∞ ]\n", + " [ OK OK OK -∞ ]\n", + " [ OK OK OK OK ]\n", + "\n", + "4. Softmax (normalize each row):\n", + " weights[i,j] = exp(scores[i,j]) / Σ(exp(scores[i,k])) for all k\n", + "\n", + "5. Apply to Values:\n", + " output[i] = Σ(weights[i,j] × V[j]) for all j\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "08019321", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "attention-function", + "locked": false, + "solution": true + } + }, + "outputs": [], + "source": [ + "def scaled_dot_product_attention(Q: Tensor, K: Tensor, V: Tensor, mask: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]:\n", + " \"\"\"\n", + " Compute scaled dot-product attention.\n", + "\n", + " This is the fundamental attention operation that powers all transformer models.\n", + " We'll implement it with explicit loops first to show the O(n²) complexity.\n", + "\n", + " TODO: Implement scaled dot-product attention step by step\n", + "\n", + " APPROACH:\n", + " 1. Extract dimensions and validate inputs\n", + " 2. Compute attention scores with explicit nested loops (show O(n²) complexity)\n", + " 3. Scale by 1/√d_k for numerical stability\n", + " 4. Apply causal mask if provided (set masked positions to -inf)\n", + " 5. Apply softmax to get attention weights\n", + " 6. Apply values with attention weights (another O(n²) operation)\n", + " 7. Return output and attention weights\n", + "\n", + " Args:\n", + " Q: Query tensor of shape (batch_size, seq_len, d_model)\n", + " K: Key tensor of shape (batch_size, seq_len, d_model)\n", + " V: Value tensor of shape (batch_size, seq_len, d_model)\n", + " mask: Optional causal mask, True=allow, False=mask (batch_size, seq_len, seq_len)\n", + "\n", + " Returns:\n", + " output: Attended values (batch_size, seq_len, d_model)\n", + " attention_weights: Attention matrix (batch_size, seq_len, seq_len)\n", + "\n", + " EXAMPLE:\n", + " >>> Q = Tensor(np.random.randn(2, 4, 64)) # batch=2, seq=4, dim=64\n", + " >>> K = Tensor(np.random.randn(2, 4, 64))\n", + " >>> V = Tensor(np.random.randn(2, 4, 64))\n", + " >>> output, weights = scaled_dot_product_attention(Q, K, V)\n", + " >>> print(output.shape) # (2, 4, 64)\n", + " >>> print(weights.shape) # (2, 4, 4)\n", + " >>> print(weights.data[0].sum(axis=1)) # Each row sums to ~1.0\n", + "\n", + " HINTS:\n", + " - Use explicit nested loops to compute Q[i] @ K[j] for educational purposes\n", + " - Scale factor is 1/√d_k where d_k is the last dimension of Q\n", + " - Masked positions should be set to -1e9 before softmax\n", + " - Remember that softmax normalizes along the last dimension\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " # Step 1: Extract dimensions and validate\n", + " batch_size, seq_len, d_model = Q.shape\n", + " assert K.shape == (batch_size, seq_len, d_model), f\"K shape {K.shape} doesn't match Q shape {Q.shape}\"\n", + " assert V.shape == (batch_size, seq_len, d_model), f\"V shape {V.shape} doesn't match Q shape {Q.shape}\"\n", + "\n", + " # Step 2: Compute attention scores with explicit loops (educational O(n²) demonstration)\n", + " scores = np.zeros((batch_size, seq_len, seq_len))\n", + "\n", + " # Show the quadratic complexity explicitly\n", + " for b in range(batch_size): # For each batch\n", + " for i in range(seq_len): # For each query position\n", + " for j in range(seq_len): # Attend to each key position\n", + " # Compute dot product between query i and key j\n", + " score = 0.0\n", + " for d in range(d_model): # Dot product across embedding dimension\n", + " score += Q.data[b, i, d] * K.data[b, j, d]\n", + " scores[b, i, j] = score\n", + "\n", + " # Step 3: Scale by 1/√d_k for numerical stability\n", + " scale_factor = 1.0 / math.sqrt(d_model)\n", + " scores = scores * scale_factor\n", + "\n", + " # Step 4: Apply causal mask if provided\n", + " if mask is not None:\n", + " # mask[i,j] = False means position j should not attend to position i\n", + " mask_value = -1e9 # Large negative value becomes 0 after softmax\n", + " for b in range(batch_size):\n", + " for i in range(seq_len):\n", + " for j in range(seq_len):\n", + " if not mask.data[b, i, j]: # If mask is False, block attention\n", + " scores[b, i, j] = mask_value\n", + "\n", + " # Step 5: Apply softmax to get attention weights (probability distribution)\n", + " attention_weights = np.zeros_like(scores)\n", + " for b in range(batch_size):\n", + " for i in range(seq_len):\n", + " # Softmax over the j dimension (what this query attends to)\n", + " row = scores[b, i, :]\n", + " max_val = np.max(row) # Numerical stability\n", + " exp_row = np.exp(row - max_val)\n", + " sum_exp = np.sum(exp_row)\n", + " attention_weights[b, i, :] = exp_row / sum_exp\n", + "\n", + " # Step 6: Apply attention weights to values (another O(n²) operation)\n", + " output = np.zeros((batch_size, seq_len, d_model))\n", + "\n", + " # Again, show the quadratic complexity\n", + " for b in range(batch_size): # For each batch\n", + " for i in range(seq_len): # For each output position\n", + " for j in range(seq_len): # Weighted sum over all value positions\n", + " weight = attention_weights[b, i, j]\n", + " for d in range(d_model): # Accumulate across embedding dimension\n", + " output[b, i, d] += weight * V.data[b, j, d]\n", + "\n", + " return Tensor(output), Tensor(attention_weights)\n", + " ### END SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63ffcc32", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test-attention-basic", + "locked": true, + "points": 10 + } + }, + "outputs": [], + "source": [ + "def test_unit_scaled_dot_product_attention():\n", + " \"\"\"🔬 Unit Test: Scaled Dot-Product Attention\"\"\"\n", + " print(\"🔬 Unit Test: Scaled Dot-Product Attention...\")\n", + "\n", + " # Test basic functionality\n", + " batch_size, seq_len, d_model = 2, 4, 8\n", + " Q = Tensor(np.random.randn(batch_size, seq_len, d_model))\n", + " K = Tensor(np.random.randn(batch_size, seq_len, d_model))\n", + " V = Tensor(np.random.randn(batch_size, seq_len, d_model))\n", + "\n", + " output, weights = scaled_dot_product_attention(Q, K, V)\n", + "\n", + " # Check output shapes\n", + " assert output.shape == (batch_size, seq_len, d_model), f\"Output shape {output.shape} incorrect\"\n", + " assert weights.shape == (batch_size, seq_len, seq_len), f\"Weights shape {weights.shape} incorrect\"\n", + "\n", + " # Check attention weights sum to 1 (probability distribution)\n", + " weights_sum = weights.data.sum(axis=2) # Sum over last dimension\n", + " expected_sum = np.ones((batch_size, seq_len))\n", + " assert np.allclose(weights_sum, expected_sum, atol=1e-6), \"Attention weights don't sum to 1\"\n", + "\n", + " # Test with causal mask\n", + " mask = Tensor(np.tril(np.ones((batch_size, seq_len, seq_len)), k=0)) # Lower triangular\n", + " output_masked, weights_masked = scaled_dot_product_attention(Q, K, V, mask)\n", + "\n", + " # Check that future positions have zero attention\n", + " for b in range(batch_size):\n", + " for i in range(seq_len):\n", + " for j in range(i + 1, seq_len): # Future positions\n", + " assert abs(weights_masked.data[b, i, j]) < 1e-6, f\"Future attention not masked at ({i},{j})\"\n", + "\n", + " print(\"✅ scaled_dot_product_attention works correctly!\")\n", + "\n", + "test_unit_scaled_dot_product_attention()" + ] + }, + { + "cell_type": "markdown", + "id": "ef857d5e", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "### 🧪 Unit Test: Scaled Dot-Product Attention\n", + "\n", + "This test validates our core attention mechanism:\n", + "- **Output shapes**: Ensures attention preserves sequence dimensions\n", + "- **Probability constraint**: Attention weights must sum to 1 per query\n", + "- **Causal masking**: Future positions should have zero attention weight\n", + "\n", + "**Why attention weights sum to 1**: Each query position creates a probability distribution over all key positions. This ensures the output is a proper weighted average of values.\n", + "\n", + "**Why causal masking matters**: In language modeling, positions shouldn't attend to future tokens (information they wouldn't have during generation).\n", + "\n", + "**The O(n²) complexity you just witnessed**: Our explicit loops show exactly why attention scales quadratically - every query position must compare with every key position." + ] + }, + { + "cell_type": "markdown", + "id": "5d7802cf", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## Part 4: Implementation - Multi-Head Attention\n", + "\n", + "Multi-head attention runs multiple attention \"heads\" in parallel, each learning to focus on different types of relationships. Think of it as having multiple specialists: one for syntax, one for semantics, one for long-range dependencies, etc.\n", + "\n", + "### Understanding Multi-Head Architecture\n", + "\n", + "```\n", + "Single-Head vs Multi-Head Attention:\n", + "\n", + "SINGLE HEAD (Limited):\n", + "Input → [Linear] → Q,K,V → [Attention] → Output\n", + " 512×512 512×512 512\n", + "\n", + "MULTI-HEAD (Rich):\n", + "Input → [Linear] → Q₁,K₁,V₁ → [Attention₁] → Head₁ (64 dims)\n", + " → [Linear] → Q₂,K₂,V₂ → [Attention₂] → Head₂ (64 dims)\n", + " → [Linear] → Q₃,K₃,V₃ → [Attention₃] → Head₃ (64 dims)\n", + " ...\n", + " → [Linear] → Q₈,K₈,V₈ → [Attention₈] → Head₈ (64 dims)\n", + " ↓\n", + " [Concatenate]\n", + " ↓\n", + " [Linear Mix] → Output (512)\n", + "```\n", + "\n", + "### The Multi-Head Process Detailed\n", + "\n", + "```\n", + "Step 1: Project to Q, K, V\n", + "Input (512 dims) → Linear → Q, K, V (512 dims each)\n", + "\n", + "Step 2: Split into Heads\n", + "Q (512) → Reshape → 8 heads × 64 dims per head\n", + "K (512) → Reshape → 8 heads × 64 dims per head\n", + "V (512) → Reshape → 8 heads × 64 dims per head\n", + "\n", + "Step 3: Parallel Attention (for each of 8 heads)\n", + "Head 1: Q₁(64) attends to K₁(64) → weights₁ → output₁(64)\n", + "Head 2: Q₂(64) attends to K₂(64) → weights₂ → output₂(64)\n", + "...\n", + "Head 8: Q₈(64) attends to K₈(64) → weights₈ → output₈(64)\n", + "\n", + "Step 4: Concatenate and Mix\n", + "[output₁ ∥ output₂ ∥ ... ∥ output₈] (512) → Linear → Final(512)\n", + "```\n", + "\n", + "### Why Multiple Heads Are Powerful\n", + "\n", + "Each head can specialize in different patterns:\n", + "- **Head 1**: Short-range syntax (\"the cat\" → subject-article relationship)\n", + "- **Head 2**: Long-range coreference (\"John...he\" → pronoun resolution)\n", + "- **Head 3**: Semantic similarity (\"dog\" ↔ \"pet\" connections)\n", + "- **Head 4**: Positional patterns (attending to specific distances)\n", + "\n", + "This parallelization allows the model to attend to different representation subspaces simultaneously." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c02f9af2", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "multihead-attention", + "locked": false, + "solution": true + } + }, + "outputs": [], + "source": [ + "class MultiHeadAttention:\n", + " \"\"\"\n", + " Multi-head attention mechanism.\n", + "\n", + " Runs multiple attention heads in parallel, each learning different relationships.\n", + " This is the core component of transformer architectures.\n", + " \"\"\"\n", + "\n", + " def __init__(self, embed_dim: int, num_heads: int):\n", + " \"\"\"\n", + " Initialize multi-head attention.\n", + "\n", + " TODO: Set up linear projections and validate configuration\n", + "\n", + " APPROACH:\n", + " 1. Validate that embed_dim is divisible by num_heads\n", + " 2. Calculate head_dim (embed_dim // num_heads)\n", + " 3. Create linear layers for Q, K, V projections\n", + " 4. Create output projection layer\n", + " 5. Store configuration parameters\n", + "\n", + " Args:\n", + " embed_dim: Embedding dimension (d_model)\n", + " num_heads: Number of parallel attention heads\n", + "\n", + " EXAMPLE:\n", + " >>> mha = MultiHeadAttention(embed_dim=512, num_heads=8)\n", + " >>> mha.head_dim # 64 (512 / 8)\n", + " >>> len(mha.parameters()) # 4 linear layers * 2 params each = 8 tensors\n", + "\n", + " HINTS:\n", + " - head_dim = embed_dim // num_heads must be integer\n", + " - Need 4 Linear layers: q_proj, k_proj, v_proj, out_proj\n", + " - Each projection maps embed_dim → embed_dim\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " assert embed_dim % num_heads == 0, f\"embed_dim ({embed_dim}) must be divisible by num_heads ({num_heads})\"\n", + "\n", + " self.embed_dim = embed_dim\n", + " self.num_heads = num_heads\n", + " self.head_dim = embed_dim // num_heads\n", + "\n", + " # Linear projections for queries, keys, values\n", + " self.q_proj = Linear(embed_dim, embed_dim)\n", + " self.k_proj = Linear(embed_dim, embed_dim)\n", + " self.v_proj = Linear(embed_dim, embed_dim)\n", + "\n", + " # Output projection to mix information across heads\n", + " self.out_proj = Linear(embed_dim, embed_dim)\n", + " ### END SOLUTION\n", + "\n", + " def forward(self, x: Tensor, mask: Optional[Tensor] = None) -> Tensor:\n", + " \"\"\"\n", + " Forward pass through multi-head attention.\n", + "\n", + " TODO: Implement the complete multi-head attention forward pass\n", + "\n", + " APPROACH:\n", + " 1. Extract input dimensions (batch_size, seq_len, embed_dim)\n", + " 2. Project input to Q, K, V using linear layers\n", + " 3. Reshape projections to separate heads: (batch, seq, heads, head_dim)\n", + " 4. Transpose to (batch, heads, seq, head_dim) for parallel processing\n", + " 5. Apply scaled dot-product attention to each head\n", + " 6. Transpose back and reshape to merge heads\n", + " 7. Apply output projection\n", + "\n", + " Args:\n", + " x: Input tensor (batch_size, seq_len, embed_dim)\n", + " mask: Optional attention mask (batch_size, seq_len, seq_len)\n", + "\n", + " Returns:\n", + " output: Attended representation (batch_size, seq_len, embed_dim)\n", + "\n", + " EXAMPLE:\n", + " >>> mha = MultiHeadAttention(embed_dim=64, num_heads=8)\n", + " >>> x = Tensor(np.random.randn(2, 10, 64)) # batch=2, seq=10, dim=64\n", + " >>> output = mha.forward(x)\n", + " >>> print(output.shape) # (2, 10, 64) - same as input\n", + "\n", + " HINTS:\n", + " - Reshape: (batch, seq, embed_dim) → (batch, seq, heads, head_dim)\n", + " - Transpose: (batch, seq, heads, head_dim) → (batch, heads, seq, head_dim)\n", + " - After attention: reverse the process to merge heads\n", + " - Use scaled_dot_product_attention for each head\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " # Step 1: Extract dimensions\n", + " batch_size, seq_len, embed_dim = x.shape\n", + " assert embed_dim == self.embed_dim, f\"Input dim {embed_dim} doesn't match expected {self.embed_dim}\"\n", + "\n", + " # Step 2: Project to Q, K, V\n", + " Q = self.q_proj.forward(x) # (batch, seq, embed_dim)\n", + " K = self.k_proj.forward(x)\n", + " V = self.v_proj.forward(x)\n", + "\n", + " # Step 3: Reshape to separate heads\n", + " # From (batch, seq, embed_dim) to (batch, seq, num_heads, head_dim)\n", + " Q_heads = Q.data.reshape(batch_size, seq_len, self.num_heads, self.head_dim)\n", + " K_heads = K.data.reshape(batch_size, seq_len, self.num_heads, self.head_dim)\n", + " V_heads = V.data.reshape(batch_size, seq_len, self.num_heads, self.head_dim)\n", + "\n", + " # Step 4: Transpose to (batch, num_heads, seq, head_dim) for parallel processing\n", + " Q_heads = np.transpose(Q_heads, (0, 2, 1, 3))\n", + " K_heads = np.transpose(K_heads, (0, 2, 1, 3))\n", + " V_heads = np.transpose(V_heads, (0, 2, 1, 3))\n", + "\n", + " # Step 5: Apply attention to each head\n", + " head_outputs = []\n", + " for h in range(self.num_heads):\n", + " # Extract this head's Q, K, V\n", + " Q_h = Tensor(Q_heads[:, h, :, :]) # (batch, seq, head_dim)\n", + " K_h = Tensor(K_heads[:, h, :, :])\n", + " V_h = Tensor(V_heads[:, h, :, :])\n", + "\n", + " # Apply attention for this head\n", + " head_out, _ = scaled_dot_product_attention(Q_h, K_h, V_h, mask)\n", + " head_outputs.append(head_out.data)\n", + "\n", + " # Step 6: Concatenate heads back together\n", + " # Stack: list of (batch, seq, head_dim) → (batch, num_heads, seq, head_dim)\n", + " concat_heads = np.stack(head_outputs, axis=1)\n", + "\n", + " # Transpose back: (batch, num_heads, seq, head_dim) → (batch, seq, num_heads, head_dim)\n", + " concat_heads = np.transpose(concat_heads, (0, 2, 1, 3))\n", + "\n", + " # Reshape: (batch, seq, num_heads, head_dim) → (batch, seq, embed_dim)\n", + " concat_output = concat_heads.reshape(batch_size, seq_len, self.embed_dim)\n", + "\n", + " # Step 7: Apply output projection\n", + " output = self.out_proj.forward(Tensor(concat_output))\n", + "\n", + " return output\n", + " ### END SOLUTION\n", + "\n", + " def parameters(self) -> List[Tensor]:\n", + " \"\"\"\n", + " Return all trainable parameters.\n", + "\n", + " TODO: Collect parameters from all linear layers\n", + "\n", + " APPROACH:\n", + " 1. Get parameters from q_proj, k_proj, v_proj, out_proj\n", + " 2. Combine into single list\n", + "\n", + " Returns:\n", + " List of all parameter tensors\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " params = []\n", + " params.extend(self.q_proj.parameters())\n", + " params.extend(self.k_proj.parameters())\n", + " params.extend(self.v_proj.parameters())\n", + " params.extend(self.out_proj.parameters())\n", + " return params\n", + " ### END SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38708375", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test-multihead", + "locked": true, + "points": 15 + } + }, + "outputs": [], + "source": [ + "def test_unit_multihead_attention():\n", + " \"\"\"🔬 Unit Test: Multi-Head Attention\"\"\"\n", + " print(\"🔬 Unit Test: Multi-Head Attention...\")\n", + "\n", + " # Test initialization\n", + " embed_dim, num_heads = 64, 8\n", + " mha = MultiHeadAttention(embed_dim, num_heads)\n", + "\n", + " # Check configuration\n", + " assert mha.embed_dim == embed_dim\n", + " assert mha.num_heads == num_heads\n", + " assert mha.head_dim == embed_dim // num_heads\n", + "\n", + " # Test parameter counting (4 linear layers, each has weight + bias)\n", + " params = mha.parameters()\n", + " assert len(params) == 8, f\"Expected 8 parameters (4 layers × 2), got {len(params)}\"\n", + "\n", + " # Test forward pass\n", + " batch_size, seq_len = 2, 6\n", + " x = Tensor(np.random.randn(batch_size, seq_len, embed_dim))\n", + "\n", + " output = mha.forward(x)\n", + "\n", + " # Check output shape preservation\n", + " assert output.shape == (batch_size, seq_len, embed_dim), f\"Output shape {output.shape} incorrect\"\n", + "\n", + " # Test with causal mask\n", + " mask = Tensor(np.tril(np.ones((batch_size, seq_len, seq_len))))\n", + " output_masked = mha.forward(x, mask)\n", + " assert output_masked.shape == (batch_size, seq_len, embed_dim)\n", + "\n", + " # Test different head configurations\n", + " mha_small = MultiHeadAttention(embed_dim=32, num_heads=4)\n", + " x_small = Tensor(np.random.randn(1, 5, 32))\n", + " output_small = mha_small.forward(x_small)\n", + " assert output_small.shape == (1, 5, 32)\n", + "\n", + " print(\"✅ MultiHeadAttention works correctly!\")\n", + "\n", + "test_unit_multihead_attention()" + ] + }, + { + "cell_type": "markdown", + "id": "3cd02d15", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "### 🧪 Unit Test: Multi-Head Attention\n", + "\n", + "This test validates our multi-head attention implementation:\n", + "- **Configuration**: Correct head dimension calculation and parameter setup\n", + "- **Parameter counting**: 4 linear layers × 2 parameters each = 8 total\n", + "- **Shape preservation**: Output maintains input dimensions\n", + "- **Masking support**: Causal masks work correctly with multiple heads\n", + "\n", + "**Why multi-head attention works**: Different heads can specialize in different types of relationships (syntactic, semantic, positional), providing richer representations than single-head attention.\n", + "\n", + "**Architecture insight**: The split → attend → concat pattern allows parallel processing of different representation subspaces, dramatically increasing the model's capacity to understand complex relationships." + ] + }, + { + "cell_type": "markdown", + "id": "58152928", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## Part 5: Systems Analysis - Attention's Computational Reality\n", + "\n", + "Now let's analyze the computational and memory characteristics that make attention both powerful and challenging at scale.\n", + "\n", + "### Memory Complexity Visualization\n", + "\n", + "```\n", + "Attention Memory Scaling (per layer):\n", + "\n", + "Sequence Length = 128:\n", + "┌────────────────────────────────┐\n", + "│ Attention Matrix: 128×128 │ = 16K values\n", + "│ Memory: 64 KB (float32) │\n", + "└────────────────────────────────┘\n", + "\n", + "Sequence Length = 512:\n", + "┌────────────────────────────────┐\n", + "│ Attention Matrix: 512×512 │ = 262K values\n", + "│ Memory: 1 MB (float32) │ ← 16× larger!\n", + "└────────────────────────────────┘\n", + "\n", + "Sequence Length = 2048 (GPT-3):\n", + "┌────────────────────────────────┐\n", + "│ Attention Matrix: 2048×2048 │ = 4.2M values\n", + "│ Memory: 16 MB (float32) │ ← 256× larger than 128!\n", + "└────────────────────────────────┘\n", + "\n", + "For a 96-layer model (GPT-3):\n", + "Total Attention Memory = 96 layers × 16 MB = 1.5 GB\n", + "Just for attention matrices!\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6e672761", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "attention-complexity", + "locked": false, + "solution": true + } + }, + "outputs": [], + "source": [ + "def analyze_attention_complexity():\n", + " \"\"\"📊 Analyze attention computational complexity and memory scaling.\"\"\"\n", + " print(\"📊 Analyzing Attention Complexity...\")\n", + "\n", + " # Test different sequence lengths to show O(n²) scaling\n", + " embed_dim = 64\n", + " sequence_lengths = [16, 32, 64, 128, 256]\n", + "\n", + " print(\"\\nSequence Length vs Attention Matrix Size:\")\n", + " print(\"Seq Len | Attention Matrix | Memory (KB) | Complexity\")\n", + " print(\"-\" * 55)\n", + "\n", + " for seq_len in sequence_lengths:\n", + " # Calculate attention matrix size\n", + " attention_matrix_size = seq_len * seq_len\n", + "\n", + " # Memory for attention weights (float32 = 4 bytes)\n", + " attention_memory_kb = (attention_matrix_size * 4) / 1024\n", + "\n", + " # Total complexity (Q@K + softmax + weights@V)\n", + " complexity = 2 * seq_len * seq_len * embed_dim + seq_len * seq_len\n", + "\n", + " print(f\"{seq_len:7d} | {attention_matrix_size:14d} | {attention_memory_kb:10.2f} | {complexity:10.0f}\")\n", + "\n", + " print(f\"\\n💡 Attention memory scales as O(n²) with sequence length\")\n", + " print(f\"🚀 For seq_len=1024, attention matrix alone needs {(1024*1024*4)/1024/1024:.1f} MB\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86c3011a", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "attention-timing", + "locked": false, + "solution": true + } + }, + "outputs": [], + "source": [ + "def analyze_attention_timing():\n", + " \"\"\"📊 Measure attention computation time vs sequence length.\"\"\"\n", + " print(\"\\n📊 Analyzing Attention Timing...\")\n", + "\n", + " embed_dim, num_heads = 64, 8\n", + " sequence_lengths = [32, 64, 128, 256]\n", + "\n", + " print(\"\\nSequence Length vs Computation Time:\")\n", + " print(\"Seq Len | Time (ms) | Ops/sec | Scaling\")\n", + " print(\"-\" * 40)\n", + "\n", + " prev_time = None\n", + " for seq_len in sequence_lengths:\n", + " # Create test input\n", + " x = Tensor(np.random.randn(1, seq_len, embed_dim))\n", + " mha = MultiHeadAttention(embed_dim, num_heads)\n", + "\n", + " # Time multiple runs for stability\n", + " times = []\n", + " for _ in range(5):\n", + " start_time = time.time()\n", + " _ = mha.forward(x)\n", + " end_time = time.time()\n", + " times.append((end_time - start_time) * 1000) # Convert to ms\n", + "\n", + " avg_time = np.mean(times)\n", + " ops_per_sec = 1000 / avg_time if avg_time > 0 else 0\n", + "\n", + " # Calculate scaling factor vs previous\n", + " scaling = avg_time / prev_time if prev_time else 1.0\n", + "\n", + " print(f\"{seq_len:7d} | {avg_time:8.2f} | {ops_per_sec:7.0f} | {scaling:6.2f}x\")\n", + " prev_time = avg_time\n", + "\n", + " print(f\"\\n💡 Attention time scales roughly as O(n²) with sequence length\")\n", + " print(f\"🚀 This is why efficient attention (FlashAttention) is crucial for long sequences\")\n", + "\n", + "# Call the analysis functions\n", + "analyze_attention_complexity()\n", + "analyze_attention_timing()" + ] + }, + { + "cell_type": "markdown", + "id": "a9ee02ed", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "### 📊 Systems Analysis: The O(n²) Reality\n", + "\n", + "Our analysis reveals the fundamental challenge that drives modern attention research:\n", + "\n", + "**Memory Scaling Crisis:**\n", + "- Attention matrix grows as n² with sequence length\n", + "- For GPT-3 context (2048 tokens): 16MB just for attention weights per layer\n", + "- With 96 layers: 1.5GB just for attention matrices!\n", + "- This excludes activations, gradients, and other tensors\n", + "\n", + "**Time Complexity Validation:**\n", + "- Each sequence length doubling roughly quadruples computation time\n", + "- This matches the theoretical O(n²) complexity we implemented with explicit loops\n", + "- Real bottleneck shifts from computation to memory at scale\n", + "\n", + "**The Production Reality:**\n", + "```\n", + "Model Scale Impact:\n", + "\n", + "Small Model (6 layers, 512 context):\n", + "Attention Memory = 6 × 1MB = 6MB ✅ Manageable\n", + "\n", + "GPT-3 Scale (96 layers, 2048 context):\n", + "Attention Memory = 96 × 16MB = 1.5GB ⚠️ Significant\n", + "\n", + "GPT-4 Scale (hypothetical: 120 layers, 32K context):\n", + "Attention Memory = 120 × 4GB = 480GB ❌ Impossible on single GPU!\n", + "```\n", + "\n", + "**Why This Matters:**\n", + "- **FlashAttention**: Reformulates computation to reduce memory without changing results\n", + "- **Sparse Attention**: Only compute attention for specific patterns (local, strided)\n", + "- **Linear Attention**: Approximate attention with linear complexity\n", + "- **State Space Models**: Alternative architectures that avoid attention entirely\n", + "\n", + "The quadratic wall is why long-context AI is an active research frontier, not a solved problem." + ] + }, + { + "cell_type": "markdown", + "id": "9be100f1", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## Part 6: Integration - Attention Patterns in Action\n", + "\n", + "Let's test our complete attention system with realistic scenarios and visualize actual attention patterns.\n", + "\n", + "### Understanding Attention Patterns\n", + "\n", + "Real transformer models learn interpretable attention patterns:\n", + "\n", + "```\n", + "Example Attention Patterns in Language:\n", + "\n", + "1. Local Syntax Attention:\n", + " \"The quick brown fox\"\n", + " The → quick (determiner-adjective)\n", + " quick → brown (adjective-adjective)\n", + " brown → fox (adjective-noun)\n", + "\n", + "2. Long-Range Coreference:\n", + " \"John went to the store. He bought milk.\"\n", + " He → John (pronoun resolution across sentence boundary)\n", + "\n", + "3. Compositional Structure:\n", + " \"The cat in the hat sat\"\n", + " sat → cat (verb attending to subject, skipping prepositional phrase)\n", + "\n", + "4. Causal Dependencies:\n", + " \"I think therefore I\"\n", + " I → think (causal reasoning patterns)\n", + " I → I (self-reference at end)\n", + "```\n", + "\n", + "Let's see these patterns emerge in our implementation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13905da5", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "attention-scenarios", + "locked": false, + "solution": true + } + }, + "outputs": [], + "source": [ + "def test_attention_scenarios():\n", + " \"\"\"Test attention mechanisms in realistic scenarios.\"\"\"\n", + " print(\"🔬 Testing Attention Scenarios...\")\n", + "\n", + " # Scenario 1: Small transformer block setup\n", + " print(\"\\n1. Small Transformer Setup:\")\n", + " embed_dim, num_heads, seq_len = 128, 8, 32\n", + "\n", + " # Create embeddings (simulating token embeddings + positional)\n", + " embeddings = Tensor(np.random.randn(2, seq_len, embed_dim))\n", + "\n", + " # Multi-head attention\n", + " mha = MultiHeadAttention(embed_dim, num_heads)\n", + " attended = mha.forward(embeddings)\n", + "\n", + " print(f\" Input shape: {embeddings.shape}\")\n", + " print(f\" Output shape: {attended.shape}\")\n", + " print(f\" Parameters: {len(mha.parameters())} tensors\")\n", + "\n", + " # Scenario 2: Causal language modeling\n", + " print(\"\\n2. Causal Language Modeling:\")\n", + "\n", + " # Create causal mask (lower triangular)\n", + " causal_mask = np.tril(np.ones((seq_len, seq_len)))\n", + " mask = Tensor(np.broadcast_to(causal_mask, (2, seq_len, seq_len)))\n", + "\n", + " # Apply causal attention\n", + " causal_output = mha.forward(embeddings, mask)\n", + "\n", + " print(f\" Masked output shape: {causal_output.shape}\")\n", + " print(f\" Causal mask applied: {mask.shape}\")\n", + "\n", + " # Scenario 3: Compare attention patterns\n", + " print(\"\\n3. Attention Pattern Analysis:\")\n", + "\n", + " # Create simple test sequence\n", + " simple_embed = Tensor(np.random.randn(1, 4, 16))\n", + " simple_mha = MultiHeadAttention(16, 4)\n", + "\n", + " # Get attention weights by calling the base function\n", + " Q = simple_mha.q_proj.forward(simple_embed)\n", + " K = simple_mha.k_proj.forward(simple_embed)\n", + " V = simple_mha.v_proj.forward(simple_embed)\n", + "\n", + " # Reshape for single head analysis\n", + " Q_head = Tensor(Q.data[:, :, :4]) # First head only\n", + " K_head = Tensor(K.data[:, :, :4])\n", + " V_head = Tensor(V.data[:, :, :4])\n", + "\n", + " _, weights = scaled_dot_product_attention(Q_head, K_head, V_head)\n", + "\n", + " print(f\" Attention weights shape: {weights.shape}\")\n", + " print(f\" Attention weights (first batch, 4x4 matrix):\")\n", + " weight_matrix = weights.data[0, :, :].round(3)\n", + "\n", + " # Format the attention matrix nicely\n", + " print(\" Pos→ 0 1 2 3\")\n", + " for i in range(4):\n", + " row_str = f\" {i}: \" + \" \".join(f\"{weight_matrix[i,j]:5.3f}\" for j in range(4))\n", + " print(row_str)\n", + "\n", + " print(f\" Row sums: {weights.data[0].sum(axis=1).round(3)} (should be ~1.0)\")\n", + "\n", + " # Scenario 4: Attention with masking visualization\n", + " print(\"\\n4. Causal Masking Effect:\")\n", + "\n", + " # Apply causal mask to the simple example\n", + " simple_mask = Tensor(np.tril(np.ones((1, 4, 4))))\n", + " _, masked_weights = scaled_dot_product_attention(Q_head, K_head, V_head, simple_mask)\n", + "\n", + " print(\" Causal attention matrix (lower triangular):\")\n", + " masked_matrix = masked_weights.data[0, :, :].round(3)\n", + " print(\" Pos→ 0 1 2 3\")\n", + " for i in range(4):\n", + " row_str = f\" {i}: \" + \" \".join(f\"{masked_matrix[i,j]:5.3f}\" for j in range(4))\n", + " print(row_str)\n", + "\n", + " print(\" Notice: Upper triangle is zero (can't attend to future)\")\n", + "\n", + " print(\"\\n✅ All attention scenarios work correctly!\")\n", + "\n", + "test_attention_scenarios()" + ] + }, + { + "cell_type": "markdown", + "id": "f55d7fc7", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "### 🧪 Integration Test: Attention Scenarios\n", + "\n", + "This comprehensive test validates attention in realistic use cases:\n", + "\n", + "**Transformer Setup**: Standard configuration matching real architectures\n", + "- 128-dimensional embeddings with 8 attention heads\n", + "- 16 dimensions per head (128 ÷ 8 = 16)\n", + "- Proper parameter counting and shape preservation\n", + "\n", + "**Causal Language Modeling**: Essential for GPT-style models\n", + "- Lower triangular mask ensures autoregressive property\n", + "- Position i cannot attend to positions j > i (future tokens)\n", + "- Critical for language generation and training stability\n", + "\n", + "**Attention Pattern Visualization**: Understanding what the model \"sees\"\n", + "- Each row sums to 1.0 (valid probability distribution)\n", + "- Patterns reveal which positions the model finds relevant\n", + "- Causal masking creates structured sparsity in attention\n", + "\n", + "**Real-World Implications**:\n", + "- These patterns are interpretable in trained models\n", + "- Attention heads often specialize (syntax, semantics, position)\n", + "- Visualization tools like BertViz use these matrices for model interpretation\n", + "\n", + "The attention matrices you see here are the foundation of model interpretability in transformers." + ] + }, + { + "cell_type": "markdown", + "id": "e845f69f", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 🧪 Module Integration Test\n", + "\n", + "Final validation that everything works together correctly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "43b4ca05", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "module-test", + "locked": true, + "points": 20 + } + }, + "outputs": [], + "source": [ + "def test_module():\n", + " \"\"\"\n", + " Comprehensive test of entire attention module functionality.\n", + "\n", + " This final test runs before module summary to ensure:\n", + " - All unit tests pass\n", + " - Functions work together correctly\n", + " - Module is ready for integration with TinyTorch\n", + " \"\"\"\n", + " print(\"🧪 RUNNING MODULE INTEGRATION TEST\")\n", + " print(\"=\" * 50)\n", + "\n", + " # Run all unit tests\n", + " print(\"Running unit tests...\")\n", + " test_unit_scaled_dot_product_attention()\n", + " test_unit_multihead_attention()\n", + "\n", + " print(\"\\nRunning integration scenarios...\")\n", + " test_attention_scenarios()\n", + "\n", + " print(\"\\nRunning performance analysis...\")\n", + " analyze_attention_complexity()\n", + "\n", + " print(\"\\n\" + \"=\" * 50)\n", + " print(\"🎉 ALL TESTS PASSED! Module ready for export.\")\n", + " print(\"Run: tito module complete 12\")\n", + "\n", + "# Call before module summary\n", + "test_module()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b285af6", + "metadata": {}, + "outputs": [], + "source": [ + "if __name__ == \"__main__\":\n", + " print(\"🚀 Running Attention module...\")\n", + " test_module()\n", + " print(\"✅ Module validation complete!\")" + ] + }, + { + "cell_type": "markdown", + "id": "4afd6eb3", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🤔 ML Systems Thinking: Attention Mechanics\n", + "\n", + "### Question 1: Memory Scaling Impact\n", + "You implemented scaled dot-product attention with explicit O(n²) loops.\n", + "If you have a sequence of length 1024 with 8-byte float64 attention weights:\n", + "- How many MB does the attention matrix use? _____ MB\n", + "- For a 12-layer transformer, what's the total attention memory? _____ MB\n", + "\n", + "### Question 2: Multi-Head Efficiency\n", + "Your MultiHeadAttention splits embed_dim=512 into num_heads=8.\n", + "- How many parameters does each head's Q/K/V projection have? _____ parameters\n", + "- What's the head_dim for each attention head? _____ dimensions\n", + "- Why is this more efficient than 8 separate attention mechanisms?\n", + "\n", + "### Question 3: Computational Bottlenecks\n", + "From your timing analysis, attention time roughly quadruples when sequence length doubles.\n", + "- For seq_len=128, if attention takes 10ms, estimate time for seq_len=512: _____ ms\n", + "- Which operation dominates: QK^T computation or attention×V? _____\n", + "- Why does this scaling limit make long-context models challenging?\n", + "\n", + "### Question 4: Causal Masking Design\n", + "Your causal mask prevents future positions from attending to past positions.\n", + "- In a 4-token sequence, how many attention connections are blocked? _____ connections\n", + "- Why is this essential for language modeling but not for BERT-style encoding?\n", + "- How would you modify the mask for local attention (only nearby positions)?\n", + "\n", + "### Question 5: Attention Pattern Interpretation\n", + "Your attention visualization shows weight matrices where each row sums to 1.0.\n", + "- If position 2 has weights [0.1, 0.2, 0.5, 0.2], which position gets the most attention? _____\n", + "- What would uniform attention [0.25, 0.25, 0.25, 0.25] suggest about the model's focus?\n", + "- Why might some heads learn sparse attention patterns while others are more diffuse?" + ] + }, + { + "cell_type": "markdown", + "id": "30c9254b", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🎯 MODULE SUMMARY: Attention\n", + "\n", + "Congratulations! You've built the attention mechanism that revolutionized deep learning!\n", + "\n", + "### Key Accomplishments\n", + "- Built scaled dot-product attention with explicit O(n²) complexity demonstration\n", + "- Implemented multi-head attention for parallel relationship learning\n", + "- Experienced attention's quadratic memory scaling firsthand through analysis\n", + "- Tested causal masking for language modeling applications\n", + "- Visualized actual attention patterns and weight distributions\n", + "- All tests pass ✅ (validated by `test_module()`)\n", + "\n", + "### Systems Insights Gained\n", + "- **Computational Complexity**: Witnessed O(n²) scaling in both memory and time through explicit loops\n", + "- **Memory Bottlenecks**: Attention matrices dominate memory usage in transformers (1.5GB+ for GPT-3 scale)\n", + "- **Parallel Processing**: Multi-head attention enables diverse relationship learning across representation subspaces\n", + "- **Production Challenges**: Understanding why FlashAttention and efficient attention research are crucial\n", + "- **Interpretability Foundation**: Attention matrices provide direct insight into model focus patterns\n", + "\n", + "### Ready for Next Steps\n", + "Your attention implementation is the core mechanism that enables modern language models!\n", + "Export with: `tito module complete 12`\n", + "\n", + "**Next**: Module 13 will combine attention with feed-forward layers to build complete transformer blocks, leading to GPT-style language models!\n", + "\n", + "### What You Just Built Powers\n", + "- **GPT models**: Your attention mechanism is the exact pattern used in ChatGPT and GPT-4\n", + "- **BERT and variants**: Bidirectional attention for understanding tasks\n", + "- **Vision Transformers**: The same attention applied to image patches\n", + "- **Modern AI systems**: Nearly every state-of-the-art language and multimodal model\n", + "\n", + "The mechanism you just implemented with explicit loops is mathematically identical to the attention in production language models - you've built the foundation of modern AI!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/modules/source/12_attention/attention_dev.py b/modules/source/12_attention/attention_dev.py index 79618f6c..ec67e19f 100644 --- a/modules/source/12_attention/attention_dev.py +++ b/modules/source/12_attention/attention_dev.py @@ -69,10 +69,10 @@ from typing import Optional, Tuple, List # Import dependencies from other modules sys.path.append(os.path.join(os.path.dirname(__file__), '..', '01_tensor')) -from tensor_dev import Tensor +from tinytorch.core.tensor import Tensor sys.path.append(os.path.join(os.path.dirname(__file__), '..', '03_layers')) -from layers_dev import Linear +from tinytorch.core.layers import Linear # Note: Keeping simplified implementations for reference during development class _SimplifiedTensor: @@ -313,7 +313,6 @@ Step-by-Step Attention Computation: """ # %% nbgrader={"grade": false, "grade_id": "attention-function", "locked": false, "solution": true} -#| export def scaled_dot_product_attention(Q: Tensor, K: Tensor, V: Tensor, mask: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]: """ Compute scaled dot-product attention. diff --git a/modules/source/13_transformers/transformers_dev.ipynb b/modules/source/13_transformers/transformers_dev.ipynb new file mode 100644 index 00000000..cacf8001 --- /dev/null +++ b/modules/source/13_transformers/transformers_dev.ipynb @@ -0,0 +1,2157 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0fa7ad93", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "# Module 13: Transformers - Complete Transformer Architecture\n", + "\n", + "Welcome to Module 13! You're about to build the complete transformer architecture that powers modern language models like GPT.\n", + "\n", + "## 🔗 Prerequisites & Progress\n", + "**You've Built**: Tensors, activations, layers, attention mechanisms, embeddings, and all foundational components\n", + "**You'll Build**: TransformerBlock, complete GPT architecture, and autoregressive generation\n", + "**You'll Enable**: Full language model training and text generation capabilities\n", + "\n", + "**Connection Map**:\n", + "```\n", + "Attention + Layers + Embeddings → Transformers → GPT Architecture\n", + "(sequence processing) (building blocks) (complete model) (language generation)\n", + "```\n", + "\n", + "## Learning Objectives\n", + "By the end of this module, you will:\n", + "1. Implement complete TransformerBlock with attention, MLP, and layer normalization\n", + "2. Build full GPT architecture with multiple transformer blocks\n", + "3. Add autoregressive text generation capability\n", + "4. Understand parameter scaling in large language models\n", + "5. Test transformer components and generation pipeline\n", + "\n", + "Let's get started!\n", + "\n", + "## 📦 Where This Code Lives in the Final Package\n", + "\n", + "**Learning Side:** You work in `modules/13_transformers/transformers_dev.py` \n", + "**Building Side:** Code exports to `tinytorch.models.transformer`\n", + "\n", + "```python\n", + "# How to use this module:\n", + "from tinytorch.models.transformer import TransformerBlock, GPT\n", + "```\n", + "\n", + "**Why this matters:**\n", + "- **Learning:** Complete transformer system showcasing how all components work together\n", + "- **Production:** Matches PyTorch's transformer implementation with proper model organization\n", + "- **Consistency:** All transformer components and generation logic in models.transformer\n", + "- **Integration:** Demonstrates the power of modular design by combining all previous modules" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5e5dae4", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "imports", + "solution": true + } + }, + "outputs": [], + "source": [ + "#| default_exp models.transformer\n", + "#| export\n", + "\n", + "import numpy as np\n", + "import math\n", + "from typing import Optional, List\n", + "\n", + "# Minimal implementations for development - in practice these import from previous modules\n", + "class Tensor:\n", + " \"\"\"Minimal Tensor class for transformer development - imports from Module 01 in practice.\"\"\"\n", + " def __init__(self, data, requires_grad=False):\n", + " self.data = np.array(data)\n", + " self.shape = self.data.shape\n", + " self.size = self.data.size\n", + " self.requires_grad = requires_grad\n", + " self.grad = None\n", + "\n", + " def __add__(self, other):\n", + " if isinstance(other, Tensor):\n", + " return Tensor(self.data + other.data)\n", + " return Tensor(self.data + other)\n", + "\n", + " def __mul__(self, other):\n", + " if isinstance(other, Tensor):\n", + " return Tensor(self.data * other.data)\n", + " return Tensor(self.data * other)\n", + "\n", + " def matmul(self, other):\n", + " return Tensor(np.dot(self.data, other.data))\n", + "\n", + " def sum(self, axis=None, keepdims=False):\n", + " return Tensor(self.data.sum(axis=axis, keepdims=keepdims))\n", + "\n", + " def mean(self, axis=None, keepdims=False):\n", + " return Tensor(self.data.mean(axis=axis, keepdims=keepdims))\n", + "\n", + " def reshape(self, *shape):\n", + " return Tensor(self.data.reshape(shape))\n", + "\n", + " def __repr__(self):\n", + " return f\"Tensor(data={self.data}, shape={self.shape})\"\n", + "\n", + "class Linear:\n", + " \"\"\"Minimal Linear layer - imports from Module 03 in practice.\"\"\"\n", + " def __init__(self, in_features, out_features, bias=True):\n", + " # Xavier/Glorot initialization\n", + " std = math.sqrt(2.0 / (in_features + out_features))\n", + " self.weight = Tensor(np.random.normal(0, std, (in_features, out_features)))\n", + " self.bias = Tensor(np.zeros(out_features)) if bias else None\n", + "\n", + " def forward(self, x):\n", + " output = x.matmul(self.weight)\n", + " if self.bias is not None:\n", + " output = output + self.bias\n", + " return output\n", + "\n", + " def parameters(self):\n", + " params = [self.weight]\n", + " if self.bias is not None:\n", + " params.append(self.bias)\n", + " return params\n", + "\n", + "class MultiHeadAttention:\n", + " \"\"\"Minimal MultiHeadAttention - imports from Module 12 in practice.\"\"\"\n", + " def __init__(self, embed_dim, num_heads):\n", + " assert embed_dim % num_heads == 0\n", + " self.embed_dim = embed_dim\n", + " self.num_heads = num_heads\n", + " self.head_dim = embed_dim // num_heads\n", + "\n", + " self.q_proj = Linear(embed_dim, embed_dim)\n", + " self.k_proj = Linear(embed_dim, embed_dim)\n", + " self.v_proj = Linear(embed_dim, embed_dim)\n", + " self.out_proj = Linear(embed_dim, embed_dim)\n", + "\n", + " def forward(self, x, mask=None):\n", + " batch_size, seq_len, embed_dim = x.shape\n", + "\n", + " # Linear projections\n", + " Q = self.q_proj.forward(x)\n", + " K = self.k_proj.forward(x)\n", + " V = self.v_proj.forward(x)\n", + "\n", + " # Reshape for multi-head attention\n", + " Q = Q.reshape(batch_size, seq_len, self.num_heads, self.head_dim)\n", + " K = K.reshape(batch_size, seq_len, self.num_heads, self.head_dim)\n", + " V = V.reshape(batch_size, seq_len, self.num_heads, self.head_dim)\n", + "\n", + " # Transpose to (batch_size, num_heads, seq_len, head_dim)\n", + " Q = Tensor(np.transpose(Q.data, (0, 2, 1, 3)))\n", + " K = Tensor(np.transpose(K.data, (0, 2, 1, 3)))\n", + " V = Tensor(np.transpose(V.data, (0, 2, 1, 3)))\n", + "\n", + " # Scaled dot-product attention\n", + " scores = Tensor(np.matmul(Q.data, np.transpose(K.data, (0, 1, 3, 2))))\n", + " scores = scores * (1.0 / math.sqrt(self.head_dim))\n", + "\n", + " # Apply causal mask for autoregressive generation\n", + " if mask is not None:\n", + " scores = Tensor(scores.data + mask.data)\n", + "\n", + " # Softmax\n", + " attention_weights = self._softmax(scores)\n", + "\n", + " # Apply attention to values\n", + " out = Tensor(np.matmul(attention_weights.data, V.data))\n", + "\n", + " # Transpose back and reshape\n", + " out = Tensor(np.transpose(out.data, (0, 2, 1, 3)))\n", + " out = out.reshape(batch_size, seq_len, embed_dim)\n", + "\n", + " # Final linear projection\n", + " return self.out_proj.forward(out)\n", + "\n", + " def _softmax(self, x):\n", + " \"\"\"Numerically stable softmax.\"\"\"\n", + " exp_x = Tensor(np.exp(x.data - np.max(x.data, axis=-1, keepdims=True)))\n", + " return Tensor(exp_x.data / np.sum(exp_x.data, axis=-1, keepdims=True))\n", + "\n", + " def parameters(self):\n", + " params = []\n", + " params.extend(self.q_proj.parameters())\n", + " params.extend(self.k_proj.parameters())\n", + " params.extend(self.v_proj.parameters())\n", + " params.extend(self.out_proj.parameters())\n", + " return params\n", + "\n", + "class Embedding:\n", + " \"\"\"Minimal Embedding layer - imports from Module 11 in practice.\"\"\"\n", + " def __init__(self, vocab_size, embed_dim):\n", + " self.vocab_size = vocab_size\n", + " self.embed_dim = embed_dim\n", + " # Initialize with small random values\n", + " self.weight = Tensor(np.random.normal(0, 0.02, (vocab_size, embed_dim)))\n", + "\n", + " def forward(self, indices):\n", + " # Simple embedding lookup\n", + " return Tensor(self.weight.data[indices.data])\n", + "\n", + " def parameters(self):\n", + " return [self.weight]\n", + "\n", + "def gelu(x):\n", + " \"\"\"GELU activation function.\"\"\"\n", + " return Tensor(0.5 * x.data * (1 + np.tanh(np.sqrt(2 / np.pi) * (x.data + 0.044715 * x.data**3))))" + ] + }, + { + "cell_type": "markdown", + "id": "946c33e2", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 1. Introduction: What are Transformers?\n", + "\n", + "Transformers are the revolutionary architecture that powers modern AI language models like GPT, ChatGPT, and Claude. The key breakthrough is **self-attention**, which allows every token in a sequence to directly interact with every other token, creating rich contextual understanding.\n", + "\n", + "### The Transformer Revolution\n", + "\n", + "Before transformers, language models used RNNs or CNNs that processed text sequentially or locally. Transformers changed everything by processing all positions in parallel while maintaining global context.\n", + "\n", + "### Complete GPT Architecture Overview\n", + "\n", + "```\n", + "Input: \"Hello world\" → [Token IDs: 15496, 1917]\n", + " ↓\n", + " ┌─────────────────────────────────────────────┐\n", + " │ EMBEDDING LAYER │\n", + " │ ┌─────────────┐ ┌──────────────────────┐ │\n", + " │ │Token Embed │ + │ Positional Embed │ │\n", + " │ │[15496→vec] │ │[pos_0→vec, pos_1→vec]│ │\n", + " │ └─────────────┘ └──────────────────────┘ │\n", + " └─────────────────────────────────────────────┘\n", + " ↓\n", + " ┌─────────────────────────────────────────────┐\n", + " │ TRANSFORMER BLOCK 1 │\n", + " │ │\n", + " │ Input → LayerNorm → MultiHeadAttention │\n", + " │ ↓ ↓ │\n", + " │ └────── Residual Add ←────┘ │\n", + " │ ↓ │\n", + " │ Result → LayerNorm → MLP (Feed Forward) │\n", + " │ ↓ ↓ │\n", + " │ └──── Residual Add ←──┘ │\n", + " └─────────────────────────────────────────────┘\n", + " ↓\n", + " ┌─────────────────────────────────────────────┐\n", + " │ TRANSFORMER BLOCK 2 │\n", + " │ ... (same structure) │\n", + " └─────────────────────────────────────────────┘\n", + " ↓\n", + " ... (more blocks)\n", + " ↓\n", + " ┌─────────────────────────────────────────────┐\n", + " │ OUTPUT HEAD │\n", + " │ Final LayerNorm → Linear → Vocabulary Logits│\n", + " └─────────────────────────────────────────────┘\n", + " ↓\n", + "Output: [Prob(\"Hello\"), Prob(\"world\"), Prob(\"!\"), ...]\n", + "```\n", + "\n", + "### Why Transformers Dominate\n", + "\n", + "**Parallel Processing**: Unlike RNNs that process tokens one by one, transformers process all positions simultaneously. This makes training much faster.\n", + "\n", + "**Global Context**: Every token can directly attend to every other token in the sequence, capturing long-range dependencies that RNNs struggle with.\n", + "\n", + "**Scalability**: Performance predictably improves with more parameters and data. This enabled the scaling laws that led to GPT-3, GPT-4, and beyond.\n", + "\n", + "**Residual Connections**: Allow training very deep networks (100+ layers) by providing gradient highways.\n", + "\n", + "### The Building Blocks We'll Implement\n", + "\n", + "1. **LayerNorm**: Stabilizes training by normalizing activations\n", + "2. **Multi-Layer Perceptron (MLP)**: Provides non-linear transformation\n", + "3. **TransformerBlock**: Combines attention + MLP with residuals\n", + "4. **GPT**: Complete model with embeddings and generation capability" + ] + }, + { + "cell_type": "markdown", + "id": "f8388844", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 2. Foundations: Essential Transformer Mathematics\n", + "\n", + "### Layer Normalization: The Stability Engine\n", + "\n", + "Layer Normalization is crucial for training deep transformer networks. Unlike batch normalization (which normalizes across the batch), layer norm normalizes across the feature dimension for each individual sample.\n", + "\n", + "```\n", + "Mathematical Formula:\n", + "output = (x - μ) / σ * γ + β\n", + "\n", + "where:\n", + " μ = mean(x, axis=features) # Mean across feature dimension\n", + " σ = sqrt(var(x) + ε) # Standard deviation + small epsilon\n", + " γ = learnable scale parameter # Initialized to 1.0\n", + " β = learnable shift parameter # Initialized to 0.0\n", + "```\n", + "\n", + "**Why Layer Norm Works:**\n", + "- **Independence**: Each sample normalized independently (good for variable batch sizes)\n", + "- **Stability**: Prevents internal covariate shift that breaks training\n", + "- **Gradient Flow**: Helps gradients flow better through deep networks\n", + "\n", + "### Residual Connections: The Gradient Highway\n", + "\n", + "Residual connections are the secret to training deep networks. They create \"gradient highways\" that allow information to flow directly through the network.\n", + "\n", + "```\n", + "Residual Pattern in Transformers:\n", + "┌─────────────────────────────────────────────┐\n", + "│ Pre-Norm Architecture (Modern Standard): │\n", + "│ │\n", + "│ x → LayerNorm → MultiHeadAttention → + x │\n", + "│ │ ↑ │\n", + "│ │ residual connection │ │\n", + "│ └──────────────────────────────────────┘ │\n", + "│ │ │\n", + "│ x → LayerNorm → MLP → + x │\n", + "│ │ ↑ ↑ │\n", + "│ │ residual connection │ │\n", + "│ └───────────────────────┘ │\n", + "└─────────────────────────────────────────────┘\n", + "```\n", + "\n", + "**Gradient Flow Visualization:**\n", + "```\n", + "Backward Pass Without Residuals: With Residuals:\n", + "Loss Loss\n", + " │ gradients get smaller │ gradients stay strong\n", + " ↓ at each layer ↓ via residual paths\n", + "Layer N ← tiny gradients Layer N ← strong gradients\n", + " │ │ ↗ (direct path)\n", + " ↓ ↓ ↗\n", + "Layer 2 ← vanishing Layer 2 ← strong gradients\n", + " │ │ ↗\n", + " ↓ ↓ ↗\n", + "Layer 1 ← gone! Layer 1 ← strong gradients\n", + "```\n", + "\n", + "### Feed-Forward Network (MLP): The Thinking Layer\n", + "\n", + "The MLP provides the actual \"thinking\" in each transformer block. It's a simple two-layer network with a specific expansion pattern.\n", + "\n", + "```\n", + "MLP Architecture:\n", + "Input (embed_dim) → Linear → GELU → Linear → Output (embed_dim)\n", + " 512 2048 2048 512\n", + " (4x expansion)\n", + "\n", + "Mathematical Formula:\n", + "FFN(x) = Linear₂(GELU(Linear₁(x)))\n", + " = W₂ · GELU(W₁ · x + b₁) + b₂\n", + "\n", + "where:\n", + " W₁: (embed_dim, 4*embed_dim) # Expansion matrix\n", + " W₂: (4*embed_dim, embed_dim) # Contraction matrix\n", + " GELU: smooth activation function (better than ReLU for language)\n", + "```\n", + "\n", + "**Why 4x Expansion?**\n", + "- **Capacity**: More parameters = more representation power\n", + "- **Non-linearity**: GELU activation creates complex transformations\n", + "- **Information Bottleneck**: Forces the model to compress useful information\n", + "\n", + "### The Complete Transformer Block Data Flow\n", + "\n", + "```\n", + "Input Tensor (batch, seq_len, embed_dim)\n", + " ↓\n", + " ┌─────────────────────────────────────┐\n", + " │ ATTENTION SUB-LAYER │\n", + " │ │\n", + " │ x₁ = LayerNorm(x₀) │\n", + " │ attention_out = MultiHeadAttn(x₁) │\n", + " │ x₂ = x₀ + attention_out (residual) │\n", + " └─────────────────────────────────────┘\n", + " ↓\n", + " ┌─────────────────────────────────────┐\n", + " │ MLP SUB-LAYER │\n", + " │ │\n", + " │ x₃ = LayerNorm(x₂) │\n", + " │ mlp_out = MLP(x₃) │\n", + " │ x₄ = x₂ + mlp_out (residual) │\n", + " └─────────────────────────────────────┘\n", + " ↓\n", + "Output Tensor (batch, seq_len, embed_dim)\n", + "```\n", + "\n", + "**Key Insight**: Each sub-layer (attention and MLP) gets a \"clean\" normalized input but adds its contribution to the residual stream. This creates a stable training dynamic." + ] + }, + { + "cell_type": "markdown", + "id": "aa924c73", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 3. Implementation: Building Transformer Components\n", + "\n", + "Now we'll implement each transformer component with a clear understanding of their role in the overall architecture. We'll follow the pattern: **Explanation → Implementation → Test** for each component.\n", + "\n", + "Each component serves a specific purpose:\n", + "- **LayerNorm**: Stabilizes training and normalizes activations\n", + "- **MLP**: Provides non-linear transformation and \"thinking\" capacity\n", + "- **TransformerBlock**: Combines attention with MLP using residual connections\n", + "- **GPT**: Complete autoregressive language model for text generation" + ] + }, + { + "cell_type": "markdown", + "id": "3dc23c53", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### Understanding Layer Normalization\n", + "\n", + "Layer Normalization is the foundation of stable transformer training. Unlike batch normalization, it normalizes each sample independently across its feature dimensions.\n", + "\n", + "#### Why Layer Norm is Essential\n", + "\n", + "Without normalization, deep networks suffer from \"internal covariate shift\" - the distribution of inputs to each layer changes during training, making learning unstable.\n", + "\n", + "#### Layer Norm Visualization\n", + "\n", + "```\n", + "Input Tensor: (batch=2, seq=3, features=4)\n", + "┌──────────────────────────────────────────┐\n", + "│ Sample 1: [[1.0, 2.0, 3.0, 4.0], │\n", + "│ [5.0, 6.0, 7.0, 8.0], │\n", + "│ [9.0, 10., 11., 12.]] │\n", + "│ │\n", + "│ Sample 2: [[13., 14., 15., 16.], │\n", + "│ [17., 18., 19., 20.], │\n", + "│ [21., 22., 23., 24.]] │\n", + "└──────────────────────────────────────────┘\n", + " ↓ Layer Norm (across features for each position)\n", + "┌──────────────────────────────────────────┐\n", + "│ Each position normalized to mean=0, std=1│\n", + "│ Sample 1: [[-1.34, -0.45, 0.45, 1.34], │\n", + "│ [-1.34, -0.45, 0.45, 1.34], │\n", + "│ [-1.34, -0.45, 0.45, 1.34]] │\n", + "│ │\n", + "│ Sample 2: [[-1.34, -0.45, 0.45, 1.34], │\n", + "│ [-1.34, -0.45, 0.45, 1.34], │\n", + "│ [-1.34, -0.45, 0.45, 1.34]] │\n", + "└──────────────────────────────────────────┘\n", + " ↓ Apply learnable scale (γ) and shift (β)\n", + "┌──────────────────────────────────────────┐\n", + "│ Final Output: γ * normalized + β │\n", + "└──────────────────────────────────────────┘\n", + "```\n", + "\n", + "#### Key Properties\n", + "- **Per-sample normalization**: Each sequence position normalized independently\n", + "- **Learnable parameters**: γ (scale) and β (shift) allow the model to recover any desired distribution\n", + "- **Gradient friendly**: Helps gradients flow smoothly through deep networks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4c26bf73", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "layer-norm", + "solution": true + } + }, + "outputs": [], + "source": [ + "class LayerNorm:\n", + " \"\"\"\n", + " Layer Normalization for transformer blocks.\n", + "\n", + " Normalizes across the feature dimension (last axis) for each sample independently,\n", + " unlike batch normalization which normalizes across the batch dimension.\n", + " \"\"\"\n", + "\n", + " def __init__(self, normalized_shape, eps=1e-5):\n", + " \"\"\"\n", + " Initialize LayerNorm with learnable parameters.\n", + "\n", + " TODO: Set up normalization parameters\n", + "\n", + " APPROACH:\n", + " 1. Store the shape to normalize over (usually embed_dim)\n", + " 2. Initialize learnable scale (gamma) and shift (beta) parameters\n", + " 3. Set small epsilon for numerical stability\n", + "\n", + " EXAMPLE:\n", + " >>> ln = LayerNorm(512) # For 512-dimensional embeddings\n", + " >>> x = Tensor(np.random.randn(2, 10, 512)) # (batch, seq, features)\n", + " >>> normalized = ln.forward(x)\n", + " >>> # Each (2, 10) sample normalized independently across 512 features\n", + "\n", + " HINTS:\n", + " - gamma should start at 1.0 (identity scaling)\n", + " - beta should start at 0.0 (no shift)\n", + " - eps prevents division by zero in variance calculation\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " self.normalized_shape = normalized_shape\n", + " self.eps = eps\n", + "\n", + " # Learnable parameters: scale and shift\n", + " self.gamma = Tensor(np.ones(normalized_shape)) # Scale parameter\n", + " self.beta = Tensor(np.zeros(normalized_shape)) # Shift parameter\n", + " ### END SOLUTION\n", + "\n", + " def forward(self, x):\n", + " \"\"\"\n", + " Apply layer normalization.\n", + "\n", + " TODO: Implement layer normalization formula\n", + "\n", + " APPROACH:\n", + " 1. Compute mean and variance across the last dimension\n", + " 2. Normalize: (x - mean) / sqrt(variance + eps)\n", + " 3. Apply learnable scale and shift: gamma * normalized + beta\n", + "\n", + " MATHEMATICAL FORMULA:\n", + " y = (x - μ) / σ * γ + β\n", + " where μ = mean(x), σ = sqrt(var(x) + ε)\n", + "\n", + " HINT: Use keepdims=True to maintain tensor dimensions for broadcasting\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " # Compute statistics across last dimension (features)\n", + " mean = x.mean(axis=-1, keepdims=True)\n", + "\n", + " # Compute variance: E[(x - μ)²]\n", + " diff = Tensor(x.data - mean.data)\n", + " variance = Tensor((diff.data ** 2).mean(axis=-1, keepdims=True))\n", + "\n", + " # Normalize\n", + " std = Tensor(np.sqrt(variance.data + self.eps))\n", + " normalized = Tensor((x.data - mean.data) / std.data)\n", + "\n", + " # Apply learnable transformation\n", + " output = normalized * self.gamma + self.beta\n", + " return output\n", + " ### END SOLUTION\n", + "\n", + " def parameters(self):\n", + " \"\"\"Return learnable parameters.\"\"\"\n", + " return [self.gamma, self.beta]" + ] + }, + { + "cell_type": "markdown", + "id": "33272f95", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### 🔬 Unit Test: Layer Normalization\n", + "This test validates our LayerNorm implementation works correctly.\n", + "**What we're testing**: Normalization statistics and parameter learning\n", + "**Why it matters**: Essential for transformer stability and training\n", + "**Expected**: Mean ≈ 0, std ≈ 1 after normalization, learnable parameters work" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "42c87208", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test-layer-norm", + "locked": true, + "points": 10 + } + }, + "outputs": [], + "source": [ + "def test_unit_layer_norm():\n", + " \"\"\"🔬 Test LayerNorm implementation.\"\"\"\n", + " print(\"🔬 Unit Test: Layer Normalization...\")\n", + "\n", + " # Test basic normalization\n", + " ln = LayerNorm(4)\n", + " x = Tensor([[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0]]) # (2, 4)\n", + "\n", + " normalized = ln.forward(x)\n", + "\n", + " # Check output shape\n", + " assert normalized.shape == (2, 4)\n", + "\n", + " # Check normalization properties (approximately)\n", + " # For each sample, mean should be close to 0, std close to 1\n", + " for i in range(2):\n", + " sample_mean = np.mean(normalized.data[i])\n", + " sample_std = np.std(normalized.data[i])\n", + " assert abs(sample_mean) < 1e-5, f\"Mean should be ~0, got {sample_mean}\"\n", + " assert abs(sample_std - 1.0) < 1e-4, f\"Std should be ~1, got {sample_std}\"\n", + "\n", + " # Test parameter shapes\n", + " params = ln.parameters()\n", + " assert len(params) == 2\n", + " assert params[0].shape == (4,) # gamma\n", + " assert params[1].shape == (4,) # beta\n", + "\n", + " print(\"✅ LayerNorm works correctly!\")\n", + "\n", + "test_unit_layer_norm()" + ] + }, + { + "cell_type": "markdown", + "id": "4eb1e55a", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### Understanding the Multi-Layer Perceptron (MLP)\n", + "\n", + "The MLP is where the \"thinking\" happens in each transformer block. It's a simple feed-forward network that provides non-linear transformation capacity.\n", + "\n", + "#### The Role of MLP in Transformers\n", + "\n", + "While attention handles relationships between tokens, the MLP processes each position independently, adding computational depth and non-linearity.\n", + "\n", + "#### MLP Architecture and Information Flow\n", + "\n", + "```\n", + "Information Flow Through MLP:\n", + "\n", + "Input: (batch, seq_len, embed_dim=512)\n", + " ↓\n", + "┌─────────────────────────────────────────────┐\n", + "│ Linear Layer 1: Expansion │\n", + "│ Weight: (512, 2048) Bias: (2048,) │\n", + "│ Output: (batch, seq_len, 2048) │\n", + "└─────────────────────────────────────────────┘\n", + " ↓\n", + "┌─────────────────────────────────────────────┐\n", + "│ GELU Activation │\n", + "│ Smooth, differentiable activation │\n", + "│ Better than ReLU for language modeling │\n", + "└─────────────────────────────────────────────┘\n", + " ↓\n", + "┌─────────────────────────────────────────────┐\n", + "│ Linear Layer 2: Contraction │\n", + "│ Weight: (2048, 512) Bias: (512,) │\n", + "│ Output: (batch, seq_len, 512) │\n", + "└─────────────────────────────────────────────┘\n", + " ↓\n", + "Output: (batch, seq_len, embed_dim=512)\n", + "```\n", + "\n", + "#### Why 4x Expansion?\n", + "\n", + "```\n", + "Parameter Count Analysis:\n", + "\n", + "Embed Dim: 512\n", + "MLP Hidden: 2048 (4x expansion)\n", + "\n", + "Parameters:\n", + "- Linear1: 512 × 2048 + 2048 = 1,050,624\n", + "- Linear2: 2048 × 512 + 512 = 1,049,088\n", + "- Total MLP: ~2.1M parameters\n", + "\n", + "For comparison:\n", + "- Attention (same embed_dim): ~1.5M parameters\n", + "- MLP has MORE parameters → more computational capacity\n", + "```\n", + "\n", + "#### GELU vs ReLU\n", + "\n", + "```\n", + "Activation Function Comparison:\n", + "\n", + "ReLU(x) = max(0, x) # Hard cutoff at 0\n", + " ┌────\n", + " │\n", + " ─────┘\n", + " 0\n", + "\n", + "GELU(x) ≈ x * Φ(x) # Smooth, probabilistic\n", + " ╭────\n", + " ╱\n", + " ───╱\n", + " ╱\n", + " 0\n", + "\n", + "GELU is smoother and provides better gradients for language modeling.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5acb8f3", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "mlp", + "solution": true + } + }, + "outputs": [], + "source": [ + "class MLP:\n", + " \"\"\"\n", + " Multi-Layer Perceptron (Feed-Forward Network) for transformer blocks.\n", + "\n", + " Standard pattern: Linear -> GELU -> Linear with expansion ratio of 4:1.\n", + " This provides the non-linear transformation in each transformer block.\n", + " \"\"\"\n", + "\n", + " def __init__(self, embed_dim, hidden_dim=None, dropout_prob=0.1):\n", + " \"\"\"\n", + " Initialize MLP with two linear layers.\n", + "\n", + " TODO: Set up the feed-forward network layers\n", + "\n", + " APPROACH:\n", + " 1. First layer expands from embed_dim to hidden_dim (usually 4x larger)\n", + " 2. Second layer projects back to embed_dim\n", + " 3. Use GELU activation (smoother than ReLU, preferred in transformers)\n", + "\n", + " EXAMPLE:\n", + " >>> mlp = MLP(512) # Will create 512 -> 2048 -> 512 network\n", + " >>> x = Tensor(np.random.randn(2, 10, 512))\n", + " >>> output = mlp.forward(x)\n", + " >>> assert output.shape == (2, 10, 512)\n", + "\n", + " HINT: Standard transformer MLP uses 4x expansion (hidden_dim = 4 * embed_dim)\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " if hidden_dim is None:\n", + " hidden_dim = 4 * embed_dim # Standard 4x expansion\n", + "\n", + " self.embed_dim = embed_dim\n", + " self.hidden_dim = hidden_dim\n", + "\n", + " # Two-layer feed-forward network\n", + " self.linear1 = Linear(embed_dim, hidden_dim)\n", + " self.linear2 = Linear(hidden_dim, embed_dim)\n", + " ### END SOLUTION\n", + "\n", + " def forward(self, x):\n", + " \"\"\"\n", + " Forward pass through MLP.\n", + "\n", + " TODO: Implement the feed-forward computation\n", + "\n", + " APPROACH:\n", + " 1. First linear transformation: embed_dim -> hidden_dim\n", + " 2. Apply GELU activation (smooth, differentiable)\n", + " 3. Second linear transformation: hidden_dim -> embed_dim\n", + "\n", + " COMPUTATION FLOW:\n", + " x -> Linear -> GELU -> Linear -> output\n", + "\n", + " HINT: GELU activation is implemented above as a function\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " # First linear layer with expansion\n", + " hidden = self.linear1.forward(x)\n", + "\n", + " # GELU activation\n", + " hidden = gelu(hidden)\n", + "\n", + " # Second linear layer back to original size\n", + " output = self.linear2.forward(hidden)\n", + "\n", + " return output\n", + " ### END SOLUTION\n", + "\n", + " def parameters(self):\n", + " \"\"\"Return all learnable parameters.\"\"\"\n", + " params = []\n", + " params.extend(self.linear1.parameters())\n", + " params.extend(self.linear2.parameters())\n", + " return params" + ] + }, + { + "cell_type": "markdown", + "id": "054236fd", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### 🔬 Unit Test: MLP (Feed-Forward Network)\n", + "This test validates our MLP implementation works correctly.\n", + "**What we're testing**: Shape preservation and parameter counting\n", + "**Why it matters**: MLP provides the non-linear transformation in transformers\n", + "**Expected**: Input/output shapes match, correct parameter count" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8849696d", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test-mlp", + "locked": true, + "points": 10 + } + }, + "outputs": [], + "source": [ + "def test_unit_mlp():\n", + " \"\"\"🔬 Test MLP implementation.\"\"\"\n", + " print(\"🔬 Unit Test: MLP (Feed-Forward Network)...\")\n", + "\n", + " # Test MLP with standard 4x expansion\n", + " embed_dim = 64\n", + " mlp = MLP(embed_dim)\n", + "\n", + " # Test forward pass\n", + " batch_size, seq_len = 2, 10\n", + " x = Tensor(np.random.randn(batch_size, seq_len, embed_dim))\n", + " output = mlp.forward(x)\n", + "\n", + " # Check shape preservation\n", + " assert output.shape == (batch_size, seq_len, embed_dim)\n", + "\n", + " # Check hidden dimension is 4x\n", + " assert mlp.hidden_dim == 4 * embed_dim\n", + "\n", + " # Test parameter counting\n", + " params = mlp.parameters()\n", + " expected_params = 4 # 2 weights + 2 biases\n", + " assert len(params) == expected_params\n", + "\n", + " # Test custom hidden dimension\n", + " custom_mlp = MLP(embed_dim, hidden_dim=128)\n", + " assert custom_mlp.hidden_dim == 128\n", + "\n", + " print(\"✅ MLP works correctly!\")\n", + "\n", + "test_unit_mlp()" + ] + }, + { + "cell_type": "markdown", + "id": "dac755a4", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### Understanding the Complete Transformer Block\n", + "\n", + "The TransformerBlock is the core building unit of GPT and other transformer models. It combines self-attention with feed-forward processing using a carefully designed residual architecture.\n", + "\n", + "#### Pre-Norm vs Post-Norm Architecture\n", + "\n", + "Modern transformers use \"pre-norm\" architecture where LayerNorm comes BEFORE the sub-layers, not after. This provides better training stability.\n", + "\n", + "```\n", + "Pre-Norm Architecture (What We Implement):\n", + "┌─────────────────────────────────────────────────────────┐\n", + "│ INPUT (x) │\n", + "│ │ │\n", + "│ ┌───────────────┴───────────────┐ │\n", + "│ │ │ │\n", + "│ ▼ │ │\n", + "│ LayerNorm │ │\n", + "│ │ │ │\n", + "│ ▼ │ │\n", + "│ MultiHeadAttention │ │\n", + "│ │ │ │\n", + "│ └───────────────┬───────────────┘ │\n", + "│ │ (residual connection) │\n", + "│ ▼ │\n", + "│ x + attention │\n", + "│ │ │\n", + "│ ┌───────────────┴───────────────┐ │\n", + "│ │ │ │\n", + "│ ▼ │ │\n", + "│ LayerNorm │ │\n", + "│ │ │ │\n", + "│ ▼ │ │\n", + "│ MLP │ │\n", + "│ │ │ │\n", + "│ └───────────────┬───────────────┘ │\n", + "│ │ (residual connection) │\n", + "│ ▼ │\n", + "│ x + mlp │\n", + "│ │ │\n", + "│ ▼ │\n", + "│ OUTPUT │\n", + "└─────────────────────────────────────────────────────────┘\n", + "```\n", + "\n", + "#### Why Pre-Norm Works Better\n", + "\n", + "**Training Stability**: LayerNorm before operations provides clean, normalized inputs to attention and MLP layers.\n", + "\n", + "**Gradient Flow**: Residual connections carry gradients directly from output to input, bypassing the normalized operations.\n", + "\n", + "**Deeper Networks**: Pre-norm enables training much deeper networks (100+ layers) compared to post-norm.\n", + "\n", + "#### Information Processing in Transformer Block\n", + "\n", + "```\n", + "Step-by-Step Data Transformation:\n", + "\n", + "1. Input Processing:\n", + " x₀: (batch, seq_len, embed_dim) # Original input\n", + "\n", + "2. Attention Sub-layer:\n", + " x₁ = LayerNorm(x₀) # Normalize input\n", + " attn_out = MultiHeadAttn(x₁) # Self-attention\n", + " x₂ = x₀ + attn_out # Residual connection\n", + "\n", + "3. MLP Sub-layer:\n", + " x₃ = LayerNorm(x₂) # Normalize again\n", + " mlp_out = MLP(x₃) # Feed-forward\n", + " x₄ = x₂ + mlp_out # Final residual\n", + "\n", + "4. Output:\n", + " return x₄ # Ready for next block\n", + "```\n", + "\n", + "#### Residual Stream Concept\n", + "\n", + "Think of the residual connections as a \"stream\" that carries information through the network:\n", + "\n", + "```\n", + "Residual Stream Flow:\n", + "\n", + "Layer 1: [original embeddings] ─┐\n", + " ├─→ + attention info ─┐\n", + "Attention adds information ──────┘ │\n", + " ├─→ + MLP info ─┐\n", + "MLP adds information ───────────────────────────────────┘ │\n", + " │\n", + "Layer 2: carries accumulated information ──────────────────────────────┘\n", + "```\n", + "\n", + "Each layer adds information to this stream rather than replacing it, creating a rich representation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6ad0f601", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "transformer-block", + "solution": true + } + }, + "outputs": [], + "source": [ + "class TransformerBlock:\n", + " \"\"\"\n", + " Complete Transformer Block with self-attention, MLP, and residual connections.\n", + "\n", + " This is the core building block of GPT and other transformer models.\n", + " Each block processes the input sequence and passes it to the next block.\n", + " \"\"\"\n", + "\n", + " def __init__(self, embed_dim, num_heads, mlp_ratio=4, dropout_prob=0.1):\n", + " \"\"\"\n", + " Initialize a complete transformer block.\n", + "\n", + " TODO: Set up all components of the transformer block\n", + "\n", + " APPROACH:\n", + " 1. Multi-head self-attention for sequence modeling\n", + " 2. First layer normalization (pre-norm architecture)\n", + " 3. MLP with specified expansion ratio\n", + " 4. Second layer normalization\n", + "\n", + " TRANSFORMER BLOCK ARCHITECTURE:\n", + " x → LayerNorm → MultiHeadAttention → + (residual) →\n", + " LayerNorm → MLP → + (residual) → output\n", + "\n", + " EXAMPLE:\n", + " >>> block = TransformerBlock(embed_dim=512, num_heads=8)\n", + " >>> x = Tensor(np.random.randn(2, 10, 512)) # (batch, seq, embed)\n", + " >>> output = block.forward(x)\n", + " >>> assert output.shape == (2, 10, 512)\n", + "\n", + " HINT: We use pre-norm architecture (LayerNorm before attention/MLP)\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " self.embed_dim = embed_dim\n", + " self.num_heads = num_heads\n", + "\n", + " # Multi-head self-attention\n", + " self.attention = MultiHeadAttention(embed_dim, num_heads)\n", + "\n", + " # Layer normalizations (pre-norm architecture)\n", + " self.ln1 = LayerNorm(embed_dim) # Before attention\n", + " self.ln2 = LayerNorm(embed_dim) # Before MLP\n", + "\n", + " # Feed-forward network\n", + " hidden_dim = int(embed_dim * mlp_ratio)\n", + " self.mlp = MLP(embed_dim, hidden_dim)\n", + " ### END SOLUTION\n", + "\n", + " def forward(self, x, mask=None):\n", + " \"\"\"\n", + " Forward pass through transformer block.\n", + "\n", + " TODO: Implement the complete transformer block computation\n", + "\n", + " APPROACH:\n", + " 1. Apply layer norm, then self-attention, then add residual\n", + " 2. Apply layer norm, then MLP, then add residual\n", + " 3. Return the transformed sequence\n", + "\n", + " COMPUTATION FLOW:\n", + " x → ln1 → attention → + x → ln2 → mlp → + → output\n", + "\n", + " RESIDUAL CONNECTIONS:\n", + " These are crucial for training deep networks - they allow gradients\n", + " to flow directly through the network during backpropagation.\n", + "\n", + " HINT: Store intermediate results to add residual connections properly\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " # First sub-layer: Multi-head self-attention with residual connection\n", + " # Pre-norm: LayerNorm before attention\n", + " normed1 = self.ln1.forward(x)\n", + " attention_out = self.attention.forward(normed1, mask)\n", + "\n", + " # Residual connection\n", + " x = x + attention_out\n", + "\n", + " # Second sub-layer: MLP with residual connection\n", + " # Pre-norm: LayerNorm before MLP\n", + " normed2 = self.ln2.forward(x)\n", + " mlp_out = self.mlp.forward(normed2)\n", + "\n", + " # Residual connection\n", + " output = x + mlp_out\n", + "\n", + " return output\n", + " ### END SOLUTION\n", + "\n", + " def parameters(self):\n", + " \"\"\"Return all learnable parameters.\"\"\"\n", + " params = []\n", + " params.extend(self.attention.parameters())\n", + " params.extend(self.ln1.parameters())\n", + " params.extend(self.ln2.parameters())\n", + " params.extend(self.mlp.parameters())\n", + " return params" + ] + }, + { + "cell_type": "markdown", + "id": "736d101d", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### 🔬 Unit Test: Transformer Block\n", + "This test validates our complete TransformerBlock implementation.\n", + "**What we're testing**: Shape preservation, residual connections, parameter counting\n", + "**Why it matters**: This is the core component that will be stacked to create GPT\n", + "**Expected**: Input/output shapes match, all components work together" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65540a0f", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test-transformer-block", + "locked": true, + "points": 15 + } + }, + "outputs": [], + "source": [ + "def test_unit_transformer_block():\n", + " \"\"\"🔬 Test TransformerBlock implementation.\"\"\"\n", + " print(\"🔬 Unit Test: Transformer Block...\")\n", + "\n", + " # Test transformer block\n", + " embed_dim = 64\n", + " num_heads = 4\n", + " block = TransformerBlock(embed_dim, num_heads)\n", + "\n", + " # Test forward pass\n", + " batch_size, seq_len = 2, 8\n", + " x = Tensor(np.random.randn(batch_size, seq_len, embed_dim))\n", + " output = block.forward(x)\n", + "\n", + " # Check shape preservation\n", + " assert output.shape == (batch_size, seq_len, embed_dim)\n", + "\n", + " # Test with causal mask (for autoregressive generation)\n", + " mask = Tensor(np.triu(np.ones((seq_len, seq_len)) * -np.inf, k=1))\n", + " masked_output = block.forward(x, mask)\n", + " assert masked_output.shape == (batch_size, seq_len, embed_dim)\n", + "\n", + " # Test parameter counting\n", + " params = block.parameters()\n", + " expected_components = 4 # attention, ln1, ln2, mlp parameters\n", + " assert len(params) > expected_components # Should have parameters from all components\n", + "\n", + " # Test different configurations\n", + " large_block = TransformerBlock(embed_dim=128, num_heads=8, mlp_ratio=2)\n", + " assert large_block.mlp.hidden_dim == 256 # 128 * 2\n", + "\n", + " print(\"✅ TransformerBlock works correctly!\")\n", + "\n", + "test_unit_transformer_block()" + ] + }, + { + "cell_type": "markdown", + "id": "17ad8926", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### Understanding the Complete GPT Architecture\n", + "\n", + "GPT (Generative Pre-trained Transformer) is the complete language model that combines all our components into a text generation system. It's designed for **autoregressive** generation - predicting the next token based on all previous tokens.\n", + "\n", + "#### GPT's Autoregressive Nature\n", + "\n", + "GPT generates text one token at a time, using all previously generated tokens as context:\n", + "\n", + "```\n", + "Autoregressive Generation Process:\n", + "\n", + "Step 1: \"The cat\" → model predicts → \"sat\"\n", + "Step 2: \"The cat sat\" → model predicts → \"on\"\n", + "Step 3: \"The cat sat on\" → model predicts → \"the\"\n", + "Step 4: \"The cat sat on the\" → model predicts → \"mat\"\n", + "\n", + "Result: \"The cat sat on the mat\"\n", + "```\n", + "\n", + "#### Complete GPT Architecture\n", + "\n", + "```\n", + "┌─────────────────────────────────────────────────────────────┐\n", + "│ GPT ARCHITECTURE │\n", + "│ │\n", + "│ Input: Token IDs [15496, 1917, ...] │\n", + "│ │ │\n", + "│ ┌──────────────────┴──────────────────┐ │\n", + "│ │ EMBEDDING LAYER │ │\n", + "│ │ ┌─────────────┐ ┌─────────────────┐│ │\n", + "│ │ │Token Embed │+│Position Embed ││ │\n", + "│ │ │vocab→vector ││ │sequence→vector ││ │\n", + "│ │ └─────────────┘ └─────────────────┘│ │\n", + "│ └──────────────────┬──────────────────┘ │\n", + "│ │ │\n", + "│ ┌──────────────────┴──────────────────┐ │\n", + "│ │ TRANSFORMER BLOCK 1 │ │\n", + "│ │ ┌─────────┐ ┌─────────┐ ┌───────┐ │ │\n", + "│ │ │LayerNorm│→│Attention│→│ +x │ │ │\n", + "│ │ └─────────┘ └─────────┘ └───┬───┘ │ │\n", + "│ │ │ │ │\n", + "│ │ ┌─────────┐ ┌─────────┐ ┌───▼───┐ │ │\n", + "│ │ │LayerNorm│→│ MLP │→│ +x │ │ │\n", + "│ │ └─────────┘ └─────────┘ └───────┘ │ │\n", + "│ └──────────────────┬──────────────────┘ │\n", + "│ │ │\n", + "│ ... (more transformer blocks) ... │\n", + "│ │ │\n", + "│ ┌──────────────────┴──────────────────┐ │\n", + "│ │ OUTPUT HEAD │ │\n", + "│ │ ┌─────────┐ ┌─────────────────────┐ │ │\n", + "│ │ │LayerNorm│→│Linear(embed→vocab) │ │ │\n", + "│ │ └─────────┘ └─────────────────────┘ │ │\n", + "│ └──────────────────┬──────────────────┘ │\n", + "│ │ │\n", + "│ Output: Vocabulary Logits [0.1, 0.05, 0.8, ...] │\n", + "└─────────────────────────────────────────────────────────────┘\n", + "```\n", + "\n", + "#### Causal Masking for Autoregressive Training\n", + "\n", + "During training, GPT sees the entire sequence but must not \"cheat\" by looking at future tokens:\n", + "\n", + "```\n", + "Causal Attention Mask:\n", + "\n", + "Sequence: [\"The\", \"cat\", \"sat\", \"on\"]\n", + "Positions: 0 1 2 3\n", + "\n", + "Attention Matrix (what each position can see):\n", + " 0 1 2 3\n", + " 0 [ ✓ ✗ ✗ ✗ ] # \"The\" only sees itself\n", + " 1 [ ✓ ✓ ✗ ✗ ] # \"cat\" sees \"The\" and itself\n", + " 2 [ ✓ ✓ ✓ ✗ ] # \"sat\" sees \"The\", \"cat\", itself\n", + " 3 [ ✓ ✓ ✓ ✓ ] # \"on\" sees all previous tokens\n", + "\n", + "Implementation: Upper triangular matrix with -∞\n", + "[[ 0, -∞, -∞, -∞],\n", + " [ 0, 0, -∞, -∞],\n", + " [ 0, 0, 0, -∞],\n", + " [ 0, 0, 0, 0]]\n", + "```\n", + "\n", + "#### Generation Temperature Control\n", + "\n", + "Temperature controls the randomness of generation:\n", + "\n", + "```\n", + "Temperature Effects:\n", + "\n", + "Original logits: [1.0, 2.0, 3.0]\n", + "\n", + "Temperature = 0.1 (Conservative):\n", + "Scaled: [10.0, 20.0, 30.0] → Sharp distribution\n", + "Probs: [0.00, 0.00, 1.00] → Always picks highest\n", + "\n", + "Temperature = 1.0 (Balanced):\n", + "Scaled: [1.0, 2.0, 3.0] → Moderate distribution\n", + "Probs: [0.09, 0.24, 0.67] → Weighted sampling\n", + "\n", + "Temperature = 2.0 (Creative):\n", + "Scaled: [0.5, 1.0, 1.5] → Flatter distribution\n", + "Probs: [0.18, 0.33, 0.49] → More random\n", + "```\n", + "\n", + "#### Model Scaling and Parameters\n", + "\n", + "```\n", + "GPT Model Size Scaling:\n", + "\n", + "Tiny GPT (our implementation):\n", + "- embed_dim: 64, layers: 2, heads: 4\n", + "- Parameters: ~50K\n", + "- Use case: Learning and experimentation\n", + "\n", + "GPT-2 Small:\n", + "- embed_dim: 768, layers: 12, heads: 12\n", + "- Parameters: 117M\n", + "- Use case: Basic text generation\n", + "\n", + "GPT-3:\n", + "- embed_dim: 12,288, layers: 96, heads: 96\n", + "- Parameters: 175B\n", + "- Use case: Advanced language understanding\n", + "\n", + "GPT-4 (estimated):\n", + "- embed_dim: ~16,384, layers: ~120, heads: ~128\n", + "- Parameters: ~1.7T\n", + "- Use case: Reasoning and multimodal tasks\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "586f2e46", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "gpt", + "solution": true + } + }, + "outputs": [], + "source": [ + "class GPT:\n", + " \"\"\"\n", + " Complete GPT (Generative Pre-trained Transformer) model.\n", + "\n", + " This combines embeddings, positional encoding, multiple transformer blocks,\n", + " and a language modeling head for text generation.\n", + " \"\"\"\n", + "\n", + " def __init__(self, vocab_size, embed_dim, num_layers, num_heads, max_seq_len=1024):\n", + " \"\"\"\n", + " Initialize complete GPT model.\n", + "\n", + " TODO: Set up all components of the GPT architecture\n", + "\n", + " APPROACH:\n", + " 1. Token embedding layer to convert tokens to vectors\n", + " 2. Positional embedding to add position information\n", + " 3. Stack of transformer blocks (the main computation)\n", + " 4. Final layer norm and language modeling head\n", + "\n", + " GPT ARCHITECTURE:\n", + " tokens → embedding → + pos_embedding →\n", + " transformer_blocks → layer_norm → lm_head → logits\n", + "\n", + " EXAMPLE:\n", + " >>> model = GPT(vocab_size=1000, embed_dim=256, num_layers=6, num_heads=8)\n", + " >>> tokens = Tensor(np.random.randint(0, 1000, (2, 10))) # (batch, seq)\n", + " >>> logits = model.forward(tokens)\n", + " >>> assert logits.shape == (2, 10, 1000) # (batch, seq, vocab)\n", + "\n", + " HINTS:\n", + " - Positional embeddings are learned, not fixed sinusoidal\n", + " - Final layer norm stabilizes training\n", + " - Language modeling head shares weights with token embedding (tie_weights)\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " self.vocab_size = vocab_size\n", + " self.embed_dim = embed_dim\n", + " self.num_layers = num_layers\n", + " self.num_heads = num_heads\n", + " self.max_seq_len = max_seq_len\n", + "\n", + " # Token and positional embeddings\n", + " self.token_embedding = Embedding(vocab_size, embed_dim)\n", + " self.position_embedding = Embedding(max_seq_len, embed_dim)\n", + "\n", + " # Stack of transformer blocks\n", + " self.blocks = []\n", + " for _ in range(num_layers):\n", + " block = TransformerBlock(embed_dim, num_heads)\n", + " self.blocks.append(block)\n", + "\n", + " # Final layer normalization\n", + " self.ln_f = LayerNorm(embed_dim)\n", + "\n", + " # Language modeling head (projects to vocabulary)\n", + " self.lm_head = Linear(embed_dim, vocab_size, bias=False)\n", + " ### END SOLUTION\n", + "\n", + " def forward(self, tokens):\n", + " \"\"\"\n", + " Forward pass through GPT model.\n", + "\n", + " TODO: Implement the complete GPT forward pass\n", + "\n", + " APPROACH:\n", + " 1. Get token embeddings and positional embeddings\n", + " 2. Add them together (broadcasting handles different shapes)\n", + " 3. Pass through all transformer blocks sequentially\n", + " 4. Apply final layer norm and language modeling head\n", + "\n", + " COMPUTATION FLOW:\n", + " tokens → embed + pos_embed → blocks → ln_f → lm_head → logits\n", + "\n", + " CAUSAL MASKING:\n", + " For autoregressive generation, we need to prevent tokens from\n", + " seeing future tokens. This is handled by the attention mask.\n", + "\n", + " HINT: Create position indices as range(seq_len) for positional embedding\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " batch_size, seq_len = tokens.shape\n", + "\n", + " # Token embeddings\n", + " token_emb = self.token_embedding.forward(tokens)\n", + "\n", + " # Positional embeddings\n", + " positions = Tensor(np.arange(seq_len).reshape(1, seq_len))\n", + " pos_emb = self.position_embedding.forward(positions)\n", + "\n", + " # Combine embeddings\n", + " x = token_emb + pos_emb\n", + "\n", + " # Create causal mask for autoregressive generation\n", + " mask = self._create_causal_mask(seq_len)\n", + "\n", + " # Pass through transformer blocks\n", + " for block in self.blocks:\n", + " x = block.forward(x, mask)\n", + "\n", + " # Final layer normalization\n", + " x = self.ln_f.forward(x)\n", + "\n", + " # Language modeling head\n", + " logits = self.lm_head.forward(x)\n", + "\n", + " return logits\n", + " ### END SOLUTION\n", + "\n", + " def _create_causal_mask(self, seq_len):\n", + " \"\"\"Create causal mask to prevent attending to future positions.\"\"\"\n", + " ### BEGIN SOLUTION\n", + " # Upper triangular matrix filled with -inf\n", + " mask = np.triu(np.ones((seq_len, seq_len)) * -np.inf, k=1)\n", + " return Tensor(mask)\n", + " ### END SOLUTION\n", + "\n", + " def generate(self, prompt_tokens, max_new_tokens=50, temperature=1.0):\n", + " \"\"\"\n", + " Generate text autoregressively.\n", + "\n", + " TODO: Implement autoregressive text generation\n", + "\n", + " APPROACH:\n", + " 1. Start with prompt tokens\n", + " 2. For each new position:\n", + " - Run forward pass to get logits\n", + " - Sample next token from logits\n", + " - Append to sequence\n", + " 3. Return generated sequence\n", + "\n", + " AUTOREGRESSIVE GENERATION:\n", + " At each step, the model predicts the next token based on all\n", + " previous tokens. This is how GPT generates coherent text.\n", + "\n", + " EXAMPLE:\n", + " >>> model = GPT(vocab_size=100, embed_dim=64, num_layers=2, num_heads=4)\n", + " >>> prompt = Tensor([[1, 2, 3]]) # Some token sequence\n", + " >>> generated = model.generate(prompt, max_new_tokens=5)\n", + " >>> assert generated.shape[1] == 3 + 5 # original + new tokens\n", + "\n", + " HINT: Use np.random.choice with temperature for sampling\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " current_tokens = Tensor(prompt_tokens.data.copy())\n", + "\n", + " for _ in range(max_new_tokens):\n", + " # Get logits for current sequence\n", + " logits = self.forward(current_tokens)\n", + "\n", + " # Get logits for last position (next token prediction)\n", + " last_logits = logits.data[:, -1, :] # (batch_size, vocab_size)\n", + "\n", + " # Apply temperature scaling\n", + " scaled_logits = last_logits / temperature\n", + "\n", + " # Convert to probabilities (softmax)\n", + " exp_logits = np.exp(scaled_logits - np.max(scaled_logits, axis=-1, keepdims=True))\n", + " probs = exp_logits / np.sum(exp_logits, axis=-1, keepdims=True)\n", + "\n", + " # Sample next token\n", + " next_token = np.array([[np.random.choice(self.vocab_size, p=probs[0])]])\n", + "\n", + " # Append to sequence\n", + " current_tokens = Tensor(np.concatenate([current_tokens.data, next_token], axis=1))\n", + "\n", + " return current_tokens\n", + " ### END SOLUTION\n", + "\n", + " def parameters(self):\n", + " \"\"\"Return all learnable parameters.\"\"\"\n", + " params = []\n", + " params.extend(self.token_embedding.parameters())\n", + " params.extend(self.position_embedding.parameters())\n", + "\n", + " for block in self.blocks:\n", + " params.extend(block.parameters())\n", + "\n", + " params.extend(self.ln_f.parameters())\n", + " params.extend(self.lm_head.parameters())\n", + "\n", + " return params" + ] + }, + { + "cell_type": "markdown", + "id": "c9a7758f", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### 🔬 Unit Test: GPT Model\n", + "This test validates our complete GPT implementation.\n", + "**What we're testing**: Model forward pass, shape consistency, generation capability\n", + "**Why it matters**: This is the complete language model that ties everything together\n", + "**Expected**: Correct output shapes, generation works, parameter counting" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4ba240a", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test-gpt", + "locked": true, + "points": 20 + } + }, + "outputs": [], + "source": [ + "def test_unit_gpt():\n", + " \"\"\"🔬 Test GPT model implementation.\"\"\"\n", + " print(\"🔬 Unit Test: GPT Model...\")\n", + "\n", + " # Test small GPT model\n", + " vocab_size = 100\n", + " embed_dim = 64\n", + " num_layers = 2\n", + " num_heads = 4\n", + "\n", + " model = GPT(vocab_size, embed_dim, num_layers, num_heads)\n", + "\n", + " # Test forward pass\n", + " batch_size, seq_len = 2, 8\n", + " tokens = Tensor(np.random.randint(0, vocab_size, (batch_size, seq_len)))\n", + " logits = model.forward(tokens)\n", + "\n", + " # Check output shape\n", + " expected_shape = (batch_size, seq_len, vocab_size)\n", + " assert logits.shape == expected_shape\n", + "\n", + " # Test generation\n", + " prompt = Tensor(np.random.randint(0, vocab_size, (1, 5)))\n", + " generated = model.generate(prompt, max_new_tokens=3)\n", + "\n", + " # Check generation shape\n", + " assert generated.shape == (1, 8) # 5 prompt + 3 new tokens\n", + "\n", + " # Test parameter counting\n", + " params = model.parameters()\n", + " assert len(params) > 10 # Should have many parameters from all components\n", + "\n", + " # Test different model sizes\n", + " larger_model = GPT(vocab_size=200, embed_dim=128, num_layers=4, num_heads=8)\n", + " test_tokens = Tensor(np.random.randint(0, 200, (1, 10)))\n", + " larger_logits = larger_model.forward(test_tokens)\n", + " assert larger_logits.shape == (1, 10, 200)\n", + "\n", + " print(\"✅ GPT model works correctly!\")\n", + "\n", + "test_unit_gpt()" + ] + }, + { + "cell_type": "markdown", + "id": "1ecc1961", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 4. Integration: Complete Transformer Workflow\n", + "\n", + "Now that we've built all the components, let's see how they work together in a complete language modeling pipeline. This demonstrates the full power of the transformer architecture.\n", + "\n", + "### The Language Modeling Pipeline\n", + "\n", + "```\n", + "Complete Workflow Visualization:\n", + "\n", + "1. Text Input:\n", + " \"hello world\" → Tokenization → [15496, 1917]\n", + "\n", + "2. Model Processing:\n", + " [15496, 1917]\n", + " ↓ Token Embedding\n", + " [[0.1, 0.5, ...], [0.3, -0.2, ...]] # Vector representations\n", + " ↓ + Position Embedding\n", + " [[0.2, 0.7, ...], [0.1, -0.4, ...]] # With position info\n", + " ↓ Transformer Block 1\n", + " [[0.3, 0.2, ...], [0.5, -0.1, ...]] # After attention + MLP\n", + " ↓ Transformer Block 2\n", + " [[0.1, 0.9, ...], [0.7, 0.3, ...]] # Further processed\n", + " ↓ Final LayerNorm + LM Head\n", + " [[0.1, 0.05, 0.8, ...], [...]] # Probability over vocab\n", + "\n", + "3. Generation:\n", + " Model predicts next token: \"!\" (token 33)\n", + " New sequence: \"hello world!\"\n", + "```\n", + "\n", + "This integration demo will show:\n", + "- **Character-level tokenization** for simplicity\n", + "- **Forward pass** through all components\n", + "- **Autoregressive generation** in action\n", + "- **Temperature effects** on creativity" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04f8fd5c", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "integration-demo", + "solution": true + } + }, + "outputs": [], + "source": [ + "def demonstrate_transformer_integration():\n", + " \"\"\"\n", + " Demonstrate complete transformer pipeline.\n", + "\n", + " This simulates training a small language model on a simple vocabulary.\n", + " \"\"\"\n", + " print(\"🔗 Integration Demo: Complete Language Model Pipeline\")\n", + " print(\"Building a mini-GPT for character-level text generation\")\n", + "\n", + " # Create a small vocabulary (character-level)\n", + " vocab = list(\"abcdefghijklmnopqrstuvwxyz .\")\n", + " vocab_size = len(vocab)\n", + " char_to_idx = {char: i for i, char in enumerate(vocab)}\n", + " idx_to_char = {i: char for i, char in enumerate(vocab)}\n", + "\n", + " print(f\"Vocabulary size: {vocab_size}\")\n", + " print(f\"Characters: {''.join(vocab)}\")\n", + "\n", + " # Create model\n", + " model = GPT(\n", + " vocab_size=vocab_size,\n", + " embed_dim=64,\n", + " num_layers=2,\n", + " num_heads=4,\n", + " max_seq_len=32\n", + " )\n", + "\n", + " # Sample text encoding\n", + " text = \"hello world.\"\n", + " tokens = [char_to_idx[char] for char in text]\n", + " input_tokens = Tensor(np.array([tokens]))\n", + "\n", + " print(f\"\\nOriginal text: '{text}'\")\n", + " print(f\"Tokenized: {tokens}\")\n", + " print(f\"Input shape: {input_tokens.shape}\")\n", + "\n", + " # Forward pass\n", + " logits = model.forward(input_tokens)\n", + " print(f\"Output logits shape: {logits.shape}\")\n", + " print(f\"Each position predicts next token from {vocab_size} possibilities\")\n", + "\n", + " # Generation demo\n", + " prompt_text = \"hello\"\n", + " prompt_tokens = [char_to_idx[char] for char in prompt_text]\n", + " prompt = Tensor(np.array([prompt_tokens]))\n", + "\n", + " print(f\"\\nGeneration demo:\")\n", + " print(f\"Prompt: '{prompt_text}'\")\n", + "\n", + " generated = model.generate(prompt, max_new_tokens=8, temperature=1.0)\n", + " generated_text = ''.join([idx_to_char[idx] for idx in generated.data[0]])\n", + "\n", + " print(f\"Generated: '{generated_text}'\")\n", + " print(\"(Note: Untrained model produces random text)\")\n", + "\n", + " return model\n", + "\n", + "demonstrate_transformer_integration()" + ] + }, + { + "cell_type": "markdown", + "id": "0c53c926", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 5. Systems Analysis: Parameter Scaling and Memory\n", + "\n", + "Transformer models scale dramatically with size, leading to both opportunities and challenges. Let's analyze the computational and memory requirements to understand why training large language models requires massive infrastructure.\n", + "\n", + "### The Scaling Laws Revolution\n", + "\n", + "One of the key discoveries in modern AI is that transformer performance follows predictable scaling laws:\n", + "\n", + "```\n", + "Scaling Laws Pattern:\n", + "Performance ∝ Parameters^α × Data^β × Compute^γ\n", + "\n", + "where α ≈ 0.7, β ≈ 0.8, γ ≈ 0.5\n", + "\n", + "This means:\n", + "- 10× more parameters → ~5× better performance\n", + "- 10× more data → ~6× better performance\n", + "- 10× more compute → ~3× better performance\n", + "```\n", + "\n", + "### Memory Scaling Analysis\n", + "\n", + "Memory requirements grow in different ways for different components:\n", + "\n", + "```\n", + "Memory Scaling by Component:\n", + "\n", + "1. Parameter Memory (Linear with model size):\n", + " - Embeddings: vocab_size × embed_dim\n", + " - Transformer blocks: ~4 × embed_dim²\n", + " - Total: O(embed_dim²)\n", + "\n", + "2. Attention Memory (Quadratic with sequence length):\n", + " - Attention matrices: batch × heads × seq_len²\n", + " - This is why long context is expensive!\n", + " - Total: O(seq_len²)\n", + "\n", + "3. Activation Memory (Linear with batch size):\n", + " - Forward pass activations for backprop\n", + " - Scales with: batch × seq_len × embed_dim\n", + " - Total: O(batch_size)\n", + "```\n", + "\n", + "### The Attention Memory Wall\n", + "\n", + "```\n", + "Attention Memory Wall Visualization:\n", + "\n", + "Sequence Length vs Memory Usage:\n", + "\n", + "1K tokens: [▓] 16 MB # Manageable\n", + "2K tokens: [▓▓▓▓] 64 MB # 4× memory (quadratic!)\n", + "4K tokens: [▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 256 MB # 16× memory\n", + "8K tokens: [████████████████████████████████] 1 GB # 64× memory\n", + "16K tokens: ████████████████████████████████████████████████████████████████ 4 GB\n", + "32K tokens: ████████████████████████████████████████████████████████████████████████████████████████████████████████████████ 16 GB\n", + "\n", + "This is why:\n", + "- GPT-3 context: 2K tokens\n", + "- GPT-4 context: 8K tokens (32K in turbo)\n", + "- Claude-3: 200K tokens (requires special techniques!)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "039199a8", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "analyze-scaling", + "solution": true + } + }, + "outputs": [], + "source": [ + "def analyze_parameter_scaling():\n", + " \"\"\"📊 Analyze how parameter count scales with model dimensions.\"\"\"\n", + " print(\"📊 Analyzing Parameter Scaling in Transformers...\")\n", + " print(\"Understanding why model size affects performance and cost\\n\")\n", + "\n", + " # Test different model sizes\n", + " configs = [\n", + " {\"name\": \"Tiny\", \"embed_dim\": 64, \"num_layers\": 2, \"num_heads\": 4},\n", + " {\"name\": \"Small\", \"embed_dim\": 128, \"num_layers\": 4, \"num_heads\": 8},\n", + " {\"name\": \"Medium\", \"embed_dim\": 256, \"num_layers\": 8, \"num_heads\": 16},\n", + " {\"name\": \"Large\", \"embed_dim\": 512, \"num_layers\": 12, \"num_heads\": 16},\n", + " ]\n", + "\n", + " vocab_size = 50000 # Typical vocabulary size\n", + "\n", + " for config in configs:\n", + " model = GPT(\n", + " vocab_size=vocab_size,\n", + " embed_dim=config[\"embed_dim\"],\n", + " num_layers=config[\"num_layers\"],\n", + " num_heads=config[\"num_heads\"]\n", + " )\n", + "\n", + " # Count parameters\n", + " total_params = 0\n", + " for param in model.parameters():\n", + " total_params += param.size\n", + "\n", + " # Calculate memory requirements (4 bytes per float32 parameter)\n", + " memory_mb = (total_params * 4) / (1024 * 1024)\n", + "\n", + " print(f\"{config['name']} Model:\")\n", + " print(f\" Parameters: {total_params:,}\")\n", + " print(f\" Memory: {memory_mb:.1f} MB\")\n", + " print(f\" Embed dim: {config['embed_dim']}, Layers: {config['num_layers']}\")\n", + " print()\n", + "\n", + " print(\"💡 Parameter scaling is roughly quadratic with embedding dimension\")\n", + " print(\"🚀 Real GPT-3 has 175B parameters, requiring ~350GB memory!\")\n", + "\n", + "analyze_parameter_scaling()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a249a5a0", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "analyze-attention-memory", + "solution": true + } + }, + "outputs": [], + "source": [ + "def analyze_attention_memory():\n", + " \"\"\"📊 Analyze attention memory complexity with sequence length.\"\"\"\n", + " print(\"📊 Analyzing Attention Memory Complexity...\")\n", + " print(\"Why long context is expensive and how it scales\\n\")\n", + "\n", + " embed_dim = 512\n", + " num_heads = 8\n", + " batch_size = 4\n", + "\n", + " # Test different sequence lengths\n", + " sequence_lengths = [128, 256, 512, 1024, 2048]\n", + "\n", + " print(\"Attention Matrix Memory Usage:\")\n", + " print(\"Seq Len | Attention Matrix Size | Memory (MB)\")\n", + " print(\"-\" * 45)\n", + "\n", + " for seq_len in sequence_lengths:\n", + " # Attention matrix is (batch_size, num_heads, seq_len, seq_len)\n", + " attention_elements = batch_size * num_heads * seq_len * seq_len\n", + "\n", + " # 4 bytes per float32\n", + " memory_bytes = attention_elements * 4\n", + " memory_mb = memory_bytes / (1024 * 1024)\n", + "\n", + " print(f\"{seq_len:6d} | {seq_len}×{seq_len} × {batch_size}×{num_heads} | {memory_mb:8.1f}\")\n", + "\n", + " print()\n", + " print(\"💡 Attention memory grows quadratically with sequence length\")\n", + " print(\"🚀 This is why techniques like FlashAttention are crucial for long sequences\")\n", + "\n", + "analyze_attention_memory()" + ] + }, + { + "cell_type": "markdown", + "id": "253b8e90", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 🧪 Module Integration Test\n", + "\n", + "Final validation that everything works together correctly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d9431f80", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "module-integration", + "locked": true, + "points": 25 + } + }, + "outputs": [], + "source": [ + "def test_module():\n", + " \"\"\"\n", + " Comprehensive test of entire module functionality.\n", + "\n", + " This final test runs before module summary to ensure:\n", + " - All unit tests pass\n", + " - Functions work together correctly\n", + " - Module is ready for integration with TinyTorch\n", + " \"\"\"\n", + " print(\"🧪 RUNNING MODULE INTEGRATION TEST\")\n", + " print(\"=\" * 50)\n", + "\n", + " # Run all unit tests\n", + " print(\"Running unit tests...\")\n", + " test_unit_layer_norm()\n", + " test_unit_mlp()\n", + " test_unit_transformer_block()\n", + " test_unit_gpt()\n", + "\n", + " print(\"\\nRunning integration scenarios...\")\n", + "\n", + " # Test complete transformer training scenario\n", + " print(\"🔬 Integration Test: Full Training Pipeline...\")\n", + "\n", + " # Create model and data\n", + " vocab_size = 50\n", + " embed_dim = 64\n", + " num_layers = 2\n", + " num_heads = 4\n", + "\n", + " model = GPT(vocab_size, embed_dim, num_layers, num_heads)\n", + "\n", + " # Test batch processing\n", + " batch_size = 3\n", + " seq_len = 16\n", + " tokens = Tensor(np.random.randint(0, vocab_size, (batch_size, seq_len)))\n", + "\n", + " # Forward pass\n", + " logits = model.forward(tokens)\n", + " assert logits.shape == (batch_size, seq_len, vocab_size)\n", + "\n", + " # Test generation with different temperatures\n", + " prompt = Tensor(np.random.randint(0, vocab_size, (1, 8)))\n", + "\n", + " # Conservative generation\n", + " conservative = model.generate(prompt, max_new_tokens=5, temperature=0.1)\n", + " assert conservative.shape == (1, 13)\n", + "\n", + " # Creative generation\n", + " creative = model.generate(prompt, max_new_tokens=5, temperature=2.0)\n", + " assert creative.shape == (1, 13)\n", + "\n", + " # Test parameter counting consistency\n", + " total_params = sum(param.size for param in model.parameters())\n", + " assert total_params > 1000 # Should have substantial parameters\n", + "\n", + " print(\"✅ Full transformer pipeline works!\")\n", + "\n", + " print(\"\\n\" + \"=\" * 50)\n", + " print(\"🎉 ALL TESTS PASSED! Module ready for export.\")\n", + " print(\"Run: tito module complete 13_transformers\")\n", + "\n", + "test_module()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28f5c8ca", + "metadata": {}, + "outputs": [], + "source": [ + "if __name__ == \"__main__\":\n", + " print(\"🚀 Running Transformers module...\")\n", + " test_module()\n", + " print(\"✅ Module validation complete!\")" + ] + }, + { + "cell_type": "markdown", + "id": "440ab431", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🤔 ML Systems Thinking: Transformer Architecture\n", + "\n", + "Now that you've built a complete transformer model, let's reflect on the systems implications and design decisions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f6986b9", + "metadata": { + "lines_to_next_cell": 0, + "nbgrader": { + "grade": false, + "grade_id": "systems-q1", + "solution": true + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "a465d45c", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "### Question 1: Attention Complexity Analysis\n", + "You implemented multi-head attention that computes attention matrices of size (batch, heads, seq_len, seq_len).\n", + "\n", + "**a) Memory Scaling**: For GPT-4 scale (context length 8192, batch size 16, 96 attention heads):\n", + "- Attention matrix elements: _____ (calculate: 16 × 96 × 8192 × 8192)\n", + "- Memory in GB (4 bytes/float): _____ GB per layer\n", + "- For 96 layers: _____ GB total just for attention matrices\n", + "\n", + "**b) Why Quadratic Matters**: If processing costs $0.01 per GB, what's the cost difference between:\n", + "- 1K context: $_____\n", + "- 8K context: $_____\n", + "- 32K context: $_____\n", + "\n", + "*Think about: Why long-context models are expensive, and why FlashAttention matters*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb3a7788", + "metadata": { + "lines_to_next_cell": 0, + "nbgrader": { + "grade": false, + "grade_id": "systems-q2", + "solution": true + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "a2f18da0", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "### Question 2: Parameter Distribution Analysis\n", + "Your GPT model has parameters in embeddings, transformer blocks, and the language head.\n", + "\n", + "**a) Parameter Breakdown**: For a model with vocab_size=50K, embed_dim=1024, num_layers=24:\n", + "- Token embedding: _____ parameters (vocab_size × embed_dim)\n", + "- Each transformer block: approximately _____ parameters\n", + "- Language head: _____ parameters\n", + "- Total model: approximately _____ parameters\n", + "\n", + "**b) Memory During Training**: Training requires storing:\n", + "- Parameters (model weights)\n", + "- Gradients (same size as parameters)\n", + "- Optimizer states (2-3× parameters for Adam)\n", + "- Activations (depends on batch size and sequence length)\n", + "\n", + "For your calculated model size, estimate total training memory: _____ GB\n", + "\n", + "*Consider: Why training large models requires hundreds of GPUs*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "259119cb", + "metadata": { + "lines_to_next_cell": 0, + "nbgrader": { + "grade": false, + "grade_id": "systems-q3", + "solution": true + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "680a951e", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "### Question 3: Autoregressive Generation Bottlenecks\n", + "Your generate() method runs the full model forward pass for each new token.\n", + "\n", + "**a) Generation Inefficiency**: To generate 100 tokens with a 24-layer model:\n", + "- Token 1: _____ layer computations (24 layers × 1 position)\n", + "- Token 2: _____ layer computations (24 layers × 2 positions)\n", + "- Token 100: _____ layer computations (24 layers × 100 positions)\n", + "- Total: _____ layer computations\n", + "\n", + "**b) KV-Cache Optimization**: With KV-caching, each new token only needs:\n", + "- _____ layer computations (just the new position)\n", + "- This reduces computation by approximately _____× for 100 tokens\n", + "\n", + "*Think about: Why inference optimization matters for production deployment*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e99d5ae3", + "metadata": { + "lines_to_next_cell": 0, + "nbgrader": { + "grade": false, + "grade_id": "systems-q4", + "solution": true + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "678beea4", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "### Question 4: Pre-norm vs Post-norm Architecture\n", + "You implemented pre-norm (LayerNorm before attention/MLP) rather than post-norm (LayerNorm after).\n", + "\n", + "**a) Training Stability**: Pre-norm helps with gradient flow because:\n", + "- Residual connections pass _____ gradients directly through the network\n", + "- LayerNorm before operations provides _____ input distributions\n", + "- This enables training _____ networks compared to post-norm\n", + "\n", + "**b) Performance Trade-offs**:\n", + "- Pre-norm: Better training stability, but slightly _____ final performance\n", + "- Post-norm: Better performance when it trains, but requires _____ learning rates\n", + "- Most modern large models use _____ because scale requires stability\n", + "\n", + "*Consider: Why architectural choices become more important at scale*" + ] + }, + { + "cell_type": "markdown", + "id": "4e8cc6dc", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🎯 MODULE SUMMARY: Transformers\n", + "\n", + "Congratulations! You've built the complete transformer architecture that powers modern language models!\n", + "\n", + "### Key Accomplishments\n", + "- Built LayerNorm for stable training across deep networks\n", + "- Implemented MLP (feed-forward) networks with GELU activation\n", + "- Created complete TransformerBlock with self-attention, residual connections, and pre-norm architecture\n", + "- Built full GPT model with embeddings, positional encoding, and autoregressive generation\n", + "- Analyzed parameter scaling and attention memory complexity\n", + "- All tests pass ✅ (validated by `test_module()`)\n", + "\n", + "### Ready for Next Steps\n", + "Your transformer implementation is the foundation for modern language models! This architecture enables:\n", + "- **Training**: Learn patterns from massive text datasets\n", + "- **Generation**: Produce coherent, contextual text\n", + "- **Transfer Learning**: Fine-tune for specific tasks\n", + "- **Scaling**: Grow to billions of parameters for emergent capabilities\n", + "\n", + "Export with: `tito module complete 13_transformers`\n", + "\n", + "**Next**: Module 14 will add KV-caching for efficient generation, optimizing the autoregressive inference you just implemented!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/modules/source/13_transformers/transformers_dev.py b/modules/source/13_transformers/transformers_dev.py index ee34304f..cd8d1d60 100644 --- a/modules/source/13_transformers/transformers_dev.py +++ b/modules/source/13_transformers/transformers_dev.py @@ -443,6 +443,7 @@ Input Tensor: (batch=2, seq=3, features=4) """ # %% nbgrader={"grade": false, "grade_id": "layer-norm", "solution": true} +#| export class LayerNorm: """ Layer Normalization for transformer blocks. @@ -640,6 +641,7 @@ GELU is smoother and provides better gradients for language modeling. """ # %% nbgrader={"grade": false, "grade_id": "mlp", "solution": true} +#| export class MLP: """ Multi-Layer Perceptron (Feed-Forward Network) for transformer blocks. @@ -1131,6 +1133,7 @@ GPT-4 (estimated): """ # %% nbgrader={"grade": false, "grade_id": "gpt", "solution": true} +#| export class GPT: """ Complete GPT (Generative Pre-trained Transformer) model. diff --git a/modules/source/14_kvcaching/kvcaching_dev.ipynb b/modules/source/14_kvcaching/kvcaching_dev.ipynb new file mode 100644 index 00000000..bbb47315 --- /dev/null +++ b/modules/source/14_kvcaching/kvcaching_dev.ipynb @@ -0,0 +1,1623 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9f182460", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "# Module 14: KV Caching - Optimizing Autoregressive Generation\n", + "\n", + "Welcome to Module 14! You'll implement the critical optimization that makes production language models possible: Key-Value caching for 10x+ faster text generation.\n", + "\n", + "## 🔗 Prerequisites & Progress\n", + "**You've Built**: Complete transformer architecture with multi-head attention and text generation\n", + "**You'll Build**: Memory-efficient KV caching system that eliminates redundant computation\n", + "**You'll Enable**: Production-grade inference optimization and real-world serving capabilities\n", + "\n", + "**Connection Map**:\n", + "```\n", + "Transformers → KV Caching → Production Serving\n", + "(slow O(n²)) (fast O(n)) (real-world scale)\n", + "```\n", + "\n", + "## Learning Objectives\n", + "By the end of this module, you will:\n", + "1. Understand why autoregressive generation has O(n²) complexity without caching\n", + "2. Implement KVCache with efficient memory management and O(1) updates\n", + "3. Build cache-aware attention that reuses previously computed keys and values\n", + "4. Measure dramatic speedup gains and understand memory trade-offs\n", + "5. Connect to production optimization patterns used in real LLM serving\n", + "\n", + "Let's make inference blazingly fast!\n", + "\n", + "## 📦 Where This Code Lives in the Final Package\n", + "\n", + "**Learning Side:** You work in `modules/14_kvcaching/kvcaching_dev.py` \n", + "**Building Side:** Code exports to `tinytorch.generation.kv_cache`\n", + "\n", + "```python\n", + "# How to use this module:\n", + "from tinytorch.generation.kv_cache import KVCache, attention_with_cache\n", + "```\n", + "\n", + "**Why this matters:**\n", + "- **Learning:** Complete caching system in one focused module for deep understanding\n", + "- **Production:** Proper organization like Hugging Face's generation/ with all optimization components\n", + "- **Consistency:** All generation optimizations and cache management in generation.kv_cache\n", + "- **Integration:** Works seamlessly with transformers for complete inference optimization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13eddf26", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "imports", + "solution": true + } + }, + "outputs": [], + "source": [ + "#| default_exp generation.kv_cache\n", + "#| export\n", + "\n", + "import numpy as np\n", + "import time\n", + "from typing import Tuple, Optional, Dict, List\n", + "from dataclasses import dataclass\n", + "\n", + "# Import our TinyTorch components (Modules 01-13)\n", + "### BEGIN SOLUTION\n", + "# Note: In real implementation, these would import from previous modules\n", + "# For now, we'll implement minimal versions to focus on caching concepts\n", + "\n", + "class Tensor:\n", + " \"\"\"Minimal Tensor for KV Caching focus (from Module 01)\"\"\"\n", + " def __init__(self, data, requires_grad=False):\n", + " self.data = np.array(data)\n", + " self.shape = self.data.shape\n", + " self.requires_grad = requires_grad\n", + " self.grad = None\n", + "\n", + " def __getitem__(self, key):\n", + " return Tensor(self.data[key])\n", + "\n", + " def __setitem__(self, key, value):\n", + " if isinstance(value, Tensor):\n", + " self.data[key] = value.data\n", + " else:\n", + " self.data[key] = value\n", + "\n", + " def size(self, dim=None):\n", + " if dim is None:\n", + " return self.shape\n", + " return self.shape[dim]\n", + "\n", + " def view(self, *shape):\n", + " return Tensor(self.data.reshape(shape))\n", + "\n", + " def transpose(self, dim0, dim1):\n", + " axes = list(range(len(self.shape)))\n", + " axes[dim0], axes[dim1] = axes[dim1], axes[dim0]\n", + " return Tensor(np.transpose(self.data, axes))\n", + "\n", + " @staticmethod\n", + " def cat(tensors, dim=0):\n", + " \"\"\"Concatenate tensors along dimension\"\"\"\n", + " arrays = [t.data for t in tensors]\n", + " return Tensor(np.concatenate(arrays, axis=dim))\n", + "\n", + " @staticmethod\n", + " def zeros(*shape):\n", + " \"\"\"Create zero tensor\"\"\"\n", + " return Tensor(np.zeros(shape))\n", + "### END SOLUTION" + ] + }, + { + "cell_type": "markdown", + "id": "bba4366b", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🎯 Part 1: Understanding the Autoregressive Generation Problem\n", + "\n", + "### The Core Inefficiency\n", + "\n", + "When generating text token by token, transformers face a fundamental computational bottleneck. Let's visualize what happens during naive generation:\n", + "\n", + "```\n", + "Token Generation Process (Without Caching):\n", + "\n", + "Step 1: Generate \"Hello\"\n", + "Input: [START]\n", + "Attention: Q₁ × [K₁] × [V₁] ← 1 computation\n", + "\n", + "Step 2: Generate \"world\"\n", + "Input: [START, Hello]\n", + "Attention: Q₂ × [K₁, K₂] × [V₁, V₂] ← 2 computations (K₁,V₁ RECOMPUTED!)\n", + "\n", + "Step 3: Generate \"!\"\n", + "Input: [START, Hello, world]\n", + "Attention: Q₃ × [K₁, K₂, K₃] × [V₁, V₂, V₃] ← 3 computations (K₁,V₁,K₂,V₂ RECOMPUTED!)\n", + "```\n", + "\n", + "**The Problem**: For each new token, we recompute ALL previous key-value pairs even though they never change!\n", + "\n", + "### Computational Complexity Analysis\n", + "\n", + "```\n", + "Naive Generation Complexity:\n", + "Step 1: 1 K,V computation\n", + "Step 2: 2 K,V computations\n", + "Step 3: 3 K,V computations\n", + "...\n", + "Step n: n K,V computations\n", + "\n", + "Total: 1 + 2 + 3 + ... + n = n(n+1)/2 = O(n²) complexity!\n", + "```\n", + "\n", + "For a 1000-token sequence, this means **500,500 redundant computations**!\n", + "\n", + "### Real-World Impact\n", + "\n", + "This inefficiency makes production LLM serving economically impossible without optimization:\n", + "- **ChatGPT/GPT-4**: Would be too slow for real-time chat without caching\n", + "- **Code completion**: IDEs couldn't provide instant suggestions\n", + "- **Mobile deployment**: On-device generation would drain batteries instantly\n", + "- **API serving**: Server costs would be 10x+ higher\n", + "\n", + "**The Solution**: Cache key-value pairs after computing them once, transforming O(n²) into O(n)." + ] + }, + { + "cell_type": "markdown", + "id": "db62451e", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🧮 Part 2: The Key-Value Caching Insight\n", + "\n", + "### Mathematical Foundation\n", + "\n", + "The core insight comes from understanding what changes during autoregressive generation:\n", + "\n", + "```\n", + "Attention Computation Breakdown:\n", + "\n", + "Q = new_token @ W_q ← Only new token (changes each step)\n", + "K = all_tokens @ W_k ← Includes old tokens (mostly redundant!)\n", + "V = all_tokens @ W_v ← Includes old tokens (mostly redundant!)\n", + "\n", + "attention_output = softmax(Q @ K.T) @ V\n", + "```\n", + "\n", + "**Key Insight**: K and V matrices for previous tokens NEVER change!\n", + "\n", + "```\n", + "Token Dependencies:\n", + "K₁ = token₁ @ W_k ← Computed once, never changes\n", + "K₂ = token₂ @ W_k ← Computed once, never changes\n", + "K₃ = token₃ @ W_k ← Computed once, never changes\n", + "\n", + "Same for V₁, V₂, V₃...\n", + "```\n", + "\n", + "### Cache-Optimized Generation\n", + "\n", + "```\n", + "Optimized Generation Process (With Caching):\n", + "\n", + "Step 1: Generate \"Hello\"\n", + "Compute: K₁, V₁ → Store in cache\n", + "Attention: Q₁ × cached[K₁] × cached[V₁]\n", + "\n", + "Step 2: Generate \"world\"\n", + "Compute: K₂, V₂ → Append to cache\n", + "Attention: Q₂ × cached[K₁, K₂] × cached[V₁, V₂]\n", + "\n", + "Step 3: Generate \"!\"\n", + "Compute: K₃, V₃ → Append to cache\n", + "Attention: Q₃ × cached[K₁, K₂, K₃] × cached[V₁, V₂, V₃]\n", + "```\n", + "\n", + "**Result**: Each step computes only ONE new K,V pair instead of recomputing ALL!\n", + "\n", + "### Memory Layout Visualization\n", + "\n", + "```\n", + "Traditional Approach (Recompute Everything):\n", + "Step 1: [K₁, V₁] ← Compute 1 pair\n", + "Step 2: [K₁, V₁, K₂, V₂] ← Compute 2 pairs (recompute K₁,V₁)\n", + "Step 3: [K₁, V₁, K₂, V₂, K₃, V₃] ← Compute 3 pairs (recompute all!)\n", + "\n", + "Cached Approach (Store and Reuse):\n", + "Step 1: [K₁, V₁] → Cache ← Compute 1, store 1\n", + "Step 2: Cache + [K₂, V₂] → Cache ← Compute 1, append 1\n", + "Step 3: Cache + [K₃, V₃] → Cache ← Compute 1, append 1\n", + "```\n", + "\n", + "**Trade-off**: Use O(seq_len × hidden_dim) memory to save O(seq_len²) computation." + ] + }, + { + "cell_type": "markdown", + "id": "06a99e38", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 🏗️ Part 3: KVCache Class Design\n", + "\n", + "### Core Requirements\n", + "\n", + "Our KVCache needs to efficiently handle:\n", + "\n", + "1. **Multi-layer storage**: Each transformer layer needs its own K,V cache\n", + "2. **Multi-head attention**: Each attention head has separate K,V pairs\n", + "3. **Batch processing**: Support multiple sequences simultaneously\n", + "4. **Dynamic updates**: Efficiently append new tokens without copying data\n", + "5. **Memory management**: Pre-allocate space to avoid dynamic resizing\n", + "\n", + "### Cache Architecture Visualization\n", + "\n", + "```\n", + "KVCache Memory Layout:\n", + "┌─────────────────────────────────────────────────────────┐\n", + "│ KVCache Object │\n", + "├─────────────────────────────────────────────────────────┤\n", + "│ Layer 0: ┌─────────────┬─────────────┐ │\n", + "│ │ Key Cache │ Value Cache │ │\n", + "│ │ (B,H,S,D) │ (B,H,S,D) │ │\n", + "│ └─────────────┴─────────────┘ │\n", + "├─────────────────────────────────────────────────────────┤\n", + "│ Layer 1: ┌─────────────┬─────────────┐ │\n", + "│ │ Key Cache │ Value Cache │ │\n", + "│ │ (B,H,S,D) │ (B,H,S,D) │ │\n", + "│ └─────────────┴─────────────┘ │\n", + "├─────────────────────────────────────────────────────────┤\n", + "│ ... ┌─────────────┬─────────────┐ │\n", + "│ Layer N: │ Key Cache │ Value Cache │ │\n", + "│ │ (B,H,S,D) │ (B,H,S,D) │ │\n", + "│ └─────────────┴─────────────┘ │\n", + "└─────────────────────────────────────────────────────────┘\n", + "\n", + "Where:\n", + "B = batch_size (number of sequences)\n", + "H = num_heads (attention heads per layer)\n", + "S = max_seq_len (maximum sequence length)\n", + "D = head_dim (dimension per attention head)\n", + "```\n", + "\n", + "### Update Operation Visualization\n", + "\n", + "```\n", + "Cache Update Process:\n", + " seq_pos = 2\n", + " ↓\n", + "┌─────┬─────┬─────┬─────┬─────┬─────┐\n", + "│ K₁ │ K₂ │ ??? │ ??? │ ??? │ ??? │ ← Key Cache\n", + "├─────┼─────┼─────┼─────┼─────┼─────┤\n", + "│ V₁ │ V₂ │ ??? │ ??? │ ??? │ ??? │ ← Value Cache\n", + "└─────┴─────┴─────┴─────┴─────┴─────┘\n", + "\n", + "New token arrives: K₃, V₃\n", + "\n", + " seq_pos = 2\n", + " ↓\n", + "┌─────┬─────┬─────┬─────┬─────┬─────┐\n", + "│ K₁ │ K₂ │ K₃ │ ??? │ ??? │ ??? │ ← Write K₃ here\n", + "├─────┼─────┼─────┼─────┼─────┼─────┤\n", + "│ V₁ │ V₂ │ V₃ │ ??? │ ??? │ ??? │ ← Write V₃ here\n", + "└─────┴─────┴─────┴─────┴─────┴─────┘\n", + "\n", + "Then: seq_pos += 1 (advance to position 3)\n", + "```\n", + "\n", + "This design enables **O(1) updates** - just write to the next position!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44db7cf8", + "metadata": { + "lines_to_next_cell": 0, + "nbgrader": { + "grade": false, + "grade_id": "kv_cache_class", + "solution": true + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f3d5157", + "metadata": {}, + "outputs": [], + "source": [ + "class KVCache:\n", + " \"\"\"\n", + " Efficient key-value cache for autoregressive generation.\n", + "\n", + " Stores K,V matrices for each transformer layer to avoid recomputation\n", + " during sequential token generation.\n", + "\n", + " TODO: Implement the complete caching system for production-speed inference\n", + "\n", + " APPROACH:\n", + " 1. Pre-allocate cache tensors with maximum sequence length\n", + " 2. Track current sequence position for efficient O(1) updates\n", + " 3. Provide update() method to append new K,V pairs without copying\n", + " 4. Provide get() method to retrieve cached values for attention\n", + " 5. Handle multiple layers and attention heads properly\n", + "\n", + " CACHE LAYOUT:\n", + " ```\n", + " Layer 0: [Key_cache, Value_cache] # Shape: (batch, num_heads, max_seq, head_dim)\n", + " Layer 1: [Key_cache, Value_cache]\n", + " ...\n", + " Layer N: [Key_cache, Value_cache]\n", + " ```\n", + "\n", + " MEMORY OPTIMIZATION:\n", + " - Pre-allocate maximum size to avoid dynamic resizing overhead\n", + " - Use efficient indexing for cache updates (no data copying)\n", + " - Store only essential data needed for attention computation\n", + "\n", + " HINTS:\n", + " - Use list of tuples: [(key_cache₀, value_cache₀), (key_cache₁, value_cache₁), ...]\n", + " - Track seq_pos to know where to write new values\n", + " - Consider batch dimension for efficient multi-sequence serving\n", + " \"\"\"\n", + "\n", + " def __init__(self, batch_size: int, max_seq_len: int, num_layers: int,\n", + " num_heads: int, head_dim: int):\n", + " \"\"\"\n", + " Initialize KV cache for efficient generation.\n", + "\n", + " Args:\n", + " batch_size: Number of sequences to generate simultaneously\n", + " max_seq_len: Maximum sequence length to support\n", + " num_layers: Number of transformer layers\n", + " num_heads: Number of attention heads per layer\n", + " head_dim: Dimension of each attention head\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " self.batch_size = batch_size\n", + " self.max_seq_len = max_seq_len\n", + " self.num_layers = num_layers\n", + " self.num_heads = num_heads\n", + " self.head_dim = head_dim\n", + "\n", + " # Current sequence position (how many tokens are cached)\n", + " self.seq_pos = 0\n", + "\n", + " # Cache storage: list of (key_cache, value_cache) tuples per layer\n", + " self.caches = []\n", + "\n", + " for layer_idx in range(num_layers):\n", + " # Pre-allocate cache tensors with maximum size\n", + " # Shape: (batch_size, num_heads, max_seq_len, head_dim)\n", + " key_cache = Tensor.zeros(batch_size, num_heads, max_seq_len, head_dim)\n", + " value_cache = Tensor.zeros(batch_size, num_heads, max_seq_len, head_dim)\n", + "\n", + " self.caches.append((key_cache, value_cache))\n", + "\n", + " # Track which positions are valid (for debugging and masking)\n", + " self.valid_positions = Tensor.zeros(batch_size, max_seq_len)\n", + " ### END SOLUTION\n", + "\n", + " def update(self, layer_idx: int, key: Tensor, value: Tensor) -> None:\n", + " \"\"\"\n", + " Update cache with new key-value pairs for given layer.\n", + "\n", + " TODO: Efficiently append new K,V to the cache without recomputation\n", + "\n", + " APPROACH:\n", + " 1. Get current cache for the specified layer\n", + " 2. Write new key,value at current sequence position (O(1) operation)\n", + " 3. Mark position as valid for attention masking\n", + "\n", + " Args:\n", + " layer_idx: Which transformer layer (0 to num_layers-1)\n", + " key: New key tensor, shape (batch_size, num_heads, 1, head_dim)\n", + " value: New value tensor, shape (batch_size, num_heads, 1, head_dim)\n", + "\n", + " PERFORMANCE NOTE:\n", + " This operation should be O(1) - just indexing assignment, no large array copying\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " if layer_idx >= self.num_layers:\n", + " raise ValueError(f\"Layer index {layer_idx} >= num_layers {self.num_layers}\")\n", + "\n", + " if self.seq_pos >= self.max_seq_len:\n", + " raise ValueError(f\"Sequence position {self.seq_pos} >= max_seq_len {self.max_seq_len}\")\n", + "\n", + " # Get cache for this layer\n", + " key_cache, value_cache = self.caches[layer_idx]\n", + "\n", + " # Update cache at current position (efficient O(1) write)\n", + " # Remove the sequence dimension since we're writing to a specific position\n", + " key_cache[:, :, self.seq_pos:self.seq_pos+1, :] = key\n", + " value_cache[:, :, self.seq_pos:self.seq_pos+1, :] = value\n", + "\n", + " # Mark this position as valid for attention\n", + " self.valid_positions[:, self.seq_pos] = 1.0\n", + "\n", + " # Note: seq_pos is advanced externally via advance() after all layers process the token\n", + " ### END SOLUTION\n", + "\n", + " def get(self, layer_idx: int) -> Tuple[Tensor, Tensor]:\n", + " \"\"\"\n", + " Retrieve cached key-value pairs for attention computation.\n", + "\n", + " TODO: Return the cached K,V up to current sequence position\n", + "\n", + " APPROACH:\n", + " 1. Get cache for specified layer\n", + " 2. Slice to current sequence position (don't return unused space)\n", + " 3. Return properly shaped tensors for attention\n", + "\n", + " Args:\n", + " layer_idx: Which transformer layer to get cache for\n", + "\n", + " Returns:\n", + " (cached_keys, cached_values): Tensors shaped for attention\n", + " Keys: (batch_size, num_heads, seq_pos+1, head_dim)\n", + " Values: (batch_size, num_heads, seq_pos+1, head_dim)\n", + "\n", + " MEMORY EFFICIENCY:\n", + " Only return the valid portion of cache, not the entire pre-allocated space\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " if layer_idx >= self.num_layers:\n", + " raise ValueError(f\"Layer index {layer_idx} >= num_layers {self.num_layers}\")\n", + "\n", + " # Get cache for this layer\n", + " key_cache, value_cache = self.caches[layer_idx]\n", + "\n", + " # Return only the valid portion (up to current sequence position + 1)\n", + " # seq_pos tracks where to write next, so seq_pos tokens have been written\n", + " valid_len = self.seq_pos\n", + "\n", + " cached_keys = key_cache[:, :, :valid_len, :]\n", + " cached_values = value_cache[:, :, :valid_len, :]\n", + "\n", + " return cached_keys, cached_values\n", + " ### END SOLUTION\n", + "\n", + " def advance(self) -> None:\n", + " \"\"\"\n", + " Advance sequence position after processing current token.\n", + "\n", + " Call this after all layers have processed the current token.\n", + "\n", + " TODO: Move to next position for subsequent cache updates\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " self.seq_pos += 1\n", + " ### END SOLUTION\n", + "\n", + " def reset(self) -> None:\n", + " \"\"\"\n", + " Reset cache for new generation sequence.\n", + "\n", + " TODO: Clear cache state for fresh generation\n", + "\n", + " APPROACH:\n", + " 1. Reset sequence position to 0\n", + " 2. Clear valid position markers\n", + " 3. Optionally zero out cache data (not strictly necessary)\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " self.seq_pos = 0\n", + " # Reset valid positions\n", + " self.valid_positions = Tensor.zeros(self.batch_size, self.max_seq_len)\n", + "\n", + " # Optional: zero out caches (not strictly necessary since we track valid positions)\n", + " for layer_idx in range(self.num_layers):\n", + " key_cache, value_cache = self.caches[layer_idx]\n", + " key_cache.data.fill(0.0)\n", + " value_cache.data.fill(0.0)\n", + " ### END SOLUTION\n", + "\n", + " def get_memory_usage(self) -> Dict[str, float]:\n", + " \"\"\"\n", + " Calculate memory usage of the cache system.\n", + "\n", + " Returns:\n", + " Dictionary with memory statistics in MB\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " # Calculate size of one cache tensor\n", + " cache_size = self.batch_size * self.num_heads * self.max_seq_len * self.head_dim\n", + " bytes_per_float = 4 # float32\n", + "\n", + " # Each layer has key_cache + value_cache\n", + " total_cache_tensors = self.num_layers * 2\n", + " total_elements = cache_size * total_cache_tensors\n", + " total_bytes = total_elements * bytes_per_float\n", + " total_mb = total_bytes / (1024 * 1024)\n", + "\n", + " return {\n", + " 'total_mb': total_mb,\n", + " 'per_layer_mb': total_mb / self.num_layers,\n", + " 'cache_tensors': total_cache_tensors,\n", + " 'total_elements': total_elements\n", + " }\n", + " ### END SOLUTION\n", + "\n", + "def test_unit_kv_cache():\n", + " \"\"\"🔬 Test KVCache implementation with realistic transformer dimensions.\"\"\"\n", + " print(\"🔬 Unit Test: KV Cache Implementation...\")\n", + "\n", + " # Test parameters (small transformer)\n", + " batch_size, max_seq_len = 2, 8\n", + " num_layers, num_heads, head_dim = 3, 4, 16\n", + "\n", + " # Create cache\n", + " cache = KVCache(batch_size, max_seq_len, num_layers, num_heads, head_dim)\n", + "\n", + " # Test 1: Initial state\n", + " assert cache.seq_pos == 0\n", + " assert cache.get_memory_usage()['total_mb'] > 0\n", + " print(f\"✅ Cache initialized: {cache.get_memory_usage()['total_mb']:.2f} MB\")\n", + "\n", + " # Test 2: Update and retrieve\n", + " # Simulate first token (batch=2, heads=4, seq=1, head_dim=16)\n", + " key1 = Tensor(np.random.randn(batch_size, num_heads, 1, head_dim))\n", + " value1 = Tensor(np.random.randn(batch_size, num_heads, 1, head_dim))\n", + "\n", + " # Update layer 0\n", + " cache.update(0, key1, value1)\n", + " cached_k, cached_v = cache.get(0)\n", + "\n", + " assert cached_k.shape == (batch_size, num_heads, 0, head_dim) # Before advance\n", + " assert cached_v.shape == (batch_size, num_heads, 0, head_dim)\n", + "\n", + " # Advance to next position\n", + " cache.advance()\n", + "\n", + " # Now cache should have 1 token\n", + " cached_k, cached_v = cache.get(0)\n", + " assert cached_k.shape == (batch_size, num_heads, 1, head_dim)\n", + " assert cached_v.shape == (batch_size, num_heads, 1, head_dim)\n", + "\n", + " # Add second token\n", + " key2 = Tensor(np.random.randn(batch_size, num_heads, 1, head_dim))\n", + " value2 = Tensor(np.random.randn(batch_size, num_heads, 1, head_dim))\n", + " cache.update(0, key2, value2)\n", + " cache.advance()\n", + "\n", + " # Now cache should have 2 tokens\n", + " cached_k, cached_v = cache.get(0)\n", + " assert cached_k.shape == (batch_size, num_heads, 2, head_dim)\n", + " assert cached_v.shape == (batch_size, num_heads, 2, head_dim)\n", + "\n", + " print(\"✅ Cache update and retrieval works correctly!\")\n", + "\n", + " # Test 3: Multiple layers\n", + " cache.reset()\n", + " cache.update(0, key1, value1) # Layer 0\n", + " cache.update(1, key1, value1) # Layer 1\n", + " cache.update(2, key1, value1) # Layer 2\n", + " cache.advance()\n", + "\n", + " for layer_idx in range(num_layers):\n", + " cached_k, cached_v = cache.get(layer_idx)\n", + " assert cached_k.shape[2] == 1 # One token in each layer cache\n", + "\n", + " print(\"✅ Multi-layer caching works correctly!\")\n", + "\n", + " # Test 4: Reset functionality\n", + " cache.reset()\n", + " assert cache.seq_pos == 0\n", + " cached_k, cached_v = cache.get(0)\n", + " assert cached_k.shape == (batch_size, num_heads, 0, head_dim) # Should be empty after reset\n", + "\n", + " print(\"✅ Cache reset works correctly!\")\n", + " print(\"✅ KVCache implementation is working perfectly!\")\n", + "\n", + "test_unit_kv_cache()" + ] + }, + { + "cell_type": "markdown", + "id": "960d1a1d", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 🔧 Part 4: Cache-Aware Attention Implementation\n", + "\n", + "### The Integration Challenge\n", + "\n", + "Now we need to modify attention to work seamlessly with our cache. The key insight is that we only compute K,V for NEW tokens, then combine with cached history for the full attention computation.\n", + "\n", + "### Traditional vs Cached Attention Flow\n", + "\n", + "```\n", + "Traditional Attention (Inefficient):\n", + "┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐\n", + "│ All Tokens │───▶│ Compute Q,K,V │───▶│ Attention │\n", + "│ [tok₁,tok₂,tok₃]│ │ (redundant) │ │ Output │\n", + "└─────────────────┘ └─────────────────┘ └─────────────────┘\n", + " ↑\n", + " Recomputes K₁,V₁,K₂,V₂\n", + " every single step!\n", + "\n", + "Cached Attention (Efficient):\n", + "┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐\n", + "│ New Token │───▶│ Compute Q,K₃,V₃ │───▶│ Cache.update() │\n", + "│ [tok₃] │ │ (only new!) │ │ │\n", + "└─────────────────┘ └─────────────────┘ └─────────────────┘\n", + " │\n", + " ▼\n", + "┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐\n", + "│ Attention │◀───│ Cache.get() │◀───│ Cached History │\n", + "│ Output │ │ K₁,V₁,K₂,V₂,K₃,V₃│ │ K₁,V₁,K₂,V₂ │\n", + "└─────────────────┘ └─────────────────┘ └─────────────────┘\n", + "```\n", + "\n", + "### Attention Computation with Cache\n", + "\n", + "```\n", + "Step-by-Step Process:\n", + "1. Input: Q₃ (query for new token), K₃,V₃ (key,value for new token)\n", + "2. Cache Update: Store K₃,V₃ → Cache now has [K₁,V₁,K₂,V₂,K₃,V₃]\n", + "3. Cache Retrieval: Get all cached K,V → [K₁,K₂,K₃], [V₁,V₂,V₃]\n", + "4. Attention: Q₃ @ [K₁,K₂,K₃]ᵀ → attention weights\n", + "5. Output: attention_weights @ [V₁,V₂,V₃] → final result\n", + "\n", + "Memory Access Pattern:\n", + "Write: O(1) - just append K₃,V₃ to cache\n", + "Read: O(seq_len) - retrieve full cached history\n", + "Total: O(seq_len) instead of O(seq_len²)!\n", + "```\n", + "\n", + "### Causal Masking Integration\n", + "\n", + "```\n", + "Causal Mask Application:\n", + "┌─────┬─────┬─────┐\n", + "│ 0 │-inf │-inf │ ← Position 0 can only see itself\n", + "├─────┼─────┼─────┤\n", + "│ 0 │ 0 │-inf │ ← Position 1 can see 0,1\n", + "├─────┼─────┼─────┤\n", + "│ 0 │ 0 │ 0 │ ← Position 2 can see 0,1,2\n", + "└─────┴─────┴─────┘\n", + "\n", + "For cached attention:\n", + "- Mask shape: (max_seq_len, max_seq_len)\n", + "- Slice needed: (1, current_seq_len) for current query\n", + "- Apply before softmax to prevent future token access\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "346d005a", + "metadata": { + "lines_to_next_cell": 0, + "nbgrader": { + "grade": false, + "grade_id": "attention_with_cache", + "solution": true + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "00d5d995", + "metadata": {}, + "outputs": [], + "source": [ + "def attention_with_cache(\n", + " query: Tensor,\n", + " key: Tensor,\n", + " value: Tensor,\n", + " cache: KVCache,\n", + " layer_idx: int,\n", + " mask: Optional[Tensor] = None\n", + ") -> Tensor:\n", + " \"\"\"\n", + " Compute attention using KV cache for efficient autoregressive generation.\n", + "\n", + " This is the core optimization: instead of recomputing K,V for all tokens,\n", + " we cache them and only compute for the new token.\n", + "\n", + " TODO: Implement cache-aware attention that's 10x+ faster than naive approach\n", + "\n", + " APPROACH:\n", + " 1. Update cache with new key,value pairs for current token\n", + " 2. Retrieve full cached history (all previous + current)\n", + " 3. Compute attention using query vs full cached K,V\n", + " 4. Apply causal masking to ensure autoregressive property\n", + " 5. Return attention output (cache position advanced externally)\n", + "\n", + " ATTENTION COMPUTATION:\n", + " ```\n", + " scores = query @ cached_keys.transpose(-2, -1) / sqrt(head_dim)\n", + " if mask: scores = mask_attention(scores, mask)\n", + " attention_weights = softmax(scores)\n", + " output = attention_weights @ cached_values\n", + " ```\n", + "\n", + " Args:\n", + " query: Query tensor for current token (batch, num_heads, 1, head_dim)\n", + " key: Key tensor for current token (batch, num_heads, 1, head_dim)\n", + " value: Value tensor for current token (batch, num_heads, 1, head_dim)\n", + " cache: KVCache instance to store/retrieve K,V pairs\n", + " layer_idx: Which transformer layer this attention belongs to\n", + " mask: Optional attention mask for preventing future token access\n", + "\n", + " Returns:\n", + " attention_output: Computed attention for current token (batch, num_heads, 1, head_dim)\n", + "\n", + " PERFORMANCE:\n", + " - Time: O(seq_len) instead of O(seq_len²) for generation\n", + " - Memory: O(seq_len × hidden_dim) cache overhead\n", + " - Speedup: 10x+ for long sequences\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " batch_size, num_heads, seq_len_q, head_dim = query.shape\n", + "\n", + " # Step 1: Update cache with new key,value for current token\n", + " cache.update(layer_idx, key, value)\n", + "\n", + " # Step 2: Retrieve full cached K,V (all previous + current token)\n", + " cached_keys, cached_values = cache.get(layer_idx)\n", + "\n", + " # If cache is empty (first token), add current token\n", + " if cached_keys.shape[2] == 0:\n", + " cached_keys = key\n", + " cached_values = value\n", + " else:\n", + " # Concatenate new token with cached history\n", + " cached_keys = Tensor.cat([cached_keys, key], dim=2)\n", + " cached_values = Tensor.cat([cached_values, value], dim=2)\n", + "\n", + " # Step 3: Compute attention scores\n", + " # query: (batch, heads, 1, head_dim)\n", + " # cached_keys: (batch, heads, seq_len_k, head_dim)\n", + " # Need: (batch, heads, 1, seq_len_k)\n", + " scores = np.matmul(query.data, cached_keys.transpose(-1, -2).data)\n", + "\n", + " # Scale by sqrt(head_dim) for numerical stability\n", + " scores = scores / np.sqrt(head_dim)\n", + "\n", + " # Step 4: Apply causal mask if provided\n", + " if mask is not None:\n", + " # Mask should be shape (max_seq_len, max_seq_len)\n", + " # We need to slice to (1, seq_len_k) for current query position\n", + " seq_len_k = cached_keys.shape[2]\n", + " query_pos = seq_len_k - 1 # Current query position\n", + "\n", + " if mask.shape[-1] >= seq_len_k and mask.shape[-2] > query_pos:\n", + " # For current query position, take the corresponding row up to seq_len_k columns\n", + " mask_slice = mask.data[query_pos:query_pos+1, :seq_len_k] # Shape: (1, seq_len_k)\n", + " # Reshape to match scores: (batch, heads, 1, seq_len_k)\n", + " mask_broadcast = mask_slice.reshape(1, 1, 1, seq_len_k)\n", + " scores = scores + mask_broadcast # Apply mask (already has -1e9 values)\n", + "\n", + " # Step 5: Compute attention weights via softmax\n", + " # Numerical stability: subtract max before exp\n", + " scores_max = np.max(scores, axis=-1, keepdims=True)\n", + " scores_stable = scores - scores_max\n", + " exp_scores = np.exp(scores_stable)\n", + " attention_weights = exp_scores / np.sum(exp_scores, axis=-1, keepdims=True)\n", + "\n", + " # Step 6: Compute final attention output\n", + " # attention_weights: (batch, heads, 1, seq_len_k)\n", + " # cached_values: (batch, heads, seq_len_k, head_dim)\n", + " # output: (batch, heads, 1, head_dim)\n", + " output_data = np.matmul(attention_weights, cached_values.data)\n", + " attention_output = Tensor(output_data)\n", + "\n", + " # Note: cache.advance() should be called externally after all layers process this token\n", + " return attention_output\n", + " ### END SOLUTION\n", + "\n", + "def test_unit_attention_with_cache():\n", + " \"\"\"🔬 Test cache-aware attention against naive implementation.\"\"\"\n", + " print(\"🔬 Unit Test: Attention with Cache...\")\n", + "\n", + " # Setup small test case\n", + " batch_size, num_heads, head_dim = 1, 2, 8\n", + " max_seq_len = 4\n", + "\n", + " cache = KVCache(batch_size, max_seq_len, 1, num_heads, head_dim)\n", + "\n", + " # Test generation sequence: 3 tokens\n", + " for step in range(3):\n", + " print(f\" Generation step {step + 1}...\")\n", + "\n", + " # Create QKV for current token\n", + " q = Tensor(np.random.randn(batch_size, num_heads, 1, head_dim))\n", + " k = Tensor(np.random.randn(batch_size, num_heads, 1, head_dim))\n", + " v = Tensor(np.random.randn(batch_size, num_heads, 1, head_dim))\n", + "\n", + " # Compute attention with cache\n", + " output = attention_with_cache(q, k, v, cache, layer_idx=0)\n", + "\n", + " # Verify output shape\n", + " assert output.shape == (batch_size, num_heads, 1, head_dim)\n", + "\n", + " # Advance cache position\n", + " cache.advance()\n", + "\n", + " # Verify cache grows correctly\n", + " # After processing step i and advancing, we should have i+1 elements cached\n", + " cached_k, cached_v = cache.get(0)\n", + " expected_cache_len = step + 1\n", + " print(f\" Step {step}: cache has {cached_k.shape[2]} elements, expected {expected_cache_len}\")\n", + " assert cached_k.shape[2] == expected_cache_len\n", + " assert cached_v.shape[2] == expected_cache_len\n", + "\n", + " print(\"✅ Cache-aware attention works correctly!\")\n", + "\n", + " # Test with causal mask\n", + " print(\" Testing with causal masking...\")\n", + " cache.reset()\n", + "\n", + " # Create causal mask (lower triangular)\n", + " causal_mask = Tensor(np.triu(np.ones((max_seq_len, max_seq_len)) * -1e9, k=1))\n", + "\n", + " q = Tensor(np.random.randn(batch_size, num_heads, 1, head_dim))\n", + " k = Tensor(np.random.randn(batch_size, num_heads, 1, head_dim))\n", + " v = Tensor(np.random.randn(batch_size, num_heads, 1, head_dim))\n", + "\n", + " output_masked = attention_with_cache(q, k, v, cache, layer_idx=0, mask=causal_mask)\n", + " cache.advance()\n", + "\n", + " print(f\" Masked output shape: {output_masked.shape}\")\n", + " assert output_masked.shape == (batch_size, num_heads, 1, head_dim)\n", + "\n", + " print(\"✅ Causal masking works correctly!\")\n", + " print(\"✅ Cache-aware attention implementation complete!\")\n", + "\n", + "test_unit_attention_with_cache()" + ] + }, + { + "cell_type": "markdown", + "id": "c304da93", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 📊 Part 5: Performance Analysis - Measuring the Speedup\n", + "\n", + "### Understanding the Performance Gains\n", + "\n", + "Let's measure the dramatic improvements KV caching provides. We'll compare naive recomputation vs cached attention across different sequence lengths to understand the scaling benefits.\n", + "\n", + "### What We're Measuring\n", + "\n", + "```\n", + "Complexity Comparison:\n", + "┌─────────────────┬─────────────────┬─────────────────┐\n", + "│ Approach │ Time Complexity │ Memory Usage │\n", + "├─────────────────┼─────────────────┼─────────────────┤\n", + "│ Naive │ O(n²) │ O(n) │\n", + "│ Recomputation │ │ │\n", + "├─────────────────┼─────────────────┼─────────────────┤\n", + "│ KV Caching │ O(n) │ O(n×hidden) │\n", + "│ │ │ │\n", + "└─────────────────┴─────────────────┴─────────────────┘\n", + "\n", + "Trade-off: Use more memory to achieve quadratic speedup!\n", + "```\n", + "\n", + "### Real-World Impact Visualization\n", + "\n", + "```\n", + "Production Serving Scenario:\n", + "Without Caching: With Caching:\n", + "┌─────────────────┐ ┌─────────────────┐\n", + "│ User Request │ │ User Request │\n", + "│ \"Write a story\" │ │ \"Write a story\" │\n", + "└─────────┬───────┘ └─────────┬───────┘\n", + " │ │\n", + " ▼ ▼\n", + "┌─────────────────┐ ┌─────────────────┐\n", + "│ Token 1: 1 ops │ │ Token 1: 1 ops │\n", + "│ Token 2: 2 ops │ │ Token 2: 1 ops │\n", + "│ Token 3: 3 ops │ │ Token 3: 1 ops │\n", + "│ ... │ │ ... │\n", + "│ Token 100: 100 │ │ Token 100: 1 op │\n", + "└─────────────────┘ └─────────────────┘\n", + "Total: 5,050 ops Total: 100 ops\n", + "Response: 5+ seconds Response: 0.1 seconds\n", + "Cost: $$$$$ Cost: $\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d272c1a9", + "metadata": { + "lines_to_next_cell": 0, + "nbgrader": { + "grade": false, + "grade_id": "performance_analysis", + "solution": true + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0056cbd0", + "metadata": {}, + "outputs": [], + "source": [ + "def analyze_kv_cache_performance():\n", + " \"\"\"📊 Measure dramatic performance gains from KV caching.\"\"\"\n", + " print(\"📊 Analyzing KV Cache Performance vs Naive Recomputation...\")\n", + "\n", + " # Test configuration (realistic transformer)\n", + " batch_size, num_heads, head_dim = 1, 8, 64\n", + " num_layers = 12\n", + "\n", + " sequence_lengths = [16, 32, 64, 128, 256] # Realistic generation lengths\n", + "\n", + " print(\"\\n=== Performance Comparison ===\")\n", + " print(\"Seq Len | Naive Ops | Cached Ops | Speedup | Cache Memory\")\n", + " print(\"-\" * 65)\n", + "\n", + " for seq_len in sequence_lengths:\n", + " # Calculate theoretical operation counts\n", + "\n", + " # Naive approach: At each step i, recompute attention for all i+1 tokens\n", + " naive_ops = 0\n", + " for step in range(seq_len):\n", + " current_seq_len = step + 1\n", + " # K,V computation: current_seq_len × head_dim per head per layer\n", + " kv_ops = current_seq_len * head_dim * num_heads * num_layers\n", + " # Attention: current_seq_len × head_dim per head per layer\n", + " attn_ops = current_seq_len * head_dim * num_heads * num_layers\n", + " naive_ops += kv_ops + attn_ops\n", + "\n", + " # Cached approach: Compute K,V only for new token, attention with cached history\n", + " cached_ops = 0\n", + " for step in range(seq_len):\n", + " current_seq_len = step + 1\n", + " # K,V computation: only 1 new token × head_dim per head per layer\n", + " kv_ops = 1 * head_dim * num_heads * num_layers\n", + " # Attention: current_seq_len × head_dim per head per layer (with cache)\n", + " attn_ops = current_seq_len * head_dim * num_heads * num_layers\n", + " cached_ops += kv_ops + attn_ops\n", + "\n", + " # Calculate metrics\n", + " speedup = naive_ops / cached_ops if cached_ops > 0 else float('inf')\n", + "\n", + " # Memory usage for cache\n", + " cache = KVCache(batch_size, seq_len, num_layers, num_heads, head_dim)\n", + " cache_memory = cache.get_memory_usage()['total_mb']\n", + "\n", + " print(f\"{seq_len:7d} | {naive_ops/1000:8.0f}K | {cached_ops/1000:9.0f}K | {speedup:6.1f}x | {cache_memory:8.1f}MB\")\n", + "\n", + " print(\"\\n💡 Key Insights:\")\n", + " print(\"• Speedup grows with sequence length (O(n²) vs O(n) complexity)\")\n", + " print(\"• Memory overhead is manageable and constant per layer\")\n", + " print(\"• Essential for production serving at any reasonable scale\")\n", + "\n", + " # Theoretical complexity analysis\n", + " print(\"\\n=== Theoretical Complexity Analysis ===\")\n", + " n = 256 # Example sequence length\n", + "\n", + " # For naive approach: sum of 1+2+3+...+n computations\n", + " naive_complexity = n * (n + 1) // 2 # Sum from 1 to n\n", + " # For cached approach: n computations (1 per step)\n", + " cached_complexity = n # Linear in sequence length\n", + "\n", + " print(f\"For {n}-token generation:\")\n", + " print(f\" Naive approach: O(n²) = {naive_complexity:,} operations\")\n", + " print(f\" Cached approach: O(n) = {cached_complexity:,} operations\")\n", + " print(f\" Theoretical speedup: {naive_complexity/cached_complexity:.0f}x\")\n", + "\n", + " print(\"\\n🚀 Production Impact:\")\n", + " print(\"• Enables real-time chat interfaces (ChatGPT, Claude)\")\n", + " print(\"• Reduces serving costs by 10x+ for long conversations\")\n", + " print(\"• Makes on-device generation feasible (mobile, edge)\")\n", + " print(\"• Critical for any autoregressive model deployment\")\n", + "\n", + " # Real-world serving scenarios\n", + " print(\"\\n=== Real-World Serving Analysis ===\")\n", + "\n", + " scenarios = [\n", + " (\"Chat Response\", 50, \"Real-time requirement\"),\n", + " (\"Code Completion\", 200, \"IDE integration\"),\n", + " (\"Document Summary\", 500, \"Batch processing\"),\n", + " (\"Long Conversation\", 1000, \"Extended context\")\n", + " ]\n", + "\n", + " print(\"Scenario | Tokens | Without Cache | With Cache | Savings\")\n", + " print(\"-\" * 70)\n", + "\n", + " for scenario, tokens, context in scenarios:\n", + " without_cache = tokens * (tokens + 1) // 2\n", + " with_cache = tokens\n", + " savings = without_cache / with_cache\n", + "\n", + " print(f\"{scenario:16s} | {tokens:6d} | {without_cache:12,} | {with_cache:9,} | {savings:5.0f}x\")\n", + "\n", + "analyze_kv_cache_performance()" + ] + }, + { + "cell_type": "markdown", + "id": "a128d8c4", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 🔧 Part 6: Advanced Optimization Strategies\n", + "\n", + "### Production KV Caching Patterns\n", + "\n", + "Real production systems implement several sophisticated optimizations beyond basic caching. Let's explore the advanced patterns used in state-of-the-art serving systems.\n", + "\n", + "### Memory Optimization Strategies\n", + "\n", + "```\n", + "Precision Trade-offs:\n", + "┌─────────────┬─────────────┬─────────────┬─────────────┐\n", + "│ Precision │ Memory │ Quality │ Use Case │\n", + "├─────────────┼─────────────┼─────────────┼─────────────┤\n", + "│ FP32 │ 100% │ Perfect │ Development │\n", + "│ FP16 │ 50% │ Minimal loss│ Production │\n", + "│ INT8 │ 25% │ Some loss │ Edge/Mobile │\n", + "│ INT4 │ 12.5% │ Quality loss│ Extreme opt │\n", + "└─────────────┴─────────────┴─────────────┴─────────────┘\n", + "```\n", + "\n", + "### Sliding Window Attention\n", + "\n", + "```\n", + "Fixed Context Window vs Sliding Window:\n", + "\n", + "Fixed Window (Traditional):\n", + "┌─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┐\n", + "│ T₁ │ T₂ │ T₃ │ T₄ │ T₅ │ T₆ │ T₇ │ T₈ │\n", + "└─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┘\n", + " ↑\n", + " Current token sees ALL history\n", + " Memory: O(n), but limited to max_seq_len\n", + "\n", + "Sliding Window (Advanced):\n", + "┌─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┐\n", + "│ │ │ T₃ │ T₄ │ T₅ │ T₆ │ T₇ │ T₈ │\n", + "└─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┘\n", + " ↑─────────────window_size──────────↑\n", + " Current token sees recent history only\n", + " Memory: O(window), enables infinite generation\n", + "```\n", + "\n", + "### Prefix Caching Optimization\n", + "\n", + "```\n", + "Shared Prefix Caching:\n", + "User A: \"Write a Python function that\" → Cache prefix\n", + "User B: \"Write a Python function that\" → Reuse cached prefix!\n", + "User C: \"Write a Python script to\" → Different, new cache\n", + "\n", + "Cache Hit Rate Impact:\n", + "┌─────────────────┬─────────────────┬─────────────────┐\n", + "│ Cache Scenario │ Hit Rate │ Speedup │\n", + "├─────────────────┼─────────────────┼─────────────────┤\n", + "│ No Sharing │ 0% │ 1x │\n", + "│ Common Prompts │ 30% │ 1.4x │\n", + "│ Chat Templates │ 60% │ 2.5x │\n", + "│ Code Patterns │ 80% │ 5x │\n", + "└─────────────────┴─────────────────┴─────────────────┘\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44f2dead", + "metadata": { + "lines_to_next_cell": 0, + "nbgrader": { + "grade": false, + "grade_id": "optimization_insights", + "solution": true + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b265570", + "metadata": {}, + "outputs": [], + "source": [ + "def analyze_advanced_caching_strategies():\n", + " \"\"\"📊 Explore advanced caching strategies and production trade-offs.\"\"\"\n", + " print(\"📊 Advanced KV Caching Strategies Analysis...\")\n", + "\n", + " # Configuration for large-scale analysis (reduced for educational demonstration)\n", + " seq_len, batch_size = 512, 4\n", + " num_layers, num_heads, head_dim = 12, 16, 64 # Realistic scale for demonstration\n", + "\n", + " print(\"\\n=== Memory Footprint by Precision ===\")\n", + "\n", + " # Standard FP32 cache\n", + " cache_fp32 = KVCache(batch_size, seq_len, num_layers, num_heads, head_dim)\n", + " fp32_memory = cache_fp32.get_memory_usage()['total_mb']\n", + "\n", + " # Simulated precision variants\n", + " precisions = [\n", + " (\"FP32\", fp32_memory, 1.0, \"No quality loss\"),\n", + " (\"FP16\", fp32_memory / 2, 0.5, \"Minimal quality loss\"),\n", + " (\"INT8\", fp32_memory / 4, 0.25, \"Some quality loss\"),\n", + " (\"INT4\", fp32_memory / 8, 0.125, \"Significant loss\")\n", + " ]\n", + "\n", + " print(\"Precision | Memory Usage | Reduction | Quality Impact\")\n", + " print(\"-\" * 55)\n", + " for precision, memory, factor, quality in precisions:\n", + " print(f\"{precision:8s} | {memory:8.0f} MB | {factor:4.2f}x | {quality}\")\n", + "\n", + " print(\"\\n=== Sliding Window Analysis ===\")\n", + "\n", + " # Compare different window sizes for memory usage\n", + " full_seq_len = 2048 # Realistic long sequence for demonstration\n", + " window_sizes = [256, 512, 1024, 2048]\n", + "\n", + " print(\"Window Size | Memory vs Full | Tokens Lost | Use Case\")\n", + " print(\"-\" * 60)\n", + "\n", + " for window_size in window_sizes:\n", + " # Memory scales with window size\n", + " full_cache = KVCache(batch_size, full_seq_len, num_layers, num_heads, head_dim)\n", + " window_cache = KVCache(batch_size, window_size, num_layers, num_heads, head_dim)\n", + "\n", + " full_memory = full_cache.get_memory_usage()['total_mb']\n", + " window_memory = window_cache.get_memory_usage()['total_mb']\n", + " reduction = full_memory / window_memory\n", + " tokens_lost = max(0, full_seq_len - window_size)\n", + "\n", + " if window_size <= 1024:\n", + " use_case = \"Chat/Code completion\"\n", + " elif window_size <= 2048:\n", + " use_case = \"Document analysis\"\n", + " else:\n", + " use_case = \"Long context tasks\"\n", + "\n", + " print(f\"{window_size:10d} | {reduction:9.1f}x | {tokens_lost:10d} | {use_case}\")\n", + "\n", + " print(\"\\n=== Multi-GPU Scaling Strategy ===\")\n", + "\n", + " # Analyze how caching scales across multiple GPUs\n", + " gpu_configs = [1, 2, 4, 8]\n", + " large_batch = 16 # Reasonable batch for demonstration\n", + "\n", + " print(\"GPUs | Batch/GPU | Cache/GPU | Total Memory | Throughput\")\n", + " print(\"-\" * 60)\n", + "\n", + " for num_gpus in gpu_configs:\n", + " batch_per_gpu = large_batch // num_gpus\n", + " cache_per_gpu = KVCache(batch_per_gpu, seq_len, num_layers, num_heads, head_dim)\n", + " memory_per_gpu = cache_per_gpu.get_memory_usage()['total_mb']\n", + " total_memory = memory_per_gpu * num_gpus\n", + " throughput_scale = num_gpus # Linear scaling assumption\n", + "\n", + " print(f\"{num_gpus:4d} | {batch_per_gpu:8d} | {memory_per_gpu:8.0f}MB | {total_memory:9.0f}MB | {throughput_scale:8.0f}x\")\n", + "\n", + " print(\"\\n=== Production Serving Scenarios ===\")\n", + "\n", + " scenarios = [\n", + " (\"Real-time Chat\", 512, 1, \"Low latency critical\"),\n", + " (\"Code Completion\", 1024, 4, \"IDE integration\"),\n", + " (\"Batch Translation\", 2048, 8, \"High throughput\"),\n", + " (\"Long Document\", 2048, 4, \"Context preservation\")\n", + " ]\n", + "\n", + " print(\"Scenario | Max Len | Batch | Memory | Optimal Strategy\")\n", + " print(\"-\" * 70)\n", + "\n", + " for name, max_len, batch, priority in scenarios:\n", + " # Calculate memory for each scenario\n", + " scenario_cache = KVCache(batch, max_len, num_layers, num_heads, head_dim)\n", + " scenario_memory = scenario_cache.get_memory_usage()['total_mb']\n", + "\n", + " # Determine optimal strategy based on memory usage\n", + " if scenario_memory < 500: # < 0.5GB\n", + " strategy = \"FP32 cache\"\n", + " elif scenario_memory < 2000: # < 2GB\n", + " strategy = \"FP16 cache\"\n", + " elif scenario_memory < 8000: # < 8GB\n", + " strategy = \"FP16 + sliding window\"\n", + " else: # > 8GB\n", + " strategy = \"Multi-GPU + quantization\"\n", + "\n", + " print(f\"{name:15s} | {max_len:7d} | {batch:5d} | {scenario_memory:6.0f}MB | {strategy}\")\n", + "\n", + " print(\"\\n💡 Advanced Optimization Insights:\")\n", + " print(\"• FP16 provides 2x memory savings with negligible quality loss\")\n", + " print(\"• Sliding windows enable unlimited generation with fixed memory\")\n", + " print(\"• Multi-GPU scaling is linear for both memory and throughput\")\n", + " print(\"• Quantization beyond FP16 requires careful quality evaluation\")\n", + "\n", + " print(\"\\n🚀 Production Implementation Recommendations:\")\n", + " print(\"• Start with FP16 caching as the baseline optimization\")\n", + " print(\"• Implement sliding windows for sequences > 4K tokens\")\n", + " print(\"• Use prefix caching for common prompt patterns\")\n", + " print(\"• Consider multi-GPU distribution for high-throughput serving\")\n", + " print(\"• Monitor cache hit rates and memory utilization in production\")\n", + "\n", + " # Cache hit rate simulation\n", + " print(\"\\n=== Prefix Caching Effectiveness ===\")\n", + "\n", + " prefix_scenarios = [\n", + " (\"No Sharing\", 0.0, 1.0),\n", + " (\"Common Prompts\", 0.3, 1.4),\n", + " (\"Chat Templates\", 0.6, 2.5),\n", + " (\"Code Patterns\", 0.8, 5.0)\n", + " ]\n", + "\n", + " print(\"Scenario | Hit Rate | Effective Speedup | Memory Efficiency\")\n", + " print(\"-\" * 65)\n", + "\n", + " for scenario, hit_rate, speedup in prefix_scenarios:\n", + " memory_efficiency = 1.0 + hit_rate * 0.5 # Shared prefixes reduce memory\n", + " print(f\"{scenario:14s} | {hit_rate:7.1%} | {speedup:12.1f}x | {memory_efficiency:14.1f}x\")\n", + "\n", + "analyze_advanced_caching_strategies()" + ] + }, + { + "cell_type": "markdown", + "id": "d81b4147", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 🧪 Part 7: Module Integration Test\n", + "\n", + "Our KV caching system is complete! Time for comprehensive testing to ensure all components work together seamlessly and deliver the promised performance improvements.\n", + "\n", + "### Integration Test Coverage\n", + "\n", + "We'll validate:\n", + "1. **Multi-layer caching**: All transformer layers cache correctly\n", + "2. **Generation simulation**: End-to-end token generation workflow\n", + "3. **Memory efficiency**: Large-scale cache allocation and management\n", + "4. **Performance consistency**: Speedup measurements are reliable\n", + "5. **Cache lifecycle**: Reset, reuse, and state management" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d8f73bd", + "metadata": { + "lines_to_next_cell": 0, + "nbgrader": { + "grade": true, + "grade_id": "test_module", + "locked": true, + "points": 20 + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9fa7dd6", + "metadata": {}, + "outputs": [], + "source": [ + "def test_module():\n", + " \"\"\"\n", + " Comprehensive test of entire Module 14: KV Caching functionality.\n", + "\n", + " This final test runs before module summary to ensure:\n", + " - All unit tests pass\n", + " - KVCache works correctly with realistic parameters\n", + " - Cache-aware attention produces correct results\n", + " - Performance analysis runs successfully\n", + " - Module is ready for integration with TinyTorch\n", + " \"\"\"\n", + " print(\"🧪 RUNNING MODULE 14 INTEGRATION TEST\")\n", + " print(\"=\" * 50)\n", + "\n", + " # Run all unit tests\n", + " print(\"Running unit tests...\")\n", + " test_unit_kv_cache()\n", + " test_unit_attention_with_cache()\n", + "\n", + " print(\"\\nRunning integration scenarios...\")\n", + "\n", + " # Integration Test 1: Multi-layer generation simulation\n", + " print(\"🔬 Integration Test: Multi-layer transformer generation...\")\n", + "\n", + " batch_size, max_seq_len = 2, 16\n", + " num_layers, num_heads, head_dim = 4, 8, 32\n", + "\n", + " # Create cache system\n", + " cache = KVCache(batch_size, max_seq_len, num_layers, num_heads, head_dim)\n", + "\n", + " # Simulate 8-token generation across all layers\n", + " for token_idx in range(8):\n", + " for layer_idx in range(num_layers):\n", + " # Generate random QKV for current token\n", + " q = Tensor(np.random.randn(batch_size, num_heads, 1, head_dim))\n", + " k = Tensor(np.random.randn(batch_size, num_heads, 1, head_dim))\n", + " v = Tensor(np.random.randn(batch_size, num_heads, 1, head_dim))\n", + "\n", + " # Compute attention with cache\n", + " output = attention_with_cache(q, k, v, cache, layer_idx)\n", + "\n", + " # Verify output shape\n", + " assert output.shape == (batch_size, num_heads, 1, head_dim)\n", + "\n", + " # Advance cache position after all layers process the token\n", + " cache.advance()\n", + "\n", + " # Verify cache state after each token\n", + " for layer_idx in range(num_layers):\n", + " cached_k, cached_v = cache.get(layer_idx)\n", + " expected_len = token_idx + 1\n", + " assert cached_k.shape[2] == expected_len\n", + " assert cached_v.shape[2] == expected_len\n", + "\n", + " print(\"✅ Multi-layer generation works correctly!\")\n", + "\n", + " # Integration Test 2: Memory efficiency validation\n", + " print(\"🔬 Integration Test: Memory efficiency...\")\n", + "\n", + " # Test large-scale cache\n", + " large_cache = KVCache(\n", + " batch_size=4,\n", + " max_seq_len=512,\n", + " num_layers=12,\n", + " num_heads=16,\n", + " head_dim=64\n", + " )\n", + "\n", + " memory_usage = large_cache.get_memory_usage()\n", + " assert memory_usage['total_mb'] > 0\n", + " assert memory_usage['per_layer_mb'] > 0\n", + "\n", + " print(f\"✅ Large cache: {memory_usage['total_mb']:.1f} MB allocated efficiently!\")\n", + "\n", + " # Integration Test 3: Cache reset and reuse\n", + " print(\"🔬 Integration Test: Cache lifecycle management...\")\n", + "\n", + " # Use cache for one sequence\n", + " q = Tensor(np.random.randn(batch_size, num_heads, 1, head_dim))\n", + " k = Tensor(np.random.randn(batch_size, num_heads, 1, head_dim))\n", + " v = Tensor(np.random.randn(batch_size, num_heads, 1, head_dim))\n", + "\n", + " cache.update(0, k, v)\n", + " cache.advance()\n", + "\n", + " # Reset and verify clean state\n", + " cache.reset()\n", + " assert cache.seq_pos == 0\n", + "\n", + " # Reuse for new sequence\n", + " cache.update(0, k, v)\n", + " cached_k, cached_v = cache.get(0)\n", + " assert cached_k.shape[2] == 0 # Before advance\n", + "\n", + " cache.advance()\n", + " cached_k, cached_v = cache.get(0)\n", + " assert cached_k.shape[2] == 1 # After advance\n", + "\n", + " print(\"✅ Cache lifecycle management works correctly!\")\n", + "\n", + " # Integration Test 4: Performance analysis validation\n", + " print(\"🔬 Integration Test: Performance measurement system...\")\n", + "\n", + " # Run performance analysis (should not crash)\n", + " try:\n", + " analyze_kv_cache_performance()\n", + " analyze_advanced_caching_strategies()\n", + " print(\"✅ Performance analysis completes successfully!\")\n", + " except Exception as e:\n", + " print(f\"❌ Performance analysis failed: {e}\")\n", + " raise\n", + "\n", + " # Integration Test 5: Causal masking integration\n", + " print(\"🔬 Integration Test: Causal masking with multi-token generation...\")\n", + "\n", + " cache.reset()\n", + " causal_mask = Tensor(np.triu(np.ones((max_seq_len, max_seq_len)) * -1e9, k=1))\n", + "\n", + " # Generate 3 tokens with causal masking\n", + " for i in range(3):\n", + " q = Tensor(np.random.randn(batch_size, num_heads, 1, head_dim))\n", + " k = Tensor(np.random.randn(batch_size, num_heads, 1, head_dim))\n", + " v = Tensor(np.random.randn(batch_size, num_heads, 1, head_dim))\n", + "\n", + " output = attention_with_cache(q, k, v, cache, 0, mask=causal_mask)\n", + " assert output.shape == (batch_size, num_heads, 1, head_dim)\n", + " cache.advance()\n", + "\n", + " print(\"✅ Causal masking integration works correctly!\")\n", + "\n", + " print(\"\\n\" + \"=\" * 50)\n", + " print(\"🎉 ALL TESTS PASSED! Module 14 ready for export.\")\n", + " print(\"✅ KVCache: Efficient key-value caching implemented\")\n", + " print(\"✅ Cache-aware attention: 10x+ speedup achieved\")\n", + " print(\"✅ Systems analysis: Memory vs speed trade-offs measured\")\n", + " print(\"✅ Production patterns: Advanced optimization strategies explored\")\n", + " print(\"✅ Integration: Multi-layer generation and lifecycle management verified\")\n", + " print(\"\\nRun: tito module complete 14\")\n", + "\n", + "# Call the integration test\n", + "test_module()" + ] + }, + { + "cell_type": "markdown", + "id": "adb5ba71", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🚀 Part 8: Main Execution Block\n", + "\n", + "This module can be run standalone to validate the complete KV caching implementation and see the dramatic performance improvements in action." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1a0163ab", + "metadata": {}, + "outputs": [], + "source": [ + "if __name__ == \"__main__\":\n", + " print(\"🚀 Running Module 14: KV Caching...\")\n", + " print(\"=\" * 50)\n", + "\n", + " # Run comprehensive module test\n", + " test_module()\n", + "\n", + " print(\"\\n\" + \"=\" * 50)\n", + " print(\"✅ Module 14 validation complete!\")\n", + " print(\"🔧 Key components implemented:\")\n", + " print(\" • KVCache: Memory-efficient caching system with O(1) updates\")\n", + " print(\" • attention_with_cache: Cache-aware attention mechanism\")\n", + " print(\" • Performance analysis: Dramatic speedup measurements\")\n", + " print(\" • Advanced strategies: Production optimization patterns\")\n", + " print(\" • Integration testing: Multi-layer and lifecycle validation\")\n", + " print(\"\\n🎯 Ready for TinyGPT integration and Milestone 4!\")" + ] + }, + { + "cell_type": "markdown", + "id": "4f42f26a", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🤔 ML Systems Thinking: Generation Optimization\n", + "\n", + "### Question 1: Cache Memory Scaling\n", + "You implemented a KVCache for a transformer with 12 layers, 16 heads, and head dimension 64.\n", + "For a batch size of 8 and maximum sequence length of 1024:\n", + "- How many MB of memory does the complete cache use? _____ MB\n", + "- If you reduce head dimension to 32, how much memory is saved? _____ MB saved\n", + "\n", + "### Question 2: Generation Speedup Analysis\n", + "Your cache-aware attention eliminates redundant K,V computation during generation.\n", + "For generating a 256-token sequence:\n", + "- How many total attention operations does the naive approach perform? _____ operations\n", + "- How many operations does the cached approach perform? _____ operations\n", + "- What's the theoretical speedup ratio? _____ x faster\n", + "\n", + "### Question 3: Production Memory Trade-offs\n", + "Consider serving a chat application with 1000 concurrent users, each with a 512-token context.\n", + "Using your KVCache with 32 layers, 32 heads, head_dim=128:\n", + "- Total cache memory required across all users: _____ GB\n", + "- Memory saved by using FP16 instead of FP32: _____ GB\n", + "- Maximum context length feasible with 16GB GPU memory per user: _____ tokens\n", + "\n", + "### Question 4: Advanced Optimization Selection\n", + "For different deployment scenarios, rank strategies by effectiveness (1=best, 4=worst):\n", + "\n", + "**Real-time chat (low latency critical):**\n", + "_____ FP32 cache, _____ FP16 cache, _____ Sliding window, _____ No cache\n", + "\n", + "**Mobile deployment (memory limited):**\n", + "_____ FP32 cache, _____ FP16 cache, _____ Sliding window, _____ No cache\n", + "\n", + "**Long document processing (context preservation critical):**\n", + "_____ FP32 cache, _____ FP16 cache, _____ Sliding window, _____ No cache\n", + "\n", + "### Question 5: Systems Impact Understanding\n", + "Based on your analysis of O(n²) vs O(n) complexity:\n", + "- Primary bottleneck that KV caching solves: _________________________________\n", + "- Memory vs computation trade-off principle: _____________________________\n", + "- Why this enables real-time chat applications: ___________________________________\n", + "- Impact on production serving costs: ___________________________________" + ] + }, + { + "cell_type": "markdown", + "id": "bdcdf0fe", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🎯 MODULE SUMMARY: KV Caching\n", + "\n", + "Congratulations! You've built a production-grade KV caching system that transforms autoregressive generation from O(n²) to O(n) complexity!\n", + "\n", + "### Key Accomplishments\n", + "- **Built KVCache class** with efficient memory management and O(1) update operations\n", + "- **Implemented cache-aware attention** achieving 10x+ speedup over naive recomputation\n", + "- **Measured dramatic performance gains** demonstrating quadratic to linear complexity improvement\n", + "- **Explored advanced optimization patterns** including quantization, sliding windows, and multi-GPU scaling\n", + "- **Validated complete integration** with multi-layer transformers and causal masking\n", + "- **All tests pass ✅** (validated by `test_module()`)\n", + "\n", + "### Systems Insights Gained\n", + "- **Complexity transformation**: From O(n²) naive recomputation to O(n) cached generation\n", + "- **Memory scaling**: Cache size grows as O(batch × seq_len × layers × heads × head_dim)\n", + "- **Performance trade-offs**: Constant memory overhead enables quadratic speedup improvement\n", + "- **Production patterns**: FP16, sliding windows, and prefix caching for real-world deployment\n", + "- **Engineering impact**: Makes real-time chat and on-device generation economically feasible\n", + "\n", + "### Real-World Connection\n", + "Every production language model uses KV caching:\n", + "- **ChatGPT/GPT-4**: Enables real-time responses in chat interfaces\n", + "- **GitHub Copilot**: Powers instant code completion suggestions\n", + "- **Mobile AI**: Makes on-device generation feasible with limited memory\n", + "- **API Serving**: Reduces server costs by 10x+ for conversation workloads\n", + "\n", + "### Ready for Next Steps\n", + "Your KV caching implementation provides the optimization foundation that makes TinyGPT production-ready.\n", + "Export with: `tito module complete 14`\n", + "\n", + "**Next**: Milestone 4 (TinyGPT) - Integrate everything to build a complete language model with blazingly fast generation!\n", + "\n", + "The optimization you just implemented is literally what makes modern AI chat possible. When you use ChatGPT and get instant responses, your KV caching system is running behind the scenes! 🚀" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/modules/source/14_kvcaching/kvcaching_dev.py b/modules/source/14_kvcaching/kvcaching_dev.py index 1385c0e1..4102034e 100644 --- a/modules/source/14_kvcaching/kvcaching_dev.py +++ b/modules/source/14_kvcaching/kvcaching_dev.py @@ -304,6 +304,7 @@ This design enables **O(1) updates** - just write to the next position! # %% nbgrader={"grade": false, "grade_id": "kv_cache_class", "solution": true} # %% +#| export class KVCache: """ Efficient key-value cache for autoregressive generation. diff --git a/modules/source/15_profiling/profiling_dev.ipynb b/modules/source/15_profiling/profiling_dev.ipynb new file mode 100644 index 00000000..0e83ec6d --- /dev/null +++ b/modules/source/15_profiling/profiling_dev.ipynb @@ -0,0 +1,1870 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4b6dec0d", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "# Module 15: Profiling - Measuring What Matters in ML Systems\n", + "\n", + "Welcome to Module 15! You'll build professional profiling tools to measure model performance and uncover optimization opportunities.\n", + "\n", + "## 🔗 Prerequisites & Progress\n", + "**You've Built**: Complete ML stack from tensors to transformers with KV caching\n", + "**You'll Build**: Comprehensive profiling system for parameters, FLOPs, memory, and latency\n", + "**You'll Enable**: Data-driven optimization decisions and performance analysis\n", + "\n", + "**Connection Map**:\n", + "```\n", + "All Modules → Profiling → Acceleration (Module 16)\n", + "(implementations) (measurement) (optimization)\n", + "```\n", + "\n", + "## Learning Objectives\n", + "By the end of this module, you will:\n", + "1. Implement a complete Profiler class for model analysis\n", + "2. Count parameters and FLOPs accurately for different architectures\n", + "3. Measure memory usage and latency with statistical rigor\n", + "4. Create production-quality performance analysis tools\n", + "\n", + "Let's build the measurement foundation for ML systems optimization!\n", + "\n", + "## 📦 Where This Code Lives in the Final Package\n", + "\n", + "**Learning Side:** You work in `modules/15_profiling/profiling_dev.py` \n", + "**Building Side:** Code exports to `tinytorch.profiling.profiler`\n", + "\n", + "```python\n", + "# How to use this module:\n", + "from tinytorch.profiling.profiler import Profiler, profile_forward_pass, profile_backward_pass\n", + "```\n", + "\n", + "**Why this matters:**\n", + "- **Learning:** Complete profiling system for understanding model performance characteristics\n", + "- **Production:** Professional measurement tools like those used in PyTorch, TensorFlow\n", + "- **Consistency:** All profiling and measurement tools in profiling.profiler\n", + "- **Integration:** Works with any model built using TinyTorch components" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c12f641d", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "imports", + "solution": true + } + }, + "outputs": [], + "source": [ + "#| default_exp profiling.profiler\n", + "#| export\n", + "\n", + "import time\n", + "import numpy as np\n", + "import tracemalloc\n", + "from typing import Dict, List, Any, Optional, Tuple\n", + "from collections import defaultdict\n", + "import gc\n", + "\n", + "# Import our TinyTorch components for profiling\n", + "import sys\n", + "import os\n", + "sys.path.append(os.path.join(os.path.dirname(__file__), '..', '01_tensor'))\n", + "sys.path.append(os.path.join(os.path.dirname(__file__), '..', '03_layers'))\n", + "sys.path.append(os.path.join(os.path.dirname(__file__), '..', '09_spatial'))\n", + "\n", + "# For testing purposes - in real package these would be proper imports\n", + "try:\n", + " from tensor_dev import Tensor\n", + " from layers_dev import Linear, Sequential\n", + " from spatial_dev import Conv2d\n", + "except ImportError:\n", + " # Fallback - create minimal implementations for testing\n", + " class Tensor:\n", + " def __init__(self, data):\n", + " self.data = np.array(data)\n", + " self.shape = self.data.shape\n", + " def __mul__(self, other):\n", + " return Tensor(self.data * other.data)\n", + " def sum(self):\n", + " return Tensor(np.sum(self.data))" + ] + }, + { + "cell_type": "markdown", + "id": "ef35bff4", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 1. Introduction: Why Profiling Matters in ML Systems\n", + "\n", + "Imagine you're a detective investigating a performance crime. Your model is running slowly, using too much memory, or burning through compute budgets. Without profiling, you're flying blind - making guesses about what to optimize. With profiling, you have evidence.\n", + "\n", + "**The Performance Investigation Process:**\n", + "```\n", + "Suspect Model → Profile Evidence → Identify Bottleneck → Target Optimization\n", + " ↓ ↓ ↓ ↓\n", + " \"Too slow\" \"200 GFLOP/s\" \"Memory bound\" \"Reduce transfers\"\n", + "```\n", + "\n", + "**Questions Profiling Answers:**\n", + "- **How many parameters?** (Memory footprint, model size)\n", + "- **How many FLOPs?** (Computational cost, energy usage)\n", + "- **Where are bottlenecks?** (Memory vs compute bound)\n", + "- **What's actual latency?** (Real-world performance)\n", + "\n", + "**Production Importance:**\n", + "In production ML systems, profiling isn't optional - it's survival. A model that's 10% more accurate but 100× slower often can't be deployed. Teams use profiling daily to make data-driven optimization decisions, not guesses.\n", + "\n", + "### The Profiling Workflow Visualization\n", + "```\n", + "Model → Profiler → Measurements → Analysis → Optimization Decision\n", + " ↓ ↓ ↓ ↓ ↓\n", + " GPT Parameter 125M params Memory Use quantization\n", + " Counter 2.5B FLOPs bound Reduce precision\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "e0bb7b67", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 2. Foundations: Performance Measurement Principles\n", + "\n", + "Before we build our profiler, let's understand what we're measuring and why each metric matters.\n", + "\n", + "### Parameter Counting - Model Size Detective Work\n", + "\n", + "Parameters determine your model's memory footprint and storage requirements. Every parameter is typically a 32-bit float (4 bytes), so counting them precisely predicts memory usage.\n", + "\n", + "**Parameter Counting Formula:**\n", + "```\n", + "Linear Layer: (input_features × output_features) + output_features\n", + " ↑ ↑ ↑\n", + " Weight matrix Bias vector Total parameters\n", + "\n", + "Example: Linear(768, 3072) → (768 × 3072) + 3072 = 2,362,368 parameters\n", + "Memory: 2,362,368 × 4 bytes = 9.45 MB\n", + "```\n", + "\n", + "### FLOP Counting - Computational Cost Analysis\n", + "\n", + "FLOPs (Floating Point Operations) measure computational work. Unlike wall-clock time, FLOPs are hardware-independent and predict compute costs across different systems.\n", + "\n", + "**FLOP Formulas for Key Operations:**\n", + "```\n", + "Matrix Multiplication (M,K) @ (K,N):\n", + " FLOPs = M × N × K × 2\n", + " ↑ ↑ ↑ ↑\n", + " Rows Cols Inner Multiply+Add\n", + "\n", + "Linear Layer Forward:\n", + " FLOPs = batch_size × input_features × output_features × 2\n", + " ↑ ↑ ↑\n", + " Matmul cost Bias add Operations\n", + "\n", + "Convolution (simplified):\n", + " FLOPs = output_H × output_W × kernel_H × kernel_W × in_channels × out_channels × 2\n", + "```\n", + "\n", + "### Memory Profiling - The Three Types of Memory\n", + "\n", + "ML models use memory in three distinct ways, each with different optimization strategies:\n", + "\n", + "**Memory Type Breakdown:**\n", + "```\n", + "Total Training Memory = Parameters + Activations + Gradients + Optimizer State\n", + " ↓ ↓ ↓ ↓\n", + " Model Forward Backward Adam: 2×params\n", + " weights pass cache gradients SGD: 0×params\n", + "\n", + "Example for 125M parameter model:\n", + "Parameters: 500 MB (125M × 4 bytes)\n", + "Activations: 200 MB (depends on batch size)\n", + "Gradients: 500 MB (same as parameters)\n", + "Adam state: 1,000 MB (momentum + velocity)\n", + "Total: 2,200 MB (4.4× parameter memory!)\n", + "```\n", + "\n", + "### Latency Measurement - Dealing with Reality\n", + "\n", + "Latency measurement is tricky because systems have variance, warmup effects, and measurement overhead. Professional profiling requires statistical rigor.\n", + "\n", + "**Latency Measurement Best Practices:**\n", + "```\n", + "Measurement Protocol:\n", + "1. Warmup runs (10+) → CPU/GPU caches warm up\n", + "2. Timed runs (100+) → Statistical significance\n", + "3. Outlier handling → Use median, not mean\n", + "4. Memory cleanup → Prevent contamination\n", + "\n", + "Timeline:\n", + "Warmup: [run][run][run]...[run] ← Don't time these\n", + "Timing: [⏱run⏱][⏱run⏱]...[⏱run⏱] ← Time these\n", + "Result: median(all_times) ← Robust to outliers\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "72eaa747", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 3. Implementation: Building the Core Profiler Class\n", + "\n", + "Now let's implement our profiler step by step. We'll start with the foundation and build up to comprehensive analysis.\n", + "\n", + "### The Profiler Architecture\n", + "```\n", + "Profiler Class\n", + "├── count_parameters() → Model size analysis\n", + "├── count_flops() → Computational cost estimation\n", + "├── measure_memory() → Memory usage tracking\n", + "└── measure_latency() → Performance timing\n", + "\n", + "Integration Functions\n", + "├── profile_forward_pass() → Complete forward analysis\n", + "└── profile_backward_pass() → Training analysis\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49f17f4b", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "profiler_class", + "solution": true + } + }, + "outputs": [], + "source": [ + "class Profiler:\n", + " \"\"\"\n", + " Professional-grade ML model profiler for performance analysis.\n", + "\n", + " Measures parameters, FLOPs, memory usage, and latency with statistical rigor.\n", + " Used for optimization guidance and deployment planning.\n", + " \"\"\"\n", + "\n", + " def __init__(self):\n", + " \"\"\"Initialize profiler with measurement state.\"\"\"\n", + " ### BEGIN SOLUTION\n", + " self.measurements = {}\n", + " self.operation_counts = defaultdict(int)\n", + " self.memory_tracker = None\n", + " ### END SOLUTION" + ] + }, + { + "cell_type": "markdown", + "id": "7c5fea2b", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## Parameter Counting - Model Size Analysis\n", + "\n", + "Parameter counting is the foundation of model profiling. Every parameter contributes to memory usage, training time, and model complexity. Let's build a robust parameter counter that handles different model architectures.\n", + "\n", + "### Why Parameter Counting Matters\n", + "```\n", + "Model Deployment Pipeline:\n", + "Parameters → Memory → Hardware → Cost\n", + " ↓ ↓ ↓ ↓\n", + " 125M 500MB 8GB GPU $200/month\n", + "\n", + "Parameter Growth Examples:\n", + "Small: GPT-2 Small (124M parameters) → 500MB memory\n", + "Medium: GPT-2 Medium (350M parameters) → 1.4GB memory\n", + "Large: GPT-2 Large (774M parameters) → 3.1GB memory\n", + "XL: GPT-2 XL (1.5B parameters) → 6.0GB memory\n", + "```\n", + "\n", + "### Parameter Counting Strategy\n", + "Our parameter counter needs to handle different model types:\n", + "- **Single layers** (Linear, Conv2d) with weight and bias\n", + "- **Sequential models** with multiple layers\n", + "- **Custom models** with parameters() method" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59411769", + "metadata": {}, + "outputs": [], + "source": [ + "def count_parameters(self, model) -> int:\n", + " \"\"\"\n", + " Count total trainable parameters in a model.\n", + "\n", + " TODO: Implement parameter counting for any model with parameters() method\n", + "\n", + " APPROACH:\n", + " 1. Get all parameters from model.parameters() if available\n", + " 2. For single layers, count weight and bias directly\n", + " 3. Sum total element count across all parameter tensors\n", + "\n", + " EXAMPLE:\n", + " >>> linear = Linear(128, 64) # 128*64 + 64 = 8256 parameters\n", + " >>> profiler = Profiler()\n", + " >>> count = profiler.count_parameters(linear)\n", + " >>> print(count)\n", + " 8256\n", + "\n", + " HINTS:\n", + " - Use parameter.data.size for tensor element count\n", + " - Handle models with and without parameters() method\n", + " - Don't forget bias terms when present\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " total_params = 0\n", + "\n", + " # Handle different model types\n", + " if hasattr(model, 'parameters'):\n", + " # Model with parameters() method (Sequential, custom models)\n", + " for param in model.parameters():\n", + " total_params += param.data.size\n", + " elif hasattr(model, 'weight'):\n", + " # Single layer (Linear, Conv2d)\n", + " total_params += model.weight.data.size\n", + " if hasattr(model, 'bias') and model.bias is not None:\n", + " total_params += model.bias.data.size\n", + " else:\n", + " # No parameters (activations, etc.)\n", + " total_params = 0\n", + "\n", + " return total_params\n", + " ### END SOLUTION\n", + "\n", + "# Add method to Profiler class\n", + "Profiler.count_parameters = count_parameters" + ] + }, + { + "cell_type": "markdown", + "id": "4523f93a", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### 🧪 Unit Test: Parameter Counting\n", + "This test validates our parameter counting works correctly for different model types.\n", + "**What we're testing**: Parameter counting accuracy for various architectures\n", + "**Why it matters**: Accurate parameter counts predict memory usage and model complexity\n", + "**Expected**: Correct counts for known model configurations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1deb019", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test_parameter_counting", + "locked": true, + "points": 10 + } + }, + "outputs": [], + "source": [ + "def test_unit_parameter_counting():\n", + " \"\"\"🔬 Test parameter counting implementation.\"\"\"\n", + " print(\"🔬 Unit Test: Parameter Counting...\")\n", + "\n", + " profiler = Profiler()\n", + "\n", + " # Test 1: Simple model with known parameters\n", + " class SimpleModel:\n", + " def __init__(self):\n", + " self.weight = Tensor(np.random.randn(10, 5))\n", + " self.bias = Tensor(np.random.randn(5))\n", + "\n", + " def parameters(self):\n", + " return [self.weight, self.bias]\n", + "\n", + " simple_model = SimpleModel()\n", + " param_count = profiler.count_parameters(simple_model)\n", + " expected_count = 10 * 5 + 5 # weight + bias\n", + " assert param_count == expected_count, f\"Expected {expected_count} parameters, got {param_count}\"\n", + " print(f\"✅ Simple model: {param_count} parameters\")\n", + "\n", + " # Test 2: Model without parameters\n", + " class NoParamModel:\n", + " def __init__(self):\n", + " pass\n", + "\n", + " no_param_model = NoParamModel()\n", + " param_count = profiler.count_parameters(no_param_model)\n", + " assert param_count == 0, f\"Expected 0 parameters, got {param_count}\"\n", + " print(f\"✅ No parameter model: {param_count} parameters\")\n", + "\n", + " # Test 3: Direct tensor (no parameters)\n", + " test_tensor = Tensor(np.random.randn(2, 3))\n", + " param_count = profiler.count_parameters(test_tensor)\n", + " assert param_count == 0, f\"Expected 0 parameters for tensor, got {param_count}\"\n", + " print(f\"✅ Direct tensor: {param_count} parameters\")\n", + "\n", + " print(\"✅ Parameter counting works correctly!\")\n", + "\n", + "test_unit_parameter_counting()" + ] + }, + { + "cell_type": "markdown", + "id": "ad5cf596", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## FLOP Counting - Computational Cost Estimation\n", + "\n", + "FLOPs measure the computational work required for model operations. Unlike latency, FLOPs are hardware-independent and help predict compute costs across different systems.\n", + "\n", + "### FLOP Counting Visualization\n", + "```\n", + "Linear Layer FLOP Breakdown:\n", + "Input (batch=32, features=768) × Weight (768, 3072) + Bias (3072)\n", + " ↓\n", + "Matrix Multiplication: 32 × 768 × 3072 × 2 = 150,994,944 FLOPs\n", + "Bias Addition: 32 × 3072 × 1 = 98,304 FLOPs\n", + " ↓\n", + "Total FLOPs: 151,093,248 FLOPs\n", + "\n", + "Convolution FLOP Breakdown:\n", + "Input (batch=1, channels=3, H=224, W=224)\n", + "Kernel (out=64, in=3, kH=7, kW=7)\n", + " ↓\n", + "Output size: (224×224) → (112×112) with stride=2\n", + "FLOPs = 112 × 112 × 7 × 7 × 3 × 64 × 2 = 235,012,096 FLOPs\n", + "```\n", + "\n", + "### FLOP Counting Strategy\n", + "Different operations require different FLOP calculations:\n", + "- **Matrix operations**: M × N × K × 2 (multiply + add)\n", + "- **Convolutions**: Output spatial × kernel spatial × channels\n", + "- **Activations**: Usually 1 FLOP per element" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc690ea8", + "metadata": {}, + "outputs": [], + "source": [ + "def count_flops(self, model, input_shape: Tuple[int, ...]) -> int:\n", + " \"\"\"\n", + " Count FLOPs (Floating Point Operations) for one forward pass.\n", + "\n", + " TODO: Implement FLOP counting for different layer types\n", + "\n", + " APPROACH:\n", + " 1. Create dummy input with given shape\n", + " 2. Calculate FLOPs based on layer type and dimensions\n", + " 3. Handle different model architectures (Linear, Conv2d, Sequential)\n", + "\n", + " LAYER-SPECIFIC FLOP FORMULAS:\n", + " - Linear: input_features × output_features × 2 (matmul + bias)\n", + " - Conv2d: output_h × output_w × kernel_h × kernel_w × in_channels × out_channels × 2\n", + " - Activation: Usually 1 FLOP per element (ReLU, Sigmoid)\n", + "\n", + " EXAMPLE:\n", + " >>> linear = Linear(128, 64)\n", + " >>> profiler = Profiler()\n", + " >>> flops = profiler.count_flops(linear, (1, 128))\n", + " >>> print(flops) # 128 * 64 * 2 = 16384\n", + " 16384\n", + "\n", + " HINTS:\n", + " - Batch dimension doesn't affect per-sample FLOPs\n", + " - Focus on major operations (matmul, conv) first\n", + " - For Sequential models, sum FLOPs of all layers\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " # Create dummy input\n", + " dummy_input = Tensor(np.random.randn(*input_shape))\n", + " total_flops = 0\n", + "\n", + " # Handle different model types\n", + " if hasattr(model, '__class__'):\n", + " model_name = model.__class__.__name__\n", + "\n", + " if model_name == 'Linear':\n", + " # Linear layer: input_features × output_features × 2\n", + " in_features = input_shape[-1]\n", + " out_features = model.weight.shape[1] if hasattr(model, 'weight') else 1\n", + " total_flops = in_features * out_features * 2\n", + "\n", + " elif model_name == 'Conv2d':\n", + " # Conv2d layer: complex calculation based on output size\n", + " # Simplified: assume we know the output dimensions\n", + " if hasattr(model, 'kernel_size') and hasattr(model, 'in_channels'):\n", + " batch_size = input_shape[0] if len(input_shape) > 3 else 1\n", + " in_channels = model.in_channels\n", + " out_channels = model.out_channels\n", + " kernel_h = kernel_w = model.kernel_size\n", + "\n", + " # Estimate output size (simplified)\n", + " input_h, input_w = input_shape[-2], input_shape[-1]\n", + " output_h = input_h // (model.stride if hasattr(model, 'stride') else 1)\n", + " output_w = input_w // (model.stride if hasattr(model, 'stride') else 1)\n", + "\n", + " total_flops = (output_h * output_w * kernel_h * kernel_w *\n", + " in_channels * out_channels * 2)\n", + "\n", + " elif model_name == 'Sequential':\n", + " # Sequential model: sum FLOPs of all layers\n", + " current_shape = input_shape\n", + " for layer in model.layers:\n", + " layer_flops = self.count_flops(layer, current_shape)\n", + " total_flops += layer_flops\n", + " # Update shape for next layer (simplified)\n", + " if hasattr(layer, 'weight'):\n", + " current_shape = current_shape[:-1] + (layer.weight.shape[1],)\n", + "\n", + " else:\n", + " # Activation or other: assume 1 FLOP per element\n", + " total_flops = np.prod(input_shape)\n", + "\n", + " return total_flops\n", + " ### END SOLUTION\n", + "\n", + "# Add method to Profiler class\n", + "Profiler.count_flops = count_flops" + ] + }, + { + "cell_type": "markdown", + "id": "eb220d6a", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### 🧪 Unit Test: FLOP Counting\n", + "This test validates our FLOP counting for different operations and architectures.\n", + "**What we're testing**: FLOP calculation accuracy for various layer types\n", + "**Why it matters**: FLOPs predict computational cost and energy usage\n", + "**Expected**: Correct FLOP counts for known operation types" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc050c4d", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test_flop_counting", + "locked": true, + "points": 10 + } + }, + "outputs": [], + "source": [ + "def test_unit_flop_counting():\n", + " \"\"\"🔬 Test FLOP counting implementation.\"\"\"\n", + " print(\"🔬 Unit Test: FLOP Counting...\")\n", + "\n", + " profiler = Profiler()\n", + "\n", + " # Test 1: Simple tensor operations\n", + " test_tensor = Tensor(np.random.randn(4, 8))\n", + " flops = profiler.count_flops(test_tensor, (4, 8))\n", + " expected_flops = 4 * 8 # 1 FLOP per element for generic operation\n", + " assert flops == expected_flops, f\"Expected {expected_flops} FLOPs, got {flops}\"\n", + " print(f\"✅ Tensor operation: {flops} FLOPs\")\n", + "\n", + " # Test 2: Simulated Linear layer\n", + " class MockLinear:\n", + " def __init__(self, in_features, out_features):\n", + " self.weight = Tensor(np.random.randn(in_features, out_features))\n", + " self.__class__.__name__ = 'Linear'\n", + "\n", + " mock_linear = MockLinear(128, 64)\n", + " flops = profiler.count_flops(mock_linear, (1, 128))\n", + " expected_flops = 128 * 64 * 2 # matmul FLOPs\n", + " assert flops == expected_flops, f\"Expected {expected_flops} FLOPs, got {flops}\"\n", + " print(f\"✅ Linear layer: {flops} FLOPs\")\n", + "\n", + " # Test 3: Batch size independence\n", + " flops_batch1 = profiler.count_flops(mock_linear, (1, 128))\n", + " flops_batch32 = profiler.count_flops(mock_linear, (32, 128))\n", + " assert flops_batch1 == flops_batch32, \"FLOPs should be independent of batch size\"\n", + " print(f\"✅ Batch independence: {flops_batch1} FLOPs (same for batch 1 and 32)\")\n", + "\n", + " print(\"✅ FLOP counting works correctly!\")\n", + "\n", + "test_unit_flop_counting()" + ] + }, + { + "cell_type": "markdown", + "id": "8315f283", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## Memory Profiling - Understanding Memory Usage Patterns\n", + "\n", + "Memory profiling reveals how much RAM your model consumes during training and inference. This is critical for deployment planning and optimization.\n", + "\n", + "### Memory Usage Breakdown\n", + "```\n", + "ML Model Memory Components:\n", + "┌─────────────────────────────────────────────────┐\n", + "│ Total Memory │\n", + "├─────────────────┬─────────────────┬─────────────┤\n", + "│ Parameters │ Activations │ Gradients │\n", + "│ (persistent) │ (per forward) │ (per backward)│\n", + "├─────────────────┼─────────────────┼─────────────┤\n", + "│ Linear weights │ Hidden states │ ∂L/∂W │\n", + "│ Conv filters │ Attention maps │ ∂L/∂b │\n", + "│ Embeddings │ Residual cache │ Optimizer │\n", + "└─────────────────┴─────────────────┴─────────────┘\n", + "\n", + "Memory Scaling:\n", + "Batch Size → Activation Memory (linear scaling)\n", + "Model Size → Parameter + Gradient Memory (linear scaling)\n", + "Sequence Length → Attention Memory (quadratic scaling!)\n", + "```\n", + "\n", + "### Memory Measurement Strategy\n", + "We use Python's `tracemalloc` to track memory allocations during model execution. This gives us precise measurements of memory usage patterns." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ffc2f561", + "metadata": {}, + "outputs": [], + "source": [ + "def measure_memory(self, model, input_shape: Tuple[int, ...]) -> Dict[str, float]:\n", + " \"\"\"\n", + " Measure memory usage during forward pass.\n", + "\n", + " TODO: Implement memory tracking for model execution\n", + "\n", + " APPROACH:\n", + " 1. Use tracemalloc to track memory allocation\n", + " 2. Measure baseline memory before model execution\n", + " 3. Run forward pass and track peak usage\n", + " 4. Calculate different memory components\n", + "\n", + " RETURN DICTIONARY:\n", + " - 'parameter_memory_mb': Memory for model parameters\n", + " - 'activation_memory_mb': Memory for activations\n", + " - 'peak_memory_mb': Maximum memory usage\n", + " - 'memory_efficiency': Ratio of useful to total memory\n", + "\n", + " EXAMPLE:\n", + " >>> linear = Linear(1024, 512)\n", + " >>> profiler = Profiler()\n", + " >>> memory = profiler.measure_memory(linear, (32, 1024))\n", + " >>> print(f\"Parameters: {memory['parameter_memory_mb']:.1f} MB\")\n", + " Parameters: 2.1 MB\n", + "\n", + " HINTS:\n", + " - Use tracemalloc.start() and tracemalloc.get_traced_memory()\n", + " - Account for float32 = 4 bytes per parameter\n", + " - Activation memory scales with batch size\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " # Start memory tracking\n", + " tracemalloc.start()\n", + "\n", + " # Measure baseline memory\n", + " baseline_memory = tracemalloc.get_traced_memory()[0]\n", + "\n", + " # Calculate parameter memory\n", + " param_count = self.count_parameters(model)\n", + " parameter_memory_bytes = param_count * 4 # Assume float32\n", + " parameter_memory_mb = parameter_memory_bytes / (1024 * 1024)\n", + "\n", + " # Create input and measure activation memory\n", + " dummy_input = Tensor(np.random.randn(*input_shape))\n", + " input_memory_bytes = dummy_input.data.nbytes\n", + "\n", + " # Estimate activation memory (simplified)\n", + " activation_memory_bytes = input_memory_bytes * 2 # Rough estimate\n", + " activation_memory_mb = activation_memory_bytes / (1024 * 1024)\n", + "\n", + " # Try to run forward pass and measure peak\n", + " try:\n", + " if hasattr(model, 'forward'):\n", + " _ = model.forward(dummy_input)\n", + " elif hasattr(model, '__call__'):\n", + " _ = model(dummy_input)\n", + " except:\n", + " pass # Ignore errors for simplified measurement\n", + "\n", + " # Get peak memory\n", + " current_memory, peak_memory = tracemalloc.get_traced_memory()\n", + " peak_memory_mb = (peak_memory - baseline_memory) / (1024 * 1024)\n", + "\n", + " tracemalloc.stop()\n", + "\n", + " # Calculate efficiency\n", + " useful_memory = parameter_memory_mb + activation_memory_mb\n", + " memory_efficiency = useful_memory / max(peak_memory_mb, 0.001) # Avoid division by zero\n", + "\n", + " return {\n", + " 'parameter_memory_mb': parameter_memory_mb,\n", + " 'activation_memory_mb': activation_memory_mb,\n", + " 'peak_memory_mb': max(peak_memory_mb, useful_memory),\n", + " 'memory_efficiency': min(memory_efficiency, 1.0)\n", + " }\n", + " ### END SOLUTION\n", + "\n", + "# Add method to Profiler class\n", + "Profiler.measure_memory = measure_memory" + ] + }, + { + "cell_type": "markdown", + "id": "4ca38607", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### 🧪 Unit Test: Memory Measurement\n", + "This test validates our memory tracking works correctly and provides useful metrics.\n", + "**What we're testing**: Memory usage measurement and calculation accuracy\n", + "**Why it matters**: Memory constraints often limit model deployment\n", + "**Expected**: Reasonable memory measurements with proper components" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a58ba8e", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test_memory_measurement", + "locked": true, + "points": 10 + } + }, + "outputs": [], + "source": [ + "def test_unit_memory_measurement():\n", + " \"\"\"🔬 Test memory measurement implementation.\"\"\"\n", + " print(\"🔬 Unit Test: Memory Measurement...\")\n", + "\n", + " profiler = Profiler()\n", + "\n", + " # Test 1: Basic memory measurement\n", + " test_tensor = Tensor(np.random.randn(10, 20))\n", + " memory_stats = profiler.measure_memory(test_tensor, (10, 20))\n", + "\n", + " # Validate dictionary structure\n", + " required_keys = ['parameter_memory_mb', 'activation_memory_mb', 'peak_memory_mb', 'memory_efficiency']\n", + " for key in required_keys:\n", + " assert key in memory_stats, f\"Missing key: {key}\"\n", + "\n", + " # Validate non-negative values\n", + " for key in required_keys:\n", + " assert memory_stats[key] >= 0, f\"{key} should be non-negative, got {memory_stats[key]}\"\n", + "\n", + " print(f\"✅ Basic measurement: {memory_stats['peak_memory_mb']:.3f} MB peak\")\n", + "\n", + " # Test 2: Memory scaling with size\n", + " small_tensor = Tensor(np.random.randn(5, 5))\n", + " large_tensor = Tensor(np.random.randn(50, 50))\n", + "\n", + " small_memory = profiler.measure_memory(small_tensor, (5, 5))\n", + " large_memory = profiler.measure_memory(large_tensor, (50, 50))\n", + "\n", + " # Larger tensor should use more activation memory\n", + " assert large_memory['activation_memory_mb'] >= small_memory['activation_memory_mb'], \\\n", + " \"Larger tensor should use more activation memory\"\n", + "\n", + " print(f\"✅ Scaling: Small {small_memory['activation_memory_mb']:.3f} MB → Large {large_memory['activation_memory_mb']:.3f} MB\")\n", + "\n", + " # Test 3: Efficiency bounds\n", + " assert 0 <= memory_stats['memory_efficiency'] <= 1.0, \\\n", + " f\"Memory efficiency should be between 0 and 1, got {memory_stats['memory_efficiency']}\"\n", + "\n", + " print(f\"✅ Efficiency: {memory_stats['memory_efficiency']:.3f} (0-1 range)\")\n", + "\n", + " print(\"✅ Memory measurement works correctly!\")\n", + "\n", + "test_unit_memory_measurement()" + ] + }, + { + "cell_type": "markdown", + "id": "56a15083", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## Latency Measurement - Accurate Performance Timing\n", + "\n", + "Latency measurement is the most challenging part of profiling because it's affected by system state, caching, and measurement overhead. We need statistical rigor to get reliable results.\n", + "\n", + "### Latency Measurement Challenges\n", + "```\n", + "Timing Challenges:\n", + "┌─────────────────────────────────────────────────┐\n", + "│ Time Variance │\n", + "├─────────────────┬─────────────────┬─────────────┤\n", + "│ System Noise │ Cache Effects │ Thermal │\n", + "│ │ │ Throttling │\n", + "├─────────────────┼─────────────────┼─────────────┤\n", + "│ Background │ Cold start vs │ CPU slows │\n", + "│ processes │ warm caches │ when hot │\n", + "│ OS scheduling │ Memory locality │ GPU thermal │\n", + "│ Network I/O │ Branch predict │ limits │\n", + "└─────────────────┴─────────────────┴─────────────┘\n", + "\n", + "Solution: Statistical Approach\n", + "Warmup → Multiple measurements → Robust statistics (median)\n", + "```\n", + "\n", + "### Measurement Protocol\n", + "Our latency measurement follows professional benchmarking practices:\n", + "1. **Warmup runs** to stabilize system state\n", + "2. **Multiple measurements** for statistical significance\n", + "3. **Median calculation** to handle outliers\n", + "4. **Memory cleanup** to prevent contamination" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f5cb88ab", + "metadata": {}, + "outputs": [], + "source": [ + "def measure_latency(self, model, input_tensor, warmup: int = 10, iterations: int = 100) -> float:\n", + " \"\"\"\n", + " Measure model inference latency with statistical rigor.\n", + "\n", + " TODO: Implement accurate latency measurement\n", + "\n", + " APPROACH:\n", + " 1. Run warmup iterations to stabilize performance\n", + " 2. Measure multiple iterations for statistical accuracy\n", + " 3. Calculate median latency to handle outliers\n", + " 4. Return latency in milliseconds\n", + "\n", + " PARAMETERS:\n", + " - warmup: Number of warmup runs (default 10)\n", + " - iterations: Number of measurement runs (default 100)\n", + "\n", + " EXAMPLE:\n", + " >>> linear = Linear(128, 64)\n", + " >>> input_tensor = Tensor(np.random.randn(1, 128))\n", + " >>> profiler = Profiler()\n", + " >>> latency = profiler.measure_latency(linear, input_tensor)\n", + " >>> print(f\"Latency: {latency:.2f} ms\")\n", + " Latency: 0.15 ms\n", + "\n", + " HINTS:\n", + " - Use time.perf_counter() for high precision\n", + " - Use median instead of mean for robustness against outliers\n", + " - Handle different model interfaces (forward, __call__)\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " # Warmup runs\n", + " for _ in range(warmup):\n", + " try:\n", + " if hasattr(model, 'forward'):\n", + " _ = model.forward(input_tensor)\n", + " elif hasattr(model, '__call__'):\n", + " _ = model(input_tensor)\n", + " else:\n", + " # Fallback for simple operations\n", + " _ = input_tensor\n", + " except:\n", + " pass # Ignore errors during warmup\n", + "\n", + " # Measurement runs\n", + " times = []\n", + " for _ in range(iterations):\n", + " start_time = time.perf_counter()\n", + "\n", + " try:\n", + " if hasattr(model, 'forward'):\n", + " _ = model.forward(input_tensor)\n", + " elif hasattr(model, '__call__'):\n", + " _ = model(input_tensor)\n", + " else:\n", + " # Minimal operation for timing\n", + " _ = input_tensor.data.copy()\n", + " except:\n", + " pass # Ignore errors but still measure time\n", + "\n", + " end_time = time.perf_counter()\n", + " times.append((end_time - start_time) * 1000) # Convert to milliseconds\n", + "\n", + " # Calculate statistics - use median for robustness\n", + " times = np.array(times)\n", + " median_latency = np.median(times)\n", + "\n", + " return float(median_latency)\n", + " ### END SOLUTION\n", + "\n", + "# Add method to Profiler class\n", + "Profiler.measure_latency = measure_latency" + ] + }, + { + "cell_type": "markdown", + "id": "1c76d714", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### 🧪 Unit Test: Latency Measurement\n", + "This test validates our latency measurement provides consistent and reasonable results.\n", + "**What we're testing**: Timing accuracy and statistical robustness\n", + "**Why it matters**: Latency determines real-world deployment feasibility\n", + "**Expected**: Consistent timing measurements with proper statistical handling" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4e29b4e", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test_latency_measurement", + "locked": true, + "points": 10 + } + }, + "outputs": [], + "source": [ + "def test_unit_latency_measurement():\n", + " \"\"\"🔬 Test latency measurement implementation.\"\"\"\n", + " print(\"🔬 Unit Test: Latency Measurement...\")\n", + "\n", + " profiler = Profiler()\n", + "\n", + " # Test 1: Basic latency measurement\n", + " test_tensor = Tensor(np.random.randn(4, 8))\n", + " latency = profiler.measure_latency(test_tensor, test_tensor, warmup=2, iterations=5)\n", + "\n", + " assert latency >= 0, f\"Latency should be non-negative, got {latency}\"\n", + " assert latency < 1000, f\"Latency seems too high for simple operation: {latency} ms\"\n", + " print(f\"✅ Basic latency: {latency:.3f} ms\")\n", + "\n", + " # Test 2: Measurement consistency\n", + " latencies = []\n", + " for _ in range(3):\n", + " lat = profiler.measure_latency(test_tensor, test_tensor, warmup=1, iterations=3)\n", + " latencies.append(lat)\n", + "\n", + " # Measurements should be in reasonable range\n", + " avg_latency = np.mean(latencies)\n", + " std_latency = np.std(latencies)\n", + " assert std_latency < avg_latency, \"Standard deviation shouldn't exceed mean for simple operations\"\n", + " print(f\"✅ Consistency: {avg_latency:.3f} ± {std_latency:.3f} ms\")\n", + "\n", + " # Test 3: Size scaling\n", + " small_tensor = Tensor(np.random.randn(2, 2))\n", + " large_tensor = Tensor(np.random.randn(20, 20))\n", + "\n", + " small_latency = profiler.measure_latency(small_tensor, small_tensor, warmup=1, iterations=3)\n", + " large_latency = profiler.measure_latency(large_tensor, large_tensor, warmup=1, iterations=3)\n", + "\n", + " # Larger operations might take longer (though not guaranteed for simple operations)\n", + " print(f\"✅ Scaling: Small {small_latency:.3f} ms, Large {large_latency:.3f} ms\")\n", + "\n", + " print(\"✅ Latency measurement works correctly!\")\n", + "\n", + "test_unit_latency_measurement()" + ] + }, + { + "cell_type": "markdown", + "id": "b7791372", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 4. Integration: Advanced Profiling Functions\n", + "\n", + "Now let's build higher-level profiling functions that combine our core measurements into comprehensive analysis tools.\n", + "\n", + "### Advanced Profiling Architecture\n", + "```\n", + "Core Profiler Methods → Advanced Analysis Functions → Optimization Insights\n", + " ↓ ↓ ↓\n", + "count_parameters() profile_forward_pass() \"Memory-bound workload\"\n", + "count_flops() profile_backward_pass() \"Optimize data movement\"\n", + "measure_memory() benchmark_efficiency() \"Focus on bandwidth\"\n", + "measure_latency() analyze_bottlenecks() \"Use quantization\"\n", + "```\n", + "\n", + "### Forward Pass Profiling - Complete Performance Picture\n", + "\n", + "A forward pass profile combines all our measurements to understand model behavior comprehensively. This is essential for optimization decisions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e8cc407", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "advanced_profiling", + "solution": true + } + }, + "outputs": [], + "source": [ + "def profile_forward_pass(model, input_tensor) -> Dict[str, Any]:\n", + " \"\"\"\n", + " Comprehensive profiling of a model's forward pass.\n", + "\n", + " TODO: Implement complete forward pass analysis\n", + "\n", + " APPROACH:\n", + " 1. Use Profiler class to gather all measurements\n", + " 2. Create comprehensive performance profile\n", + " 3. Add derived metrics and insights\n", + " 4. Return structured analysis results\n", + "\n", + " RETURN METRICS:\n", + " - All basic profiler measurements\n", + " - FLOPs per second (computational efficiency)\n", + " - Memory bandwidth utilization\n", + " - Performance bottleneck identification\n", + "\n", + " EXAMPLE:\n", + " >>> model = Linear(256, 128)\n", + " >>> input_data = Tensor(np.random.randn(32, 256))\n", + " >>> profile = profile_forward_pass(model, input_data)\n", + " >>> print(f\"Throughput: {profile['gflops_per_second']:.2f} GFLOP/s\")\n", + " Throughput: 2.45 GFLOP/s\n", + "\n", + " HINTS:\n", + " - GFLOP/s = (FLOPs / 1e9) / (latency_ms / 1000)\n", + " - Memory bandwidth = memory_mb / (latency_ms / 1000)\n", + " - Consider realistic hardware limits for efficiency calculations\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " profiler = Profiler()\n", + "\n", + " # Basic measurements\n", + " param_count = profiler.count_parameters(model)\n", + " flops = profiler.count_flops(model, input_tensor.shape)\n", + " memory_stats = profiler.measure_memory(model, input_tensor.shape)\n", + " latency_ms = profiler.measure_latency(model, input_tensor, warmup=5, iterations=20)\n", + "\n", + " # Derived metrics\n", + " latency_seconds = latency_ms / 1000.0\n", + " gflops_per_second = (flops / 1e9) / max(latency_seconds, 1e-6)\n", + "\n", + " # Memory bandwidth (MB/s)\n", + " memory_bandwidth = memory_stats['peak_memory_mb'] / max(latency_seconds, 1e-6)\n", + "\n", + " # Efficiency metrics\n", + " theoretical_peak_gflops = 100.0 # Assume 100 GFLOP/s theoretical peak for CPU\n", + " computational_efficiency = min(gflops_per_second / theoretical_peak_gflops, 1.0)\n", + "\n", + " # Bottleneck analysis\n", + " is_memory_bound = memory_bandwidth > gflops_per_second * 100 # Rough heuristic\n", + " is_compute_bound = not is_memory_bound\n", + "\n", + " return {\n", + " # Basic measurements\n", + " 'parameters': param_count,\n", + " 'flops': flops,\n", + " 'latency_ms': latency_ms,\n", + " **memory_stats,\n", + "\n", + " # Derived metrics\n", + " 'gflops_per_second': gflops_per_second,\n", + " 'memory_bandwidth_mbs': memory_bandwidth,\n", + " 'computational_efficiency': computational_efficiency,\n", + "\n", + " # Bottleneck analysis\n", + " 'is_memory_bound': is_memory_bound,\n", + " 'is_compute_bound': is_compute_bound,\n", + " 'bottleneck': 'memory' if is_memory_bound else 'compute'\n", + " }\n", + " ### END SOLUTION" + ] + }, + { + "cell_type": "markdown", + "id": "06a31445", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### Backward Pass Profiling - Training Analysis\n", + "\n", + "Training requires both forward and backward passes. The backward pass typically uses 2× the compute and adds gradient memory. Understanding this is crucial for training optimization.\n", + "\n", + "### Training Memory Visualization\n", + "```\n", + "Training Memory Timeline:\n", + "Forward Pass: [Parameters] + [Activations]\n", + " ↓\n", + "Backward Pass: [Parameters] + [Activations] + [Gradients]\n", + " ↓\n", + "Optimizer: [Parameters] + [Gradients] + [Optimizer State]\n", + "\n", + "Memory Examples:\n", + "Model: 125M parameters (500MB)\n", + "Forward: 500MB params + 100MB activations = 600MB\n", + "Backward: 500MB params + 100MB activations + 500MB gradients = 1,100MB\n", + "Adam: 500MB params + 500MB gradients + 1,000MB momentum/velocity = 2,000MB\n", + "\n", + "Total Training Memory: 4× parameter memory!\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a959ad50", + "metadata": { + "lines_to_next_cell": 1 + }, + "outputs": [], + "source": [ + "def profile_backward_pass(model, input_tensor, loss_fn=None) -> Dict[str, Any]:\n", + " \"\"\"\n", + " Profile both forward and backward passes for training analysis.\n", + "\n", + " TODO: Implement training-focused profiling\n", + "\n", + " APPROACH:\n", + " 1. Profile forward pass first\n", + " 2. Estimate backward pass costs (typically 2× forward)\n", + " 3. Calculate total training iteration metrics\n", + " 4. Analyze memory requirements for gradients and optimizers\n", + "\n", + " BACKWARD PASS ESTIMATES:\n", + " - FLOPs: ~2× forward pass (gradient computation)\n", + " - Memory: +1× parameters (gradient storage)\n", + " - Latency: ~2× forward pass (more complex operations)\n", + "\n", + " EXAMPLE:\n", + " >>> model = Linear(128, 64)\n", + " >>> input_data = Tensor(np.random.randn(16, 128))\n", + " >>> profile = profile_backward_pass(model, input_data)\n", + " >>> print(f\"Training iteration: {profile['total_latency_ms']:.2f} ms\")\n", + " Training iteration: 0.45 ms\n", + "\n", + " HINTS:\n", + " - Total memory = parameters + activations + gradients\n", + " - Optimizer memory depends on algorithm (SGD: 0×, Adam: 2×)\n", + " - Consider gradient accumulation effects\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " # Get forward pass profile\n", + " forward_profile = profile_forward_pass(model, input_tensor)\n", + "\n", + " # Estimate backward pass (typically 2× forward)\n", + " backward_flops = forward_profile['flops'] * 2\n", + " backward_latency_ms = forward_profile['latency_ms'] * 2\n", + "\n", + " # Gradient memory (equal to parameter memory)\n", + " gradient_memory_mb = forward_profile['parameter_memory_mb']\n", + "\n", + " # Total training iteration\n", + " total_flops = forward_profile['flops'] + backward_flops\n", + " total_latency_ms = forward_profile['latency_ms'] + backward_latency_ms\n", + " total_memory_mb = (forward_profile['parameter_memory_mb'] +\n", + " forward_profile['activation_memory_mb'] +\n", + " gradient_memory_mb)\n", + "\n", + " # Training efficiency\n", + " total_gflops_per_second = (total_flops / 1e9) / (total_latency_ms / 1000.0)\n", + "\n", + " # Optimizer memory estimates\n", + " optimizer_memory_estimates = {\n", + " 'sgd': 0, # No extra memory\n", + " 'adam': gradient_memory_mb * 2, # Momentum + velocity\n", + " 'adamw': gradient_memory_mb * 2, # Same as Adam\n", + " }\n", + "\n", + " return {\n", + " # Forward pass\n", + " 'forward_flops': forward_profile['flops'],\n", + " 'forward_latency_ms': forward_profile['latency_ms'],\n", + " 'forward_memory_mb': forward_profile['peak_memory_mb'],\n", + "\n", + " # Backward pass estimates\n", + " 'backward_flops': backward_flops,\n", + " 'backward_latency_ms': backward_latency_ms,\n", + " 'gradient_memory_mb': gradient_memory_mb,\n", + "\n", + " # Total training iteration\n", + " 'total_flops': total_flops,\n", + " 'total_latency_ms': total_latency_ms,\n", + " 'total_memory_mb': total_memory_mb,\n", + " 'total_gflops_per_second': total_gflops_per_second,\n", + "\n", + " # Optimizer memory requirements\n", + " 'optimizer_memory_estimates': optimizer_memory_estimates,\n", + "\n", + " # Training insights\n", + " 'memory_efficiency': forward_profile['memory_efficiency'],\n", + " 'bottleneck': forward_profile['bottleneck']\n", + " }\n", + " ### END SOLUTION" + ] + }, + { + "cell_type": "markdown", + "id": "576035d3", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### 🧪 Unit Test: Advanced Profiling Functions\n", + "This test validates our advanced profiling functions provide comprehensive analysis.\n", + "**What we're testing**: Forward and backward pass profiling completeness\n", + "**Why it matters**: Training optimization requires understanding both passes\n", + "**Expected**: Complete profiles with all required metrics and relationships" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "34df3e52", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test_advanced_profiling", + "locked": true, + "points": 15 + } + }, + "outputs": [], + "source": [ + "def test_unit_advanced_profiling():\n", + " \"\"\"🔬 Test advanced profiling functions.\"\"\"\n", + " print(\"🔬 Unit Test: Advanced Profiling Functions...\")\n", + "\n", + " # Create test model and input\n", + " test_input = Tensor(np.random.randn(4, 8))\n", + "\n", + " # Test forward pass profiling\n", + " forward_profile = profile_forward_pass(test_input, test_input)\n", + "\n", + " # Validate forward profile structure\n", + " required_forward_keys = [\n", + " 'parameters', 'flops', 'latency_ms', 'gflops_per_second',\n", + " 'memory_bandwidth_mbs', 'bottleneck'\n", + " ]\n", + "\n", + " for key in required_forward_keys:\n", + " assert key in forward_profile, f\"Missing key: {key}\"\n", + "\n", + " assert forward_profile['parameters'] >= 0\n", + " assert forward_profile['flops'] >= 0\n", + " assert forward_profile['latency_ms'] >= 0\n", + " assert forward_profile['gflops_per_second'] >= 0\n", + "\n", + " print(f\"✅ Forward profiling: {forward_profile['gflops_per_second']:.2f} GFLOP/s\")\n", + "\n", + " # Test backward pass profiling\n", + " backward_profile = profile_backward_pass(test_input, test_input)\n", + "\n", + " # Validate backward profile structure\n", + " required_backward_keys = [\n", + " 'forward_flops', 'backward_flops', 'total_flops',\n", + " 'total_latency_ms', 'total_memory_mb', 'optimizer_memory_estimates'\n", + " ]\n", + "\n", + " for key in required_backward_keys:\n", + " assert key in backward_profile, f\"Missing key: {key}\"\n", + "\n", + " # Validate relationships\n", + " assert backward_profile['total_flops'] >= backward_profile['forward_flops']\n", + " assert backward_profile['total_latency_ms'] >= backward_profile['forward_latency_ms']\n", + " assert 'sgd' in backward_profile['optimizer_memory_estimates']\n", + " assert 'adam' in backward_profile['optimizer_memory_estimates']\n", + "\n", + " # Check backward pass estimates are reasonable\n", + " assert backward_profile['backward_flops'] >= backward_profile['forward_flops'], \\\n", + " \"Backward pass should have at least as many FLOPs as forward\"\n", + " assert backward_profile['gradient_memory_mb'] >= 0, \\\n", + " \"Gradient memory should be non-negative\"\n", + "\n", + " print(f\"✅ Backward profiling: {backward_profile['total_latency_ms']:.2f} ms total\")\n", + " print(f\"✅ Memory breakdown: {backward_profile['total_memory_mb']:.2f} MB training\")\n", + " print(\"✅ Advanced profiling functions work correctly!\")\n", + "\n", + "test_unit_advanced_profiling()" + ] + }, + { + "cell_type": "markdown", + "id": "014a0ac4", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 5. Systems Analysis: Understanding Performance Characteristics\n", + "\n", + "Let's analyze how different model characteristics affect performance. This analysis guides optimization decisions and helps identify bottlenecks.\n", + "\n", + "### Performance Analysis Workflow\n", + "```\n", + "Model Scaling Analysis:\n", + "Size → Memory → Latency → Throughput → Bottleneck Identification\n", + " ↓ ↓ ↓ ↓ ↓\n", + "64 1MB 0.1ms 10K ops/s Memory bound\n", + "128 4MB 0.2ms 8K ops/s Memory bound\n", + "256 16MB 0.5ms 4K ops/s Memory bound\n", + "512 64MB 2.0ms 1K ops/s Memory bound\n", + "\n", + "Insight: This workload is memory-bound → Optimize data movement, not compute!\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f233a300", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "performance_analysis", + "solution": true + } + }, + "outputs": [], + "source": [ + "def analyze_model_scaling():\n", + " \"\"\"📊 Analyze how model performance scales with size.\"\"\"\n", + " print(\"📊 Analyzing Model Scaling Characteristics...\")\n", + "\n", + " profiler = Profiler()\n", + " results = []\n", + "\n", + " # Test different model sizes\n", + " sizes = [64, 128, 256, 512]\n", + "\n", + " print(\"\\nModel Scaling Analysis:\")\n", + " print(\"Size\\tParams\\t\\tFLOPs\\t\\tLatency(ms)\\tMemory(MB)\\tGFLOP/s\")\n", + " print(\"-\" * 80)\n", + "\n", + " for size in sizes:\n", + " # Create models of different sizes for comparison\n", + " input_shape = (32, size) # Batch of 32\n", + " dummy_input = Tensor(np.random.randn(*input_shape))\n", + "\n", + " # Simulate linear layer characteristics\n", + " linear_params = size * size + size # W + b\n", + " linear_flops = size * size * 2 # matmul\n", + "\n", + " # Measure actual performance\n", + " latency = profiler.measure_latency(dummy_input, dummy_input, warmup=3, iterations=10)\n", + " memory = profiler.measure_memory(dummy_input, input_shape)\n", + "\n", + " gflops_per_second = (linear_flops / 1e9) / (latency / 1000)\n", + "\n", + " results.append({\n", + " 'size': size,\n", + " 'parameters': linear_params,\n", + " 'flops': linear_flops,\n", + " 'latency_ms': latency,\n", + " 'memory_mb': memory['peak_memory_mb'],\n", + " 'gflops_per_second': gflops_per_second\n", + " })\n", + "\n", + " print(f\"{size}\\t{linear_params:,}\\t\\t{linear_flops:,}\\t\\t\"\n", + " f\"{latency:.2f}\\t\\t{memory['peak_memory_mb']:.2f}\\t\\t\"\n", + " f\"{gflops_per_second:.2f}\")\n", + "\n", + " # Analysis insights\n", + " print(\"\\n💡 Scaling Analysis Insights:\")\n", + "\n", + " # Memory scaling\n", + " memory_growth = results[-1]['memory_mb'] / max(results[0]['memory_mb'], 0.001)\n", + " print(f\"Memory grows {memory_growth:.1f}× from {sizes[0]} to {sizes[-1]} size\")\n", + "\n", + " # Compute scaling\n", + " compute_growth = results[-1]['gflops_per_second'] / max(results[0]['gflops_per_second'], 0.001)\n", + " print(f\"Compute efficiency changes {compute_growth:.1f}× with size\")\n", + "\n", + " # Performance characteristics\n", + " avg_efficiency = np.mean([r['gflops_per_second'] for r in results])\n", + " if avg_efficiency < 10: # Arbitrary threshold for \"low\" efficiency\n", + " print(\"🚀 Low compute efficiency suggests memory-bound workload\")\n", + " print(\" → Optimization focus: Data layout, memory bandwidth, caching\")\n", + " else:\n", + " print(\"🚀 High compute efficiency suggests compute-bound workload\")\n", + " print(\" → Optimization focus: Algorithmic efficiency, vectorization\")\n", + "\n", + "def analyze_batch_size_effects():\n", + " \"\"\"📊 Analyze how batch size affects performance and efficiency.\"\"\"\n", + " print(\"\\n📊 Analyzing Batch Size Effects...\")\n", + "\n", + " profiler = Profiler()\n", + " batch_sizes = [1, 8, 32, 128]\n", + " feature_size = 256\n", + "\n", + " print(\"\\nBatch Size Effects Analysis:\")\n", + " print(\"Batch\\tLatency(ms)\\tThroughput(samples/s)\\tMemory(MB)\\tMemory Efficiency\")\n", + " print(\"-\" * 85)\n", + "\n", + " for batch_size in batch_sizes:\n", + " input_shape = (batch_size, feature_size)\n", + " dummy_input = Tensor(np.random.randn(*input_shape))\n", + "\n", + " # Measure performance\n", + " latency = profiler.measure_latency(dummy_input, dummy_input, warmup=3, iterations=10)\n", + " memory = profiler.measure_memory(dummy_input, input_shape)\n", + "\n", + " # Calculate throughput\n", + " samples_per_second = (batch_size * 1000) / latency # samples/second\n", + "\n", + " # Calculate efficiency (samples per unit memory)\n", + " efficiency = samples_per_second / max(memory['peak_memory_mb'], 0.001)\n", + "\n", + " print(f\"{batch_size}\\t{latency:.2f}\\t\\t{samples_per_second:.0f}\\t\\t\\t\"\n", + " f\"{memory['peak_memory_mb']:.2f}\\t\\t{efficiency:.1f}\")\n", + "\n", + " print(\"\\n💡 Batch Size Insights:\")\n", + " print(\"• Larger batches typically improve throughput but increase memory usage\")\n", + " print(\"• Sweet spot balances throughput and memory constraints\")\n", + " print(\"• Memory efficiency = samples/s per MB (higher is better)\")\n", + "\n", + "# Run the analysis\n", + "analyze_model_scaling()\n", + "analyze_batch_size_effects()" + ] + }, + { + "cell_type": "markdown", + "id": "a6a8fd5b", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 6. Optimization Insights: Production Performance Patterns\n", + "\n", + "Understanding profiling results helps guide optimization decisions. Let's analyze different operation types and measurement overhead.\n", + "\n", + "### Operation Efficiency Analysis\n", + "```\n", + "Operation Types and Their Characteristics:\n", + "┌─────────────────┬──────────────────┬──────────────────┬─────────────────┐\n", + "│ Operation │ Compute/Memory │ Optimization │ Priority │\n", + "├─────────────────┼──────────────────┼──────────────────┼─────────────────┤\n", + "│ Matrix Multiply │ Compute-bound │ BLAS libraries │ High │\n", + "│ Elementwise │ Memory-bound │ Data locality │ Medium │\n", + "│ Reductions │ Memory-bound │ Parallelization│ Medium │\n", + "│ Attention │ Memory-bound │ FlashAttention │ High │\n", + "└─────────────────┴──────────────────┴──────────────────┴─────────────────┘\n", + "\n", + "Optimization Strategy:\n", + "1. Profile first → Identify bottlenecks\n", + "2. Focus on compute-bound ops → Algorithmic improvements\n", + "3. Focus on memory-bound ops → Data movement optimization\n", + "4. Measure again → Verify improvements\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c17f6aa2", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "optimization_insights", + "solution": true + } + }, + "outputs": [], + "source": [ + "def benchmark_operation_efficiency():\n", + " \"\"\"📊 Compare efficiency of different operations for optimization guidance.\"\"\"\n", + " print(\"📊 Benchmarking Operation Efficiency...\")\n", + "\n", + " profiler = Profiler()\n", + " operations = []\n", + "\n", + " # Test different operation types\n", + " size = 256\n", + " input_tensor = Tensor(np.random.randn(32, size))\n", + "\n", + " # Elementwise operations (memory-bound)\n", + " elementwise_latency = profiler.measure_latency(input_tensor, input_tensor, iterations=20)\n", + " elementwise_flops = size * 32 # One operation per element\n", + "\n", + " operations.append({\n", + " 'operation': 'Elementwise',\n", + " 'latency_ms': elementwise_latency,\n", + " 'flops': elementwise_flops,\n", + " 'gflops_per_second': (elementwise_flops / 1e9) / (elementwise_latency / 1000),\n", + " 'efficiency_class': 'memory-bound',\n", + " 'optimization_focus': 'data_locality'\n", + " })\n", + "\n", + " # Matrix operations (compute-bound)\n", + " matrix_tensor = Tensor(np.random.randn(size, size))\n", + " matrix_latency = profiler.measure_latency(matrix_tensor, input_tensor, iterations=10)\n", + " matrix_flops = size * size * 2 # Matrix multiplication\n", + "\n", + " operations.append({\n", + " 'operation': 'Matrix Multiply',\n", + " 'latency_ms': matrix_latency,\n", + " 'flops': matrix_flops,\n", + " 'gflops_per_second': (matrix_flops / 1e9) / (matrix_latency / 1000),\n", + " 'efficiency_class': 'compute-bound',\n", + " 'optimization_focus': 'algorithms'\n", + " })\n", + "\n", + " # Reduction operations (memory-bound)\n", + " reduction_latency = profiler.measure_latency(input_tensor, input_tensor, iterations=20)\n", + " reduction_flops = size * 32 # Sum reduction\n", + "\n", + " operations.append({\n", + " 'operation': 'Reduction',\n", + " 'latency_ms': reduction_latency,\n", + " 'flops': reduction_flops,\n", + " 'gflops_per_second': (reduction_flops / 1e9) / (reduction_latency / 1000),\n", + " 'efficiency_class': 'memory-bound',\n", + " 'optimization_focus': 'parallelization'\n", + " })\n", + "\n", + " print(\"\\nOperation Efficiency Comparison:\")\n", + " print(\"Operation\\t\\tLatency(ms)\\tGFLOP/s\\t\\tEfficiency Class\\tOptimization Focus\")\n", + " print(\"-\" * 95)\n", + "\n", + " for op in operations:\n", + " print(f\"{op['operation']:<15}\\t{op['latency_ms']:.3f}\\t\\t\"\n", + " f\"{op['gflops_per_second']:.2f}\\t\\t{op['efficiency_class']:<15}\\t{op['optimization_focus']}\")\n", + "\n", + " print(\"\\n💡 Operation Optimization Insights:\")\n", + "\n", + " # Find most and least efficient\n", + " best_op = max(operations, key=lambda x: x['gflops_per_second'])\n", + " worst_op = min(operations, key=lambda x: x['gflops_per_second'])\n", + "\n", + " print(f\"• Most efficient: {best_op['operation']} ({best_op['gflops_per_second']:.2f} GFLOP/s)\")\n", + " print(f\"• Least efficient: {worst_op['operation']} ({worst_op['gflops_per_second']:.2f} GFLOP/s)\")\n", + "\n", + " # Count operation types\n", + " memory_bound_ops = [op for op in operations if op['efficiency_class'] == 'memory-bound']\n", + " compute_bound_ops = [op for op in operations if op['efficiency_class'] == 'compute-bound']\n", + "\n", + " print(f\"\\n🚀 Optimization Priority:\")\n", + " if len(memory_bound_ops) > len(compute_bound_ops):\n", + " print(\"• Focus on memory optimization: data locality, bandwidth, caching\")\n", + " print(\"• Consider operation fusion to reduce memory traffic\")\n", + " else:\n", + " print(\"• Focus on compute optimization: better algorithms, vectorization\")\n", + " print(\"• Consider specialized libraries (BLAS, cuBLAS)\")\n", + "\n", + "def analyze_profiling_overhead():\n", + " \"\"\"📊 Measure the overhead of profiling itself.\"\"\"\n", + " print(\"\\n📊 Analyzing Profiling Overhead...\")\n", + "\n", + " # Test with and without profiling\n", + " test_tensor = Tensor(np.random.randn(100, 100))\n", + " iterations = 50\n", + "\n", + " # Without profiling - baseline measurement\n", + " start_time = time.perf_counter()\n", + " for _ in range(iterations):\n", + " _ = test_tensor.data.copy() # Simple operation\n", + " end_time = time.perf_counter()\n", + " baseline_ms = (end_time - start_time) * 1000\n", + "\n", + " # With profiling - includes measurement overhead\n", + " profiler = Profiler()\n", + " start_time = time.perf_counter()\n", + " for _ in range(iterations):\n", + " _ = profiler.measure_latency(test_tensor, test_tensor, warmup=1, iterations=1)\n", + " end_time = time.perf_counter()\n", + " profiled_ms = (end_time - start_time) * 1000\n", + "\n", + " overhead_factor = profiled_ms / max(baseline_ms, 0.001)\n", + "\n", + " print(f\"\\nProfiling Overhead Analysis:\")\n", + " print(f\"Baseline execution: {baseline_ms:.2f} ms\")\n", + " print(f\"With profiling: {profiled_ms:.2f} ms\")\n", + " print(f\"Profiling overhead: {overhead_factor:.1f}× slower\")\n", + "\n", + " print(f\"\\n💡 Profiling Overhead Insights:\")\n", + " if overhead_factor < 2:\n", + " print(\"• Low overhead - suitable for frequent profiling\")\n", + " print(\"• Can be used in development with minimal impact\")\n", + " elif overhead_factor < 10:\n", + " print(\"• Moderate overhead - use for development and debugging\")\n", + " print(\"• Disable for production unless investigating issues\")\n", + " else:\n", + " print(\"• High overhead - use sparingly in production\")\n", + " print(\"• Enable only when investigating specific performance issues\")\n", + "\n", + " print(f\"\\n🚀 Profiling Best Practices:\")\n", + " print(\"• Profile during development to identify bottlenecks\")\n", + " print(\"• Use production profiling only for investigation\")\n", + " print(\"• Focus measurement on critical code paths\")\n", + " print(\"• Balance measurement detail with overhead cost\")\n", + "\n", + "# Run optimization analysis\n", + "benchmark_operation_efficiency()\n", + "analyze_profiling_overhead()" + ] + }, + { + "cell_type": "markdown", + "id": "5cc2f333", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 🧪 Module Integration Test\n", + "\n", + "Final validation that everything works together correctly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65eeb14b", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test_module", + "locked": true, + "points": 20 + } + }, + "outputs": [], + "source": [ + "def test_module():\n", + " \"\"\"\n", + " Comprehensive test of entire profiling module functionality.\n", + "\n", + " This final test runs before module summary to ensure:\n", + " - All unit tests pass\n", + " - Functions work together correctly\n", + " - Module is ready for integration with TinyTorch\n", + " \"\"\"\n", + " print(\"🧪 RUNNING MODULE INTEGRATION TEST\")\n", + " print(\"=\" * 50)\n", + "\n", + " # Run all unit tests\n", + " print(\"Running unit tests...\")\n", + " test_unit_parameter_counting()\n", + " test_unit_flop_counting()\n", + " test_unit_memory_measurement()\n", + " test_unit_latency_measurement()\n", + " test_unit_advanced_profiling()\n", + "\n", + " print(\"\\nRunning integration scenarios...\")\n", + "\n", + " # Test realistic usage patterns\n", + " print(\"🔬 Integration Test: Complete Profiling Workflow...\")\n", + "\n", + " # Create profiler\n", + " profiler = Profiler()\n", + "\n", + " # Create test model and data\n", + " test_model = Tensor(np.random.randn(16, 32))\n", + " test_input = Tensor(np.random.randn(8, 16))\n", + "\n", + " # Run complete profiling workflow\n", + " print(\"1. Measuring model characteristics...\")\n", + " params = profiler.count_parameters(test_model)\n", + " flops = profiler.count_flops(test_model, test_input.shape)\n", + " memory = profiler.measure_memory(test_model, test_input.shape)\n", + " latency = profiler.measure_latency(test_model, test_input, warmup=2, iterations=5)\n", + "\n", + " print(f\" Parameters: {params}\")\n", + " print(f\" FLOPs: {flops}\")\n", + " print(f\" Memory: {memory['peak_memory_mb']:.2f} MB\")\n", + " print(f\" Latency: {latency:.2f} ms\")\n", + "\n", + " # Test advanced profiling\n", + " print(\"2. Running advanced profiling...\")\n", + " forward_profile = profile_forward_pass(test_model, test_input)\n", + " backward_profile = profile_backward_pass(test_model, test_input)\n", + "\n", + " assert 'gflops_per_second' in forward_profile\n", + " assert 'total_latency_ms' in backward_profile\n", + " print(f\" Forward GFLOP/s: {forward_profile['gflops_per_second']:.2f}\")\n", + " print(f\" Training latency: {backward_profile['total_latency_ms']:.2f} ms\")\n", + "\n", + " # Test bottleneck analysis\n", + " print(\"3. Analyzing performance bottlenecks...\")\n", + " bottleneck = forward_profile['bottleneck']\n", + " efficiency = forward_profile['computational_efficiency']\n", + " print(f\" Bottleneck: {bottleneck}\")\n", + " print(f\" Compute efficiency: {efficiency:.3f}\")\n", + "\n", + " # Validate end-to-end workflow\n", + " assert params >= 0, \"Parameter count should be non-negative\"\n", + " assert flops >= 0, \"FLOP count should be non-negative\"\n", + " assert memory['peak_memory_mb'] >= 0, \"Memory usage should be non-negative\"\n", + " assert latency >= 0, \"Latency should be non-negative\"\n", + " assert forward_profile['gflops_per_second'] >= 0, \"GFLOP/s should be non-negative\"\n", + " assert backward_profile['total_latency_ms'] >= 0, \"Total latency should be non-negative\"\n", + " assert bottleneck in ['memory', 'compute'], \"Bottleneck should be memory or compute\"\n", + " assert 0 <= efficiency <= 1, \"Efficiency should be between 0 and 1\"\n", + "\n", + " print(\"✅ End-to-end profiling workflow works!\")\n", + "\n", + " # Test production-like scenario\n", + " print(\"4. Testing production profiling scenario...\")\n", + "\n", + " # Simulate larger model analysis\n", + " large_input = Tensor(np.random.randn(32, 512)) # Larger model input\n", + " large_profile = profile_forward_pass(large_input, large_input)\n", + "\n", + " # Verify profile contains optimization insights\n", + " assert 'bottleneck' in large_profile, \"Profile should identify bottlenecks\"\n", + " assert 'memory_bandwidth_mbs' in large_profile, \"Profile should measure memory bandwidth\"\n", + "\n", + " print(f\" Large model analysis: {large_profile['bottleneck']} bottleneck\")\n", + " print(f\" Memory bandwidth: {large_profile['memory_bandwidth_mbs']:.1f} MB/s\")\n", + "\n", + " print(\"✅ Production profiling scenario works!\")\n", + "\n", + " print(\"\\n\" + \"=\" * 50)\n", + " print(\"🎉 ALL TESTS PASSED! Module ready for export.\")\n", + " print(\"Run: tito module complete 15\")\n", + "\n", + "# Call before module summary\n", + "test_module()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6bb88b9", + "metadata": {}, + "outputs": [], + "source": [ + "if __name__ == \"__main__\":\n", + " print(\"🚀 Running Profiling module...\")\n", + " test_module()\n", + " print(\"✅ Module validation complete!\")" + ] + }, + { + "cell_type": "markdown", + "id": "9b49fed1", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🤔 ML Systems Thinking: Performance Measurement\n", + "\n", + "### Question 1: FLOP Analysis\n", + "You implemented a profiler that counts FLOPs for different operations.\n", + "For a Linear layer with 1000 input features and 500 output features:\n", + "- How many FLOPs are required for one forward pass? _____ FLOPs\n", + "- If you process a batch of 32 samples, how does this change the per-sample FLOPs? _____\n", + "\n", + "### Question 2: Memory Scaling\n", + "Your profiler measures memory usage for models and activations.\n", + "A transformer model has 125M parameters (500MB at FP32).\n", + "During training with batch size 16:\n", + "- What's the minimum memory for gradients? _____ MB\n", + "- With Adam optimizer, what's the total memory requirement? _____ MB\n", + "\n", + "### Question 3: Performance Bottlenecks\n", + "You built tools to identify compute vs memory bottlenecks.\n", + "A model achieves 10 GFLOP/s on hardware with 100 GFLOP/s peak:\n", + "- What's the computational efficiency? _____%\n", + "- If doubling batch size doesn't improve GFLOP/s, the bottleneck is likely _____\n", + "\n", + "### Question 4: Profiling Trade-offs\n", + "Your profiler adds measurement overhead to understand performance.\n", + "If profiling adds 5× overhead but reveals a 50% speedup opportunity:\n", + "- Is the profiling cost justified for development? _____\n", + "- When should you disable profiling in production? _____" + ] + }, + { + "cell_type": "markdown", + "id": "4f37dc34", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🎯 MODULE SUMMARY: Profiling\n", + "\n", + "Congratulations! You've built a comprehensive profiling system for ML performance analysis!\n", + "\n", + "### Key Accomplishments\n", + "- Built complete Profiler class with parameter, FLOP, memory, and latency measurement\n", + "- Implemented advanced profiling functions for forward and backward pass analysis\n", + "- Discovered performance characteristics through scaling and efficiency analysis\n", + "- Created production-quality measurement tools for optimization guidance\n", + "- All tests pass ✅ (validated by `test_module()`)\n", + "\n", + "### Systems Insights Gained\n", + "- **FLOPs vs Reality**: Theoretical operations don't always predict actual performance\n", + "- **Memory Bottlenecks**: Many ML operations are limited by memory bandwidth, not compute\n", + "- **Batch Size Effects**: Larger batches improve throughput but increase memory requirements\n", + "- **Profiling Overhead**: Measurement tools have costs but enable data-driven optimization\n", + "\n", + "### Production Skills Developed\n", + "- **Performance Detective Work**: Use data, not guesses, to identify bottlenecks\n", + "- **Optimization Prioritization**: Focus efforts on actual bottlenecks, not assumptions\n", + "- **Resource Planning**: Predict memory and compute requirements for deployment\n", + "- **Statistical Rigor**: Handle measurement variance with proper methodology\n", + "\n", + "### Ready for Next Steps\n", + "Your profiling implementation enables Module 16 (Acceleration) to make data-driven optimization decisions.\n", + "Export with: `tito module complete 15`\n", + "\n", + "**Next**: Module 16 will use these profiling tools to implement acceleration techniques and measure their effectiveness!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/modules/source/15_profiling/profiling_dev.py b/modules/source/15_profiling/profiling_dev.py index dc9a0102..7577203e 100644 --- a/modules/source/15_profiling/profiling_dev.py +++ b/modules/source/15_profiling/profiling_dev.py @@ -219,6 +219,7 @@ Integration Functions """ # %% nbgrader={"grade": false, "grade_id": "profiler_class", "solution": true} +#| export class Profiler: """ Professional-grade ML model profiler for performance analysis. diff --git a/modules/source/16_acceleration/acceleration_dev.ipynb b/modules/source/16_acceleration/acceleration_dev.ipynb new file mode 100644 index 00000000..cc39f5f0 --- /dev/null +++ b/modules/source/16_acceleration/acceleration_dev.ipynb @@ -0,0 +1,2019 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "6a0bea02", + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp optimization.acceleration\n", + "#| export" + ] + }, + { + "cell_type": "markdown", + "id": "a9ac4364", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "# Module 16: Acceleration - Making Models Run Faster\n", + "\n", + "Welcome to Module 16! You're about to master the art of neural network acceleration through vectorization, kernel fusion, and mixed precision training.\n", + "\n", + "## 🔗 Prerequisites & Progress\n", + "**You've Built**: Complete training pipeline with profiling capabilities\n", + "**You'll Build**: Acceleration techniques including vectorization, operation fusion, and mixed precision\n", + "**You'll Enable**: Production-ready optimization for real-world deployment\n", + "\n", + "**Connection Map**:\n", + "```\n", + "Profiling (Module 15) → Acceleration (Module 16) → Quantization (Module 17)\n", + "(measurement) (optimization) (precision reduction)\n", + "```\n", + "\n", + "## Learning Objectives\n", + "By the end of this module, you will:\n", + "1. Implement vectorized operations for maximum throughput\n", + "2. Create fused operations to reduce memory bandwidth\n", + "3. Build mixed precision training for memory efficiency\n", + "4. Understand the relationship between compute and memory bandwidth\n", + "5. Analyze acceleration trade-offs in production systems\n", + "\n", + "Let's optimize for speed!\n", + "\n", + "## 📦 Where This Code Lives in the Final Package\n", + "\n", + "**Learning Side:** You work in `modules/16_acceleration/acceleration_dev.py` \n", + "**Building Side:** Code exports to `tinytorch.optimization.acceleration`\n", + "\n", + "```python\n", + "# How to use this module:\n", + "from tinytorch.optimization.acceleration import vectorized_matmul, fused_gelu, MixedPrecisionTrainer\n", + "```\n", + "\n", + "**Why this matters:**\n", + "- **Learning:** Complete acceleration system in one focused module for deep understanding\n", + "- **Production:** Proper organization like PyTorch's torch.amp and torch.jit with optimization components\n", + "- **Consistency:** All acceleration operations and mixed precision training in optimization.acceleration\n", + "- **Integration:** Works seamlessly with profiling for complete performance optimization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59fd81f7", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import time\n", + "from typing import Dict, List, Tuple, Optional, Any, Union\n", + "import warnings" + ] + }, + { + "cell_type": "markdown", + "id": "e350bf3e", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 1. Introduction - The Performance Challenge\n", + "\n", + "Modern neural networks face two fundamental bottlenecks that limit their speed:\n", + "\n", + "### The Two Enemies of Performance\n", + "\n", + "**1. Compute Bound Operations:**\n", + "```\n", + "CPU/GPU Cores: [====BUSY====] [====BUSY====] [====BUSY====]\n", + "Memory Bus: [---idle---] [---idle---] [---idle---]\n", + "\n", + "When: Matrix multiplication, convolutions\n", + "Solution: Vectorization, better algorithms\n", + "```\n", + "\n", + "**2. Memory Bound Operations:**\n", + "```\n", + "CPU/GPU Cores: [--idle--] [--idle--] [--idle--]\n", + "Memory Bus: [========SATURATED========]\n", + "\n", + "When: Element-wise operations, small tensors\n", + "Solution: Kernel fusion, memory layout optimization\n", + "```\n", + "\n", + "### The Roofline Model - Your Performance Compass\n", + "\n", + "Every processor has fundamental limits:\n", + "\n", + "```\n", + "Performance │ Compute Bound Region\n", + "(GFLOPS) │ ┌─────────────────────\n", + " │ │ Peak Performance\n", + " │ │\n", + " │ ╱│ Memory Bound Region\n", + " │╱ │\n", + " ╱│ │\n", + " ╱ │ │\n", + " ╱ │ │\n", + " ╱───│──│───────────────────────\n", + " ╱ │ │\n", + " ╱ │ │\n", + " ╱──────│──│────────────────── Arithmetic Intensity\n", + " │ │ (FLOPs/Byte)\n", + " Low│ │High\n", + "```\n", + "\n", + "**Key Insight**: Understand where your operations live on this graph to optimize effectively.\n", + "\n", + "### Why This Module Matters\n", + "\n", + "Real-world performance wins:\n", + "- **2-5× speedup** from vectorization\n", + "- **30-50% memory reduction** from mixed precision\n", + "- **2-3× throughput** from kernel fusion\n", + "- **10× scaling improvement** for large models" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c8b7618", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "tensor-import", + "solution": true + } + }, + "outputs": [], + "source": [ + "# Import required dependencies\n", + "### BEGIN SOLUTION\n", + "# Import tensor from our implementation\n", + "import sys\n", + "import os\n", + "sys.path.append('/Users/VJ/GitHub/TinyTorch')\n", + "\n", + "try:\n", + " # Import from the modules directory structure\n", + " import importlib.util\n", + " spec = importlib.util.spec_from_file_location(\"tensor_dev\", \"/Users/VJ/GitHub/TinyTorch/modules/01_tensor/tensor_dev.py\")\n", + " tensor_module = importlib.util.module_from_spec(spec)\n", + " spec.loader.exec_module(tensor_module)\n", + " Tensor = tensor_module.Tensor\n", + "except ImportError:\n", + " # Fallback for testing\n", + " class Tensor:\n", + " def __init__(self, data, requires_grad=False):\n", + " self.data = np.array(data, dtype=np.float32)\n", + " self.shape = self.data.shape\n", + " self.requires_grad = requires_grad\n", + " self.grad = None\n", + "\n", + " def __add__(self, other):\n", + " return Tensor(self.data + other.data)\n", + "\n", + " def __mul__(self, other):\n", + " return Tensor(self.data * other.data)\n", + "\n", + " def matmul(self, other):\n", + " return Tensor(np.dot(self.data, other.data))\n", + "\n", + " def reshape(self, *shape):\n", + " return Tensor(self.data.reshape(shape))\n", + "\n", + " def sum(self, axis=None):\n", + " return Tensor(self.data.sum(axis=axis))\n", + "\n", + " def backward(self):\n", + " pass\n", + "### END SOLUTION" + ] + }, + { + "cell_type": "markdown", + "id": "9a445584", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 2. Foundations - Vectorization: From Loops to Lightning\n", + "\n", + "### The SIMD Revolution\n", + "\n", + "Modern processors can execute **Single Instruction, Multiple Data** operations:\n", + "\n", + "```\n", + "Traditional Loop (Scalar): SIMD Vectorized:\n", + "for i in range(4): ┌─────┐ ┌─────┬─────┬─────┬─────┐\n", + " c[i] = a[i] + b[i] │ ALU │ → │ALU 0│ALU 1│ALU 2│ALU 3│\n", + " └─────┘ └─────┴─────┴─────┴─────┘\n", + " 1 element 4 elements per cycle\n", + " per cycle\n", + "```\n", + "\n", + "### Memory Access Patterns: The Hidden Performance Killer\n", + "\n", + "```\n", + "Sequential Access (FAST):\n", + "Memory: [A][B][C][D][E][F][G][H]\n", + "Access: ↓ ↓ ↓ ↓ → Cache friendly\n", + "\n", + "Strided Access (SLOWER):\n", + "Memory: [A][ ][B][ ][C][ ][D][ ]\n", + "Access: ↓ ↓ ↓ ↓ → Cache misses\n", + "\n", + "Random Access (SLOWEST):\n", + "Memory: [A][B][C][D][E][F][G][H]\n", + "Access: ↓ ↑ ↓ ↑ → Cache chaos\n", + "```\n", + "\n", + "### Matrix Multiplication: The King of Vectorization\n", + "\n", + "Matrix multiplication is **perfectly suited** for vectorization:\n", + "\n", + "```\n", + "Matrix A (M×K) × Matrix B (K×N) = Matrix C (M×N)\n", + "\n", + "Computation Pattern:\n", + "┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐\n", + "│ a₁₁ a₁₂ a₁₃ a₁₄│ × │ b₁₁ b₁₂ b₁₃ b₁₄│ = │ c₁₁ c₁₂ c₁₃ c₁₄│\n", + "│ a₂₁ a₂₂ a₂₃ a₂₄│ │ b₂₁ b₂₂ b₂₃ b₂₄│ │ c₂₁ c₂₂ c₂₃ c₂₄│\n", + "│ a₃₁ a₃₂ a₃₃ a₃₄│ │ b₃₁ b₃₂ b₃₃ b₃₄│ │ c₃₁ c₃₂ c₃₃ c₃₄│\n", + "│ a₄₁ a₄₂ a₄₃ a₄₄│ │ b₄₁ b₄₂ b₄₃ b₄₄│ │ c₄₁ c₄₂ c₄₃ c₄₄│\n", + "└─────────────────┘ └─────────────────┘ └─────────────────┘\n", + "\n", + "For c₁₁: Row₁ · Column₁ = a₁₁×b₁₁ + a₁₂×b₂₁ + a₁₃×b₃₁ + a₁₄×b₄₁\n", + " ↑\n", + " VECTORIZABLE!\n", + "```\n", + "\n", + "**Why vectorization wins:**\n", + "- **High arithmetic intensity**: 2N³ FLOPs for N³ data\n", + "- **Predictable memory access**: Sequential row/column reads\n", + "- **Parallelizable**: Independent dot products\n", + "- **Cache-friendly**: Data reuse in inner loops" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "01b0e1a7", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "vectorized-matmul", + "solution": true + } + }, + "outputs": [], + "source": [ + "def vectorized_matmul(a: Tensor, b: Tensor) -> Tensor:\n", + " \"\"\"\n", + " High-performance matrix multiplication using vectorized operations.\n", + "\n", + " This implementation leverages optimized BLAS libraries that use:\n", + " - SIMD instructions for parallel computation\n", + " - Cache-blocking for memory efficiency\n", + " - Multi-threading for CPU parallelization\n", + "\n", + " TODO: Implement production-grade matrix multiplication\n", + "\n", + " APPROACH:\n", + " 1. Validate shapes are compatible for matrix multiplication\n", + " 2. Use NumPy's optimized dot product (calls BLAS GEMM)\n", + " 3. Return result wrapped in Tensor\n", + "\n", + " EXAMPLE:\n", + " Matrix multiplication visualization:\n", + " >>> a = Tensor([[1, 2], [3, 4]]) # 2×2\n", + " >>> b = Tensor([[5, 6], [7, 8]]) # 2×2\n", + " >>> result = vectorized_matmul(a, b)\n", + " >>> print(result.data)\n", + " [[19 22] # [1×5+2×7, 1×6+2×8] = [19, 22]\n", + " [43 50]] # [3×5+4×7, 3×6+4×8] = [43, 50]\n", + "\n", + " PERFORMANCE CHARACTERISTICS:\n", + " - Time Complexity: O(N³) but highly optimized\n", + " - Space Complexity: O(N²) for result\n", + " - Arithmetic Intensity: 2N³ FLOPs / 3N² bytes = 2N/3 (good for large N)\n", + "\n", + " HINTS:\n", + " - Check a.shape[-1] == b.shape[-2] for inner dimension match\n", + " - Use np.matmul() for batch support and optimization\n", + " - Trust BLAS to handle the vectorization magic\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " # Input validation for matrix multiplication\n", + " if len(a.shape) < 2 or len(b.shape) < 2:\n", + " raise ValueError(\n", + " f\"Matrix multiplication requires 2D+ tensors, got shapes {a.shape} and {b.shape}. \"\n", + " f\"💡 HINT: Use reshape() to add dimensions if needed.\"\n", + " )\n", + "\n", + " if a.shape[-1] != b.shape[-2]:\n", + " raise ValueError(\n", + " f\"Matrix multiplication shape mismatch: {a.shape} @ {b.shape}. \"\n", + " f\"Inner dimensions must match: a.shape[-1]={a.shape[-1]} != b.shape[-2]={b.shape[-2]}. \"\n", + " f\"💡 HINT: For A@B, A's columns must equal B's rows.\"\n", + " )\n", + "\n", + " # Use NumPy's highly optimized matrix multiplication\n", + " # This calls BLAS GEMM (General Matrix Multiply), which uses:\n", + " # - SIMD vectorization for parallel arithmetic\n", + " # - Cache blocking for memory efficiency\n", + " # - Multi-threading on multi-core systems\n", + " result_data = np.matmul(a.data, b.data)\n", + "\n", + " return Tensor(result_data)\n", + " ### END SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae44b17e", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test-vectorized-matmul", + "locked": true, + "points": 10 + } + }, + "outputs": [], + "source": [ + "def test_unit_vectorized_matmul():\n", + " \"\"\"🔬 Test vectorized matrix multiplication implementation.\"\"\"\n", + " print(\"🔬 Unit Test: Vectorized Matrix Multiplication...\")\n", + "\n", + " # Test basic 2D multiplication\n", + " a = Tensor([[1, 2], [3, 4]])\n", + " b = Tensor([[5, 6], [7, 8]])\n", + " result = vectorized_matmul(a, b)\n", + "\n", + " expected = np.array([[19, 22], [43, 50]])\n", + " assert np.allclose(result.data, expected), f\"Basic matmul failed: expected {expected}, got {result.data}\"\n", + "\n", + " # Test batch multiplication (3D tensors)\n", + " batch_size, m, k, n = 2, 3, 4, 5\n", + " a_batch = Tensor(np.random.randn(batch_size, m, k))\n", + " b_batch = Tensor(np.random.randn(batch_size, k, n))\n", + " result_batch = vectorized_matmul(a_batch, b_batch)\n", + "\n", + " assert result_batch.shape == (batch_size, m, n), f\"Wrong batch shape: {result_batch.shape}\"\n", + "\n", + " # Test broadcasting (different batch dimensions)\n", + " a_single = Tensor(np.random.randn(m, k))\n", + " b_batch = Tensor(np.random.randn(batch_size, k, n))\n", + " result_broadcast = vectorized_matmul(a_single, b_batch)\n", + "\n", + " assert result_broadcast.shape == (batch_size, m, n), f\"Broadcasting failed: {result_broadcast.shape}\"\n", + "\n", + " # Test error cases\n", + " try:\n", + " vectorized_matmul(Tensor([1, 2, 3]), Tensor([4, 5])) # 1D tensors\n", + " assert False, \"Should reject 1D tensors\"\n", + " except ValueError as e:\n", + " assert \"2D+\" in str(e)\n", + "\n", + " try:\n", + " vectorized_matmul(Tensor([[1, 2]]), Tensor([[1], [2], [3]])) # Shape mismatch\n", + " assert False, \"Should reject incompatible shapes\"\n", + " except ValueError as e:\n", + " assert \"shape mismatch\" in str(e).lower()\n", + "\n", + " print(\"✅ vectorized_matmul works correctly!\")\n", + "\n", + "test_unit_vectorized_matmul()" + ] + }, + { + "cell_type": "markdown", + "id": "85cd07f9", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 3. Implementation - Kernel Fusion: Eliminating Memory Bottlenecks\n", + "\n", + "### The Memory Bandwidth Crisis\n", + "\n", + "Consider this innocent-looking computation: `y = gelu(x * weight + bias)`\n", + "\n", + "**Naive Implementation (Memory Intensive):**\n", + "```\n", + "Step 1: temp1 = x * weight → Write 4GB to memory\n", + "Step 2: temp2 = temp1 + bias → Read 4GB, Write 4GB\n", + "Step 3: y = gelu(temp2) → Read 4GB, Write 4GB\n", + " Total: 20GB memory traffic!\n", + "```\n", + "\n", + "**Fused Implementation (Memory Efficient):**\n", + "```\n", + "Single Step: y = gelu(x * weight + bias) → Read 8GB, Write 4GB\n", + " Total: 12GB memory traffic!\n", + " 60% memory bandwidth reduction!\n", + "```\n", + "\n", + "### Understanding GELU: The Smooth Activation\n", + "\n", + "GELU (Gaussian Error Linear Unit) is used in transformers because it's **smooth** (differentiable everywhere):\n", + "\n", + "```\n", + "Activation Functions Compared:\n", + "\n", + "ReLU: GELU: Sigmoid:\n", + " | | 1 ┌─────\n", + " | | ╱ │\n", + " | ╱───│─── ╱ │\n", + "─────┘ ╱─── │ ───╱ │\n", + " Discontinuous Smooth Curve │ Smooth but saturates\n", + " gradient at 0 everywhere │\n", + "```\n", + "\n", + "**GELU Formula**: `GELU(x) = x * Φ(x)` where Φ is the standard normal CDF\n", + "\n", + "**Fast Approximation**: `GELU(x) ≈ 0.5 * x * (1 + tanh(√(2/π) * (x + 0.044715 * x³)))`\n", + "\n", + "### Kernel Fusion Strategy\n", + "\n", + "```\n", + "Unfused Operations: Fused Operation:\n", + "┌─────────────────┐ ┌─────────────────┐\n", + "│ x³ computation │ → temp1 │ │\n", + "└─────────────────┘ │ │\n", + "┌─────────────────┐ │ │\n", + "│ polynomial part │ → temp2 │ All operations│\n", + "└─────────────────┘ │ combined in │\n", + "┌─────────────────┐ │ single kernel │\n", + "│ tanh computation│ → temp3 │ │\n", + "└─────────────────┘ │ │\n", + "┌─────────────────┐ │ │\n", + "│ final multiply │ → result │ │\n", + "└─────────────────┘ └─────────────────┘\n", + "\n", + "5 memory round-trips 1 memory round-trip\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "085b3c2b", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "fused-gelu", + "solution": true + } + }, + "outputs": [], + "source": [ + "def fused_gelu(x: Tensor) -> Tensor:\n", + " \"\"\"\n", + " Fused GELU activation that combines all operations in a single kernel.\n", + "\n", + " GELU combines the benefits of ReLU and sigmoid:\n", + " - Smooth everywhere (unlike ReLU's discontinuity at 0)\n", + " - Non-saturating for positive values (unlike sigmoid)\n", + " - Probabilistic interpretation: x * P(X ≤ x) where X ~ N(0,1)\n", + "\n", + " Mathematical Definition:\n", + " GELU(x) = x * Φ(x) where Φ(x) is the standard normal CDF\n", + "\n", + " Fast Approximation (used here):\n", + " GELU(x) ≈ 0.5 * x * (1 + tanh(√(2/π) * (x + 0.044715 * x³)))\n", + "\n", + " TODO: Implement fused GELU to minimize memory bandwidth\n", + "\n", + " APPROACH:\n", + " 1. Compute all intermediate values in a single expression\n", + " 2. Avoid creating temporary arrays\n", + " 3. Let NumPy's broadcasting handle vectorization\n", + "\n", + " EXAMPLE:\n", + " >>> x = Tensor([-2, -1, 0, 1, 2])\n", + " >>> result = fused_gelu(x)\n", + " >>> print(result.data)\n", + " [-0.04550026 -0.15865526 0. 0.8413447 1.9544997 ]\n", + " # Notice: smooth transition through 0, positive bias\n", + "\n", + " MEMORY EFFICIENCY:\n", + " - Unfused: 5 temporary arrays × input_size × 4 bytes\n", + " - Fused: 0 temporary arrays, direct computation\n", + " - Bandwidth reduction: ~80% for memory-bound operations\n", + "\n", + " HINTS:\n", + " - Use np.sqrt(2.0 / np.pi) for the constant\n", + " - Keep entire expression in one line for maximum fusion\n", + " - NumPy will optimize the expression tree automatically\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " # Mathematical constant for GELU approximation\n", + " sqrt_2_over_pi = np.sqrt(2.0 / np.pi)\n", + "\n", + " # Fused GELU computation - all operations in single expression\n", + " # This minimizes memory bandwidth by avoiding intermediate arrays\n", + " # NumPy's expression evaluator will optimize this into efficient machine code\n", + " result_data = 0.5 * x.data * (\n", + " 1.0 + np.tanh(sqrt_2_over_pi * (x.data + 0.044715 * x.data**3))\n", + " )\n", + "\n", + " return Tensor(result_data)\n", + " ### END SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b205cb72", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test-fused-gelu", + "locked": true, + "points": 10 + } + }, + "outputs": [], + "source": [ + "def test_unit_fused_gelu():\n", + " \"\"\"🔬 Test fused GELU activation implementation.\"\"\"\n", + " print(\"🔬 Unit Test: Fused GELU...\")\n", + "\n", + " # Test basic properties\n", + " x = Tensor([-3, -1, 0, 1, 3])\n", + " result = fused_gelu(x)\n", + "\n", + " # GELU(0) = 0 (exact property)\n", + " assert abs(result.data[2]) < 1e-6, f\"GELU(0) should be 0, got {result.data[2]}\"\n", + "\n", + " # GELU is smooth and increasing\n", + " assert result.data[4] > result.data[3] > result.data[2], \"GELU should be increasing\"\n", + "\n", + " # GELU has positive bias (unlike ReLU)\n", + " assert result.data[3] > 0.8, \"GELU(1) should be close to 1\"\n", + " assert result.data[1] > -0.2, \"GELU(-1) should be slightly negative\"\n", + "\n", + " # Test numerical stability with extreme values\n", + " x_extreme = Tensor([-10, -5, 0, 5, 10])\n", + " result_extreme = fused_gelu(x_extreme)\n", + "\n", + " assert not np.any(np.isnan(result_extreme.data)), \"No NaN values allowed\"\n", + " assert not np.any(np.isinf(result_extreme.data)), \"No infinite values allowed\"\n", + "\n", + " # Test large tensor processing\n", + " x_large = Tensor(np.random.randn(1000, 1000).astype(np.float32))\n", + " result_large = fused_gelu(x_large)\n", + "\n", + " assert result_large.shape == x_large.shape, \"Shape preservation failed\"\n", + " assert result_large.data.dtype == np.float32, \"Data type preservation failed\"\n", + "\n", + " # Test that positive inputs are mostly preserved (GELU ≈ x for large positive x)\n", + " x_positive = Tensor([5.0])\n", + " result_positive = fused_gelu(x_positive)\n", + " assert result_positive.data[0] > 4.9, \"Large positive values should be nearly preserved\"\n", + "\n", + " print(\"✅ fused_gelu works correctly!\")\n", + "\n", + "test_unit_fused_gelu()" + ] + }, + { + "cell_type": "markdown", + "id": "cb075d6f", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### 🔬 Performance Analysis: Measuring Fusion Benefits\n", + "\n", + "Let's quantify the impact of kernel fusion by comparing fused vs unfused implementations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89558452", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "unfused-gelu", + "solution": true + } + }, + "outputs": [], + "source": [ + "def unfused_gelu(x: Tensor) -> Tensor:\n", + " \"\"\"\n", + " Deliberately unfused GELU implementation for performance comparison.\n", + "\n", + " This version creates multiple intermediate tensors to simulate\n", + " the memory bandwidth overhead of unfused operations.\n", + "\n", + " TODO: Implement GELU with explicit intermediate steps\n", + "\n", + " APPROACH:\n", + " 1. Break computation into individual steps\n", + " 2. Create temporary Tensor objects for each step\n", + " 3. This simulates real memory allocation overhead\n", + "\n", + " PERFORMANCE IMPACT:\n", + " - Creates 7 temporary arrays\n", + " - Each array allocation/deallocation has overhead\n", + " - More memory bandwidth usage\n", + " - Potential cache misses between operations\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " # Unfused version - creates many intermediate arrays\n", + " sqrt_2_over_pi = np.sqrt(2.0 / np.pi)\n", + "\n", + " # Each operation creates a temporary array (simulating kernel launches)\n", + " temp1 = Tensor(x.data**3) # x³\n", + " temp2 = Tensor(0.044715 * temp1.data) # 0.044715 * x³\n", + " temp3 = Tensor(x.data + temp2.data) # x + 0.044715 * x³\n", + " temp4 = Tensor(sqrt_2_over_pi * temp3.data) # √(2/π) * (...)\n", + " temp5 = Tensor(np.tanh(temp4.data)) # tanh(...)\n", + " temp6 = Tensor(1.0 + temp5.data) # 1 + tanh(...)\n", + " temp7 = Tensor(x.data * temp6.data) # x * (1 + tanh(...))\n", + " result = Tensor(0.5 * temp7.data) # 0.5 * x * (...)\n", + "\n", + " return result\n", + " ### END SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a50536a", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test-fusion-speedup", + "locked": true, + "points": 10 + } + }, + "outputs": [], + "source": [ + "def test_unit_fusion_speedup():\n", + " \"\"\"🔬 Measure the performance impact of kernel fusion.\"\"\"\n", + " print(\"🔬 Unit Test: Kernel Fusion Performance Impact...\")\n", + "\n", + " # Create moderately large tensor for meaningful timing\n", + " size = 2000\n", + " x = Tensor(np.random.randn(size, size).astype(np.float32))\n", + " warmup_iterations = 2\n", + " timing_iterations = 5\n", + "\n", + " # Warmup both implementations\n", + " for _ in range(warmup_iterations):\n", + " _ = unfused_gelu(x)\n", + " _ = fused_gelu(x)\n", + "\n", + " # Time unfused version\n", + " start = time.time()\n", + " for _ in range(timing_iterations):\n", + " result_unfused = unfused_gelu(x)\n", + " unfused_time = time.time() - start\n", + "\n", + " # Time fused version\n", + " start = time.time()\n", + " for _ in range(timing_iterations):\n", + " result_fused = fused_gelu(x)\n", + " fused_time = time.time() - start\n", + "\n", + " # Verify numerical correctness\n", + " assert np.allclose(result_unfused.data, result_fused.data, atol=1e-6), \\\n", + " \"Fused and unfused implementations must be numerically equivalent\"\n", + "\n", + " # Calculate performance metrics\n", + " speedup = unfused_time / fused_time if fused_time > 0 else 1.0\n", + " unfused_per_elem = (unfused_time / timing_iterations) / (size * size) * 1e9 # ns per element\n", + " fused_per_elem = (fused_time / timing_iterations) / (size * size) * 1e9\n", + "\n", + " print(f\"📊 Kernel Fusion Performance Analysis:\")\n", + " print(f\" Tensor size: {size}×{size} = {size*size:,} elements\")\n", + " print(f\" Unfused time: {unfused_time/timing_iterations*1000:.2f} ms\")\n", + " print(f\" Fused time: {fused_time/timing_iterations*1000:.2f} ms\")\n", + " print(f\" Speedup: {speedup:.2f}× faster\")\n", + " print(f\" Per-element: {unfused_per_elem:.1f} ns → {fused_per_elem:.1f} ns\")\n", + "\n", + " # Memory bandwidth estimate\n", + " bytes_per_elem = 4 # float32\n", + " unfused_memory_ops = 7 # 7 intermediate arrays\n", + " fused_memory_ops = 2 # read input, write output\n", + "\n", + " unfused_bandwidth = (unfused_memory_ops * size * size * bytes_per_elem) / (unfused_time / timing_iterations) / 1e9\n", + " fused_bandwidth = (fused_memory_ops * size * size * bytes_per_elem) / (fused_time / timing_iterations) / 1e9\n", + "\n", + " print(f\" Memory efficiency: {unfused_memory_ops}→{fused_memory_ops} memory ops\")\n", + " print(f\" Effective bandwidth: {unfused_bandwidth:.1f}→{fused_bandwidth:.1f} GB/s\")\n", + "\n", + " # Interpret results\n", + " if speedup > 1.5:\n", + " print(\"🚀 Excellent! Kernel fusion providing significant speedup\")\n", + " elif speedup > 1.1:\n", + " print(\"✅ Good! Kernel fusion providing measurable benefit\")\n", + " else:\n", + " print(\"⚠️ Limited speedup - may be compute-bound or small tensor size\")\n", + "\n", + " print(\"✅ Fusion performance analysis completed!\")\n", + "\n", + "test_unit_fusion_speedup()" + ] + }, + { + "cell_type": "markdown", + "id": "adb97e5a", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 4. Integration - Mixed Precision Training: Memory and Speed\n", + "\n", + "### The Mixed Precision Revolution\n", + "\n", + "Modern GPUs (like V100, A100) have specialized **Tensor Cores** that can perform FP16 operations much faster than FP32:\n", + "\n", + "```\n", + "Performance Comparison (Theoretical Peak):\n", + "┌─────────────────┬────────────────┬────────────────┐\n", + "│ Precision │ V100 TFLOPS │ A100 TFLOPS │\n", + "├─────────────────┼────────────────┼────────────────┤\n", + "│ FP32 (float) │ 15.7 │ 19.5 │\n", + "│ FP16 (half) │ 125.0 │ 312.0 │\n", + "│ Speedup │ 8× │ 16× │\n", + "└─────────────────┴────────────────┴────────────────┘\n", + "```\n", + "\n", + "### The Challenge: FP16 Precision Limitations\n", + "\n", + "FP16 has a much smaller range than FP32:\n", + "\n", + "```\n", + "FP32 (32-bit): FP16 (16-bit):\n", + "┌─────────────────────────────┐ ┌───────────────┐\n", + "│ Sign │ 8-bit │ 23-bit │ │Sign│5-bit│10-bit│\n", + "│ bit │ Exp │ Mantissa │ │bit │ Exp │Mant. │\n", + "└─────────────────────────────┘ └───────────────┘\n", + "Range: ±3.4 × 10³⁸ Range: ±6.5 × 10⁴\n", + "Precision: ~7 decimal digits Precision: ~3 decimal digits\n", + "\n", + "Problem: Small gradients (< 6e-5) become ZERO in FP16!\n", + "```\n", + "\n", + "### The Solution: Automatic Loss Scaling\n", + "\n", + "```\n", + "Training Step Without Scaling: Training Step With Scaling:\n", + "\n", + "Loss = 0.0001 Loss = 0.0001\n", + " ↓ ↓\n", + "Gradients = 0.00001 Scale × 1024\n", + " ↓ ↓\n", + "Convert to FP16 Loss = 0.1024\n", + " ↓ ↓\n", + "Gradients = 0.0 (UNDERFLOW!) Gradients = 0.01024\n", + " ↓ ↓\n", + "No learning! Convert to FP16: 0.01024 ✓\n", + " ↓\n", + " Unscale: 0.01024 / 1024 = 0.00001\n", + " ↓\n", + " Successful learning!\n", + "```\n", + "\n", + "### Mixed Precision Memory Benefits\n", + "\n", + "```\n", + "Model Component Breakdown:\n", + "┌─────────────────┬─────────────┬─────────────┬─────────────┐\n", + "│ Component │ FP32 Memory │ FP16 Memory │ Savings │\n", + "├─────────────────┼─────────────┼─────────────┼─────────────┤\n", + "│ Parameters │ 4N │ 4N │ 0% │\n", + "│ Gradients │ 4N │ 2N │ 50% │\n", + "│ Activations │ 4A │ 2A │ 50% │\n", + "│ Optimizer State │ 8N │ 8N │ 0% │\n", + "├─────────────────┼─────────────┼─────────────┼─────────────┤\n", + "│ Total Typical │ ~20N │ ~16N │ 20% │\n", + "│ Activation-Heavy│ ~40N │ ~24N │ 40% │\n", + "└─────────────────┴─────────────┴─────────────┴─────────────┘\n", + "\n", + "N = parameter count, A = activation memory\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7a19b2a6", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "mixed-precision-trainer", + "solution": true + } + }, + "outputs": [], + "source": [ + "class MixedPrecisionTrainer:\n", + " \"\"\"\n", + " Mixed precision trainer with automatic loss scaling.\n", + "\n", + " Implements the same pattern as PyTorch's Automatic Mixed Precision (AMP):\n", + " 1. Forward pass in FP16 for speed and memory efficiency\n", + " 2. Loss scaling to prevent gradient underflow\n", + " 3. Gradient computation and unscaling\n", + " 4. Parameter updates in FP32 for numerical stability\n", + "\n", + " The key insight: keep different parts of training in optimal precision.\n", + " \"\"\"\n", + "\n", + " def __init__(self, model, optimizer, loss_scale: float = 1024.0, max_loss_scale: float = 65536.0):\n", + " \"\"\"\n", + " Initialize mixed precision training infrastructure.\n", + "\n", + " TODO: Set up automatic loss scaling and overflow detection\n", + "\n", + " APPROACH:\n", + " 1. Store model and optimizer references\n", + " 2. Initialize dynamic loss scaling parameters\n", + " 3. Set up overflow detection and scale adjustment logic\n", + "\n", + " Args:\n", + " model: Neural network model\n", + " optimizer: Parameter optimizer (SGD, Adam, etc.)\n", + " loss_scale: Initial scaling factor for gradients\n", + " max_loss_scale: Maximum allowed loss scale\n", + "\n", + " LOSS SCALING STRATEGY:\n", + " - Start with reasonable scale (1024)\n", + " - Increase gradually if no overflow (better precision)\n", + " - Decrease immediately on overflow (stability)\n", + " - This balances numerical precision with training stability\n", + "\n", + " HINTS:\n", + " - Track consecutive successful steps for scale increases\n", + " - Use exponential backoff on overflow detection\n", + " - Keep scale within reasonable bounds [1, 65536]\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " self.model = model\n", + " self.optimizer = optimizer\n", + "\n", + " # Loss scaling parameters\n", + " self.loss_scale = loss_scale\n", + " self.max_loss_scale = max_loss_scale\n", + " self.min_loss_scale = 1.0\n", + "\n", + " # Dynamic scaling parameters\n", + " self.scale_growth_factor = 2.0 # Multiply by 2 when increasing\n", + " self.scale_backoff_factor = 0.5 # Divide by 2 when decreasing\n", + " self.growth_interval = 2000 # Steps between scale increases\n", + " self.steps_since_last_scale_update = 0\n", + "\n", + " # Overflow tracking\n", + " self.overflow_detected = False\n", + " ### END SOLUTION\n", + "\n", + " def scale_loss(self, loss: Tensor) -> Tensor:\n", + " \"\"\"\n", + " Scale loss to prevent gradient underflow in FP16.\n", + "\n", + " The fundamental challenge: FP16 can only represent values ≥ 6e-5.\n", + " Small gradients (common in deep networks) become zero without scaling.\n", + "\n", + " TODO: Apply loss scaling for mixed precision stability\n", + "\n", + " APPROACH:\n", + " 1. Multiply loss by current scale factor\n", + " 2. This amplifies gradients proportionally\n", + " 3. Return scaled loss for backward pass\n", + "\n", + " MATHEMATICAL INSIGHT:\n", + " If loss = 1e-6 and scale = 1024:\n", + " scaled_loss = 1e-6 × 1024 = 1.024e-3\n", + "\n", + " After backward pass:\n", + " scaled_gradients = 1.024e-3 × dloss/dparam = 1024 × gradients\n", + "\n", + " These larger gradients survive FP16 conversion!\n", + "\n", + " EXAMPLE:\n", + " >>> trainer = MixedPrecisionTrainer(model, optimizer)\n", + " >>> loss = Tensor([0.0001]) # Small loss\n", + " >>> scaled = trainer.scale_loss(loss)\n", + " >>> print(scaled.data) # [0.1024] (0.0001 × 1024)\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " # Scale the loss to amplify gradients\n", + " # This prevents gradient underflow in FP16 arithmetic\n", + " scaled_data = loss.data * self.loss_scale\n", + " return Tensor(scaled_data)\n", + " ### END SOLUTION\n", + "\n", + " def unscale_gradients(self, parameters: List[Tensor]) -> bool:\n", + " \"\"\"\n", + " Unscale gradients and detect overflow from FP16 conversion.\n", + "\n", + " After backward pass on scaled loss, gradients are scaled too.\n", + " We must unscale them AND check for overflow/underflow.\n", + "\n", + " TODO: Implement gradient unscaling with overflow detection\n", + "\n", + " APPROACH:\n", + " 1. Divide all gradients by loss scale (restore original magnitude)\n", + " 2. Check for inf/nan values (indicates FP16 overflow)\n", + " 3. Return True if gradients are valid, False if overflow detected\n", + "\n", + " OVERFLOW DETECTION:\n", + " inf/nan in gradients indicates:\n", + " - Gradient magnitude too large for FP16\n", + " - Numerical instability in computation\n", + " - Loss scale too aggressive\n", + "\n", + " When overflow occurs:\n", + " - Skip parameter update (unstable gradients)\n", + " - Reduce loss scale for next iteration\n", + " - Continue training with lower scale\n", + "\n", + " HINTS:\n", + " - Use np.isfinite() to detect inf/nan efficiently\n", + " - Process all parameters even if overflow found\n", + " - Set self.overflow_detected flag for scale adjustment\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " self.overflow_detected = False\n", + "\n", + " # Unscale all gradients and check for overflow\n", + " for param in parameters:\n", + " if param.grad is not None:\n", + " # Unscale gradients to original magnitude\n", + " param.grad.data = param.grad.data / self.loss_scale\n", + "\n", + " # Check for overflow/underflow (inf/nan values)\n", + " if not np.all(np.isfinite(param.grad.data)):\n", + " self.overflow_detected = True\n", + " # Continue processing to unscale all gradients\n", + "\n", + " return not self.overflow_detected\n", + " ### END SOLUTION\n", + "\n", + " def update_loss_scale(self):\n", + " \"\"\"\n", + " Dynamically adjust loss scale based on training stability.\n", + "\n", + " Implements the \"Goldilocks\" principle for loss scaling:\n", + " - Too low: precision loss from small gradients\n", + " - Too high: overflow and instability\n", + " - Just right: maximum precision without overflow\n", + "\n", + " TODO: Implement adaptive loss scale adjustment\n", + "\n", + " APPROACH:\n", + " 1. If overflow detected: reduce scale immediately (stability)\n", + " 2. If no overflow for many steps: increase scale (precision)\n", + " 3. Keep scale within reasonable bounds\n", + "\n", + " SCALING STRATEGY:\n", + " - Aggressive reduction on overflow (×0.5)\n", + " - Conservative growth during stability (×2 every 2000 steps)\n", + " - This favors stability over maximum precision\n", + "\n", + " WHY THIS WORKS:\n", + " - Most training is stable (gradual scale increase)\n", + " - Occasional instability (rapid scale decrease)\n", + " - Converges to optimal scale for current training phase\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " if self.overflow_detected:\n", + " # Immediately reduce scale on overflow\n", + " self.loss_scale = max(\n", + " self.min_loss_scale,\n", + " self.loss_scale * self.scale_backoff_factor\n", + " )\n", + " self.steps_since_last_scale_update = 0\n", + " else:\n", + " # Gradually increase scale if stable\n", + " self.steps_since_last_scale_update += 1\n", + " if self.steps_since_last_scale_update >= self.growth_interval:\n", + " self.loss_scale = min(\n", + " self.max_loss_scale,\n", + " self.loss_scale * self.scale_growth_factor\n", + " )\n", + " self.steps_since_last_scale_update = 0\n", + " ### END SOLUTION\n", + "\n", + " def train_step(self, batch: Tuple[Tensor, Tensor]) -> Dict[str, float]:\n", + " \"\"\"\n", + " Execute complete mixed precision training step.\n", + "\n", + " Orchestrates the entire mixed precision training process:\n", + " 1. Forward pass (FP16 in real implementation)\n", + " 2. Loss computation and scaling\n", + " 3. Backward pass on scaled loss\n", + " 4. Gradient unscaling and overflow detection\n", + " 5. Conditional parameter update\n", + " 6. Loss scale adjustment\n", + "\n", + " TODO: Implement end-to-end mixed precision training step\n", + "\n", + " APPROACH:\n", + " 1. Clear gradients from previous step\n", + " 2. Forward pass through model\n", + " 3. Compute and scale loss\n", + " 4. Backward pass to compute scaled gradients\n", + " 5. Unscale gradients and check for overflow\n", + " 6. Update parameters only if no overflow\n", + " 7. Adjust loss scale based on stability\n", + "\n", + " CRITICAL INSIGHT:\n", + " Skip parameter updates on overflow! Unstable gradients\n", + " would move parameters in wrong direction.\n", + "\n", + " RETURN FORMAT:\n", + " Dictionary with training metrics:\n", + " - loss: unscaled loss value\n", + " - loss_scale: current scaling factor\n", + " - overflow: whether overflow occurred\n", + " - gradients_valid: whether update was applied\n", + "\n", + " HINTS:\n", + " - Use self.optimizer.zero_grad() to clear gradients\n", + " - Get parameters with gradients for unscaling\n", + " - Only call optimizer.step() if gradients are valid\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " inputs, targets = batch\n", + "\n", + " # Clear gradients from previous step\n", + " self.optimizer.zero_grad()\n", + "\n", + " # Forward pass (would use FP16 autocast in real implementation)\n", + " # For simulation, we work in FP32 but apply scaling principles\n", + " outputs = self.model(inputs)\n", + "\n", + " # Compute loss (unscaled)\n", + " loss = self._compute_loss(outputs, targets)\n", + "\n", + " # Scale loss for mixed precision\n", + " scaled_loss = self.scale_loss(loss)\n", + "\n", + " # Backward pass on scaled loss\n", + " scaled_loss.backward()\n", + "\n", + " # Get all parameters with gradients\n", + " parameters = [p for p in self.model.parameters() if p.grad is not None]\n", + "\n", + " # Unscale gradients and detect overflow\n", + " gradients_valid = self.unscale_gradients(parameters)\n", + "\n", + " # Update parameters only if no overflow\n", + " if gradients_valid:\n", + " self.optimizer.step()\n", + "\n", + " # Adjust loss scale based on stability\n", + " self.update_loss_scale()\n", + "\n", + " # Return training metrics\n", + " return {\n", + " 'loss': loss.data.item() if hasattr(loss.data, 'item') else float(loss.data),\n", + " 'loss_scale': self.loss_scale,\n", + " 'overflow': self.overflow_detected,\n", + " 'gradients_valid': gradients_valid\n", + " }\n", + " ### END SOLUTION\n", + "\n", + " def _compute_loss(self, outputs: Tensor, targets: Tensor) -> Tensor:\n", + " \"\"\"Simple MSE loss for demonstration purposes.\"\"\"\n", + " diff = Tensor(outputs.data - targets.data)\n", + " return Tensor(np.mean(diff.data**2))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "650bf77c", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test-mixed-precision", + "locked": true, + "points": 15 + } + }, + "outputs": [], + "source": [ + "def test_unit_mixed_precision():\n", + " \"\"\"🔬 Test mixed precision training components comprehensively.\"\"\"\n", + " print(\"🔬 Unit Test: Mixed Precision Training...\")\n", + "\n", + " # Create mock model and optimizer for testing\n", + " class MockModel:\n", + " def __init__(self):\n", + " self.weight = Tensor(np.random.randn(10, 5).astype(np.float32))\n", + " self.weight.grad = None\n", + "\n", + " def __call__(self, x):\n", + " return x.matmul(self.weight)\n", + "\n", + " def parameters(self):\n", + " return [self.weight]\n", + "\n", + " class MockOptimizer:\n", + " def __init__(self, params):\n", + " self.params = params\n", + " self.updates_applied = 0\n", + "\n", + " def zero_grad(self):\n", + " for p in self.params:\n", + " p.grad = None\n", + "\n", + " def step(self):\n", + " for p in self.params:\n", + " if p.grad is not None:\n", + " p.data = p.data - 0.01 * p.grad.data\n", + " self.updates_applied += 1\n", + "\n", + " # Initialize mixed precision trainer\n", + " model = MockModel()\n", + " optimizer = MockOptimizer(model.parameters())\n", + " trainer = MixedPrecisionTrainer(model, optimizer, loss_scale=1024.0)\n", + "\n", + " # Test 1: Loss scaling\n", + " print(\" Testing loss scaling...\")\n", + " loss = Tensor([0.001])\n", + " scaled_loss = trainer.scale_loss(loss)\n", + " expected_scaled = 0.001 * 1024.0\n", + " assert np.isclose(scaled_loss.data[0], expected_scaled), \\\n", + " f\"Loss scaling failed: expected {expected_scaled}, got {scaled_loss.data[0]}\"\n", + "\n", + " # Test 2: Gradient unscaling (normal case)\n", + " print(\" Testing gradient unscaling...\")\n", + " model.weight.grad = Tensor(np.full((10, 5), 1024.0)) # Simulate scaled gradients\n", + " valid = trainer.unscale_gradients([model.weight])\n", + " assert valid, \"Should detect valid gradients\"\n", + " assert np.allclose(model.weight.grad.data, 1.0), \"Gradient unscaling failed\"\n", + "\n", + " # Test 3: Overflow detection\n", + " print(\" Testing overflow detection...\")\n", + " model.weight.grad = Tensor(np.full((10, 5), np.inf)) # Simulate overflow\n", + " valid = trainer.unscale_gradients([model.weight])\n", + " assert not valid, \"Should detect overflow\"\n", + " assert trainer.overflow_detected, \"Overflow flag not set\"\n", + "\n", + " # Test 4: Loss scale adjustment after overflow\n", + " print(\" Testing loss scale adjustment...\")\n", + " initial_scale = trainer.loss_scale\n", + " trainer.update_loss_scale() # Should reduce scale due to overflow\n", + " assert trainer.loss_scale < initial_scale, \\\n", + " f\"Scale should decrease after overflow: {initial_scale} → {trainer.loss_scale}\"\n", + "\n", + " # Test 5: Loss scale increase during stability\n", + " print(\" Testing loss scale increase...\")\n", + " trainer.overflow_detected = False\n", + " trainer.steps_since_last_scale_update = 2000 # Simulate stable training\n", + " scale_before = trainer.loss_scale\n", + " trainer.update_loss_scale()\n", + " assert trainer.loss_scale > scale_before, \"Scale should increase during stability\"\n", + "\n", + " # Test 6: End-to-end training step\n", + " print(\" Testing complete training step...\")\n", + " inputs = Tensor(np.random.randn(8, 10).astype(np.float32))\n", + " targets = Tensor(np.random.randn(8, 5).astype(np.float32))\n", + "\n", + " initial_updates = optimizer.updates_applied\n", + " metrics = trainer.train_step((inputs, targets))\n", + "\n", + " # Verify metrics structure\n", + " required_keys = ['loss', 'loss_scale', 'overflow', 'gradients_valid']\n", + " for key in required_keys:\n", + " assert key in metrics, f\"Missing metric: {key}\"\n", + "\n", + " # Verify loss is reasonable\n", + " assert isinstance(metrics['loss'], (int, float)), \"Loss should be numeric\"\n", + " assert metrics['loss'] >= 0, \"Loss should be non-negative\"\n", + "\n", + " # Verify loss scale is positive\n", + " assert metrics['loss_scale'] > 0, \"Loss scale should be positive\"\n", + "\n", + " print(\"✅ Mixed precision training works correctly!\")\n", + "\n", + "test_unit_mixed_precision()" + ] + }, + { + "cell_type": "markdown", + "id": "de9e4b44", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 5. Systems Analysis - Performance Scaling Patterns\n", + "\n", + "Let's analyze how our acceleration techniques perform across different scenarios and understand their scaling characteristics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f7edfee", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "analyze-vectorization", + "solution": true + } + }, + "outputs": [], + "source": [ + "def analyze_vectorization_scaling():\n", + " \"\"\"📊 Analyze vectorization performance across different tensor sizes.\"\"\"\n", + " print(\"📊 Analyzing vectorization scaling behavior...\")\n", + "\n", + " # Test sizes spanning different cache regimes\n", + " sizes = [64, 128, 256, 512, 1024, 2048]\n", + "\n", + " print(\"\\n🔍 Vectorization Scaling Analysis:\")\n", + " print(\"┌─────────┬─────────────┬─────────────┬─────────────┬─────────────┐\")\n", + " print(\"│ Size │ Time (ms) │ GFLOPS │ Bandwidth │ Efficiency │\")\n", + " print(\"│ │ │ │ (GB/s) │ (% of peak) │\")\n", + " print(\"├─────────┼─────────────┼─────────────┼─────────────┼─────────────┤\")\n", + "\n", + " for size in sizes:\n", + " # Create test matrices\n", + " a = Tensor(np.random.randn(size, size).astype(np.float32))\n", + " b = Tensor(np.random.randn(size, size).astype(np.float32))\n", + "\n", + " # Warm up\n", + " for _ in range(2):\n", + " _ = vectorized_matmul(a, b)\n", + "\n", + " # Time vectorized implementation\n", + " iterations = max(1, 100 // (size // 64)) # Fewer iterations for larger sizes\n", + " start = time.time()\n", + " for _ in range(iterations):\n", + " result = vectorized_matmul(a, b)\n", + " elapsed = (time.time() - start) / iterations\n", + "\n", + " # Calculate performance metrics\n", + " flops = 2 * size**3 # 2N³ FLOPs for matrix multiplication\n", + " gflops = flops / (elapsed * 1e9)\n", + "\n", + " bytes_accessed = 3 * size * size * 4 # 3 matrices × size² × 4 bytes\n", + " bandwidth = bytes_accessed / (elapsed * 1e9)\n", + "\n", + " # Estimate efficiency (rough baseline: modern CPU ~100-500 GFLOPS peak)\n", + " estimated_peak_gflops = 200 # Conservative estimate\n", + " efficiency = min(100, gflops / estimated_peak_gflops * 100)\n", + "\n", + " print(f\"│ {size:6d} │ {elapsed*1000:9.2f} │ {gflops:9.1f} │ {bandwidth:9.1f} │ {efficiency:9.1f} │\")\n", + "\n", + " print(\"└─────────┴─────────────┴─────────────┴─────────────┴─────────────┘\")\n", + "\n", + " print(f\"\\n💡 Vectorization insights:\")\n", + " print(f\" • Small matrices: Limited by overhead and cache effects\")\n", + " print(f\" • Medium matrices: Sweet spot for cache reuse\")\n", + " print(f\" • Large matrices: Memory bandwidth becomes limiting factor\")\n", + " print(f\" • BLAS libraries automatically optimize for each size regime\")\n", + " print(\"🚀 Vectorization effectiveness depends on problem size and hardware\")\n", + "\n", + "analyze_vectorization_scaling()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5972a039", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "analyze-arithmetic-intensity", + "solution": true + } + }, + "outputs": [], + "source": [ + "def analyze_arithmetic_intensity():\n", + " \"\"\"📊 Demonstrate the roofline model with different operations.\"\"\"\n", + " print(\"📊 Analyzing arithmetic intensity patterns...\")\n", + "\n", + " size = 1024\n", + " iterations = 10\n", + "\n", + " operations = []\n", + "\n", + " # Create test data\n", + " x = Tensor(np.random.randn(size, size).astype(np.float32))\n", + " y = Tensor(np.random.randn(size, size).astype(np.float32))\n", + "\n", + " print(\"\\n🎯 Arithmetic Intensity Analysis:\")\n", + " print(\"┌─────────────────────┬─────────┬─────────────┬─────────────┬─────────────┐\")\n", + " print(\"│ Operation │ AI │ Time (ms) │ GFLOPS │ GB/s │\")\n", + " print(\"│ │(FLOPs/B)│ │ │ │\")\n", + " print(\"├─────────────────────┼─────────┼─────────────┼─────────────┼─────────────┤\")\n", + "\n", + " # 1. Element-wise addition (very low arithmetic intensity)\n", + " start = time.time()\n", + " for _ in range(iterations):\n", + " _ = Tensor(x.data + y.data)\n", + " add_time = (time.time() - start) / iterations\n", + "\n", + " add_flops = size * size # One addition per element\n", + " add_bytes = 3 * size * size * 4 # Read x, read y, write result\n", + " add_ai = add_flops / add_bytes\n", + " add_gflops = add_flops / (add_time * 1e9)\n", + " add_bandwidth = add_bytes / (add_time * 1e9)\n", + "\n", + " print(f\"│ Element-wise Add │ {add_ai:6.3f} │ {add_time*1000:9.2f} │ {add_gflops:9.1f} │ {add_bandwidth:9.1f} │\")\n", + "\n", + " # 2. Element-wise multiply (still low, but slightly higher)\n", + " start = time.time()\n", + " for _ in range(iterations):\n", + " _ = Tensor(x.data * y.data)\n", + " mul_time = (time.time() - start) / iterations\n", + "\n", + " mul_flops = size * size\n", + " mul_bytes = 3 * size * size * 4\n", + " mul_ai = mul_flops / mul_bytes\n", + " mul_gflops = mul_flops / (mul_time * 1e9)\n", + " mul_bandwidth = mul_bytes / (mul_time * 1e9)\n", + "\n", + " print(f\"│ Element-wise Mult │ {mul_ai:6.3f} │ {mul_time*1000:9.2f} │ {mul_gflops:9.1f} │ {mul_bandwidth:9.1f} │\")\n", + "\n", + " # 3. GELU (medium arithmetic intensity)\n", + " start = time.time()\n", + " for _ in range(iterations):\n", + " _ = fused_gelu(x)\n", + " gelu_time = (time.time() - start) / iterations\n", + "\n", + " gelu_flops = size * size * 8 # Approximate: x³, add, mul, tanh, etc.\n", + " gelu_bytes = 2 * size * size * 4 # Read x, write result\n", + " gelu_ai = gelu_flops / gelu_bytes\n", + " gelu_gflops = gelu_flops / (gelu_time * 1e9)\n", + " gelu_bandwidth = gelu_bytes / (gelu_time * 1e9)\n", + "\n", + " print(f\"│ Fused GELU │ {gelu_ai:6.3f} │ {gelu_time*1000:9.2f} │ {gelu_gflops:9.1f} │ {gelu_bandwidth:9.1f} │\")\n", + "\n", + " # 4. Matrix multiplication (high arithmetic intensity)\n", + " start = time.time()\n", + " for _ in range(iterations):\n", + " _ = vectorized_matmul(x, y)\n", + " matmul_time = (time.time() - start) / iterations\n", + "\n", + " matmul_flops = 2 * size**3 # 2N³ FLOPs\n", + " matmul_bytes = 3 * size * size * 4 # 3 matrices\n", + " matmul_ai = matmul_flops / matmul_bytes\n", + " matmul_gflops = matmul_flops / (matmul_time * 1e9)\n", + " matmul_bandwidth = matmul_bytes / (matmul_time * 1e9)\n", + "\n", + " print(f\"│ Matrix Multiply │ {matmul_ai:6.3f} │ {matmul_time*1000:9.2f} │ {matmul_gflops:9.1f} │ {matmul_bandwidth:9.1f} │\")\n", + "\n", + " print(\"└─────────────────────┴─────────┴─────────────┴─────────────┴─────────────┘\")\n", + "\n", + " print(f\"\\n💡 Roofline Model Insights:\")\n", + " print(f\" 📊 Low AI (< 1): Memory bound - limited by bandwidth\")\n", + " print(f\" 📊 Med AI (1-10): Transitional - depends on implementation\")\n", + " print(f\" 📊 High AI (> 10): Compute bound - limited by ALU throughput\")\n", + " print(f\" 🎯 Matrix multiplication ({matmul_ai:.1f} AI) is ideal for GPUs/TPUs\")\n", + " print(f\" ⚡ Element-wise ops ({add_ai:.3f} AI) need memory optimization\")\n", + " print(\"🚀 Design algorithms with high arithmetic intensity for performance\")\n", + "\n", + "analyze_arithmetic_intensity()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7a539cd5", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "analyze-mixed-precision-benefits", + "solution": true + } + }, + "outputs": [], + "source": [ + "def analyze_mixed_precision_benefits():\n", + " \"\"\"📊 Quantify mixed precision memory and performance benefits.\"\"\"\n", + " print(\"📊 Analyzing mixed precision benefits across model sizes...\")\n", + "\n", + " # Define representative model configurations\n", + " model_configs = [\n", + " (\"Tiny CNN\", {\"params\": 50_000, \"activations\": 100_000}),\n", + " (\"Small BERT\", {\"params\": 10_000_000, \"activations\": 5_000_000}),\n", + " (\"Medium GPT\", {\"params\": 100_000_000, \"activations\": 50_000_000}),\n", + " (\"Large Transformer\", {\"params\": 1_000_000_000, \"activations\": 500_000_000}),\n", + " ]\n", + "\n", + " print(\"\\n🧮 Mixed Precision Memory Analysis:\")\n", + " print(\"┌─────────────────┬─────────────┬─────────────┬─────────────┬─────────────┐\")\n", + " print(\"│ Model Type │ Parameters │ FP32 Memory │ FP16 Memory │ Savings │\")\n", + " print(\"│ │ │ (GB) │ (GB) │ (%) │\")\n", + " print(\"├─────────────────┼─────────────┼─────────────┼─────────────┼─────────────┤\")\n", + "\n", + " for name, config in model_configs:\n", + " param_count = config[\"params\"]\n", + " activation_count = config[\"activations\"]\n", + "\n", + " # Memory calculation (bytes)\n", + " # Parameters: always FP32 for stability\n", + " param_memory = param_count * 4\n", + "\n", + " # FP32 training memory\n", + " fp32_activations = activation_count * 4\n", + " fp32_gradients = param_count * 4\n", + " fp32_optimizer = param_count * 8 # Adam: momentum + velocity\n", + " fp32_total = param_memory + fp32_activations + fp32_gradients + fp32_optimizer\n", + "\n", + " # Mixed precision memory\n", + " fp16_activations = activation_count * 2 # FP16 activations\n", + " fp16_gradients = param_count * 2 # FP16 gradients during backward\n", + " mixed_total = param_memory + fp16_activations + fp16_gradients + fp32_optimizer\n", + "\n", + " # Calculate savings\n", + " savings_gb = (fp32_total - mixed_total) / 1e9\n", + " savings_pct = (fp32_total - mixed_total) / fp32_total * 100\n", + "\n", + " print(f\"│ {name:14s} │ {param_count:10,d} │ {fp32_total/1e9:9.1f} │ {mixed_total/1e9:9.1f} │ {savings_pct:9.1f} │\")\n", + "\n", + " print(\"└─────────────────┴─────────────┴─────────────┴─────────────┴─────────────┘\")\n", + "\n", + " # Performance simulation\n", + " print(f\"\\n⚡ Mixed Precision Performance Simulation:\")\n", + "\n", + " # Simulate different batch sizes to show memory pressure\n", + " batch_sizes = [8, 16, 32, 64]\n", + " hidden_size = 1024\n", + " seq_length = 512\n", + "\n", + " print(\"┌─────────────┬─────────────┬─────────────┬─────────────┬─────────────┐\")\n", + " print(\"│ Batch Size │ FP32 Mem │ FP16 Mem │ Throughput │ Efficiency │\")\n", + " print(\"│ │ (GB) │ (GB) │ Gain │ Gain │\")\n", + " print(\"├─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤\")\n", + "\n", + " for batch_size in batch_sizes:\n", + " # Memory for activations (dominant for large models)\n", + " elements = batch_size * seq_length * hidden_size\n", + "\n", + " fp32_mem = elements * 4 / 1e9 # 4 bytes per FP32\n", + " fp16_mem = elements * 2 / 1e9 # 2 bytes per FP16\n", + "\n", + " # Simulate throughput gains (based on Tensor Core speedups)\n", + " # Real speedups depend on hardware and operation mix\n", + " throughput_gain = 1.4 # Conservative estimate for mixed workloads\n", + "\n", + " # Memory efficiency enables larger batch sizes\n", + " max_fp32_batch = 32 # Assume memory limit\n", + " max_fp16_batch = 64 # Double capacity with FP16\n", + "\n", + " efficiency_gain = max_fp16_batch / max_fp32_batch if batch_size <= max_fp32_batch else \"OOM\"\n", + " efficiency_str = f\"{efficiency_gain:.1f}×\" if isinstance(efficiency_gain, float) else efficiency_gain\n", + "\n", + " print(f\"│ {batch_size:10d} │ {fp32_mem:9.2f} │ {fp16_mem:9.2f} │ {throughput_gain:9.1f}× │ {efficiency_str:9s} │\")\n", + "\n", + " print(\"└─────────────┴─────────────┴─────────────┴─────────────┴─────────────┘\")\n", + "\n", + " print(f\"\\n💡 Mixed Precision Key Benefits:\")\n", + " print(f\" 🎯 Memory: 20-40% reduction enables larger models/batches\")\n", + " print(f\" ⚡ Speed: 1.3-2× throughput on modern hardware (V100+)\")\n", + " print(f\" 📈 Scale: Essential for billion-parameter models\")\n", + " print(f\" ⚠️ Complexity: Requires careful loss scaling and overflow handling\")\n", + " print(\"🚀 Mixed precision is crucial for competitive ML training\")\n", + "\n", + "analyze_mixed_precision_benefits()" + ] + }, + { + "cell_type": "markdown", + "id": "d42aa6ff", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 6. Optimization Insights - Production Acceleration Strategy\n", + "\n", + "Understanding when and how to apply different acceleration techniques in real-world scenarios." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "133b1f71", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "acceleration-decision-framework", + "solution": true + } + }, + "outputs": [], + "source": [ + "def analyze_acceleration_decision_framework():\n", + " \"\"\"📊 Decision framework for choosing acceleration techniques.\"\"\"\n", + " print(\"📊 Acceleration Technique Decision Framework...\")\n", + "\n", + " # Define workload characteristics\n", + " workloads = [\n", + " (\"Research Training\", {\n", + " \"memory_pressure\": \"medium\",\n", + " \"latency_sensitive\": False,\n", + " \"stability_critical\": False,\n", + " \"development_speed\": \"high\",\n", + " \"hardware_variety\": \"high\"\n", + " }),\n", + " (\"Production Training\", {\n", + " \"memory_pressure\": \"high\",\n", + " \"latency_sensitive\": False,\n", + " \"stability_critical\": True,\n", + " \"development_speed\": \"medium\",\n", + " \"hardware_variety\": \"low\"\n", + " }),\n", + " (\"Real-time Inference\", {\n", + " \"memory_pressure\": \"medium\",\n", + " \"latency_sensitive\": True,\n", + " \"stability_critical\": True,\n", + " \"development_speed\": \"low\",\n", + " \"hardware_variety\": \"medium\"\n", + " }),\n", + " (\"Edge Deployment\", {\n", + " \"memory_pressure\": \"very_high\",\n", + " \"latency_sensitive\": True,\n", + " \"stability_critical\": True,\n", + " \"development_speed\": \"low\",\n", + " \"hardware_variety\": \"very_high\"\n", + " }),\n", + " (\"Batch Inference\", {\n", + " \"memory_pressure\": \"low\",\n", + " \"latency_sensitive\": False,\n", + " \"stability_critical\": True,\n", + " \"development_speed\": \"medium\",\n", + " \"hardware_variety\": \"low\"\n", + " })\n", + " ]\n", + "\n", + " # Define technique characteristics\n", + " techniques = {\n", + " \"Vectorization\": {\n", + " \"implementation_cost\": \"low\",\n", + " \"memory_benefit\": \"none\",\n", + " \"latency_benefit\": \"high\",\n", + " \"stability_risk\": \"none\",\n", + " \"hardware_dependency\": \"low\"\n", + " },\n", + " \"Kernel Fusion\": {\n", + " \"implementation_cost\": \"medium\",\n", + " \"memory_benefit\": \"medium\",\n", + " \"latency_benefit\": \"medium\",\n", + " \"stability_risk\": \"low\",\n", + " \"hardware_dependency\": \"medium\"\n", + " },\n", + " \"Mixed Precision\": {\n", + " \"implementation_cost\": \"high\",\n", + " \"memory_benefit\": \"high\",\n", + " \"latency_benefit\": \"high\",\n", + " \"stability_risk\": \"medium\",\n", + " \"hardware_dependency\": \"high\"\n", + " },\n", + " \"Graph Optimization\": {\n", + " \"implementation_cost\": \"very_high\",\n", + " \"memory_benefit\": \"medium\",\n", + " \"latency_benefit\": \"very_high\",\n", + " \"stability_risk\": \"low\",\n", + " \"hardware_dependency\": \"very_high\"\n", + " }\n", + " }\n", + "\n", + " print(\"\\n🎯 Acceleration Technique Recommendations:\")\n", + " print(\"┌─────────────────────┬─────────────┬─────────────┬─────────────┬─────────────┐\")\n", + " print(\"│ Workload │ Vectorize │ Fuse Kernels│ Mixed Prec │ Graph Opt │\")\n", + " print(\"├─────────────────────┼─────────────┼─────────────┼─────────────┼─────────────┤\")\n", + "\n", + " for workload_name, workload_chars in workloads:\n", + " recommendations = []\n", + "\n", + " for technique_name in [\"Vectorization\", \"Kernel Fusion\", \"Mixed Precision\", \"Graph Optimization\"]:\n", + " tech_chars = techniques[technique_name]\n", + " score = 0\n", + "\n", + " # Benefit vs requirement matching\n", + " if workload_chars[\"memory_pressure\"] in [\"high\", \"very_high\"]:\n", + " if tech_chars[\"memory_benefit\"] in [\"medium\", \"high\"]:\n", + " score += 2\n", + "\n", + " if workload_chars[\"latency_sensitive\"]:\n", + " if tech_chars[\"latency_benefit\"] in [\"medium\", \"high\", \"very_high\"]:\n", + " score += 2\n", + "\n", + " # Risk vs tolerance matching\n", + " if workload_chars[\"stability_critical\"]:\n", + " if tech_chars[\"stability_risk\"] in [\"none\", \"low\"]:\n", + " score += 1\n", + " elif tech_chars[\"stability_risk\"] == \"medium\":\n", + " score -= 1\n", + "\n", + " # Implementation cost vs development speed\n", + " if workload_chars[\"development_speed\"] == \"high\":\n", + " if tech_chars[\"implementation_cost\"] in [\"low\", \"medium\"]:\n", + " score += 1\n", + " elif tech_chars[\"implementation_cost\"] in [\"high\", \"very_high\"]:\n", + " score -= 1\n", + "\n", + " # Hardware dependency vs variety\n", + " if workload_chars[\"hardware_variety\"] in [\"high\", \"very_high\"]:\n", + " if tech_chars[\"hardware_dependency\"] in [\"low\", \"medium\"]:\n", + " score += 1\n", + " elif tech_chars[\"hardware_dependency\"] in [\"high\", \"very_high\"]:\n", + " score -= 2\n", + "\n", + " # Convert score to recommendation\n", + " if score >= 3:\n", + " rec = \"✅ High\"\n", + " elif score >= 1:\n", + " rec = \"⚡ Medium\"\n", + " elif score >= 0:\n", + " rec = \"⚠️ Low\"\n", + " else:\n", + " rec = \"❌ Skip\"\n", + "\n", + " recommendations.append(rec)\n", + "\n", + " rec_line = \" │ \".join(f\"{rec:10s}\" for rec in recommendations)\n", + " print(f\"│ {workload_name:18s} │ {rec_line} │\")\n", + "\n", + " print(\"└─────────────────────┴─────────────┴─────────────┴─────────────┴─────────────┘\")\n", + "\n", + " # Implementation priority framework\n", + " print(f\"\\n🛠️ Implementation Priority Framework:\")\n", + " print(f\" 📊 Phase 1 (Always): Vectorization\")\n", + " print(f\" • Low risk, high reward\")\n", + " print(f\" • Works on any hardware\")\n", + " print(f\" • Foundation for other optimizations\")\n", + " print(f\" \")\n", + " print(f\" 📊 Phase 2 (Memory constrained): Kernel Fusion\")\n", + " print(f\" • Targets memory-bound operations\")\n", + " print(f\" • Moderate complexity\")\n", + " print(f\" • Significant wins on element-wise ops\")\n", + " print(f\" \")\n", + " print(f\" 📊 Phase 3 (Large models): Mixed Precision\")\n", + " print(f\" • Essential for large model training\")\n", + " print(f\" • Requires careful validation\")\n", + " print(f\" • Hardware-dependent benefits\")\n", + " print(f\" \")\n", + " print(f\" 📊 Phase 4 (Production): Graph Optimization\")\n", + " print(f\" • Maximum performance extraction\")\n", + " print(f\" • High implementation cost\")\n", + " print(f\" • Deployment-specific tuning\")\n", + "\n", + " print(f\"\\n💡 Key Decision Factors:\")\n", + " print(f\" 🎯 Start simple: Vectorization first, always\")\n", + " print(f\" 📈 Scale up: Add complexity only when needed\")\n", + " print(f\" ⚡ Measure impact: Profile before and after each optimization\")\n", + " print(f\" 🔄 Iterate: Optimization is an ongoing process, not one-time\")\n", + " print(\"🚀 Systematic acceleration beats random optimization\")\n", + "\n", + "analyze_acceleration_decision_framework()" + ] + }, + { + "cell_type": "markdown", + "id": "541be4f4", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 7. Module Integration Test\n", + "\n", + "Final validation that all acceleration components work together correctly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "05244210", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test-module", + "locked": true, + "points": 20 + } + }, + "outputs": [], + "source": [ + "def test_module():\n", + " \"\"\"\n", + " Comprehensive test of entire acceleration module functionality.\n", + "\n", + " This final test ensures:\n", + " - All acceleration techniques work correctly\n", + " - Performance improvements are measurable\n", + " - Mixed precision training is stable\n", + " - Components integrate seamlessly\n", + " - Module is ready for production use\n", + " \"\"\"\n", + " print(\"🧪 RUNNING MODULE INTEGRATION TEST\")\n", + " print(\"=\" * 50)\n", + "\n", + " # Run all unit tests\n", + " print(\"Running unit tests...\")\n", + " test_unit_vectorized_matmul()\n", + " test_unit_fused_gelu()\n", + " test_unit_fusion_speedup()\n", + " test_unit_mixed_precision()\n", + "\n", + " print(\"\\nRunning integration scenarios...\")\n", + "\n", + " # Test realistic acceleration pipeline\n", + " print(\"🔬 Integration Test: Complete acceleration pipeline...\")\n", + "\n", + " # Create realistic model scenario\n", + " batch_size, seq_len, hidden_dim = 16, 64, 256\n", + " print(f\" Model config: batch={batch_size}, seq_len={seq_len}, hidden={hidden_dim}\")\n", + "\n", + " # Test data\n", + " x = Tensor(np.random.randn(batch_size, seq_len, hidden_dim).astype(np.float32))\n", + " weight = Tensor(np.random.randn(hidden_dim, hidden_dim).astype(np.float32))\n", + " print(f\" Input tensor: {x.shape}, Weight tensor: {weight.shape}\")\n", + "\n", + " # Test complete pipeline: reshape → matmul → activation → mixed precision\n", + " print(\" Testing vectorized operations...\")\n", + "\n", + " # Reshape for matrix multiplication (flatten batch and sequence)\n", + " x_reshaped = Tensor(x.data.reshape(-1, hidden_dim))\n", + " assert x_reshaped.shape == (batch_size * seq_len, hidden_dim)\n", + "\n", + " # Vectorized matrix multiplication\n", + " linear_output = vectorized_matmul(x_reshaped, weight)\n", + " assert linear_output.shape == (batch_size * seq_len, hidden_dim)\n", + " print(f\" ✅ Matrix multiplication: {x_reshaped.shape} @ {weight.shape} → {linear_output.shape}\")\n", + "\n", + " # Fused activation\n", + " activated = fused_gelu(linear_output)\n", + " assert activated.shape == linear_output.shape\n", + " print(f\" ✅ Fused GELU activation: {linear_output.shape} → {activated.shape}\")\n", + "\n", + " # Reshape back to original structure\n", + " final_output = Tensor(activated.data.reshape(batch_size, seq_len, hidden_dim))\n", + " assert final_output.shape == x.shape\n", + " print(f\" ✅ Output reshape: {activated.shape} → {final_output.shape}\")\n", + "\n", + " print(\" Testing mixed precision training integration...\")\n", + "\n", + " # Create complete model for mixed precision testing\n", + " class TransformerBlock:\n", + " def __init__(self, hidden_dim):\n", + " self.hidden_dim = hidden_dim\n", + " self.weight1 = Tensor(np.random.randn(hidden_dim, hidden_dim).astype(np.float32))\n", + " self.weight2 = Tensor(np.random.randn(hidden_dim, hidden_dim).astype(np.float32))\n", + " self.weight1.grad = None\n", + " self.weight2.grad = None\n", + "\n", + " def __call__(self, x):\n", + " # Simulate transformer block: linear → activation → linear\n", + " batch_size, seq_len, hidden_dim = x.shape\n", + " x_flat = Tensor(x.data.reshape(-1, hidden_dim))\n", + "\n", + " # First linear layer\n", + " h1 = vectorized_matmul(x_flat, self.weight1)\n", + " h1_activated = fused_gelu(h1)\n", + "\n", + " # Second linear layer\n", + " h2 = vectorized_matmul(h1_activated, self.weight2)\n", + "\n", + " # Reshape back\n", + " output = Tensor(h2.data.reshape(batch_size, seq_len, hidden_dim))\n", + " return output\n", + "\n", + " def parameters(self):\n", + " return [self.weight1, self.weight2]\n", + "\n", + " class SimpleOptimizer:\n", + " def __init__(self, params):\n", + " self.params = params\n", + "\n", + " def zero_grad(self):\n", + " for p in self.params:\n", + " p.grad = None\n", + "\n", + " def step(self):\n", + " for p in self.params:\n", + " if p.grad is not None:\n", + " p.data = p.data - 0.001 * p.grad.data\n", + "\n", + " # Initialize model and optimizer\n", + " model = TransformerBlock(hidden_dim)\n", + " optimizer = SimpleOptimizer(model.parameters())\n", + " trainer = MixedPrecisionTrainer(model, optimizer, loss_scale=512.0)\n", + "\n", + " print(f\" Model parameters: {len(model.parameters())}\")\n", + " print(f\" Initial loss scale: {trainer.loss_scale}\")\n", + "\n", + " # Simulate training steps\n", + " print(\" Running training steps...\")\n", + " targets = Tensor(np.random.randn(batch_size, seq_len, hidden_dim).astype(np.float32))\n", + "\n", + " training_metrics = []\n", + " for step in range(5):\n", + " metrics = trainer.train_step((x, targets))\n", + " training_metrics.append(metrics)\n", + "\n", + " # Verify metrics are reasonable\n", + " assert isinstance(metrics['loss'], (int, float))\n", + " assert metrics['loss'] >= 0\n", + " assert metrics['loss_scale'] > 0\n", + " assert isinstance(metrics['overflow'], bool)\n", + " assert isinstance(metrics['gradients_valid'], bool)\n", + "\n", + " print(f\" ✅ Completed {len(training_metrics)} training steps\")\n", + "\n", + " # Analyze training stability\n", + " losses = [m['loss'] for m in training_metrics]\n", + " overflows = [m['overflow'] for m in training_metrics]\n", + "\n", + " print(f\" Loss range: {min(losses):.6f} - {max(losses):.6f}\")\n", + " print(f\" Overflow rate: {sum(overflows)}/{len(overflows)} steps\")\n", + "\n", + " print(\" Testing performance characteristics...\")\n", + "\n", + " # Verify acceleration provides measurable benefits\n", + " test_sizes = [128, 256]\n", + " for size in test_sizes:\n", + " test_x = Tensor(np.random.randn(size, size).astype(np.float32))\n", + " test_y = Tensor(np.random.randn(size, size).astype(np.float32))\n", + "\n", + " # Time operations and verify reasonable performance\n", + " start = time.time()\n", + " _ = vectorized_matmul(test_x, test_y)\n", + " matmul_time = time.time() - start\n", + "\n", + " start = time.time()\n", + " _ = fused_gelu(test_x)\n", + " gelu_time = time.time() - start\n", + "\n", + " # Verify operations complete in reasonable time\n", + " assert matmul_time < 1.0, f\"Matrix multiplication too slow: {matmul_time:.3f}s\"\n", + " assert gelu_time < 0.1, f\"GELU activation too slow: {gelu_time:.3f}s\"\n", + "\n", + " print(f\" ✅ Size {size}: matmul={matmul_time*1000:.1f}ms, gelu={gelu_time*1000:.1f}ms\")\n", + "\n", + " print(\" Testing memory efficiency...\")\n", + "\n", + " # Verify mixed precision reduces memory usage conceptually\n", + " param_count = sum(p.data.size for p in model.parameters())\n", + " activation_count = batch_size * seq_len * hidden_dim\n", + "\n", + " fp32_memory = (param_count + activation_count) * 4 # 4 bytes per FP32\n", + " mixed_memory = param_count * 4 + activation_count * 2 # FP32 params + FP16 activations\n", + " memory_savings = (fp32_memory - mixed_memory) / fp32_memory * 100\n", + "\n", + " print(f\" Memory analysis: {memory_savings:.1f}% savings from mixed precision\")\n", + " assert memory_savings > 0, \"Mixed precision should reduce memory usage\"\n", + "\n", + " print(\"✅ End-to-end acceleration pipeline works!\")\n", + "\n", + " print(\"\\n\" + \"=\" * 50)\n", + " print(\"🎉 ALL TESTS PASSED! Module ready for export.\")\n", + " print(\"Run: tito module complete 16\")\n", + "\n", + "# Call the module test\n", + "test_module()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6531eb00", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "main-execution", + "solution": false + } + }, + "outputs": [], + "source": [ + "# Main execution block\n", + "if __name__ == \"__main__\":\n", + " print(\"🚀 Running Acceleration module...\")\n", + " test_module()\n", + " print(\"✅ Module validation complete!\")" + ] + }, + { + "cell_type": "markdown", + "id": "e1054af9", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🤔 ML Systems Thinking: Acceleration and Performance\n", + "\n", + "### Question 1: Arithmetic Intensity Analysis\n", + "You implemented vectorized matrix multiplication and fused GELU.\n", + "- Matrix multiplication (1024×1024): Performs ~2.1 billion FLOPs, reads ~12 MB data\n", + "- Arithmetic intensity: _____ FLOPs/byte\n", + "- Compared to element-wise addition (0.33 FLOPs/byte): _____× higher intensity\n", + "- Why does this make matrix multiplication ideal for GPUs? _____\n", + "\n", + "### Question 2: Kernel Fusion Memory Benefits\n", + "Your fused_gelu combines 7 operations into a single expression.\n", + "- Unfused version memory accesses: 7 reads + 7 writes = _____ per element\n", + "- Fused version memory accesses: 1 read + 1 write = _____ per element\n", + "- Memory bandwidth reduction: _____%\n", + "- Why is this critical for transformer inference? _____\n", + "\n", + "### Question 3: Mixed Precision Memory Calculation\n", + "Your MixedPrecisionTrainer uses FP16 activations, FP32 parameters.\n", + "For a 100M parameter model with 50M activation elements:\n", + "- FP32 memory: (100M + 50M) × 4 bytes = _____ MB\n", + "- Mixed precision memory: 100M × 4 + 50M × 2 = _____ MB\n", + "- Memory reduction: _____%\n", + "\n", + "### Question 4: Loss Scaling Strategy\n", + "Your trainer starts with loss_scale=1024, grows by 2×, shrinks by 0.5×.\n", + "- Minimum FP16 representable value: ~6e-5\n", + "- Without scaling, gradients < _____ become zero\n", + "- With 1024× scaling, gradients down to _____ are preserved\n", + "- Why increase scale gradually but decrease immediately? _____\n", + "\n", + "### Question 5: Production Optimization Strategy\n", + "Based on your decision framework analysis:\n", + "For edge deployment (memory critical, stability required, hardware diverse):\n", + "- Priority 1 technique: _____ (low risk, universal)\n", + "- Priority 2 technique: _____ (memory benefits)\n", + "- Skip technique: _____ (why: _____)\n", + "- What's the primary constraint: memory, compute, or power? _____" + ] + }, + { + "cell_type": "markdown", + "id": "2fcecfae", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🎯 MODULE SUMMARY: Acceleration\n", + "\n", + "Congratulations! You've mastered the fundamental techniques for accelerating neural networks!\n", + "\n", + "### Key Accomplishments\n", + "- Built **vectorized operations** leveraging SIMD and optimized BLAS for 2-5× speedups\n", + "- Implemented **kernel fusion** reducing memory bandwidth by 60-80% for element-wise operations\n", + "- Created **mixed precision training** with automatic loss scaling for 20-40% memory savings\n", + "- Analyzed **arithmetic intensity patterns** and their impact on the roofline model\n", + "- Developed **production decision framework** for systematic optimization\n", + "- All tests pass ✅ (validated by `test_module()`)\n", + "\n", + "### Systems Insights Discovered\n", + "- **Roofline Model**: Operations with high arithmetic intensity (FLOPs/byte) scale better\n", + "- **Memory Bandwidth**: Often the limiting factor for modern accelerators\n", + "- **Kernel Fusion**: Critical for memory-bound workloads, reduces intermediate storage overhead\n", + "- **Mixed Precision**: Essential for large model training, requires careful gradient scaling\n", + "- **Optimization Strategy**: Start simple (vectorization), add complexity as needed\n", + "\n", + "### Production Impact\n", + "Your acceleration techniques enable:\n", + "- **Training larger models** within memory constraints\n", + "- **Faster iteration cycles** during research and development\n", + "- **Better hardware utilization** across different deployment targets\n", + "- **Cost reduction** through improved efficiency\n", + "\n", + "### Ready for Next Steps\n", + "Your acceleration implementations provide the foundation for quantization techniques in Module 17.\n", + "The performance analysis skills transfer directly to production optimization workflows.\n", + "\n", + "Export with: `tito module complete 16`\n", + "\n", + "**Next**: Module 17 will add quantization to further reduce memory and increase throughput while maintaining accuracy!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/modules/source/16_acceleration/acceleration_dev.py b/modules/source/16_acceleration/acceleration_dev.py index 550967f8..7ea37a02 100644 --- a/modules/source/16_acceleration/acceleration_dev.py +++ b/modules/source/16_acceleration/acceleration_dev.py @@ -681,6 +681,7 @@ N = parameter count, A = activation memory """ # %% nbgrader={"grade": false, "grade_id": "mixed-precision-trainer", "solution": true} +#| export class MixedPrecisionTrainer: """ Mixed precision trainer with automatic loss scaling. diff --git a/modules/source/17_quantization/quantization_dev.ipynb b/modules/source/17_quantization/quantization_dev.ipynb new file mode 100644 index 00000000..a487f386 --- /dev/null +++ b/modules/source/17_quantization/quantization_dev.ipynb @@ -0,0 +1,2521 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "2acc88dd", + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp optimization.quantization\n", + "#| export" + ] + }, + { + "cell_type": "markdown", + "id": "479b9fc0", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "# Module 17: Quantization - Making Models Smaller and Faster\n", + "\n", + "Welcome to Quantization! Today you'll learn how to reduce model precision from FP32 to INT8 while preserving accuracy.\n", + "\n", + "## 🔗 Prerequisites & Progress\n", + "**You've Built**: Complete ML pipeline with profiling and acceleration techniques\n", + "**You'll Build**: INT8 quantization system with calibration and memory savings\n", + "**You'll Enable**: 4× memory reduction and 2-4× speedup with minimal accuracy loss\n", + "\n", + "**Connection Map**:\n", + "```\n", + "Profiling → Quantization → Compression\n", + "(measure) (reduce bits) (remove weights)\n", + "```\n", + "\n", + "## Learning Objectives\n", + "By the end of this module, you will:\n", + "1. Implement INT8 quantization with proper scaling\n", + "2. Build quantization-aware training for minimal accuracy loss\n", + "3. Apply post-training quantization to existing models\n", + "4. Measure actual memory and compute savings\n", + "5. Understand quantization error and mitigation strategies\n", + "\n", + "Let's make models 4× smaller!" + ] + }, + { + "cell_type": "markdown", + "id": "f08c1131", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 📦 Where This Code Lives in the Final Package\n", + "\n", + "**Learning Side:** You work in `modules/17_quantization/quantization_dev.py` \n", + "**Building Side:** Code exports to `tinytorch.optimization.quantization`\n", + "\n", + "```python\n", + "# How to use this module:\n", + "from tinytorch.optimization.quantization import quantize_int8, QuantizedLinear, quantize_model\n", + "```\n", + "\n", + "**Why this matters:**\n", + "- **Learning:** Complete quantization system in one focused module for deep understanding\n", + "- **Production:** Proper organization like PyTorch's torch.quantization with all optimization components together\n", + "- **Consistency:** All quantization operations and calibration tools in optimization.quantization\n", + "- **Integration:** Works seamlessly with existing models for complete optimization pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ed30f4b2", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "imports", + "solution": true + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import time\n", + "import matplotlib.pyplot as plt\n", + "from typing import Tuple, Dict, List, Optional\n", + "import warnings\n", + "\n", + "# Smart import system for development and production compatibility\n", + "import sys\n", + "import os\n", + "\n", + "# Import dependencies from other modules\n", + "sys.path.append(os.path.join(os.path.dirname(__file__), '..', '01_tensor'))\n", + "from tensor_dev import Tensor\n", + "\n", + "sys.path.append(os.path.join(os.path.dirname(__file__), '..', '03_layers'))\n", + "from layers_dev import Linear, Sequential\n", + "\n", + "sys.path.append(os.path.join(os.path.dirname(__file__), '..', '02_activations'))\n", + "from activations_dev import ReLU\n", + "\n", + "# Note: Keeping development fallback for reference\n", + "if False: # Disabled development fallback\n", + " # Development: Import from local module files\n", + " try:\n", + " # Try to find the current directory\n", + " current_dir = os.path.dirname(os.path.abspath(__file__))\n", + " except NameError:\n", + " # Fallback when __file__ is not available (e.g., in exec context)\n", + " current_dir = os.getcwd()\n", + "\n", + " # Import Tensor from Module 01\n", + " tensor_module_path = os.path.join(current_dir, '..', '01_tensor')\n", + " sys.path.insert(0, tensor_module_path)\n", + " try:\n", + " from tensor_dev import Tensor\n", + " finally:\n", + " sys.path.pop(0)\n", + "\n", + " # Import from Module 03 layers\n", + " layers_module_path = os.path.join(current_dir, '..', '03_layers')\n", + " sys.path.insert(0, layers_module_path)\n", + " try:\n", + " from layers_dev import Linear, Sequential\n", + " finally:\n", + " sys.path.pop(0)\n", + "\n", + " # Import from Module 02 activations\n", + " activations_module_path = os.path.join(current_dir, '..', '02_activations')\n", + " sys.path.insert(0, activations_module_path)\n", + " try:\n", + " from activations_dev import ReLU\n", + " finally:\n", + " sys.path.pop(0)\n", + "\n", + " # Create dummy profiler if needed\n", + " class Profiler:\n", + " \"\"\"Dummy profiler class for development.\"\"\"\n", + " def count_parameters(self, model):\n", + " return 0\n", + " def measure_memory(self, model, input_shape):\n", + " return {\"total\": 0}\n", + "\n", + "print(\"✅ Quantization module imports complete\")" + ] + }, + { + "cell_type": "markdown", + "id": "4006fa45", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 1. Introduction - The Memory Wall Problem\n", + "\n", + "Imagine trying to fit a library in your backpack. Neural networks face the same challenge - models are getting huge, but devices have limited memory!\n", + "\n", + "### The Precision Paradox\n", + "\n", + "Modern neural networks use 32-bit floating point numbers with incredible precision:\n", + "\n", + "```\n", + "FP32 Number: 3.14159265359...\n", + " ^^^^^^^^^^^^^^^^\n", + " 32 bits = 4 bytes per weight\n", + "```\n", + "\n", + "But here's the surprising truth: **we don't need all that precision for most AI tasks!**\n", + "\n", + "### The Growing Memory Crisis\n", + "\n", + "```\n", + "Model Memory Requirements (FP32):\n", + "┌─────────────────────────────────────────────────────────────┐\n", + "│ BERT-Base: 110M params × 4 bytes = 440MB │\n", + "│ GPT-2: 1.5B params × 4 bytes = 6GB │\n", + "│ GPT-3: 175B params × 4 bytes = 700GB │\n", + "│ Your Phone: Available RAM = 4-8GB │\n", + "└─────────────────────────────────────────────────────────────┘\n", + " ↑\n", + " Problem!\n", + "```\n", + "\n", + "### The Quantization Solution\n", + "\n", + "What if we could represent each weight with just 8 bits instead of 32?\n", + "\n", + "```\n", + "Before Quantization (FP32):\n", + "┌──────────────────────────────────┐\n", + "│ 3.14159265 │ 2.71828183 │ │ 32 bits each\n", + "└──────────────────────────────────┘\n", + "\n", + "After Quantization (INT8):\n", + "┌────────┬────────┬────────┬────────┐\n", + "│ 98 │ 85 │ 72 │ 45 │ 8 bits each\n", + "└────────┴────────┴────────┴────────┘\n", + " ↑\n", + " 4× less memory!\n", + "```\n", + "\n", + "### Real-World Impact You'll Achieve\n", + "\n", + "**Memory Reduction:**\n", + "- BERT-Base: 440MB → 110MB (4× smaller)\n", + "- Fits on mobile devices!\n", + "- Faster loading from disk\n", + "- More models in GPU memory\n", + "\n", + "**Speed Improvements:**\n", + "- 2-4× faster inference (hardware dependent)\n", + "- Lower power consumption\n", + "- Better user experience\n", + "\n", + "**Accuracy Preservation:**\n", + "- <1% accuracy loss with proper techniques\n", + "- Sometimes even improves generalization!\n", + "\n", + "**Why This Matters:**\n", + "- **Mobile AI:** Deploy powerful models on phones\n", + "- **Edge Computing:** Run AI without cloud connectivity\n", + "- **Data Centers:** Serve more users with same hardware\n", + "- **Environmental:** Reduce energy consumption by 2-4×\n", + "\n", + "Today you'll build the production-quality quantization system that makes all this possible!" + ] + }, + { + "cell_type": "markdown", + "id": "bab2541f", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 2. Foundations - The Mathematics of Compression\n", + "\n", + "### Understanding the Core Challenge\n", + "\n", + "Think of quantization like converting a smooth analog signal to digital steps. We need to map infinite precision (FP32) to just 256 possible values (INT8).\n", + "\n", + "### The Quantization Mapping\n", + "\n", + "```\n", + "The Fundamental Problem:\n", + "\n", + "FP32 Numbers (Continuous): INT8 Numbers (Discrete):\n", + " ∞ possible values → 256 possible values\n", + "\n", + " ... -1.7 -1.2 -0.3 0.0 0.8 1.5 2.1 ...\n", + " ↓ ↓ ↓ ↓ ↓ ↓ ↓\n", + " -128 -95 -38 0 25 48 67 127\n", + "```\n", + "\n", + "### The Magic Formula\n", + "\n", + "Every quantization system uses this fundamental relationship:\n", + "\n", + "```\n", + "Quantization (FP32 → INT8):\n", + "┌─────────────────────────────────────────────────────────┐\n", + "│ quantized = round((float_value - zero_point) / scale) │\n", + "└─────────────────────────────────────────────────────────┘\n", + "\n", + "Dequantization (INT8 → FP32):\n", + "┌─────────────────────────────────────────────────────────┐\n", + "│ float_value = scale × quantized + zero_point │\n", + "└─────────────────────────────────────────────────────────┘\n", + "```\n", + "\n", + "### The Two Critical Parameters\n", + "\n", + "**1. Scale (s)** - How big each INT8 step is in FP32 space:\n", + "```\n", + "Small Scale (high precision): Large Scale (low precision):\n", + " FP32: [0.0, 0.255] FP32: [0.0, 25.5]\n", + " ↓ ↓ ↓ ↓ ↓ ↓\n", + " INT8: 0 128 255 INT8: 0 128 255\n", + " │ │ │ │ │ │\n", + " 0.0 0.127 0.255 0.0 12.75 25.5\n", + "\n", + " Scale = 0.001 (very precise) Scale = 0.1 (less precise)\n", + "```\n", + "\n", + "**2. Zero Point (z)** - Which INT8 value represents FP32 zero:\n", + "```\n", + "Symmetric Range: Asymmetric Range:\n", + " FP32: [-2.0, 2.0] FP32: [-1.0, 3.0]\n", + " ↓ ↓ ↓ ↓ ↓ ↓\n", + " INT8: -128 0 127 INT8: -128 64 127\n", + " │ │ │ │ │ │\n", + " -2.0 0.0 2.0 -1.0 0.0 3.0\n", + "\n", + " Zero Point = 0 Zero Point = 64\n", + "```\n", + "\n", + "### Visual Example: Weight Quantization\n", + "\n", + "```\n", + "Original FP32 Weights: Quantized INT8 Mapping:\n", + "┌─────────────────────────┐ ┌─────────────────────────┐\n", + "│ -0.8 -0.3 0.0 0.5 │ → │ -102 -38 0 64 │\n", + "│ 0.9 1.2 -0.1 0.7 │ │ 115 153 -13 89 │\n", + "└─────────────────────────┘ └─────────────────────────┘\n", + " 4 bytes each 1 byte each\n", + " Total: 32 bytes Total: 8 bytes\n", + " ↑\n", + " 4× compression!\n", + "```\n", + "\n", + "### Quantization Error Analysis\n", + "\n", + "```\n", + "Perfect Reconstruction (Impossible): Quantized Reconstruction (Reality):\n", + "\n", + "Original: 0.73 Original: 0.73\n", + " ↓ ↓\n", + "INT8: ? (can't represent exactly) INT8: 93 (closest)\n", + " ↓ ↓\n", + "Restored: 0.73 Restored: 0.728\n", + " ↑\n", + " Error: 0.002\n", + "```\n", + "\n", + "**The Quantization Trade-off:**\n", + "- **More bits** = Higher precision, larger memory\n", + "- **Fewer bits** = Lower precision, smaller memory\n", + "- **Goal:** Find the sweet spot where error is acceptable\n", + "\n", + "### Why INT8 is the Sweet Spot\n", + "\n", + "```\n", + "Precision vs Memory Trade-offs:\n", + "\n", + "FP32: ████████████████████████████████ (32 bits) - Overkill precision\n", + "FP16: ████████████████ (16 bits) - Good precision\n", + "INT8: ████████ (8 bits) - Sufficient precision ← Sweet spot!\n", + "INT4: ████ (4 bits) - Often too little\n", + "\n", + "Memory: 100% 50% 25% 12.5%\n", + "Accuracy: 100% 99.9% 99.5% 95%\n", + "```\n", + "\n", + "INT8 gives us 4× memory reduction with <1% accuracy loss - the perfect balance for production systems!" + ] + }, + { + "cell_type": "markdown", + "id": "66797259", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 3. Implementation - Building the Quantization Engine\n", + "\n", + "### Our Implementation Strategy\n", + "\n", + "We'll build quantization in logical layers, each building on the previous:\n", + "\n", + "```\n", + "Quantization System Architecture:\n", + "\n", + "┌─────────────────────────────────────────────────────────────┐\n", + "│ Layer 4: Model Quantization │\n", + "│ quantize_model() - Convert entire neural networks │\n", + "├─────────────────────────────────────────────────────────────┤\n", + "│ Layer 3: Layer Quantization │\n", + "│ QuantizedLinear - Quantized linear transformations │\n", + "├─────────────────────────────────────────────────────────────┤\n", + "│ Layer 2: Tensor Operations │\n", + "│ quantize_int8() - Core quantization algorithm │\n", + "│ dequantize_int8() - Restore to floating point │\n", + "├─────────────────────────────────────────────────────────────┤\n", + "│ Layer 1: Foundation │\n", + "│ Scale & Zero Point Calculation - Parameter optimization │\n", + "└─────────────────────────────────────────────────────────────┘\n", + "```\n", + "\n", + "### What We're About to Build\n", + "\n", + "**Core Functions:**\n", + "- `quantize_int8()` - Convert FP32 tensors to INT8\n", + "- `dequantize_int8()` - Convert INT8 back to FP32\n", + "- `QuantizedLinear` - Quantized version of Linear layers\n", + "- `quantize_model()` - Quantize entire neural networks\n", + "\n", + "**Key Features:**\n", + "- **Automatic calibration** - Find optimal quantization parameters\n", + "- **Error minimization** - Preserve accuracy during compression\n", + "- **Memory tracking** - Measure actual savings achieved\n", + "- **Production patterns** - Industry-standard algorithms\n", + "\n", + "Let's start with the fundamental building block!" + ] + }, + { + "cell_type": "markdown", + "id": "89f744ea", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### INT8 Quantization - The Foundation\n", + "\n", + "This is the core function that converts any FP32 tensor to INT8. Think of it as a smart compression algorithm that preserves the most important information.\n", + "\n", + "```\n", + "Quantization Process Visualization:\n", + "\n", + "Step 1: Analyze Range Step 2: Calculate Parameters Step 3: Apply Formula\n", + "┌─────────────────────────┐ ┌─────────────────────────┐ ┌─────────────────────────┐\n", + "│ Input: [-1.5, 0.2, 2.8] │ │ Min: -1.5 │ │ quantized = round( │\n", + "│ │ │ Max: 2.8 │ │ (value - zp*scale) │\n", + "│ Find min/max values │ → │ Range: 4.3 │ →│ / scale) │\n", + "│ │ │ Scale: 4.3/255 = 0.017 │ │ │\n", + "│ │ │ Zero Point: 88 │ │ Result: [-128, 12, 127] │\n", + "└─────────────────────────┘ └─────────────────────────┘ └─────────────────────────┘\n", + "```\n", + "\n", + "**Key Challenges This Function Solves:**\n", + "- **Dynamic Range:** Each tensor has different min/max values\n", + "- **Precision Loss:** Map 4 billion FP32 values to just 256 INT8 values\n", + "- **Zero Preservation:** Ensure FP32 zero maps exactly to an INT8 value\n", + "- **Symmetric Mapping:** Distribute quantization levels efficiently\n", + "\n", + "**Why This Algorithm:**\n", + "- **Linear mapping** preserves relative relationships between values\n", + "- **Symmetric quantization** works well for most neural network weights\n", + "- **Clipping to [-128, 127]** ensures valid INT8 range\n", + "- **Round-to-nearest** minimizes quantization error" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bccfa56e", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "quantize_int8", + "solution": true + } + }, + "outputs": [], + "source": [ + "def quantize_int8(tensor: Tensor) -> Tuple[Tensor, float, int]:\n", + " \"\"\"\n", + " Quantize FP32 tensor to INT8 using symmetric quantization.\n", + "\n", + " TODO: Implement INT8 quantization with scale and zero_point calculation\n", + "\n", + " APPROACH:\n", + " 1. Find min/max values in tensor data\n", + " 2. Calculate scale: (max_val - min_val) / 255 (INT8 range: -128 to 127)\n", + " 3. Calculate zero_point: offset to map FP32 zero to INT8 zero\n", + " 4. Apply quantization formula: round((value - zero_point) / scale)\n", + " 5. Clamp to INT8 range [-128, 127]\n", + "\n", + " EXAMPLE:\n", + " >>> tensor = Tensor([[-1.0, 0.0, 2.0], [0.5, 1.5, -0.5]])\n", + " >>> q_tensor, scale, zero_point = quantize_int8(tensor)\n", + " >>> print(f\"Scale: {scale:.4f}, Zero point: {zero_point}\")\n", + " Scale: 0.0118, Zero point: 42\n", + "\n", + " HINTS:\n", + " - Use np.round() for quantization\n", + " - Clamp with np.clip(values, -128, 127)\n", + " - Handle edge case where min_val == max_val (set scale=1.0)\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " data = tensor.data\n", + "\n", + " # Step 1: Find dynamic range\n", + " min_val = float(np.min(data))\n", + " max_val = float(np.max(data))\n", + "\n", + " # Step 2: Handle edge case (constant tensor)\n", + " if abs(max_val - min_val) < 1e-8:\n", + " scale = 1.0\n", + " zero_point = 0\n", + " quantized_data = np.zeros_like(data, dtype=np.int8)\n", + " return Tensor(quantized_data), scale, zero_point\n", + "\n", + " # Step 3: Calculate scale and zero_point for standard quantization\n", + " # Map [min_val, max_val] to [-128, 127] (INT8 range)\n", + " scale = (max_val - min_val) / 255.0\n", + " zero_point = int(np.round(-128 - min_val / scale))\n", + "\n", + " # Clamp zero_point to valid INT8 range\n", + " zero_point = int(np.clip(zero_point, -128, 127))\n", + "\n", + " # Step 4: Apply quantization formula: q = (x / scale) + zero_point\n", + " quantized_data = np.round(data / scale + zero_point)\n", + "\n", + " # Step 5: Clamp to INT8 range and convert to int8\n", + " quantized_data = np.clip(quantized_data, -128, 127).astype(np.int8)\n", + "\n", + " return Tensor(quantized_data), scale, zero_point\n", + " ### END SOLUTION\n", + "\n", + "def test_unit_quantize_int8():\n", + " \"\"\"🔬 Test INT8 quantization implementation.\"\"\"\n", + " print(\"🔬 Unit Test: INT8 Quantization...\")\n", + "\n", + " # Test basic quantization\n", + " tensor = Tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])\n", + " q_tensor, scale, zero_point = quantize_int8(tensor)\n", + "\n", + " # Verify quantized values are in INT8 range\n", + " assert np.all(q_tensor.data >= -128)\n", + " assert np.all(q_tensor.data <= 127)\n", + " assert isinstance(scale, float)\n", + " assert isinstance(zero_point, int)\n", + "\n", + " # Test dequantization preserves approximate values\n", + " dequantized = scale * (q_tensor.data - zero_point)\n", + " error = np.mean(np.abs(tensor.data - dequantized))\n", + " assert error < 0.2, f\"Quantization error too high: {error}\"\n", + "\n", + " # Test edge case: constant tensor\n", + " constant_tensor = Tensor([[2.0, 2.0], [2.0, 2.0]])\n", + " q_const, scale_const, zp_const = quantize_int8(constant_tensor)\n", + " assert scale_const == 1.0\n", + "\n", + " print(\"✅ INT8 quantization works correctly!\")\n", + "\n", + "test_unit_quantize_int8()" + ] + }, + { + "cell_type": "markdown", + "id": "10333244", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### INT8 Dequantization - Restoring Precision\n", + "\n", + "Dequantization is the inverse process - converting compressed INT8 values back to usable FP32. This is where we \"decompress\" our quantized data.\n", + "\n", + "```\n", + "Dequantization Process:\n", + "\n", + "INT8 Values + Parameters → FP32 Reconstruction\n", + "\n", + "┌─────────────────────────┐\n", + "│ Quantized: [-128, 12, 127] │\n", + "│ Scale: 0.017 │\n", + "│ Zero Point: 88 │\n", + "└─────────────────────────┘\n", + " │\n", + " ▼ Apply Formula\n", + "┌─────────────────────────┐\n", + "│ FP32 = scale × quantized │\n", + "│ + zero_point × scale │\n", + "└─────────────────────────┘\n", + " │\n", + " ▼\n", + "┌─────────────────────────┐\n", + "│ Result: [-1.496, 0.204, 2.799]│\n", + "│ Original: [-1.5, 0.2, 2.8] │\n", + "│ Error: [0.004, 0.004, 0.001] │\n", + "└─────────────────────────┘\n", + " ↑\n", + " Excellent approximation!\n", + "```\n", + "\n", + "**Why This Step Is Critical:**\n", + "- **Neural networks expect FP32** - INT8 values would confuse computations\n", + "- **Preserves computation compatibility** - works with existing matrix operations\n", + "- **Controlled precision loss** - error is bounded and predictable\n", + "- **Hardware flexibility** - can use FP32 or specialized INT8 operations\n", + "\n", + "**When Dequantization Happens:**\n", + "- **During forward pass** - before matrix multiplications\n", + "- **For gradient computation** - during backward pass\n", + "- **Educational approach** - production uses INT8 GEMM directly" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2cc24635", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "dequantize_int8", + "solution": true + } + }, + "outputs": [], + "source": [ + "def dequantize_int8(q_tensor: Tensor, scale: float, zero_point: int) -> Tensor:\n", + " \"\"\"\n", + " Dequantize INT8 tensor back to FP32.\n", + "\n", + " TODO: Implement dequantization using the inverse formula\n", + "\n", + " APPROACH:\n", + " 1. Apply inverse quantization: scale * quantized_value + zero_point * scale\n", + " 2. Return as new FP32 Tensor\n", + "\n", + " EXAMPLE:\n", + " >>> q_tensor = Tensor([[-42, 0, 85]]) # INT8 values\n", + " >>> scale, zero_point = 0.0314, 64\n", + " >>> fp32_tensor = dequantize_int8(q_tensor, scale, zero_point)\n", + " >>> print(fp32_tensor.data)\n", + " [[-1.31, 2.01, 2.67]] # Approximate original values\n", + "\n", + " HINT:\n", + " - Formula: dequantized = scale * quantized + zero_point * scale\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " # Apply inverse quantization formula\n", + " dequantized_data = scale * q_tensor.data + zero_point * scale\n", + " return Tensor(dequantized_data.astype(np.float32))\n", + " ### END SOLUTION\n", + "\n", + "def test_unit_dequantize_int8():\n", + " \"\"\"🔬 Test INT8 dequantization implementation.\"\"\"\n", + " print(\"🔬 Unit Test: INT8 Dequantization...\")\n", + "\n", + " # Test round-trip: quantize → dequantize\n", + " original = Tensor([[-1.5, 0.0, 3.2], [1.1, -0.8, 2.7]])\n", + " q_tensor, scale, zero_point = quantize_int8(original)\n", + " restored = dequantize_int8(q_tensor, scale, zero_point)\n", + "\n", + " # Verify round-trip error is small\n", + " error = np.mean(np.abs(original.data - restored.data))\n", + " assert error < 2.0, f\"Round-trip error too high: {error}\"\n", + "\n", + " # Verify output is float32\n", + " assert restored.data.dtype == np.float32\n", + "\n", + " print(\"✅ INT8 dequantization works correctly!\")\n", + "\n", + "test_unit_dequantize_int8()" + ] + }, + { + "cell_type": "markdown", + "id": "4790bbcf", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## Quantization Quality - Understanding the Impact\n", + "\n", + "### Why Distribution Matters\n", + "\n", + "Different types of data quantize differently. Let's understand how various weight distributions affect quantization quality.\n", + "\n", + "```\n", + "Quantization Quality Factors:\n", + "\n", + "┌─────────────────┬─────────────────┬─────────────────┐\n", + "│ Distribution │ Scale Usage │ Error Level │\n", + "├─────────────────┼─────────────────┼─────────────────┤\n", + "│ Uniform │ ████████████████ │ Low │\n", + "│ Normal │ ██████████████ │ Medium │\n", + "│ With Outliers │ ████ │ High │\n", + "│ Sparse (zeros) │ ████ │ High │\n", + "└─────────────────┴─────────────────┴─────────────────┘\n", + "```\n", + "\n", + "### The Scale Utilization Problem\n", + "\n", + "```\n", + "Good Quantization (Uniform): Bad Quantization (Outliers):\n", + "\n", + "Values: [-1.0 ... +1.0] Values: [-10.0, -0.1...+0.1, +10.0]\n", + " ↓ ↓\n", + "INT8: -128 ......... +127 INT8: -128 ... 0 ... +127\n", + " ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑\n", + " All levels used Most levels wasted!\n", + "\n", + "Scale: 0.0078 (good precision) Scale: 0.078 (poor precision)\n", + "Error: ~0.004 Error: ~0.04 (10× worse!)\n", + "```\n", + "\n", + "**Key Insight:** Outliers waste quantization levels and hurt precision for normal values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c745d8e", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "analyze_quantization_error", + "solution": true + } + }, + "outputs": [], + "source": [ + "def analyze_quantization_error():\n", + " \"\"\"📊 Analyze quantization error across different distributions.\"\"\"\n", + " print(\"📊 Analyzing Quantization Error Across Distributions...\")\n", + "\n", + " distributions = {\n", + " 'uniform': np.random.uniform(-1, 1, (1000,)),\n", + " 'normal': np.random.normal(0, 0.5, (1000,)),\n", + " 'outliers': np.concatenate([np.random.normal(0, 0.1, (900,)),\n", + " np.random.uniform(-2, 2, (100,))]),\n", + " 'sparse': np.random.choice([0, 0, 0, 1], size=(1000,)) * np.random.normal(0, 1, (1000,))\n", + " }\n", + "\n", + " results = {}\n", + "\n", + " for name, data in distributions.items():\n", + " # Quantize and measure error\n", + " original = Tensor(data)\n", + " q_tensor, scale, zero_point = quantize_int8(original)\n", + " restored = dequantize_int8(q_tensor, scale, zero_point)\n", + "\n", + " # Calculate metrics\n", + " mse = np.mean((original.data - restored.data) ** 2)\n", + " max_error = np.max(np.abs(original.data - restored.data))\n", + "\n", + " results[name] = {\n", + " 'mse': mse,\n", + " 'max_error': max_error,\n", + " 'scale': scale,\n", + " 'range_ratio': (np.max(data) - np.min(data)) / scale if scale > 0 else 0\n", + " }\n", + "\n", + " print(f\"{name:8}: MSE={mse:.6f}, Max Error={max_error:.4f}, Scale={scale:.4f}\")\n", + "\n", + " print(\"\\n💡 Insights:\")\n", + " print(\"- Uniform: Low error, good scale utilization\")\n", + " print(\"- Normal: Higher error at distribution tails\")\n", + " print(\"- Outliers: Poor quantization due to extreme values\")\n", + " print(\"- Sparse: Wasted quantization levels on zeros\")\n", + "\n", + " return results\n", + "\n", + "# Analyze quantization quality\n", + "error_analysis = analyze_quantization_error()" + ] + }, + { + "cell_type": "markdown", + "id": "3bf20bbe", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## QuantizedLinear - The Heart of Efficient Networks\n", + "\n", + "### Why We Need Quantized Layers\n", + "\n", + "A quantized model isn't just about storing weights in INT8 - we need layers that can work efficiently with quantized data.\n", + "\n", + "```\n", + "Regular Linear Layer: QuantizedLinear Layer:\n", + "\n", + "┌─────────────────────┐ ┌─────────────────────┐\n", + "│ Input: FP32 │ │ Input: FP32 │\n", + "│ Weights: FP32 │ │ Weights: INT8 │\n", + "│ Computation: FP32 │ VS │ Computation: Mixed │\n", + "│ Output: FP32 │ │ Output: FP32 │\n", + "│ Memory: 4× more │ │ Memory: 4× less │\n", + "└─────────────────────┘ └─────────────────────┘\n", + "```\n", + "\n", + "### The Quantized Forward Pass\n", + "\n", + "```\n", + "Quantized Linear Layer Forward Pass:\n", + "\n", + " Input (FP32) Quantized Weights (INT8)\n", + " │ │\n", + " ▼ ▼\n", + "┌─────────────────┐ ┌─────────────────┐\n", + "│ Calibrate │ │ Dequantize │\n", + "│ (optional) │ │ Weights │\n", + "└─────────────────┘ └─────────────────┘\n", + " │ │\n", + " ▼ ▼\n", + " Input (FP32) Weights (FP32)\n", + " │ │\n", + " └───────────────┬───────────────┘\n", + " ▼\n", + " ┌─────────────────┐\n", + " │ Matrix Multiply │\n", + " │ (FP32 GEMM) │\n", + " └─────────────────┘\n", + " │\n", + " ▼\n", + " Output (FP32)\n", + "\n", + "Memory Saved: 4× for weights storage!\n", + "Speed: Depends on dequantization overhead vs INT8 GEMM support\n", + "```\n", + "\n", + "### Calibration - Finding Optimal Input Quantization\n", + "\n", + "```\n", + "Calibration Process:\n", + "\n", + " Step 1: Collect Sample Inputs Step 2: Analyze Distribution Step 3: Optimize Parameters\n", + " ┌─────────────────────────┐ ┌─────────────────────────┐ ┌─────────────────────────┐\n", + " │ input_1: [-0.5, 0.2, ..] │ │ Min: -0.8 │ │ Scale: 0.00627 │\n", + " │ input_2: [-0.3, 0.8, ..] │ → │ Max: +0.8 │ → │ Zero Point: 0 │\n", + " │ input_3: [-0.1, 0.5, ..] │ │ Range: 1.6 │ │ Optimal for this data │\n", + " │ ... │ │ Distribution: Normal │ │ range and distribution │\n", + " └─────────────────────────┘ └─────────────────────────┘ └─────────────────────────┘\n", + "```\n", + "\n", + "**Why Calibration Matters:**\n", + "- **Without calibration:** Generic quantization parameters may waste precision\n", + "- **With calibration:** Parameters optimized for actual data distribution\n", + "- **Result:** Better accuracy preservation with same memory savings" + ] + }, + { + "cell_type": "markdown", + "id": "2253b351", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### QuantizedLinear Class - Efficient Neural Network Layer\n", + "\n", + "This class replaces regular Linear layers with quantized versions that use 4× less memory while preserving functionality.\n", + "\n", + "```\n", + "QuantizedLinear Architecture:\n", + "\n", + "Creation Time: Runtime:\n", + "┌─────────────────────────┐ ┌─────────────────────────┐\n", + "│ Regular Linear Layer │ │ Input (FP32) │\n", + "│ ↓ │ │ ↓ │\n", + "│ Quantize weights → INT8 │ │ Optional: quantize input│\n", + "│ Quantize bias → INT8 │ → │ ↓ │\n", + "│ Store quantization params │ │ Dequantize weights │\n", + "│ Ready for deployment! │ │ ↓ │\n", + "└─────────────────────────┘ │ Matrix multiply (FP32) │\n", + " One-time cost │ ↓ │\n", + " │ Output (FP32) │\n", + " └─────────────────────────┘\n", + " Per-inference cost\n", + "```\n", + "\n", + "**Key Design Decisions:**\n", + "\n", + "1. **Store original layer reference** - for debugging and comparison\n", + "2. **Separate quantization parameters** - weights and bias may need different scales\n", + "3. **Calibration support** - optimize input quantization using real data\n", + "4. **FP32 computation** - educational approach, production uses INT8 GEMM\n", + "5. **Memory tracking** - measure actual compression achieved\n", + "\n", + "**Memory Layout Comparison:**\n", + "```\n", + "Regular Linear Layer: QuantizedLinear Layer:\n", + "┌─────────────────────────┐ ┌─────────────────────────┐\n", + "│ weights: FP32 × N │ │ q_weights: INT8 × N │\n", + "│ bias: FP32 × M │ │ q_bias: INT8 × M │\n", + "│ │ → │ weight_scale: 1 float │\n", + "│ Total: 4×(N+M) bytes │ │ weight_zero_point: 1 int│\n", + "└─────────────────────────┘ │ bias_scale: 1 float │\n", + " │ bias_zero_point: 1 int │\n", + " │ │\n", + " │ Total: (N+M) + 16 bytes │\n", + " └─────────────────────────┘\n", + " ↑\n", + " ~4× smaller!\n", + "```\n", + "\n", + "**Production vs Educational Trade-off:**\n", + "- **Our approach:** Dequantize → FP32 computation (easier to understand)\n", + "- **Production:** INT8 GEMM operations (faster, more complex)\n", + "- **Both achieve:** Same memory savings, similar accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1cfe87e1", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "quantized_linear", + "solution": true + } + }, + "outputs": [], + "source": [ + "class QuantizedLinear:\n", + " \"\"\"Quantized version of Linear layer using INT8 arithmetic.\"\"\"\n", + "\n", + " def __init__(self, linear_layer: Linear):\n", + " \"\"\"\n", + " Create quantized version of existing linear layer.\n", + "\n", + " TODO: Quantize weights and bias, store quantization parameters\n", + "\n", + " APPROACH:\n", + " 1. Quantize weights using quantize_int8\n", + " 2. Quantize bias if it exists\n", + " 3. Store original layer reference for forward pass\n", + " 4. Store quantization parameters for dequantization\n", + "\n", + " IMPLEMENTATION STRATEGY:\n", + " - Store quantized weights, scales, and zero points\n", + " - Implement forward pass using dequantized computation (educational approach)\n", + " - Production: Would use INT8 matrix multiplication libraries\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " self.original_layer = linear_layer\n", + "\n", + " # Quantize weights\n", + " self.q_weight, self.weight_scale, self.weight_zero_point = quantize_int8(linear_layer.weight)\n", + "\n", + " # Quantize bias if it exists\n", + " if linear_layer.bias is not None:\n", + " self.q_bias, self.bias_scale, self.bias_zero_point = quantize_int8(linear_layer.bias)\n", + " else:\n", + " self.q_bias = None\n", + " self.bias_scale = None\n", + " self.bias_zero_point = None\n", + "\n", + " # Store input quantization parameters (set during calibration)\n", + " self.input_scale = None\n", + " self.input_zero_point = None\n", + " ### END SOLUTION\n", + "\n", + " def calibrate(self, sample_inputs: List[Tensor]):\n", + " \"\"\"\n", + " Calibrate input quantization parameters using sample data.\n", + "\n", + " TODO: Calculate optimal input quantization parameters\n", + "\n", + " APPROACH:\n", + " 1. Collect statistics from sample inputs\n", + " 2. Calculate optimal scale and zero_point for inputs\n", + " 3. Store for use in forward pass\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " # Collect all input values\n", + " all_values = []\n", + " for inp in sample_inputs:\n", + " all_values.extend(inp.data.flatten())\n", + "\n", + " all_values = np.array(all_values)\n", + "\n", + " # Calculate input quantization parameters\n", + " min_val = float(np.min(all_values))\n", + " max_val = float(np.max(all_values))\n", + "\n", + " if abs(max_val - min_val) < 1e-8:\n", + " self.input_scale = 1.0\n", + " self.input_zero_point = 0\n", + " else:\n", + " self.input_scale = (max_val - min_val) / 255.0\n", + " self.input_zero_point = int(np.round(-128 - min_val / self.input_scale))\n", + " self.input_zero_point = np.clip(self.input_zero_point, -128, 127)\n", + " ### END SOLUTION\n", + "\n", + " def forward(self, x: Tensor) -> Tensor:\n", + " \"\"\"\n", + " Forward pass with quantized computation.\n", + "\n", + " TODO: Implement quantized forward pass\n", + "\n", + " APPROACH:\n", + " 1. Quantize input (if calibrated)\n", + " 2. Dequantize weights and input for computation (educational approach)\n", + " 3. Perform matrix multiplication\n", + " 4. Return FP32 result\n", + "\n", + " NOTE: Production quantization uses INT8 GEMM libraries for speed\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " # For educational purposes, we dequantize and compute in FP32\n", + " # Production systems use specialized INT8 GEMM operations\n", + "\n", + " # Dequantize weights\n", + " weight_fp32 = dequantize_int8(self.q_weight, self.weight_scale, self.weight_zero_point)\n", + "\n", + " # Perform computation (same as original layer)\n", + " result = x.matmul(weight_fp32)\n", + "\n", + " # Add bias if it exists\n", + " if self.q_bias is not None:\n", + " bias_fp32 = dequantize_int8(self.q_bias, self.bias_scale, self.bias_zero_point)\n", + " result = Tensor(result.data + bias_fp32.data)\n", + "\n", + " return result\n", + " ### END SOLUTION\n", + "\n", + " def parameters(self) -> List[Tensor]:\n", + " \"\"\"Return quantized parameters.\"\"\"\n", + " params = [self.q_weight]\n", + " if self.q_bias is not None:\n", + " params.append(self.q_bias)\n", + " return params\n", + "\n", + " def memory_usage(self) -> Dict[str, float]:\n", + " \"\"\"Calculate memory usage in bytes.\"\"\"\n", + " ### BEGIN SOLUTION\n", + " # Original FP32 usage\n", + " original_weight_bytes = self.original_layer.weight.data.size * 4 # 4 bytes per FP32\n", + " original_bias_bytes = 0\n", + " if self.original_layer.bias is not None:\n", + " original_bias_bytes = self.original_layer.bias.data.size * 4\n", + "\n", + " # Quantized INT8 usage\n", + " quantized_weight_bytes = self.q_weight.data.size * 1 # 1 byte per INT8\n", + " quantized_bias_bytes = 0\n", + " if self.q_bias is not None:\n", + " quantized_bias_bytes = self.q_bias.data.size * 1\n", + "\n", + " # Add overhead for scales and zero points (small)\n", + " overhead_bytes = 8 * 2 # 2 floats + 2 ints for weight/bias quantization params\n", + "\n", + " return {\n", + " 'original_bytes': original_weight_bytes + original_bias_bytes,\n", + " 'quantized_bytes': quantized_weight_bytes + quantized_bias_bytes + overhead_bytes,\n", + " 'compression_ratio': (original_weight_bytes + original_bias_bytes) /\n", + " (quantized_weight_bytes + quantized_bias_bytes + overhead_bytes)\n", + " }\n", + " ### END SOLUTION\n", + "\n", + "def test_unit_quantized_linear():\n", + " \"\"\"🔬 Test QuantizedLinear implementation.\"\"\"\n", + " print(\"🔬 Unit Test: QuantizedLinear...\")\n", + "\n", + " # Create original linear layer\n", + " original = Linear(4, 3)\n", + " original.weight = Tensor(np.random.randn(4, 3) * 0.5) # Smaller range for testing\n", + " original.bias = Tensor(np.random.randn(3) * 0.1)\n", + "\n", + " # Create quantized version\n", + " quantized = QuantizedLinear(original)\n", + "\n", + " # Test forward pass\n", + " x = Tensor(np.random.randn(2, 4) * 0.5)\n", + "\n", + " # Original forward pass\n", + " original_output = original.forward(x)\n", + "\n", + " # Quantized forward pass\n", + " quantized_output = quantized.forward(x)\n", + "\n", + " # Compare outputs (should be close but not identical due to quantization)\n", + " error = np.mean(np.abs(original_output.data - quantized_output.data))\n", + " assert error < 1.0, f\"Quantization error too high: {error}\"\n", + "\n", + " # Test memory usage\n", + " memory_info = quantized.memory_usage()\n", + " assert memory_info['compression_ratio'] > 3.0, \"Should achieve ~4× compression\"\n", + "\n", + " print(f\" Memory reduction: {memory_info['compression_ratio']:.1f}×\")\n", + " print(\"✅ QuantizedLinear works correctly!\")\n", + "\n", + "test_unit_quantized_linear()" + ] + }, + { + "cell_type": "markdown", + "id": "1a822fb8", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 4. Integration - Scaling to Full Neural Networks\n", + "\n", + "### The Model Quantization Challenge\n", + "\n", + "Quantizing individual tensors is useful, but real applications need to quantize entire neural networks with multiple layers, activations, and complex data flows.\n", + "\n", + "```\n", + "Model Quantization Process:\n", + "\n", + "Original Model: Quantized Model:\n", + "┌─────────────────────────────┐ ┌─────────────────────────────┐\n", + "│ Linear(784, 128) [FP32] │ │ QuantizedLinear(784, 128) │\n", + "│ ReLU() [FP32] │ │ ReLU() [FP32] │\n", + "│ Linear(128, 64) [FP32] │ → │ QuantizedLinear(128, 64) │\n", + "│ ReLU() [FP32] │ │ ReLU() [FP32] │\n", + "│ Linear(64, 10) [FP32] │ │ QuantizedLinear(64, 10) │\n", + "└─────────────────────────────┘ └─────────────────────────────┘\n", + " Memory: 100% Memory: ~25%\n", + " Speed: Baseline Speed: 2-4× faster\n", + "```\n", + "\n", + "### Smart Layer Selection\n", + "\n", + "Not all layers benefit equally from quantization:\n", + "\n", + "```\n", + "Layer Quantization Strategy:\n", + "\n", + "┌─────────────────┬─────────────────┬─────────────────────────────┐\n", + "│ Layer Type │ Quantize? │ Reason │\n", + "├─────────────────┼─────────────────┼─────────────────────────────┤\n", + "│ Linear/Dense │ ✅ YES │ Most parameters, big savings │\n", + "│ Convolution │ ✅ YES │ Many weights, good candidate │\n", + "│ Embedding │ ✅ YES │ Large lookup tables │\n", + "│ ReLU/Sigmoid │ ❌ NO │ No parameters to quantize │\n", + "│ BatchNorm │ 🤔 MAYBE │ Few params, may hurt │\n", + "│ First Layer │ 🤔 MAYBE │ Often sensitive to precision │\n", + "│ Last Layer │ 🤔 MAYBE │ Output quality critical │\n", + "└─────────────────┴─────────────────┴─────────────────────────────┘\n", + "```\n", + "\n", + "### Calibration Data Flow\n", + "\n", + "```\n", + "End-to-End Calibration:\n", + "\n", + "Calibration Input Layer-by-Layer Processing\n", + " │ │\n", + " ▼ ▼\n", + "┌─────────────┐ ┌──────────────────────────────────────────┐\n", + "│ Sample Data │ → │ Layer 1: Collect activation statistics │\n", + "│ [batch of │ │ ↓ │\n", + "│ real data] │ │ Layer 2: Collect activation statistics │\n", + "└─────────────┘ │ ↓ │\n", + " │ Layer 3: Collect activation statistics │\n", + " │ ↓ │\n", + " │ Optimize quantization parameters │\n", + " └──────────────────────────────────────────┘\n", + " │\n", + " ▼\n", + " Ready for deployment!\n", + "```\n", + "\n", + "### Memory Impact Visualization\n", + "\n", + "```\n", + "Model Memory Breakdown:\n", + "\n", + "Before Quantization: After Quantization:\n", + "┌─────────────────────┐ ┌─────────────────────┐\n", + "│ Layer 1: 3.1MB │ │ Layer 1: 0.8MB │ (-75%)\n", + "│ Layer 2: 0.5MB │ → │ Layer 2: 0.1MB │ (-75%)\n", + "│ Layer 3: 0.3MB │ │ Layer 3: 0.1MB │ (-75%)\n", + "│ Total: 3.9MB │ │ Total: 1.0MB │ (-74%)\n", + "└─────────────────────┘ └─────────────────────┘\n", + "\n", + " Typical mobile phone memory: 4-8GB\n", + " Model now fits: 4000× more models in memory!\n", + "```\n", + "\n", + "Now let's implement the functions that make this transformation possible!" + ] + }, + { + "cell_type": "markdown", + "id": "9c025ff3", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### Model Quantization - Scaling to Full Networks\n", + "\n", + "This function transforms entire neural networks from FP32 to quantized versions. It's like upgrading a whole building to be more energy efficient!\n", + "\n", + "```\n", + "Model Transformation Process:\n", + "\n", + "Input Model: Quantized Model:\n", + "┌─────────────────────────────┐ ┌─────────────────────────────┐\n", + "│ layers[0]: Linear(784, 128) │ │ layers[0]: QuantizedLinear │\n", + "│ layers[1]: ReLU() │ │ layers[1]: ReLU() │\n", + "│ layers[2]: Linear(128, 64) │ → │ layers[2]: QuantizedLinear │\n", + "│ layers[3]: ReLU() │ │ layers[3]: ReLU() │\n", + "│ layers[4]: Linear(64, 10) │ │ layers[4]: QuantizedLinear │\n", + "└─────────────────────────────┘ └─────────────────────────────┘\n", + " Memory: 100% Memory: ~25%\n", + " Interface: Same Interface: Identical\n", + "```\n", + "\n", + "**Smart Layer Selection Logic:**\n", + "```\n", + "Quantization Decision Tree:\n", + "\n", + "For each layer in model:\n", + " │\n", + " ├── Is it a Linear layer?\n", + " │ │\n", + " │ └── YES → Replace with QuantizedLinear\n", + " │\n", + " └── Is it ReLU/Activation?\n", + " │\n", + " └── NO → Keep unchanged (no parameters to quantize)\n", + "```\n", + "\n", + "**Calibration Integration:**\n", + "```\n", + "Calibration Data Flow:\n", + "\n", + " Input Data Layer-by-Layer Processing\n", + " │ │\n", + " ▼ ▼\n", + " ┌─────────────────┐ ┌───────────────────────────────────────────────────────────┐\n", + " │ Sample Batch 1 │ │ Layer 0: Forward → Collect activation statistics │\n", + " │ Sample Batch 2 │ → │ ↓ │\n", + " │ ... │ │ Layer 2: Forward → Collect activation statistics │\n", + " │ Sample Batch N │ │ ↓ │\n", + " └─────────────────┘ │ Layer 4: Forward → Collect activation statistics │\n", + " │ ↓ │\n", + " │ For each layer: calibrate optimal quantization │\n", + " └───────────────────────────────────────────────────────────┘\n", + "```\n", + "\n", + "**Why In-Place Modification:**\n", + "- **Preserves model structure** - Same interface, same behavior\n", + "- **Memory efficient** - No copying of large tensors\n", + "- **Drop-in replacement** - Existing code works unchanged\n", + "- **Gradual quantization** - Can selectively quantize sensitive layers\n", + "\n", + "**Deployment Benefits:**\n", + "```\n", + "Before Quantization: After Quantization:\n", + "┌─────────────────────────┐ ┌─────────────────────────┐\n", + "│ ❌ Can't fit on phone │ │ ✅ Fits on mobile device │\n", + "│ ❌ Slow cloud deployment │ │ ✅ Fast edge inference │\n", + "│ ❌ High memory usage │ → │ ✅ 4× memory efficiency │\n", + "│ ❌ Expensive to serve │ │ ✅ Lower serving costs │\n", + "│ ❌ Battery drain │ │ ✅ Extended battery life │\n", + "└─────────────────────────┘ └─────────────────────────┘\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "55ead684", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "quantize_model", + "solution": true + } + }, + "outputs": [], + "source": [ + "def quantize_model(model, calibration_data: Optional[List[Tensor]] = None) -> None:\n", + " \"\"\"\n", + " Quantize all Linear layers in a model in-place.\n", + "\n", + " TODO: Replace all Linear layers with QuantizedLinear versions\n", + "\n", + " APPROACH:\n", + " 1. Find all Linear layers in the model\n", + " 2. Replace each with QuantizedLinear version\n", + " 3. If calibration data provided, calibrate input quantization\n", + " 4. Handle Sequential containers properly\n", + "\n", + " EXAMPLE:\n", + " >>> model = Sequential(Linear(10, 5), ReLU(), Linear(5, 2))\n", + " >>> quantize_model(model)\n", + " >>> # Now model uses quantized layers\n", + "\n", + " HINT:\n", + " - Handle Sequential.layers list for layer replacement\n", + " - Use isinstance(layer, Linear) to identify layers to quantize\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " if hasattr(model, 'layers'): # Sequential model\n", + " for i, layer in enumerate(model.layers):\n", + " if isinstance(layer, Linear):\n", + " # Replace with quantized version\n", + " quantized_layer = QuantizedLinear(layer)\n", + "\n", + " # Calibrate if data provided\n", + " if calibration_data is not None:\n", + " # Run forward passes to get intermediate activations\n", + " sample_inputs = []\n", + " for data in calibration_data[:10]: # Use first 10 samples for efficiency\n", + " # Forward through layers up to this point\n", + " x = data\n", + " for j in range(i):\n", + " if hasattr(model.layers[j], 'forward'):\n", + " x = model.layers[j].forward(x)\n", + " sample_inputs.append(x)\n", + "\n", + " quantized_layer.calibrate(sample_inputs)\n", + "\n", + " model.layers[i] = quantized_layer\n", + "\n", + " elif isinstance(model, Linear): # Single Linear layer\n", + " # Can't replace in-place for single layer, user should handle\n", + " raise ValueError(\"Cannot quantize single Linear layer in-place. Use QuantizedLinear directly.\")\n", + "\n", + " else:\n", + " raise ValueError(f\"Unsupported model type: {type(model)}\")\n", + " ### END SOLUTION\n", + "\n", + "def test_unit_quantize_model():\n", + " \"\"\"🔬 Test model quantization implementation.\"\"\"\n", + " print(\"🔬 Unit Test: Model Quantization...\")\n", + "\n", + " # Create test model\n", + " model = Sequential(\n", + " Linear(4, 8),\n", + " ReLU(),\n", + " Linear(8, 3)\n", + " )\n", + "\n", + " # Initialize weights\n", + " model.layers[0].weight = Tensor(np.random.randn(4, 8) * 0.5)\n", + " model.layers[0].bias = Tensor(np.random.randn(8) * 0.1)\n", + " model.layers[2].weight = Tensor(np.random.randn(8, 3) * 0.5)\n", + " model.layers[2].bias = Tensor(np.random.randn(3) * 0.1)\n", + "\n", + " # Test original model\n", + " x = Tensor(np.random.randn(2, 4))\n", + " original_output = model.forward(x)\n", + "\n", + " # Create calibration data\n", + " calibration_data = [Tensor(np.random.randn(1, 4)) for _ in range(5)]\n", + "\n", + " # Quantize model\n", + " quantize_model(model, calibration_data)\n", + "\n", + " # Verify layers were replaced\n", + " assert isinstance(model.layers[0], QuantizedLinear)\n", + " assert isinstance(model.layers[1], ReLU) # Should remain unchanged\n", + " assert isinstance(model.layers[2], QuantizedLinear)\n", + "\n", + " # Test quantized model\n", + " quantized_output = model.forward(x)\n", + "\n", + " # Compare outputs\n", + " error = np.mean(np.abs(original_output.data - quantized_output.data))\n", + " print(f\" Model quantization error: {error:.4f}\")\n", + " assert error < 2.0, f\"Model quantization error too high: {error}\"\n", + "\n", + " print(\"✅ Model quantization works correctly!\")\n", + "\n", + "test_unit_quantize_model()" + ] + }, + { + "cell_type": "markdown", + "id": "25d42062", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### Model Size Comparison - Measuring the Impact\n", + "\n", + "This function provides detailed analysis of memory savings achieved through quantization. It's like a before/after comparison for model efficiency.\n", + "\n", + "```\n", + "Memory Analysis Framework:\n", + "\n", + "┌────────────────────────────────────────────────────────────────────────────────────┐\n", + "│ Memory Breakdown Analysis │\n", + "├─────────────────┬─────────────────┬─────────────────┬─────────────────┤\n", + "│ Component │ Original (FP32) │ Quantized (INT8) │ Savings │\n", + "├─────────────────┼─────────────────┼─────────────────┼─────────────────┤\n", + "│ Layer 1 weights │ 12.8 MB │ 3.2 MB │ 9.6 MB (75%)│\n", + "│ Layer 1 bias │ 0.5 MB │ 0.1 MB │ 0.4 MB (75%)│\n", + "│ Layer 2 weights │ 2.0 MB │ 0.5 MB │ 1.5 MB (75%)│\n", + "│ Layer 2 bias │ 0.3 MB │ 0.1 MB │ 0.2 MB (67%)│\n", + "│ Overhead │ 0.0 MB │ 0.02 MB │ -0.02 MB │\n", + "├─────────────────┼─────────────────┼─────────────────┼─────────────────┤\n", + "│ TOTAL │ 15.6 MB │ 3.92 MB │ 11.7 MB (74%)│\n", + "└─────────────────┴─────────────────┴─────────────────┴─────────────────┘\n", + " ↑\n", + " 4× compression ratio!\n", + "```\n", + "\n", + "**Comprehensive Metrics Provided:**\n", + "```\n", + "Output Dictionary:\n", + "{\n", + " 'original_params': 4000000, # Total parameter count\n", + " 'quantized_params': 4000000, # Same count, different precision\n", + " 'original_bytes': 16000000, # 4 bytes per FP32 parameter\n", + " 'quantized_bytes': 4000016, # 1 byte per INT8 + overhead\n", + " 'compression_ratio': 3.99, # Nearly 4× compression\n", + " 'memory_saved_mb': 11.7, # Absolute savings in MB\n", + " 'memory_saved_percent': 74.9 # Relative savings percentage\n", + "}\n", + "```\n", + "\n", + "**Why These Metrics Matter:**\n", + "\n", + "**For Developers:**\n", + "- **compression_ratio** - How much smaller is the model?\n", + "- **memory_saved_mb** - Actual bytes freed up\n", + "- **memory_saved_percent** - Efficiency improvement\n", + "\n", + "**For Deployment:**\n", + "- **Model fits in device memory?** Check memory_saved_mb\n", + "- **Network transfer time?** Reduced by compression_ratio\n", + "- **Disk storage savings?** Shown by memory_saved_percent\n", + "\n", + "**For Business:**\n", + "- **Cloud costs** reduced by compression_ratio\n", + "- **User experience** improved (faster downloads)\n", + "- **Device support** expanded (fits on more devices)\n", + "\n", + "**Validation Checks:**\n", + "- **Parameter count preservation** - same functionality\n", + "- **Reasonable compression ratio** - should be ~4× for INT8\n", + "- **Minimal overhead** - quantization parameters are tiny" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab7d75d0", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "compare_model_sizes", + "solution": true + } + }, + "outputs": [], + "source": [ + "def compare_model_sizes(original_model, quantized_model) -> Dict[str, float]:\n", + " \"\"\"\n", + " Compare memory usage between original and quantized models.\n", + "\n", + " TODO: Calculate comprehensive memory comparison\n", + "\n", + " APPROACH:\n", + " 1. Count parameters in both models\n", + " 2. Calculate bytes used (FP32 vs INT8)\n", + " 3. Include quantization overhead\n", + " 4. Return comparison metrics\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " # Count original model parameters\n", + " original_params = 0\n", + " original_bytes = 0\n", + "\n", + " if hasattr(original_model, 'layers'):\n", + " for layer in original_model.layers:\n", + " if hasattr(layer, 'parameters'):\n", + " params = layer.parameters()\n", + " for param in params:\n", + " original_params += param.data.size\n", + " original_bytes += param.data.size * 4 # 4 bytes per FP32\n", + "\n", + " # Count quantized model parameters\n", + " quantized_params = 0\n", + " quantized_bytes = 0\n", + "\n", + " if hasattr(quantized_model, 'layers'):\n", + " for layer in quantized_model.layers:\n", + " if isinstance(layer, QuantizedLinear):\n", + " memory_info = layer.memory_usage()\n", + " quantized_bytes += memory_info['quantized_bytes']\n", + " params = layer.parameters()\n", + " for param in params:\n", + " quantized_params += param.data.size\n", + " elif hasattr(layer, 'parameters'):\n", + " # Non-quantized layers\n", + " params = layer.parameters()\n", + " for param in params:\n", + " quantized_params += param.data.size\n", + " quantized_bytes += param.data.size * 4\n", + "\n", + " compression_ratio = original_bytes / quantized_bytes if quantized_bytes > 0 else 1.0\n", + " memory_saved = original_bytes - quantized_bytes\n", + "\n", + " return {\n", + " 'original_params': original_params,\n", + " 'quantized_params': quantized_params,\n", + " 'original_bytes': original_bytes,\n", + " 'quantized_bytes': quantized_bytes,\n", + " 'compression_ratio': compression_ratio,\n", + " 'memory_saved_mb': memory_saved / (1024 * 1024),\n", + " 'memory_saved_percent': (memory_saved / original_bytes) * 100 if original_bytes > 0 else 0\n", + " }\n", + " ### END SOLUTION\n", + "\n", + "def test_unit_compare_model_sizes():\n", + " \"\"\"🔬 Test model size comparison.\"\"\"\n", + " print(\"🔬 Unit Test: Model Size Comparison...\")\n", + "\n", + " # Create and quantize a model for testing\n", + " original_model = Sequential(Linear(100, 50), ReLU(), Linear(50, 10))\n", + " original_model.layers[0].weight = Tensor(np.random.randn(100, 50))\n", + " original_model.layers[0].bias = Tensor(np.random.randn(50))\n", + " original_model.layers[2].weight = Tensor(np.random.randn(50, 10))\n", + " original_model.layers[2].bias = Tensor(np.random.randn(10))\n", + "\n", + " # Create quantized copy\n", + " quantized_model = Sequential(Linear(100, 50), ReLU(), Linear(50, 10))\n", + " quantized_model.layers[0].weight = Tensor(np.random.randn(100, 50))\n", + " quantized_model.layers[0].bias = Tensor(np.random.randn(50))\n", + " quantized_model.layers[2].weight = Tensor(np.random.randn(50, 10))\n", + " quantized_model.layers[2].bias = Tensor(np.random.randn(10))\n", + "\n", + " quantize_model(quantized_model)\n", + "\n", + " # Compare sizes\n", + " comparison = compare_model_sizes(original_model, quantized_model)\n", + "\n", + " # Verify compression achieved\n", + " assert comparison['compression_ratio'] > 2.0, \"Should achieve significant compression\"\n", + " assert comparison['memory_saved_percent'] > 50, \"Should save >50% memory\"\n", + "\n", + " print(f\" Compression ratio: {comparison['compression_ratio']:.1f}×\")\n", + " print(f\" Memory saved: {comparison['memory_saved_percent']:.1f}%\")\n", + " print(\"✅ Model size comparison works correctly!\")\n", + "\n", + "test_unit_compare_model_sizes()" + ] + }, + { + "cell_type": "markdown", + "id": "005fda32", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 5. Systems Analysis - Real-World Performance Impact\n", + "\n", + "### Understanding Production Trade-offs\n", + "\n", + "Quantization isn't just about smaller models - it's about enabling entirely new deployment scenarios. Let's measure the real impact across different model scales.\n", + "\n", + "```\n", + "Production Deployment Scenarios:\n", + "\n", + "┌──────────────────┬──────────────────┬──────────────────┬──────────────────┐\n", + "│ Deployment │ Memory Limit │ Speed Needs │ Quantization Fit │\n", + "├──────────────────┼──────────────────┼──────────────────┼──────────────────┤\n", + "│ Mobile Phone │ 100-500MB │ <100ms latency │ ✅ Essential │\n", + "│ Edge Device │ 50-200MB │ Real-time │ ✅ Critical │\n", + "│ Cloud GPU │ 16-80GB │ High throughput │ 🤔 Optional │\n", + "│ Embedded MCU │ 1-10MB │ Ultra-low power │ ✅ Mandatory │\n", + "└──────────────────┴──────────────────┴──────────────────┴──────────────────┘\n", + "```\n", + "\n", + "### The Performance Testing Framework\n", + "\n", + "We'll measure quantization impact across three critical dimensions:\n", + "\n", + "```\n", + "Performance Analysis Framework:\n", + "\n", + "1. Memory Efficiency 2. Inference Speed 3. Accuracy Preservation\n", + "┌─────────────────────┐ ┌─────────────────────┐ ┌─────────────────────┐\n", + "│ • Model size (MB) │ │ • Forward pass time │ │ • MSE vs original │\n", + "│ • Compression ratio │ │ • Throughput (fps) │ │ • Relative error │\n", + "│ • Memory bandwidth │ │ • Latency (ms) │ │ • Distribution │\n", + "└─────────────────────┘ └─────────────────────┘ └─────────────────────┘\n", + "```\n", + "\n", + "### Expected Results Preview\n", + "\n", + "```\n", + "Typical Quantization Results:\n", + "\n", + "Model Size: Small (1-10MB) Medium (10-100MB) Large (100MB+)\n", + " ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐\n", + "Compression: │ 3.8× reduction │ │ 3.9× reduction │ │ 4.0× reduction │\n", + "Speed: │ 1.2× faster │ │ 2.1× faster │ │ 3.2× faster │\n", + "Accuracy: │ 0.1% loss │ │ 0.3% loss │ │ 0.5% loss │\n", + " └─────────────────┘ └─────────────────┘ └─────────────────┘\n", + "\n", + "Key Insight: Larger models benefit more from quantization!\n", + "```\n", + "\n", + "Let's run comprehensive tests to validate these expectations and understand the underlying patterns." + ] + }, + { + "cell_type": "markdown", + "id": "c8fa23cd", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### Performance Analysis - Real-World Benchmarking\n", + "\n", + "This comprehensive analysis measures quantization impact across the three critical dimensions: memory, speed, and accuracy.\n", + "\n", + "```\n", + "Performance Testing Strategy:\n", + "\n", + "┌────────────────────────────────────────────────────────────────────────────────────┐\n", + "│ Test Model Configurations │\n", + "├────────────────────────────┬────────────────────────────┬────────────────────────────┤\n", + "│ Model Type │ Architecture │ Use Case │\n", + "├────────────────────────────┼────────────────────────────┼────────────────────────────┤\n", + "│ Small MLP │ 64 → 32 → 10 │ Edge Device │\n", + "│ Medium MLP │ 512 → 256 → 128 → 10 │ Mobile App │\n", + "│ Large MLP │ 2048 → 1024 → 512 → 10│ Server Deployment │\n", + "└────────────────────────────┴────────────────────────────┴────────────────────────────┘\n", + "```\n", + "\n", + "**Performance Measurement Pipeline:**\n", + "```\n", + "For Each Model Configuration:\n", + "\n", + " Create Original Model Create Quantized Model Comparative Analysis\n", + " │ │ │\n", + " ▼ ▼ ▼\n", + " ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐\n", + " │ Initialize weights │ │ Copy weights │ │ Memory analysis │\n", + " │ Random test data │ │ Apply quantization│ │ Speed benchmarks │\n", + " │ Forward pass │ │ Calibrate layers │ │ Accuracy testing │\n", + " │ Timing measurements│ │ Forward pass │ │ Trade-off analysis│\n", + " └─────────────────┘ └─────────────────┘ └─────────────────┘\n", + "```\n", + "\n", + "**Expected Performance Patterns:**\n", + "```\n", + "Model Scaling Effects:\n", + "\n", + " Memory Usage Inference Speed Accuracy Loss\n", + " │ │ │\n", + " ▼ ▼ ▼\n", + "\n", + "4× │ ############### FP32 3× │ INT8 1% │ ####\n", + " │ │ ############### FP32 │\n", + "3× │ 2× │ 0.5% │ ##\n", + " │ ######### INT8 │ ########### INT8 │\n", + "2× │ 1× │ 0.1% │ #\n", + " │ │ ####### │\n", + "1× │ │ 0% └────────────────────────────────────────────────────\n", + " └──────────────────────────────────────────────────── └──────────────────────────────────────────────────── Small Medium Large\n", + " Small Medium Large Small Medium Large\n", + "\n", + "Key Insight: Larger models benefit more from quantization!\n", + "```\n", + "\n", + "**Real-World Impact Translation:**\n", + "- **Memory savings** → More models fit on device, lower cloud costs\n", + "- **Speed improvements** → Better user experience, real-time applications\n", + "- **Accuracy preservation** → Maintains model quality, no retraining needed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22e204f8", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "analyze_quantization_performance", + "solution": true + } + }, + "outputs": [], + "source": [ + "def analyze_quantization_performance():\n", + " \"\"\"📊 Comprehensive analysis of quantization benefits and trade-offs.\"\"\"\n", + " print(\"📊 Analyzing Quantization Performance Across Model Sizes...\")\n", + "\n", + " # Test different model configurations\n", + " configs = [\n", + " {'name': 'Small MLP', 'layers': [64, 32, 10], 'batch_size': 32},\n", + " {'name': 'Medium MLP', 'layers': [512, 256, 128, 10], 'batch_size': 64},\n", + " {'name': 'Large MLP', 'layers': [2048, 1024, 512, 10], 'batch_size': 128},\n", + " ]\n", + "\n", + " results = []\n", + "\n", + " for config in configs:\n", + " print(f\"\\n🔍 Testing {config['name']}...\")\n", + "\n", + " # Create original model\n", + " layers = []\n", + " for i in range(len(config['layers']) - 1):\n", + " layers.append(Linear(config['layers'][i], config['layers'][i+1]))\n", + " if i < len(config['layers']) - 2: # Add ReLU except for last layer\n", + " layers.append(ReLU())\n", + "\n", + " original_model = Sequential(*layers)\n", + "\n", + " # Initialize weights\n", + " for layer in original_model.layers:\n", + " if isinstance(layer, Linear):\n", + " layer.weight = Tensor(np.random.randn(*layer.weight.shape) * 0.1)\n", + " layer.bias = Tensor(np.random.randn(*layer.bias.shape) * 0.01)\n", + "\n", + " # Create quantized copy\n", + " quantized_model = Sequential(*layers)\n", + " for i, layer in enumerate(original_model.layers):\n", + " if isinstance(layer, Linear):\n", + " quantized_model.layers[i].weight = Tensor(layer.weight.data.copy())\n", + " quantized_model.layers[i].bias = Tensor(layer.bias.data.copy())\n", + "\n", + " # Generate calibration data\n", + " input_size = config['layers'][0]\n", + " calibration_data = [Tensor(np.random.randn(1, input_size)) for _ in range(10)]\n", + "\n", + " # Quantize model\n", + " quantize_model(quantized_model, calibration_data)\n", + "\n", + " # Measure performance\n", + " test_input = Tensor(np.random.randn(config['batch_size'], input_size))\n", + "\n", + " # Time original model\n", + " start_time = time.time()\n", + " for _ in range(10):\n", + " original_output = original_model.forward(test_input)\n", + " original_time = (time.time() - start_time) / 10\n", + "\n", + " # Time quantized model\n", + " start_time = time.time()\n", + " for _ in range(10):\n", + " quantized_output = quantized_model.forward(test_input)\n", + " quantized_time = (time.time() - start_time) / 10\n", + "\n", + " # Calculate accuracy preservation (using MSE as proxy)\n", + " mse = np.mean((original_output.data - quantized_output.data) ** 2)\n", + " relative_error = np.sqrt(mse) / (np.std(original_output.data) + 1e-8)\n", + "\n", + " # Memory comparison\n", + " memory_comparison = compare_model_sizes(original_model, quantized_model)\n", + "\n", + " result = {\n", + " 'name': config['name'],\n", + " 'original_time': original_time * 1000, # Convert to ms\n", + " 'quantized_time': quantized_time * 1000,\n", + " 'speedup': original_time / quantized_time if quantized_time > 0 else 1.0,\n", + " 'compression_ratio': memory_comparison['compression_ratio'],\n", + " 'relative_error': relative_error,\n", + " 'memory_saved_mb': memory_comparison['memory_saved_mb']\n", + " }\n", + "\n", + " results.append(result)\n", + "\n", + " print(f\" Speedup: {result['speedup']:.1f}×\")\n", + " print(f\" Compression: {result['compression_ratio']:.1f}×\")\n", + " print(f\" Error: {result['relative_error']:.1%}\")\n", + " print(f\" Memory saved: {result['memory_saved_mb']:.1f}MB\")\n", + "\n", + " # Summary analysis\n", + " print(f\"\\n📈 QUANTIZATION PERFORMANCE SUMMARY\")\n", + " print(\"=\" * 50)\n", + "\n", + " avg_speedup = np.mean([r['speedup'] for r in results])\n", + " avg_compression = np.mean([r['compression_ratio'] for r in results])\n", + " avg_error = np.mean([r['relative_error'] for r in results])\n", + " total_memory_saved = sum([r['memory_saved_mb'] for r in results])\n", + "\n", + " print(f\"Average speedup: {avg_speedup:.1f}×\")\n", + " print(f\"Average compression: {avg_compression:.1f}×\")\n", + " print(f\"Average relative error: {avg_error:.1%}\")\n", + " print(f\"Total memory saved: {total_memory_saved:.1f}MB\")\n", + "\n", + " print(f\"\\n💡 Key Insights:\")\n", + " print(f\"- Quantization achieves ~{avg_compression:.0f}× memory reduction\")\n", + " print(f\"- Typical speedup: {avg_speedup:.1f}× (varies by hardware)\")\n", + " print(f\"- Accuracy loss: <{avg_error:.1%} for well-calibrated models\")\n", + " print(f\"- Best for: Memory-constrained deployment\")\n", + "\n", + " return results\n", + "\n", + "# Run comprehensive performance analysis\n", + "performance_results = analyze_quantization_performance()" + ] + }, + { + "cell_type": "markdown", + "id": "e800a3d9", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## Quantization Error Visualization - Seeing the Impact\n", + "\n", + "### Understanding Distribution Effects\n", + "\n", + "Different weight distributions quantize with varying quality. Let's visualize this to understand when quantization works well and when it struggles.\n", + "\n", + "```\n", + "Visualization Strategy:\n", + "\n", + "┌─────────────────────────────────────────────────────────────────────────────┐\n", + "│ Weight Distribution Analysis │\n", + "├─────────────────────┬─────────────────────┬─────────────────────────────────┤\n", + "│ Distribution Type │ Expected Quality │ Key Challenge │\n", + "├─────────────────────┼─────────────────────┼─────────────────────────────────┤\n", + "│ Normal (Gaussian) │ Good │ Tail values may be clipped │\n", + "│ Uniform │ Excellent │ Perfect scale utilization │\n", + "│ Sparse (many zeros) │ Poor │ Wasted quantization levels │\n", + "│ Heavy-tailed │ Very Poor │ Outliers dominate scale │\n", + "└─────────────────────┴─────────────────────┴─────────────────────────────────┘\n", + "```\n", + "\n", + "### Quantization Quality Patterns\n", + "\n", + "```\n", + "Ideal Quantization: Problematic Quantization:\n", + "\n", + "Original: [████████████████████] Original: [██ ████ ██]\n", + " ↓ ↓\n", + "Quantized: [████████████████████] Quantized: [██....████....██]\n", + " Perfect reconstruction Lost precision\n", + "\n", + "Scale efficiently used Scale poorly used\n", + "Low quantization error High quantization error\n", + "```\n", + "\n", + "**What We'll Visualize:**\n", + "- **Before/After histograms** - See how distributions change\n", + "- **Error metrics** - Quantify the precision loss\n", + "- **Scale utilization** - Understand efficiency\n", + "- **Real examples** - Connect to practical scenarios\n", + "\n", + "This visualization will help you understand which types of neural network weights quantize well and which need special handling." + ] + }, + { + "cell_type": "markdown", + "id": "f94b8502", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### Quantization Effects Visualization - Understanding Distribution Impact\n", + "\n", + "This visualization reveals how different weight distributions respond to quantization, helping you understand when quantization works well and when it struggles.\n", + "\n", + "```\n", + "Visualization Strategy:\n", + "\n", + "┌────────────────────────────────────────────────────────────────────────────────────┐\n", + "│ Distribution Analysis Grid │\n", + "├─────────────────────┬─────────────────────┬─────────────────────┬─────────────────────┤\n", + "│ Normal (Good) │ Uniform (Best) │ Sparse (Bad) │ Heavy-Tailed (Worst)│\n", + "├─────────────────────┼─────────────────────┼─────────────────────┼─────────────────────┤\n", + "│ /\\ │ ┌──────────┐ │ | | | │ /\\ │\n", + "│ / \\ │ │ │ │ | | | │ / \\ /\\ │\n", + "│ / \\ │ │ Flat │ │ |||| | |||| │ / \\/ \\ │\n", + "│ / \\ │ │ │ │ zeros sparse │ / \\ │\n", + "│ / \\ │ └──────────┘ │ values │ / huge \\ │\n", + "│ / \\ │ │ │ / outliers \\ │\n", + "├─────────────────────┼─────────────────────┼─────────────────────┼─────────────────────┤\n", + "│ MSE: 0.001 │ MSE: 0.0001 │ MSE: 0.01 │ MSE: 0.1 │\n", + "│ Scale Usage: 80% │ Scale Usage: 100% │ Scale Usage: 10% │ Scale Usage: 5% │\n", + "└─────────────────────┴─────────────────────┴─────────────────────┴─────────────────────┘\n", + "```\n", + "\n", + "**Visual Comparison Strategy:**\n", + "```\n", + "For Each Distribution Type:\n", + " │\n", + " ├── Generate sample weights (1000 values)\n", + " │\n", + " ├── Quantize to INT8\n", + " │\n", + " ├── Dequantize back to FP32\n", + " │\n", + " ├── Plot overlaid histograms:\n", + " │ ├── Original distribution (blue)\n", + " │ └── Quantized distribution (red)\n", + " │\n", + " └── Calculate and display error metrics:\n", + " ├── Mean Squared Error (MSE)\n", + " ├── Scale utilization efficiency\n", + " └── Quantization scale value\n", + "```\n", + "\n", + "**Key Insights You'll Discover:**\n", + "\n", + "**1. Normal Distribution (Most Common):**\n", + " - Smooth bell curve preserved reasonably well\n", + " - Tail values may be clipped slightly\n", + " - Good compromise for most neural networks\n", + "\n", + "**2. Uniform Distribution (Ideal Case):**\n", + " - Perfect scale utilization\n", + " - Minimal quantization error\n", + " - Best-case scenario for quantization\n", + "\n", + "**3. Sparse Distribution (Problematic):**\n", + " - Many zeros waste quantization levels\n", + " - Poor precision for non-zero values\n", + " - Common in pruned networks\n", + "\n", + "**4. Heavy-Tailed Distribution (Worst Case):**\n", + " - Outliers dominate scale calculation\n", + " - Most values squeezed into narrow range\n", + " - Requires special handling (clipping, per-channel)\n", + "\n", + "**Practical Implications:**\n", + "- **Model design:** Prefer batch normalization to reduce outliers\n", + "- **Training:** Techniques to encourage uniform weight distributions\n", + "- **Deployment:** Advanced quantization for sparse/heavy-tailed weights" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b1823ce", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "visualize_quantization_effects", + "solution": true + } + }, + "outputs": [], + "source": [ + "def visualize_quantization_effects():\n", + " \"\"\"📊 Visualize the effects of quantization on weight distributions.\"\"\"\n", + " print(\"📊 Visualizing Quantization Effects on Weight Distributions...\")\n", + "\n", + " # Create sample weight tensors with different characteristics\n", + " weight_types = {\n", + " 'Normal': np.random.normal(0, 0.1, (1000,)),\n", + " 'Uniform': np.random.uniform(-0.2, 0.2, (1000,)),\n", + " 'Sparse': np.random.choice([0, 0, 0, 1], (1000,)) * np.random.normal(0, 0.15, (1000,)),\n", + " 'Heavy-tailed': np.concatenate([\n", + " np.random.normal(0, 0.05, (800,)),\n", + " np.random.uniform(-0.5, 0.5, (200,))\n", + " ])\n", + " }\n", + "\n", + " fig, axes = plt.subplots(2, 2, figsize=(12, 8))\n", + " axes = axes.flatten()\n", + "\n", + " for idx, (name, weights) in enumerate(weight_types.items()):\n", + " # Original weights\n", + " original_tensor = Tensor(weights)\n", + "\n", + " # Quantize and dequantize\n", + " q_tensor, scale, zero_point = quantize_int8(original_tensor)\n", + " restored_tensor = dequantize_int8(q_tensor, scale, zero_point)\n", + "\n", + " # Plot histograms\n", + " ax = axes[idx]\n", + " ax.hist(weights, bins=50, alpha=0.6, label='Original', density=True)\n", + " ax.hist(restored_tensor.data, bins=50, alpha=0.6, label='Quantized', density=True)\n", + " ax.set_title(f'{name} Weights\\nScale: {scale:.4f}')\n", + " ax.set_xlabel('Weight Value')\n", + " ax.set_ylabel('Density')\n", + " ax.legend()\n", + " ax.grid(True, alpha=0.3)\n", + "\n", + " # Calculate and display error metrics\n", + " mse = np.mean((weights - restored_tensor.data) ** 2)\n", + " ax.text(0.02, 0.98, f'MSE: {mse:.6f}', transform=ax.transAxes,\n", + " verticalalignment='top', bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))\n", + "\n", + " plt.tight_layout()\n", + " plt.savefig('/tmp/claude/quantization_effects.png', dpi=100, bbox_inches='tight')\n", + " plt.show()\n", + "\n", + " print(\"💡 Observations:\")\n", + " print(\"- Normal: Smooth quantization, good preservation\")\n", + " print(\"- Uniform: Excellent quantization, full range utilized\")\n", + " print(\"- Sparse: Many wasted quantization levels on zeros\")\n", + " print(\"- Heavy-tailed: Outliers dominate scale, poor precision for small weights\")\n", + "\n", + "# Visualize quantization effects\n", + "visualize_quantization_effects()" + ] + }, + { + "cell_type": "markdown", + "id": "302e88e4", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 6. Optimization Insights - Production Quantization Strategies\n", + "\n", + "### Beyond Basic Quantization\n", + "\n", + "Our INT8 per-tensor quantization is just the beginning. Production systems use sophisticated strategies to squeeze out every bit of performance while preserving accuracy.\n", + "\n", + "```\n", + "Quantization Strategy Evolution:\n", + "\n", + " Basic (What we built) Advanced (Production) Cutting-Edge (Research)\n", + "┌─────────────────────┐ ┌─────────────────────┐ ┌─────────────────────┐\n", + "│ • Per-tensor scale │ │ • Per-channel scale │ │ • Dynamic ranges │\n", + "│ • Uniform INT8 │ → │ • Mixed precision │ → │ • Adaptive bitwidth │\n", + "│ • Post-training │ │ • Quantization-aware│ │ • Learned quantizers│\n", + "│ • Simple calibration│ │ • Advanced calib. │ │ • Neural compression│\n", + "└─────────────────────┘ └─────────────────────┘ └─────────────────────┘\n", + " Good baseline Production systems Future research\n", + "```\n", + "\n", + "### Strategy Comparison Framework\n", + "\n", + "```\n", + "Quantization Strategy Trade-offs:\n", + "\n", + "┌─────────────────────┬─────────────┬─────────────┬─────────────┬─────────────┐\n", + "│ Strategy │ Accuracy │ Complexity │ Memory Use │ Speed Gain │\n", + "├─────────────────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n", + "│ Per-Tensor (Ours) │ ████████░░ │ ██░░░░░░░░ │ ████████░░ │ ███████░░░ │\n", + "│ Per-Channel │ █████████░ │ █████░░░░░ │ ████████░░ │ ██████░░░░ │\n", + "│ Mixed Precision │ ██████████ │ ████████░░ │ ███████░░░ │ ████████░░ │\n", + "│ Quantization-Aware │ ██████████ │ ██████████ │ ████████░░ │ ███████░░░ │\n", + "└─────────────────────┴─────────────┴─────────────┴─────────────┴─────────────┘\n", + "```\n", + "\n", + "### The Three Advanced Strategies We'll Analyze\n", + "\n", + "**1. Per-Channel Quantization:**\n", + "```\n", + "Per-Tensor: Per-Channel:\n", + "┌─────────────────────────┐ ┌─────────────────────────┐\n", + "│ [W₁₁ W₁₂ W₁₃] │ │ [W₁₁ W₁₂ W₁₃] scale₁ │\n", + "│ [W₂₁ W₂₂ W₂₃] scale │ VS │ [W₂₁ W₂₂ W₂₃] scale₂ │\n", + "│ [W₃₁ W₃₂ W₃₃] │ │ [W₃₁ W₃₂ W₃₃] scale₃ │\n", + "└─────────────────────────┘ └─────────────────────────┘\n", + " One scale for all Separate scale per channel\n", + " May waste precision Better precision per channel\n", + "```\n", + "\n", + "**2. Mixed Precision:**\n", + "```\n", + "Sensitive Layers (FP32): Regular Layers (INT8):\n", + "┌─────────────────────────┐ ┌─────────────────────────┐\n", + "│ Input Layer │ │ Hidden Layer 1 │\n", + "│ (preserve input quality)│ │ (can tolerate error) │\n", + "├─────────────────────────┤ ├─────────────────────────┤\n", + "│ Output Layer │ │ Hidden Layer 2 │\n", + "│ (preserve output) │ │ (bulk of computation) │\n", + "└─────────────────────────┘ └─────────────────────────┘\n", + " Keep high precision Maximize compression\n", + "```\n", + "\n", + "**3. Calibration Strategies:**\n", + "```\n", + "Basic Calibration: Advanced Calibration:\n", + "┌─────────────────────────┐ ┌─────────────────────────┐\n", + "│ • Use min/max range │ │ • Percentile clipping │\n", + "│ • Simple statistics │ │ • KL-divergence │\n", + "│ • Few samples │ VS │ • Multiple datasets │\n", + "│ • Generic approach │ │ • Layer-specific tuning │\n", + "└─────────────────────────┘ └─────────────────────────┘\n", + " Fast but suboptimal Optimal but expensive\n", + "```\n", + "\n", + "Let's implement and compare these strategies to understand their practical trade-offs!" + ] + }, + { + "cell_type": "markdown", + "id": "3551e3b4", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "### Advanced Quantization Strategies - Production Techniques\n", + "\n", + "This analysis compares different quantization approaches used in production systems, revealing the trade-offs between accuracy, complexity, and performance.\n", + "\n", + "```\n", + "Strategy Comparison Framework:\n", + "\n", + "┌────────────────────────────────────────────────────────────────────────────────────┐\n", + "│ Three Advanced Strategies │\n", + "├────────────────────────────┬────────────────────────────┬────────────────────────────┤\n", + "│ Strategy 1 │ Strategy 2 │ Strategy 3 │\n", + "│ Per-Tensor (Ours) │ Per-Channel Scale │ Mixed Precision │\n", + "├────────────────────────────┼────────────────────────────┼────────────────────────────┤\n", + "│ │ │ │\n", + "│ ┌──────────────────────┐ │ ┌──────────────────────┐ │ ┌──────────────────────┐ │\n", + "│ │ Weights: │ │ │ Channel 1: scale₁ │ │ │ Sensitive: FP32 │ │\n", + "│ │ [W₁₁ W₁₂ W₁₃] │ │ │ Channel 2: scale₂ │ │ │ Regular: INT8 │ │\n", + "│ │ [W₂₁ W₂₂ W₂₃] scale │ │ │ Channel 3: scale₃ │ │ │ │ │\n", + "│ │ [W₃₁ W₃₂ W₃₃] │ │ │ │ │ │ Input: FP32 │ │\n", + "│ └──────────────────────┘ │ │ Better precision │ │ │ Output: FP32 │ │\n", + "│ │ │ per channel │ │ │ Hidden: INT8 │ │\n", + "│ Simple, fast │ └──────────────────────┘ │ └──────────────────────┘ │\n", + "│ Good baseline │ │ │\n", + "│ │ More complex │ Optimal accuracy │\n", + "│ │ Better accuracy │ Selective compression │\n", + "└────────────────────────────┴────────────────────────────┴────────────────────────────┘\n", + "```\n", + "\n", + "**Strategy 1: Per-Tensor Quantization (Our Implementation)**\n", + "```\n", + "Weight Matrix: Scale Calculation:\n", + "┌─────────────────────────┐ ┌─────────────────────────┐\n", + "│ 0.1 -0.3 0.8 0.2 │ │ Global min: -0.5 │\n", + "│-0.2 0.5 -0.1 0.7 │ → │ Global max: +0.8 │\n", + "│ 0.4 -0.5 0.3 -0.4 │ │ Scale: 1.3/255 = 0.0051 │\n", + "└─────────────────────────┘ └─────────────────────────┘\n", + "\n", + "Pros: Simple, fast Cons: May waste precision\n", + "```\n", + "\n", + "**Strategy 2: Per-Channel Quantization (Advanced)**\n", + "```\n", + "Weight Matrix: Scale Calculation:\n", + "┌─────────────────────────┐ ┌─────────────────────────┐\n", + "│ 0.1 -0.3 0.8 0.2 │ │ Col 1: [-0.2,0.4] → s₁ │\n", + "│-0.2 0.5 -0.1 0.7 │ → │ Col 2: [-0.5,0.5] → s₂ │\n", + "│ 0.4 -0.5 0.3 -0.4 │ │ Col 3: [-0.1,0.8] → s₃ │\n", + "└─────────────────────────┘ │ Col 4: [-0.4,0.7] → s₄ │\n", + " └─────────────────────────┘\n", + "\n", + "Pros: Better precision Cons: More complex\n", + "```\n", + "\n", + "**Strategy 3: Mixed Precision (Production)**\n", + "```\n", + "Model Architecture: Precision Assignment:\n", + "┌─────────────────────────┐ ┌─────────────────────────┐\n", + "│ Input Layer (sensitive) │ │ Keep in FP32 (precision) │\n", + "│ Hidden 1 (bulk) │ → │ Quantize to INT8 │\n", + "│ Hidden 2 (bulk) │ │ Quantize to INT8 │\n", + "│ Output Layer (sensitive)│ │ Keep in FP32 (quality) │\n", + "└─────────────────────────┘ └─────────────────────────┘\n", + "\n", + "Pros: Optimal trade-off Cons: Requires expertise\n", + "```\n", + "\n", + "**Experimental Design:**\n", + "```\n", + "Comparative Testing Protocol:\n", + "\n", + "1. Create identical test model → 2. Apply each strategy → 3. Measure results\n", + " ┌───────────────────────┐ ┌───────────────────────┐ ┌───────────────────────┐\n", + " │ 128 → 64 → 10 MLP │ │ Per-tensor quantization │ │ MSE error calculation │\n", + " │ Identical weights │ │ Per-channel simulation │ │ Compression measurement│\n", + " │ Same test input │ │ Mixed precision setup │ │ Speed comparison │\n", + " └───────────────────────┘ └───────────────────────┘ └───────────────────────┘\n", + "```\n", + "\n", + "**Expected Strategy Rankings:**\n", + "1. **Mixed Precision** - Best accuracy, moderate complexity\n", + "2. **Per-Channel** - Good accuracy, higher complexity\n", + "3. **Per-Tensor** - Baseline accuracy, simplest implementation\n", + "\n", + "This analysis reveals which strategies work best for different deployment scenarios and accuracy requirements." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8977e67b", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "analyze_quantization_strategies", + "solution": true + } + }, + "outputs": [], + "source": [ + "def analyze_quantization_strategies():\n", + " \"\"\"📊 Compare different quantization strategies and their trade-offs.\"\"\"\n", + " print(\"📊 Analyzing Advanced Quantization Strategies...\")\n", + "\n", + " # Create test model and data\n", + " model = Sequential(Linear(128, 64), ReLU(), Linear(64, 10))\n", + " model.layers[0].weight = Tensor(np.random.randn(128, 64) * 0.1)\n", + " model.layers[0].bias = Tensor(np.random.randn(64) * 0.01)\n", + " model.layers[2].weight = Tensor(np.random.randn(64, 10) * 0.1)\n", + " model.layers[2].bias = Tensor(np.random.randn(10) * 0.01)\n", + "\n", + " test_input = Tensor(np.random.randn(32, 128))\n", + " original_output = model.forward(test_input)\n", + "\n", + " strategies = {}\n", + "\n", + " # Strategy 1: Per-tensor quantization (what we implemented)\n", + " print(\"\\n🔍 Strategy 1: Per-Tensor Quantization\")\n", + " model_copy = Sequential(Linear(128, 64), ReLU(), Linear(64, 10))\n", + " for i, layer in enumerate(model.layers):\n", + " if isinstance(layer, Linear):\n", + " model_copy.layers[i].weight = Tensor(layer.weight.data.copy())\n", + " model_copy.layers[i].bias = Tensor(layer.bias.data.copy())\n", + "\n", + " quantize_model(model_copy)\n", + " output1 = model_copy.forward(test_input)\n", + " error1 = np.mean((original_output.data - output1.data) ** 2)\n", + " strategies['per_tensor'] = {'mse': error1, 'description': 'Single scale per tensor'}\n", + " print(f\" MSE: {error1:.6f}\")\n", + "\n", + " # Strategy 2: Per-channel quantization simulation\n", + " print(\"\\n🔍 Strategy 2: Per-Channel Quantization (simulated)\")\n", + " # Simulate by quantizing each output channel separately\n", + " def per_channel_quantize(tensor):\n", + " \"\"\"Simulate per-channel quantization for 2D weight matrices.\"\"\"\n", + " if len(tensor.shape) < 2:\n", + " return quantize_int8(tensor)\n", + "\n", + " quantized_data = np.zeros_like(tensor.data, dtype=np.int8)\n", + " scales = []\n", + " zero_points = []\n", + "\n", + " for i in range(tensor.shape[1]): # Per output channel\n", + " channel_tensor = Tensor(tensor.data[:, i:i+1])\n", + " q_channel, scale, zp = quantize_int8(channel_tensor)\n", + " quantized_data[:, i] = q_channel.data.flatten()\n", + " scales.append(scale)\n", + " zero_points.append(zp)\n", + "\n", + " return Tensor(quantized_data), scales, zero_points\n", + "\n", + " # Apply per-channel quantization to weights\n", + " total_error = 0\n", + " for layer in model.layers:\n", + " if isinstance(layer, Linear):\n", + " q_weight, scales, zps = per_channel_quantize(layer.weight)\n", + " # Simulate dequantization and error\n", + " for i in range(layer.weight.shape[1]):\n", + " original_channel = layer.weight.data[:, i]\n", + " restored_channel = scales[i] * q_weight.data[:, i] + zps[i] * scales[i]\n", + " total_error += np.mean((original_channel - restored_channel) ** 2)\n", + "\n", + " strategies['per_channel'] = {'mse': total_error, 'description': 'Scale per output channel'}\n", + " print(f\" MSE: {total_error:.6f}\")\n", + "\n", + " # Strategy 3: Mixed precision simulation\n", + " print(\"\\n🔍 Strategy 3: Mixed Precision\")\n", + " # Keep sensitive layers in FP32, quantize others\n", + " sensitive_layers = [0] # First layer often most sensitive\n", + " mixed_error = 0\n", + "\n", + " for i, layer in enumerate(model.layers):\n", + " if isinstance(layer, Linear):\n", + " if i in sensitive_layers:\n", + " # Keep in FP32 (no quantization error)\n", + " pass\n", + " else:\n", + " # Quantize layer\n", + " q_weight, scale, zp = quantize_int8(layer.weight)\n", + " restored = dequantize_int8(q_weight, scale, zp)\n", + " mixed_error += np.mean((layer.weight.data - restored.data) ** 2)\n", + "\n", + " strategies['mixed_precision'] = {'mse': mixed_error, 'description': 'FP32 sensitive + INT8 others'}\n", + " print(f\" MSE: {mixed_error:.6f}\")\n", + "\n", + " # Compare strategies\n", + " print(f\"\\n📊 QUANTIZATION STRATEGY COMPARISON\")\n", + " print(\"=\" * 60)\n", + " for name, info in strategies.items():\n", + " print(f\"{name:15}: MSE={info['mse']:.6f} | {info['description']}\")\n", + "\n", + " # Find best strategy\n", + " best_strategy = min(strategies.items(), key=lambda x: x[1]['mse'])\n", + " print(f\"\\n🏆 Best Strategy: {best_strategy[0]} (MSE: {best_strategy[1]['mse']:.6f})\")\n", + "\n", + " print(f\"\\n💡 Production Insights:\")\n", + " print(\"- Per-channel: Better accuracy, more complex implementation\")\n", + " print(\"- Mixed precision: Optimal accuracy/efficiency trade-off\")\n", + " print(\"- Per-tensor: Simplest, good for most applications\")\n", + " print(\"- Hardware support varies: INT8 GEMM, per-channel scales\")\n", + "\n", + " return strategies\n", + "\n", + "# Analyze quantization strategies\n", + "strategy_analysis = analyze_quantization_strategies()" + ] + }, + { + "cell_type": "markdown", + "id": "8ec49c3f", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 7. Module Integration Test\n", + "\n", + "Final validation that our quantization system works correctly across all components." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c10d0645", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test_module", + "points": 20 + } + }, + "outputs": [], + "source": [ + "def test_module():\n", + " \"\"\"\n", + " Comprehensive test of entire quantization module functionality.\n", + "\n", + " This final test runs before module summary to ensure:\n", + " - All quantization functions work correctly\n", + " - Model quantization preserves functionality\n", + " - Memory savings are achieved\n", + " - Module is ready for integration with TinyTorch\n", + " \"\"\"\n", + " print(\"🧪 RUNNING MODULE INTEGRATION TEST\")\n", + " print(\"=\" * 50)\n", + "\n", + " # Run all unit tests\n", + " print(\"Running unit tests...\")\n", + " test_unit_quantize_int8()\n", + " test_unit_dequantize_int8()\n", + " test_unit_quantized_linear()\n", + " test_unit_quantize_model()\n", + " test_unit_compare_model_sizes()\n", + "\n", + " print(\"\\nRunning integration scenarios...\")\n", + "\n", + " # Test realistic usage scenario\n", + " print(\"🔬 Integration Test: End-to-end quantization workflow...\")\n", + "\n", + " # Create a realistic model\n", + " model = Sequential(\n", + " Linear(784, 128), # MNIST-like input\n", + " ReLU(),\n", + " Linear(128, 64),\n", + " ReLU(),\n", + " Linear(64, 10) # 10-class output\n", + " )\n", + "\n", + " # Initialize with realistic weights\n", + " for layer in model.layers:\n", + " if isinstance(layer, Linear):\n", + " # Xavier initialization\n", + " fan_in, fan_out = layer.weight.shape\n", + " std = np.sqrt(2.0 / (fan_in + fan_out))\n", + " layer.weight = Tensor(np.random.randn(fan_in, fan_out) * std)\n", + " layer.bias = Tensor(np.zeros(fan_out))\n", + "\n", + " # Generate realistic calibration data\n", + " calibration_data = [Tensor(np.random.randn(1, 784) * 0.1) for _ in range(20)]\n", + "\n", + " # Test original model\n", + " test_input = Tensor(np.random.randn(8, 784) * 0.1)\n", + " original_output = model.forward(test_input)\n", + "\n", + " # Quantize the model\n", + " quantize_model(model, calibration_data)\n", + "\n", + " # Test quantized model\n", + " quantized_output = model.forward(test_input)\n", + "\n", + " # Verify functionality is preserved\n", + " assert quantized_output.shape == original_output.shape, \"Output shape mismatch\"\n", + "\n", + " # Verify reasonable accuracy preservation\n", + " mse = np.mean((original_output.data - quantized_output.data) ** 2)\n", + " relative_error = np.sqrt(mse) / (np.std(original_output.data) + 1e-8)\n", + " assert relative_error < 0.1, f\"Accuracy degradation too high: {relative_error:.3f}\"\n", + "\n", + " # Verify memory savings\n", + " # Create equivalent original model for comparison\n", + " original_model = Sequential(\n", + " Linear(784, 128),\n", + " ReLU(),\n", + " Linear(128, 64),\n", + " ReLU(),\n", + " Linear(64, 10)\n", + " )\n", + "\n", + " for i, layer in enumerate(model.layers):\n", + " if isinstance(layer, QuantizedLinear):\n", + " # Restore original weights for comparison\n", + " original_model.layers[i].weight = dequantize_int8(\n", + " layer.q_weight, layer.weight_scale, layer.weight_zero_point\n", + " )\n", + " if layer.q_bias is not None:\n", + " original_model.layers[i].bias = dequantize_int8(\n", + " layer.q_bias, layer.bias_scale, layer.bias_zero_point\n", + " )\n", + "\n", + " memory_comparison = compare_model_sizes(original_model, model)\n", + " assert memory_comparison['compression_ratio'] > 2.0, \"Insufficient compression achieved\"\n", + "\n", + " print(f\"✅ Compression achieved: {memory_comparison['compression_ratio']:.1f}×\")\n", + " print(f\"✅ Accuracy preserved: {relative_error:.1%} relative error\")\n", + " print(f\"✅ Memory saved: {memory_comparison['memory_saved_mb']:.1f}MB\")\n", + "\n", + " # Test edge cases\n", + " print(\"🔬 Testing edge cases...\")\n", + "\n", + " # Test constant tensor quantization\n", + " constant_tensor = Tensor([[1.0, 1.0], [1.0, 1.0]])\n", + " q_const, scale_const, zp_const = quantize_int8(constant_tensor)\n", + " assert scale_const == 1.0, \"Constant tensor quantization failed\"\n", + "\n", + " # Test zero tensor\n", + " zero_tensor = Tensor([[0.0, 0.0], [0.0, 0.0]])\n", + " q_zero, scale_zero, zp_zero = quantize_int8(zero_tensor)\n", + " restored_zero = dequantize_int8(q_zero, scale_zero, zp_zero)\n", + " assert np.allclose(restored_zero.data, 0.0, atol=1e-6), \"Zero tensor restoration failed\"\n", + "\n", + " print(\"✅ Edge cases handled correctly!\")\n", + "\n", + " print(\"\\n\" + \"=\" * 50)\n", + " print(\"🎉 ALL TESTS PASSED! Module ready for export.\")\n", + " print(\"📈 Quantization system provides:\")\n", + " print(f\" • {memory_comparison['compression_ratio']:.1f}× memory reduction\")\n", + " print(f\" • <{relative_error:.1%} accuracy loss\")\n", + " print(f\" • Production-ready INT8 quantization\")\n", + " print(\"Run: tito module complete 17\")\n", + "\n", + "# Call the comprehensive test\n", + "test_module()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15e9c4fc", + "metadata": {}, + "outputs": [], + "source": [ + "if __name__ == \"__main__\":\n", + " print(\"🚀 Running Quantization module...\")\n", + " test_module()\n", + " print(\"✅ Module validation complete!\")" + ] + }, + { + "cell_type": "markdown", + "id": "eccba324", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🤔 ML Systems Thinking: Quantization in Production\n", + "\n", + "### Question 1: Memory Architecture Impact\n", + "You implemented INT8 quantization that reduces each parameter from 4 bytes to 1 byte.\n", + "For a model with 100M parameters:\n", + "- Original memory usage: _____ GB\n", + "- Quantized memory usage: _____ GB\n", + "- Memory bandwidth reduction when loading from disk: _____ ×\n", + "\n", + "### Question 2: Quantization Error Analysis\n", + "Your quantization maps a continuous range to 256 discrete values (INT8).\n", + "For weights uniformly distributed in [-0.1, 0.1]:\n", + "- Quantization scale: _____\n", + "- Maximum quantization error: _____\n", + "- Signal-to-noise ratio approximately: _____ dB\n", + "\n", + "### Question 3: Hardware Efficiency\n", + "Modern processors have specialized INT8 instructions (like AVX-512 VNNI).\n", + "Compared to FP32 operations:\n", + "- How many INT8 operations fit in one SIMD instruction vs FP32? _____ × more\n", + "- Why might actual speedup be less than this theoretical maximum? _____\n", + "- What determines whether quantization improves or hurts performance? _____\n", + "\n", + "### Question 4: Calibration Strategy Trade-offs\n", + "Your calibration process finds optimal scales using sample data.\n", + "- Too little calibration data: Risk of _____\n", + "- Too much calibration data: Cost of _____\n", + "- Per-channel vs per-tensor quantization trades _____ for _____\n", + "\n", + "### Question 5: Production Deployment\n", + "In mobile/edge deployment scenarios:\n", + "- When is 4× memory reduction worth <1% accuracy loss? _____\n", + "- Why might you keep certain layers in FP32? _____\n", + "- How does quantization affect battery life? _____" + ] + }, + { + "cell_type": "markdown", + "id": "a263016f", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🎯 MODULE SUMMARY: Quantization\n", + "\n", + "Congratulations! You've built a complete INT8 quantization system that can reduce model size by 4× with minimal accuracy loss!\n", + "\n", + "### Key Accomplishments\n", + "- **Built INT8 quantization** with proper scaling and zero-point calculation\n", + "- **Implemented QuantizedLinear** layer with calibration support\n", + "- **Created model-level quantization** for complete neural networks\n", + "- **Analyzed quantization trade-offs** across different distributions and strategies\n", + "- **Measured real memory savings** and performance improvements\n", + "- All tests pass ✅ (validated by `test_module()`)\n", + "\n", + "### Real-World Impact\n", + "Your quantization implementation achieves:\n", + "- **4× memory reduction** (FP32 → INT8)\n", + "- **2-4× inference speedup** (hardware dependent)\n", + "- **<1% accuracy loss** with proper calibration\n", + "- **Production deployment readiness** for mobile/edge applications\n", + "\n", + "### What You've Mastered\n", + "- **Quantization mathematics** - scale and zero-point calculations\n", + "- **Calibration techniques** - optimizing quantization parameters\n", + "- **Error analysis** - understanding and minimizing quantization noise\n", + "- **Systems optimization** - memory vs accuracy trade-offs\n", + "\n", + "### Ready for Next Steps\n", + "Your quantization system enables efficient model deployment on resource-constrained devices.\n", + "Export with: `tito module complete 17`\n", + "\n", + "**Next**: Module 18 will add model compression through pruning - removing unnecessary weights entirely!\n", + "\n", + "---\n", + "\n", + "**🏆 Achievement Unlocked**: You can now deploy 4× smaller models with production-quality quantization! This is a critical skill for mobile AI, edge computing, and efficient inference systems." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/modules/source/17_quantization/quantization_dev.py b/modules/source/17_quantization/quantization_dev.py index 7a94de22..69a4ea6f 100644 --- a/modules/source/17_quantization/quantization_dev.py +++ b/modules/source/17_quantization/quantization_dev.py @@ -778,6 +778,7 @@ Regular Linear Layer: QuantizedLinear Layer: """ # %% nbgrader={"grade": false, "grade_id": "quantized_linear", "solution": true} +#| export class QuantizedLinear: """Quantized version of Linear layer using INT8 arithmetic.""" diff --git a/modules/source/18_compression/compression_dev.ipynb b/modules/source/18_compression/compression_dev.ipynb new file mode 100644 index 00000000..0b2e90af --- /dev/null +++ b/modules/source/18_compression/compression_dev.ipynb @@ -0,0 +1,1728 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7c0b2b14", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "# Module 18: Compression - Making Models Smaller\n", + "\n", + "Welcome to Module 18! You're about to build model compression techniques that make neural networks smaller and more efficient while preserving their intelligence.\n", + "\n", + "## 🔗 Prerequisites & Progress\n", + "**You've Built**: Full TinyGPT pipeline with profiling, acceleration, and quantization\n", + "**You'll Build**: Pruning (magnitude & structured), knowledge distillation, and low-rank approximation\n", + "**You'll Enable**: Compressed models that maintain accuracy while using dramatically less storage and memory\n", + "\n", + "**Connection Map**:\n", + "```\n", + "Quantization → Compression → Benchmarking\n", + "(precision) (sparsity) (evaluation)\n", + "```\n", + "\n", + "## Learning Objectives\n", + "By the end of this module, you will:\n", + "1. Implement magnitude-based and structured pruning\n", + "2. Build knowledge distillation for model compression\n", + "3. Create low-rank approximations of weight matrices\n", + "4. Measure compression ratios and sparsity levels\n", + "5. Understand structured vs unstructured sparsity trade-offs\n", + "\n", + "Let's get started!\n", + "\n", + "## 📦 Where This Code Lives in the Final Package\n", + "\n", + "**Learning Side:** You work in `modules/18_compression/compression_dev.py` \n", + "**Building Side:** Code exports to `tinytorch.optimization.compression`\n", + "\n", + "```python\n", + "# How to use this module:\n", + "from tinytorch.optimization.compression import magnitude_prune, structured_prune, measure_sparsity\n", + "```\n", + "\n", + "**Why this matters:**\n", + "- **Learning:** Complete compression system in one focused module for deep understanding\n", + "- **Production:** Proper organization like real compression libraries with all techniques together\n", + "- **Consistency:** All compression operations and sparsity management in optimization.compression\n", + "- **Integration:** Works seamlessly with models and quantization for complete optimization pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37872416", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "imports", + "solution": true + } + }, + "outputs": [], + "source": [ + "#| default_exp optimization.compression\n", + "#| export\n", + "\n", + "import numpy as np\n", + "import copy\n", + "from typing import List, Dict, Any, Tuple, Optional\n", + "import time\n", + "\n", + "# Import from previous modules\n", + "# Note: In the full package, these would be imports like:\n", + "# from tinytorch.core.tensor import Tensor\n", + "# from tinytorch.core.layers import Linear\n", + "# For development, we'll create minimal implementations\n", + "\n", + "class Tensor:\n", + " \"\"\"Minimal Tensor class for compression development - imports from Module 01 in practice.\"\"\"\n", + " def __init__(self, data, requires_grad=False):\n", + " self.data = np.array(data)\n", + " self.shape = self.data.shape\n", + " self.size = self.data.size\n", + " self.requires_grad = requires_grad\n", + " self.grad = None\n", + "\n", + " def __add__(self, other):\n", + " if isinstance(other, Tensor):\n", + " return Tensor(self.data + other.data)\n", + " return Tensor(self.data + other)\n", + "\n", + " def __mul__(self, other):\n", + " if isinstance(other, Tensor):\n", + " return Tensor(self.data * other.data)\n", + " return Tensor(self.data * other)\n", + "\n", + " def matmul(self, other):\n", + " return Tensor(np.dot(self.data, other.data))\n", + "\n", + " def abs(self):\n", + " return Tensor(np.abs(self.data))\n", + "\n", + " def sum(self, axis=None):\n", + " return Tensor(self.data.sum(axis=axis))\n", + "\n", + " def __repr__(self):\n", + " return f\"Tensor(shape={self.shape})\"\n", + "\n", + "class Linear:\n", + " \"\"\"Minimal Linear layer for compression development - imports from Module 03 in practice.\"\"\"\n", + " def __init__(self, in_features, out_features, bias=True):\n", + " self.in_features = in_features\n", + " self.out_features = out_features\n", + " # Initialize with He initialization\n", + " self.weight = Tensor(np.random.randn(in_features, out_features) * np.sqrt(2.0 / in_features))\n", + " self.bias = Tensor(np.zeros(out_features)) if bias else None\n", + "\n", + " def forward(self, x):\n", + " output = x.matmul(self.weight)\n", + " if self.bias is not None:\n", + " output = output + self.bias\n", + " return output\n", + "\n", + " def parameters(self):\n", + " params = [self.weight]\n", + " if self.bias is not None:\n", + " params.append(self.bias)\n", + " return params\n", + "\n", + "class Sequential:\n", + " \"\"\"Minimal Sequential container for model compression.\"\"\"\n", + " def __init__(self, *layers):\n", + " self.layers = list(layers)\n", + "\n", + " def forward(self, x):\n", + " for layer in self.layers:\n", + " x = layer.forward(x)\n", + " return x\n", + "\n", + " def parameters(self):\n", + " params = []\n", + " for layer in self.layers:\n", + " if hasattr(layer, 'parameters'):\n", + " params.extend(layer.parameters())\n", + " return params" + ] + }, + { + "cell_type": "markdown", + "id": "252e20ce", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 1. Introduction: What is Model Compression?\n", + "\n", + "Imagine you have a massive library with millions of books, but you only reference 10% of them regularly. Model compression is like creating a curated collection that keeps the essential knowledge while dramatically reducing storage space.\n", + "\n", + "Model compression reduces the size and computational requirements of neural networks while preserving their intelligence. It's the bridge between powerful research models and practical deployment.\n", + "\n", + "### Why Compression Matters in ML Systems\n", + "\n", + "**The Storage Challenge:**\n", + "- Modern language models: 100GB+ (GPT-3 scale)\n", + "- Mobile devices: <1GB available for models\n", + "- Edge devices: <100MB realistic limits\n", + "- Network bandwidth: Slow downloads kill user experience\n", + "\n", + "**The Speed Challenge:**\n", + "- Research models: Designed for accuracy, not efficiency\n", + "- Production needs: Sub-second response times\n", + "- Battery life: Energy consumption matters for mobile\n", + "- Cost scaling: Inference costs grow with model size\n", + "\n", + "### The Compression Landscape\n", + "\n", + "```\n", + "Neural Network Compression Techniques:\n", + "\n", + "┌─────────────────────────────────────────────────────────────┐\n", + "│ COMPRESSION METHODS │\n", + "├─────────────────────────────────────────────────────────────┤\n", + "│ WEIGHT-BASED │ ARCHITECTURE-BASED │\n", + "│ ┌─────────────────────────────┐ │ ┌─────────────────────┐ │\n", + "│ │ Magnitude Pruning │ │ │ Knowledge Distillation│ │\n", + "│ │ • Remove small weights │ │ │ • Teacher → Student │ │\n", + "│ │ • 90% sparsity achievable │ │ │ • 10x size reduction │ │\n", + "│ │ │ │ │ │ │\n", + "│ │ Structured Pruning │ │ │ Neural Architecture │ │\n", + "│ │ • Remove entire channels │ │ │ Search (NAS) │ │\n", + "│ │ • Hardware-friendly │ │ │ • Automated design │ │\n", + "│ │ │ │ │ │ │\n", + "│ │ Low-Rank Approximation │ │ │ Early Exit │ │\n", + "│ │ • Matrix factorization │ │ │ • Adaptive compute │ │\n", + "│ │ • SVD decomposition │ │ │ │ │\n", + "│ └─────────────────────────────┘ │ └─────────────────────┘ │\n", + "└─────────────────────────────────────────────────────────────┘\n", + "```\n", + "\n", + "Think of compression like optimizing a recipe - you want to keep the essential ingredients that create the flavor while removing anything that doesn't contribute to the final dish." + ] + }, + { + "cell_type": "markdown", + "id": "30325dfe", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 2. Foundations: Mathematical Background\n", + "\n", + "Understanding the mathematics behind compression helps us choose the right technique for each situation and predict their effects on model performance.\n", + "\n", + "### Magnitude-Based Pruning: The Simple Approach\n", + "\n", + "The core insight: small weights contribute little to the final prediction. Magnitude pruning removes weights based on their absolute values.\n", + "\n", + "```\n", + "Mathematical Foundation:\n", + "For weight w_ij in layer l:\n", + " If |w_ij| < threshold_l → w_ij = 0\n", + "\n", + "Threshold Selection:\n", + "- Global: One threshold for entire model\n", + "- Layer-wise: Different threshold per layer\n", + "- Percentile-based: Remove bottom k% of weights\n", + "\n", + "Sparsity Calculation:\n", + " Sparsity = (Zero weights / Total weights) × 100%\n", + "```\n", + "\n", + "### Structured Pruning: Hardware-Friendly Compression\n", + "\n", + "Unlike magnitude pruning which creates scattered zeros, structured pruning removes entire computational units (neurons, channels, attention heads).\n", + "\n", + "```\n", + "Channel Importance Metrics:\n", + "\n", + "Method 1: L2 Norm\n", + " Importance(channel_i) = ||W[:,i]||₂ = √(Σⱼ W²ⱼᵢ)\n", + "\n", + "Method 2: Gradient-based\n", + " Importance(channel_i) = |∂Loss/∂W[:,i]|\n", + "\n", + "Method 3: Activation-based\n", + " Importance(channel_i) = E[|activations_i|]\n", + "\n", + "Pruning Decision:\n", + " Remove bottom k% of channels based on importance ranking\n", + "```\n", + "\n", + "### Knowledge Distillation: Learning from Teachers\n", + "\n", + "Knowledge distillation transfers knowledge from a large \"teacher\" model to a smaller \"student\" model. The student learns not just the correct answers, but the teacher's reasoning process.\n", + "\n", + "```\n", + "Distillation Loss Function:\n", + " L_total = α × L_soft + (1-α) × L_hard\n", + "\n", + "Where:\n", + " L_soft = KL_divergence(σ(z_s/T), σ(z_t/T)) # Soft targets\n", + " L_hard = CrossEntropy(σ(z_s), y_true) # Hard targets\n", + "\n", + " σ(z/T) = Softmax with temperature T\n", + " z_s = Student logits, z_t = Teacher logits\n", + " α = Balance parameter (typically 0.7)\n", + " T = Temperature parameter (typically 3-5)\n", + "\n", + "Temperature Effect:\n", + " T=1: Standard softmax (sharp probabilities)\n", + " T>1: Softer distributions (reveals teacher's uncertainty)\n", + "```\n", + "\n", + "### Low-Rank Approximation: Matrix Compression\n", + "\n", + "Large weight matrices often have redundancy that can be captured with lower-rank approximations using Singular Value Decomposition (SVD).\n", + "\n", + "```\n", + "SVD Decomposition:\n", + " W_{m×n} = U_{m×k} × Σ_{k×k} × V^T_{k×n}\n", + "\n", + "Parameter Reduction:\n", + " Original: m × n parameters\n", + " Compressed: (m × k) + k + (k × n) = k(m + n + 1) parameters\n", + "\n", + " Compression achieved when: k < mn/(m+n+1)\n", + "\n", + "Reconstruction Error:\n", + " ||W - W_approx||_F = √(Σᵢ₌ₖ₊₁ʳ σᵢ²)\n", + "\n", + " Where σᵢ are singular values, r = rank(W)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "ce0801cd", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 3. Sparsity Measurement - Understanding Model Density\n", + "\n", + "Before we can compress models, we need to understand how dense they are. Sparsity measurement tells us what percentage of weights are zero (or effectively zero).\n", + "\n", + "### Understanding Sparsity\n", + "\n", + "Sparsity is like measuring how much of a parking lot is empty. A 90% sparse model means 90% of its weights are zero - only 10% of the \"parking spaces\" are occupied.\n", + "\n", + "```\n", + "Sparsity Visualization:\n", + "\n", + "Dense Matrix (0% sparse): Sparse Matrix (75% sparse):\n", + "┌─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─┐ ┌─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─┐\n", + "│ 2.1 1.3 0.8 1.9 2.4 1.1 0.7 │ │ 2.1 0.0 0.0 1.9 0.0 0.0 0.0 │\n", + "│ 1.5 2.8 1.2 0.9 1.6 2.2 1.4 │ │ 0.0 2.8 0.0 0.0 0.0 2.2 0.0 │\n", + "│ 0.6 1.7 2.5 1.1 0.8 1.3 2.0 │ │ 0.0 0.0 2.5 0.0 0.0 0.0 2.0 │\n", + "│ 1.9 1.0 1.6 2.3 1.8 0.9 1.2 │ │ 1.9 0.0 0.0 2.3 0.0 0.0 0.0 │\n", + "└─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─┘ └─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─┘\n", + "All weights active Only 7/28 weights active\n", + "Storage: 28 values Storage: 7 values + indices\n", + "```\n", + "\n", + "Why this matters: Sparsity directly relates to memory savings, but achieving speedup requires special sparse computation libraries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4440ec7a", + "metadata": {}, + "outputs": [], + "source": [ + "def measure_sparsity(model) -> float:\n", + " \"\"\"\n", + " Calculate the percentage of zero weights in a model.\n", + "\n", + " TODO: Count zero weights and total weights across all layers\n", + "\n", + " APPROACH:\n", + " 1. Iterate through all model parameters\n", + " 2. Count zeros using np.sum(weights == 0)\n", + " 3. Count total parameters\n", + " 4. Return percentage: zeros / total * 100\n", + "\n", + " EXAMPLE:\n", + " >>> model = Sequential(Linear(10, 5), Linear(5, 2))\n", + " >>> sparsity = measure_sparsity(model)\n", + " >>> print(f\"Model sparsity: {sparsity:.1f}%\")\n", + " Model sparsity: 0.0% # Before pruning\n", + "\n", + " HINT: Use np.sum() to count zeros efficiently\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " total_params = 0\n", + " zero_params = 0\n", + "\n", + " for param in model.parameters():\n", + " total_params += param.size\n", + " zero_params += np.sum(param.data == 0)\n", + "\n", + " if total_params == 0:\n", + " return 0.0\n", + "\n", + " return (zero_params / total_params) * 100.0\n", + " ### END SOLUTION\n", + "\n", + "def test_unit_measure_sparsity():\n", + " \"\"\"🔬 Test sparsity measurement functionality.\"\"\"\n", + " print(\"🔬 Unit Test: Measure Sparsity...\")\n", + "\n", + " # Test with dense model\n", + " model = Sequential(Linear(4, 3), Linear(3, 2))\n", + " initial_sparsity = measure_sparsity(model)\n", + " assert initial_sparsity == 0.0, f\"Expected 0% sparsity, got {initial_sparsity}%\"\n", + "\n", + " # Test with manually sparse model\n", + " model.layers[0].weight.data[0, 0] = 0\n", + " model.layers[0].weight.data[1, 1] = 0\n", + " sparse_sparsity = measure_sparsity(model)\n", + " assert sparse_sparsity > 0, f\"Expected >0% sparsity, got {sparse_sparsity}%\"\n", + "\n", + " print(\"✅ measure_sparsity works correctly!\")\n", + "\n", + "test_unit_measure_sparsity()" + ] + }, + { + "cell_type": "markdown", + "id": "fc5fb46e", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 4. Magnitude-Based Pruning - Removing Small Weights\n", + "\n", + "Magnitude pruning is the simplest and most intuitive compression technique. It's based on the observation that weights with small magnitudes contribute little to the model's output.\n", + "\n", + "### How Magnitude Pruning Works\n", + "\n", + "Think of magnitude pruning like editing a document - you remove words that don't significantly change the meaning. In neural networks, we remove weights that don't significantly affect predictions.\n", + "\n", + "```\n", + "Magnitude Pruning Process:\n", + "\n", + "Step 1: Collect All Weights\n", + "┌──────────────────────────────────────────────────┐\n", + "│ Layer 1: [2.1, 0.1, -1.8, 0.05, 3.2, -0.02] │\n", + "│ Layer 2: [1.5, -0.03, 2.8, 0.08, -2.1, 0.01] │\n", + "│ Layer 3: [0.7, 2.4, -0.06, 1.9, 0.04, -1.3] │\n", + "└──────────────────────────────────────────────────┘\n", + " ↓\n", + "Step 2: Calculate Magnitudes\n", + "┌──────────────────────────────────────────────────┐\n", + "│ Magnitudes: [2.1, 0.1, 1.8, 0.05, 3.2, 0.02, │\n", + "│ 1.5, 0.03, 2.8, 0.08, 2.1, 0.01, │\n", + "│ 0.7, 2.4, 0.06, 1.9, 0.04, 1.3] │\n", + "└──────────────────────────────────────────────────┘\n", + " ↓\n", + "Step 3: Find Threshold (e.g., 70th percentile)\n", + "┌──────────────────────────────────────────────────┐\n", + "│ Sorted: [0.01, 0.02, 0.03, 0.04, 0.05, 0.06, │\n", + "│ 0.08, 0.1, 0.7, 1.3, 1.5, 1.8, │ Threshold: 0.1\n", + "│ 1.9, 2.1, 2.1, 2.4, 2.8, 3.2] │ (70% of weights removed)\n", + "└──────────────────────────────────────────────────┘\n", + " ↓\n", + "Step 4: Apply Pruning Mask\n", + "┌──────────────────────────────────────────────────┐\n", + "│ Layer 1: [2.1, 0.0, -1.8, 0.0, 3.2, 0.0] │\n", + "│ Layer 2: [1.5, 0.0, 2.8, 0.0, -2.1, 0.0] │ 70% weights → 0\n", + "│ Layer 3: [0.7, 2.4, 0.0, 1.9, 0.0, -1.3] │ 30% preserved\n", + "└──────────────────────────────────────────────────┘\n", + "\n", + "Memory Impact:\n", + "- Dense storage: 18 values\n", + "- Sparse storage: 6 values + 6 indices = 12 values (33% savings)\n", + "- Theoretical limit: 70% savings with perfect sparse format\n", + "```\n", + "\n", + "### Why Global Thresholding Works\n", + "\n", + "Global thresholding treats the entire model as one big collection of weights, finding a single threshold that achieves the target sparsity across all layers.\n", + "\n", + "**Advantages:**\n", + "- Simple to implement and understand\n", + "- Preserves overall model capacity\n", + "- Works well for uniform network architectures\n", + "\n", + "**Disadvantages:**\n", + "- May over-prune some layers, under-prune others\n", + "- Doesn't account for layer-specific importance\n", + "- Can hurt performance if layers have very different weight distributions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8f12c15", + "metadata": {}, + "outputs": [], + "source": [ + "def magnitude_prune(model, sparsity=0.9):\n", + " \"\"\"\n", + " Remove weights with smallest magnitudes to achieve target sparsity.\n", + "\n", + " TODO: Implement global magnitude-based pruning\n", + "\n", + " APPROACH:\n", + " 1. Collect all weights from the model\n", + " 2. Calculate absolute values to get magnitudes\n", + " 3. Find threshold at desired sparsity percentile\n", + " 4. Set weights below threshold to zero (in-place)\n", + "\n", + " EXAMPLE:\n", + " >>> model = Sequential(Linear(100, 50), Linear(50, 10))\n", + " >>> original_params = sum(p.size for p in model.parameters())\n", + " >>> magnitude_prune(model, sparsity=0.8)\n", + " >>> final_sparsity = measure_sparsity(model)\n", + " >>> print(f\"Achieved {final_sparsity:.1f}% sparsity\")\n", + " Achieved 80.0% sparsity\n", + "\n", + " HINTS:\n", + " - Use np.percentile() to find threshold\n", + " - Modify model parameters in-place\n", + " - Consider only weight matrices, not biases\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " # Collect all weights (excluding biases)\n", + " all_weights = []\n", + " weight_params = []\n", + "\n", + " for param in model.parameters():\n", + " # Skip biases (typically 1D)\n", + " if len(param.shape) > 1:\n", + " all_weights.extend(param.data.flatten())\n", + " weight_params.append(param)\n", + "\n", + " if not all_weights:\n", + " return\n", + "\n", + " # Calculate magnitude threshold\n", + " magnitudes = np.abs(all_weights)\n", + " threshold = np.percentile(magnitudes, sparsity * 100)\n", + "\n", + " # Apply pruning to each weight parameter\n", + " for param in weight_params:\n", + " mask = np.abs(param.data) >= threshold\n", + " param.data = param.data * mask\n", + " ### END SOLUTION\n", + "\n", + "def test_unit_magnitude_prune():\n", + " \"\"\"🔬 Test magnitude-based pruning functionality.\"\"\"\n", + " print(\"🔬 Unit Test: Magnitude Prune...\")\n", + "\n", + " # Create test model with known weights\n", + " model = Sequential(Linear(4, 3), Linear(3, 2))\n", + "\n", + " # Set specific weight values for predictable testing\n", + " model.layers[0].weight.data = np.array([\n", + " [1.0, 2.0, 3.0],\n", + " [0.1, 0.2, 0.3],\n", + " [4.0, 5.0, 6.0],\n", + " [0.01, 0.02, 0.03]\n", + " ])\n", + "\n", + " initial_sparsity = measure_sparsity(model)\n", + " assert initial_sparsity == 0.0, \"Model should start with no sparsity\"\n", + "\n", + " # Apply 50% pruning\n", + " magnitude_prune(model, sparsity=0.5)\n", + " final_sparsity = measure_sparsity(model)\n", + "\n", + " # Should achieve approximately 50% sparsity\n", + " assert 40 <= final_sparsity <= 60, f\"Expected ~50% sparsity, got {final_sparsity}%\"\n", + "\n", + " # Verify largest weights survived\n", + " remaining_weights = model.layers[0].weight.data[model.layers[0].weight.data != 0]\n", + " assert len(remaining_weights) > 0, \"Some weights should remain\"\n", + " assert np.all(np.abs(remaining_weights) >= 0.1), \"Large weights should survive\"\n", + "\n", + " print(\"✅ magnitude_prune works correctly!\")\n", + "\n", + "test_unit_magnitude_prune()" + ] + }, + { + "cell_type": "markdown", + "id": "8ddc8e18", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 5. Structured Pruning - Hardware-Friendly Compression\n", + "\n", + "While magnitude pruning creates scattered zeros throughout the network, structured pruning removes entire computational units (channels, neurons, heads). This creates sparsity patterns that modern hardware can actually accelerate.\n", + "\n", + "### Why Structured Pruning Matters\n", + "\n", + "Think of the difference between removing random words from a paragraph versus removing entire sentences. Structured pruning removes entire \"sentences\" (channels) rather than random \"words\" (individual weights).\n", + "\n", + "```\n", + "Unstructured vs Structured Sparsity:\n", + "\n", + "UNSTRUCTURED (Magnitude Pruning):\n", + "┌─────────────────────────────────────────────┐\n", + "│ Channel 0: [2.1, 0.0, 1.8, 0.0, 3.2] │ ← Sparse weights\n", + "│ Channel 1: [0.0, 2.8, 0.0, 2.1, 0.0] │ ← Sparse weights\n", + "│ Channel 2: [1.5, 0.0, 2.4, 0.0, 1.9] │ ← Sparse weights\n", + "│ Channel 3: [0.0, 1.7, 0.0, 2.0, 0.0] │ ← Sparse weights\n", + "└─────────────────────────────────────────────┘\n", + "Issues: Irregular memory access, no hardware speedup\n", + "\n", + "STRUCTURED (Channel Pruning):\n", + "┌─────────────────────────────────────────────┐\n", + "│ Channel 0: [2.1, 1.3, 1.8, 0.9, 3.2] │ ← Fully preserved\n", + "│ Channel 1: [0.0, 0.0, 0.0, 0.0, 0.0] │ ← Fully removed\n", + "│ Channel 2: [1.5, 2.2, 2.4, 1.1, 1.9] │ ← Fully preserved\n", + "│ Channel 3: [0.0, 0.0, 0.0, 0.0, 0.0] │ ← Fully removed\n", + "└─────────────────────────────────────────────┘\n", + "Benefits: Regular patterns, hardware acceleration possible\n", + "```\n", + "\n", + "### Channel Importance Ranking\n", + "\n", + "How do we decide which channels to remove? We rank them by importance using various metrics:\n", + "\n", + "```\n", + "Channel Importance Metrics:\n", + "\n", + "Method 1: L2 Norm (Most Common)\n", + " For each output channel i:\n", + " Importance_i = ||W[:, i]||_2 = √(Σⱼ w²ⱼᵢ)\n", + "\n", + " Intuition: Channels with larger weights have bigger impact\n", + "\n", + "Method 2: Activation-Based\n", + " Importance_i = E[|activation_i|] over dataset\n", + "\n", + " Intuition: Channels that activate more are more important\n", + "\n", + "Method 3: Gradient-Based\n", + " Importance_i = |∂Loss/∂W[:, i]|\n", + "\n", + " Intuition: Channels with larger gradients affect loss more\n", + "\n", + "Ranking Process:\n", + " 1. Calculate importance for all channels\n", + " 2. Sort channels by importance (ascending)\n", + " 3. Remove bottom k% (least important)\n", + " 4. Zero out entire channels, not individual weights\n", + "```\n", + "\n", + "### Hardware Benefits of Structured Sparsity\n", + "\n", + "Structured sparsity enables real hardware acceleration because:\n", + "\n", + "1. **Memory Coalescing**: Accessing contiguous memory chunks is faster\n", + "2. **SIMD Operations**: Can process multiple remaining channels in parallel\n", + "3. **No Indexing Overhead**: Don't need to track locations of sparse weights\n", + "4. **Cache Efficiency**: Better spatial locality of memory access" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ede3f6c9", + "metadata": {}, + "outputs": [], + "source": [ + "def structured_prune(model, prune_ratio=0.5):\n", + " \"\"\"\n", + " Remove entire channels/neurons based on L2 norm importance.\n", + "\n", + " TODO: Implement structured pruning for Linear layers\n", + "\n", + " APPROACH:\n", + " 1. For each Linear layer, calculate L2 norm of each output channel\n", + " 2. Rank channels by importance (L2 norm)\n", + " 3. Remove lowest importance channels by setting to zero\n", + " 4. This creates block sparsity that's hardware-friendly\n", + "\n", + " EXAMPLE:\n", + " >>> model = Sequential(Linear(100, 50), Linear(50, 10))\n", + " >>> original_shape = model.layers[0].weight.shape\n", + " >>> structured_prune(model, prune_ratio=0.3)\n", + " >>> # 30% of channels are now completely zero\n", + " >>> final_sparsity = measure_sparsity(model)\n", + " >>> print(f\"Structured sparsity: {final_sparsity:.1f}%\")\n", + " Structured sparsity: 30.0%\n", + "\n", + " HINTS:\n", + " - Calculate L2 norm along input dimension for each output channel\n", + " - Use np.linalg.norm(weights[:, channel]) for channel importance\n", + " - Set entire channels to zero (not just individual weights)\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " for layer in model.layers:\n", + " if isinstance(layer, Linear) and hasattr(layer, 'weight'):\n", + " weight = layer.weight.data\n", + "\n", + " # Calculate L2 norm for each output channel (column)\n", + " channel_norms = np.linalg.norm(weight, axis=0)\n", + "\n", + " # Find channels to prune (lowest importance)\n", + " num_channels = weight.shape[1]\n", + " num_to_prune = int(num_channels * prune_ratio)\n", + "\n", + " if num_to_prune > 0:\n", + " # Get indices of channels to prune (smallest norms)\n", + " prune_indices = np.argpartition(channel_norms, num_to_prune)[:num_to_prune]\n", + "\n", + " # Zero out entire channels\n", + " weight[:, prune_indices] = 0\n", + "\n", + " # Also zero corresponding bias elements if bias exists\n", + " if layer.bias is not None:\n", + " layer.bias.data[prune_indices] = 0\n", + " ### END SOLUTION\n", + "\n", + "def test_unit_structured_prune():\n", + " \"\"\"🔬 Test structured pruning functionality.\"\"\"\n", + " print(\"🔬 Unit Test: Structured Prune...\")\n", + "\n", + " # Create test model\n", + " model = Sequential(Linear(4, 6), Linear(6, 2))\n", + "\n", + " # Set predictable weights for testing\n", + " model.layers[0].weight.data = np.array([\n", + " [1.0, 0.1, 2.0, 0.05, 3.0, 0.01], # Channels with varying importance\n", + " [1.1, 0.11, 2.1, 0.06, 3.1, 0.02],\n", + " [1.2, 0.12, 2.2, 0.07, 3.2, 0.03],\n", + " [1.3, 0.13, 2.3, 0.08, 3.3, 0.04]\n", + " ])\n", + "\n", + " initial_sparsity = measure_sparsity(model)\n", + " assert initial_sparsity == 0.0, \"Model should start with no sparsity\"\n", + "\n", + " # Apply 33% structured pruning (2 out of 6 channels)\n", + " structured_prune(model, prune_ratio=0.33)\n", + " final_sparsity = measure_sparsity(model)\n", + "\n", + " # Check that some channels are completely zero\n", + " weight = model.layers[0].weight.data\n", + " zero_channels = np.sum(np.all(weight == 0, axis=0))\n", + " assert zero_channels >= 1, f\"Expected at least 1 zero channel, got {zero_channels}\"\n", + "\n", + " # Check that non-zero channels are completely preserved\n", + " for col in range(weight.shape[1]):\n", + " channel = weight[:, col]\n", + " assert np.all(channel == 0) or np.all(channel != 0), \"Channels should be fully zero or fully non-zero\"\n", + "\n", + " print(\"✅ structured_prune works correctly!\")\n", + "\n", + "test_unit_structured_prune()" + ] + }, + { + "cell_type": "markdown", + "id": "74c8202f", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 6. Low-Rank Approximation - Matrix Compression Through Factorization\n", + "\n", + "Low-rank approximation discovers that large weight matrices often contain redundant information that can be captured with much smaller matrices through mathematical decomposition.\n", + "\n", + "### The Intuition Behind Low-Rank Approximation\n", + "\n", + "Imagine you're storing a massive spreadsheet where many columns are highly correlated. Instead of storing all columns separately, you could store a few \"basis\" columns and coefficients for how to combine them to recreate the original data.\n", + "\n", + "```\n", + "Low-Rank Decomposition Visualization:\n", + "\n", + "Original Matrix W (large): Factorized Form (smaller):\n", + "┌─────────────────────────┐ ┌──────┐ ┌──────────────┐\n", + "│ 2.1 1.3 0.8 1.9 2.4 │ │ 1.1 │ │ 1.9 1.2 0.7│\n", + "│ 1.5 2.8 1.2 0.9 1.6 │ ≈ │ 2.4 │ @ │ 0.6 1.2 0.5│\n", + "│ 0.6 1.7 2.5 1.1 0.8 │ │ 0.8 │ │ 1.4 2.1 0.9│\n", + "│ 1.9 1.0 1.6 2.3 1.8 │ │ 1.6 │ │ 0.5 0.6 1.1│\n", + "└─────────────────────────┘ └──────┘ └──────────────┘\n", + " W (4×5) = 20 params U (4×2)=8 + V (2×5)=10 = 18 params\n", + "\n", + "Parameter Reduction:\n", + "- Original: 4 × 5 = 20 parameters\n", + "- Compressed: (4 × 2) + (2 × 5) = 18 parameters\n", + "- Compression ratio: 18/20 = 0.9 (10% savings)\n", + "\n", + "For larger matrices, savings become dramatic:\n", + "- W (1000×1000): 1M parameters → U (1000×100) + V (100×1000): 200K parameters\n", + "- Compression ratio: 0.2 (80% savings)\n", + "```\n", + "\n", + "### SVD: The Mathematical Foundation\n", + "\n", + "Singular Value Decomposition (SVD) finds the optimal low-rank approximation by identifying the most important \"directions\" in the data:\n", + "\n", + "```\n", + "SVD Decomposition:\n", + " W = U × Σ × V^T\n", + "\n", + "Where:\n", + " U: Left singular vectors (input patterns)\n", + " Σ: Singular values (importance weights)\n", + " V^T: Right singular vectors (output patterns)\n", + "\n", + "Truncated SVD (Rank-k approximation):\n", + " W ≈ U[:,:k] × Σ[:k] × V^T[:k,:]\n", + "\n", + "Quality vs Compression Trade-off:\n", + " Higher k → Better approximation, less compression\n", + " Lower k → More compression, worse approximation\n", + "\n", + "Choosing Optimal Rank:\n", + " Method 1: Fixed ratio (k = ratio × min(m,n))\n", + " Method 2: Energy threshold (keep 90% of singular value energy)\n", + " Method 3: Error threshold (reconstruction error < threshold)\n", + "```\n", + "\n", + "### When Low-Rank Works Best\n", + "\n", + "Low-rank approximation works well when:\n", + "- **Matrices are large**: Compression benefits scale with size\n", + "- **Data has structure**: Correlated patterns enable compression\n", + "- **Moderate accuracy loss acceptable**: Some precision traded for efficiency\n", + "\n", + "It works poorly when:\n", + "- **Matrices are already small**: Overhead exceeds benefits\n", + "- **Data is random**: No patterns to exploit\n", + "- **High precision required**: SVD introduces approximation error" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bdbedbf4", + "metadata": {}, + "outputs": [], + "source": [ + "def low_rank_approximate(weight_matrix, rank_ratio=0.5):\n", + " \"\"\"\n", + " Approximate weight matrix using low-rank decomposition (SVD).\n", + "\n", + " TODO: Implement SVD-based low-rank approximation\n", + "\n", + " APPROACH:\n", + " 1. Perform SVD: W = U @ S @ V^T\n", + " 2. Keep only top k singular values where k = rank_ratio * min(dimensions)\n", + " 3. Reconstruct: W_approx = U[:,:k] @ diag(S[:k]) @ V[:k,:]\n", + " 4. Return decomposed matrices for memory savings\n", + "\n", + " EXAMPLE:\n", + " >>> weight = np.random.randn(100, 50)\n", + " >>> U, S, V = low_rank_approximate(weight, rank_ratio=0.3)\n", + " >>> # Original: 100*50 = 5000 params\n", + " >>> # Compressed: 100*15 + 15*50 = 2250 params (55% reduction)\n", + "\n", + " HINTS:\n", + " - Use np.linalg.svd() for decomposition\n", + " - Choose k = int(rank_ratio * min(m, n))\n", + " - Return U[:,:k], S[:k], V[:k,:] for reconstruction\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " m, n = weight_matrix.shape\n", + "\n", + " # Perform SVD\n", + " U, S, V = np.linalg.svd(weight_matrix, full_matrices=False)\n", + "\n", + " # Determine target rank\n", + " max_rank = min(m, n)\n", + " target_rank = max(1, int(rank_ratio * max_rank))\n", + "\n", + " # Truncate to target rank\n", + " U_truncated = U[:, :target_rank]\n", + " S_truncated = S[:target_rank]\n", + " V_truncated = V[:target_rank, :]\n", + "\n", + " return U_truncated, S_truncated, V_truncated\n", + " ### END SOLUTION\n", + "\n", + "def test_unit_low_rank_approximate():\n", + " \"\"\"🔬 Test low-rank approximation functionality.\"\"\"\n", + " print(\"🔬 Unit Test: Low-Rank Approximate...\")\n", + "\n", + " # Create test weight matrix\n", + " original_weight = np.random.randn(20, 15)\n", + " original_params = original_weight.size\n", + "\n", + " # Apply low-rank approximation\n", + " U, S, V = low_rank_approximate(original_weight, rank_ratio=0.4)\n", + "\n", + " # Check dimensions\n", + " target_rank = int(0.4 * min(20, 15)) # min(20,15) = 15, so 0.4*15 = 6\n", + " assert U.shape == (20, target_rank), f\"Expected U shape (20, {target_rank}), got {U.shape}\"\n", + " assert S.shape == (target_rank,), f\"Expected S shape ({target_rank},), got {S.shape}\"\n", + " assert V.shape == (target_rank, 15), f\"Expected V shape ({target_rank}, 15), got {V.shape}\"\n", + "\n", + " # Check parameter reduction\n", + " compressed_params = U.size + S.size + V.size\n", + " compression_ratio = compressed_params / original_params\n", + " assert compression_ratio < 1.0, f\"Should compress, but ratio is {compression_ratio}\"\n", + "\n", + " # Check reconstruction quality\n", + " reconstructed = U @ np.diag(S) @ V\n", + " reconstruction_error = np.linalg.norm(original_weight - reconstructed)\n", + " relative_error = reconstruction_error / np.linalg.norm(original_weight)\n", + " assert relative_error < 0.5, f\"Reconstruction error too high: {relative_error}\"\n", + "\n", + " print(\"✅ low_rank_approximate works correctly!\")\n", + "\n", + "test_unit_low_rank_approximate()" + ] + }, + { + "cell_type": "markdown", + "id": "a51cbe39", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 7. Knowledge Distillation - Learning from Teacher Models\n", + "\n", + "Knowledge distillation is like having an expert teacher simplify complex concepts for a student. The large \"teacher\" model shares its knowledge with a smaller \"student\" model, achieving similar performance with far fewer parameters.\n", + "\n", + "### The Teacher-Student Learning Process\n", + "\n", + "Unlike traditional training where models learn from hard labels (cat/dog), knowledge distillation uses \"soft\" targets that contain richer information about the teacher's decision-making process.\n", + "\n", + "```\n", + "Knowledge Distillation Process:\n", + "\n", + " TEACHER MODEL (Large)\n", + " ┌─────────────────────┐\n", + "Input Data ────────→│ 100M parameters │\n", + " │ 95% accuracy │\n", + " │ 500ms inference │\n", + " └─────────────────────┘\n", + " │\n", + " ↓ Soft Targets\n", + " ┌─────────────────────┐\n", + " │ Logits: [2.1, 0.3, │\n", + " │ 0.8, 4.2] │ ← Rich information\n", + " └─────────────────────┘\n", + " │\n", + " ↓ Distillation Loss\n", + " ┌─────────────────────┐\n", + "Input Data ────────→│ STUDENT MODEL │\n", + "Hard Labels ───────→│ 10M parameters │ ← 10x smaller\n", + " │ 93% accuracy │ ← 2% loss\n", + " │ 50ms inference │ ← 10x faster\n", + " └─────────────────────┘\n", + "\n", + "Benefits:\n", + "• Size: 10x smaller models\n", + "• Speed: 10x faster inference\n", + "• Accuracy: Only 2-5% degradation\n", + "• Knowledge transfer: Student learns teacher's \"reasoning\"\n", + "```\n", + "\n", + "### Temperature Scaling: Softening Decisions\n", + "\n", + "Temperature scaling is a key innovation that makes knowledge distillation effective. It \"softens\" the teacher's confidence, revealing uncertainty that helps the student learn.\n", + "\n", + "```\n", + "Temperature Effect on Probability Distributions:\n", + "\n", + "Without Temperature (T=1): With Temperature (T=3):\n", + "Teacher Logits: [1.0, 2.0, 0.5] Teacher Logits: [1.0, 2.0, 0.5]\n", + " ↓ ↓ ÷ 3\n", + "Softmax: [0.09, 0.67, 0.24] Logits/T: [0.33, 0.67, 0.17]\n", + " ^ ^ ^ ↓\n", + " Low High Med Softmax: [0.21, 0.42, 0.17]\n", + " ^ ^ ^\n", + "Sharp decisions (hard to learn) Soft decisions (easier to learn)\n", + "\n", + "Why Soft Targets Help:\n", + "1. Reveal teacher's uncertainty about similar classes\n", + "2. Provide richer gradients for student learning\n", + "3. Transfer knowledge about class relationships\n", + "4. Reduce overfitting to hard labels\n", + "```\n", + "\n", + "### Loss Function Design\n", + "\n", + "The distillation loss balances learning from both the teacher's soft knowledge and the ground truth hard labels:\n", + "\n", + "```\n", + "Combined Loss Function:\n", + "\n", + "L_total = α × L_soft + (1-α) × L_hard\n", + "\n", + "Where:\n", + " L_soft = KL_divergence(Student_soft, Teacher_soft)\n", + " │\n", + " └─ Measures how well student mimics teacher\n", + "\n", + " L_hard = CrossEntropy(Student_predictions, True_labels)\n", + " │\n", + " └─ Ensures student still learns correct answers\n", + "\n", + "Balance Parameter α:\n", + "• α = 0.7: Focus mainly on teacher (typical)\n", + "• α = 0.9: Almost pure distillation\n", + "• α = 0.3: Balance teacher and ground truth\n", + "• α = 0.0: Ignore teacher (regular training)\n", + "\n", + "Temperature T:\n", + "• T = 1: No softening (standard softmax)\n", + "• T = 3-5: Good balance (typical range)\n", + "• T = 10+: Very soft (may lose information)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bf1a9ab1", + "metadata": {}, + "outputs": [], + "source": [ + "class KnowledgeDistillation:\n", + " \"\"\"\n", + " Knowledge distillation for model compression.\n", + "\n", + " Train a smaller student model to mimic a larger teacher model.\n", + " \"\"\"\n", + "\n", + " def __init__(self, teacher_model, student_model, temperature=3.0, alpha=0.7):\n", + " \"\"\"\n", + " Initialize knowledge distillation.\n", + "\n", + " TODO: Set up teacher and student models with distillation parameters\n", + "\n", + " APPROACH:\n", + " 1. Store teacher and student models\n", + " 2. Set temperature for softening probability distributions\n", + " 3. Set alpha for balancing hard vs soft targets\n", + "\n", + " Args:\n", + " teacher_model: Large, pre-trained model\n", + " student_model: Smaller model to train\n", + " temperature: Softening parameter for distributions\n", + " alpha: Weight for soft target loss (1-alpha for hard targets)\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " self.teacher_model = teacher_model\n", + " self.student_model = student_model\n", + " self.temperature = temperature\n", + " self.alpha = alpha\n", + " ### END SOLUTION\n", + "\n", + " def distillation_loss(self, student_logits, teacher_logits, true_labels):\n", + " \"\"\"\n", + " Calculate combined distillation loss.\n", + "\n", + " TODO: Implement knowledge distillation loss function\n", + "\n", + " APPROACH:\n", + " 1. Calculate hard target loss (student vs true labels)\n", + " 2. Calculate soft target loss (student vs teacher, with temperature)\n", + " 3. Combine losses: alpha * soft_loss + (1-alpha) * hard_loss\n", + "\n", + " EXAMPLE:\n", + " >>> kd = KnowledgeDistillation(teacher, student)\n", + " >>> loss = kd.distillation_loss(student_out, teacher_out, labels)\n", + " >>> print(f\"Distillation loss: {loss:.4f}\")\n", + "\n", + " HINTS:\n", + " - Use temperature to soften distributions: logits/temperature\n", + " - Soft targets use KL divergence or cross-entropy\n", + " - Hard targets use standard classification loss\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " # Convert to numpy for this implementation\n", + " if hasattr(student_logits, 'data'):\n", + " student_logits = student_logits.data\n", + " if hasattr(teacher_logits, 'data'):\n", + " teacher_logits = teacher_logits.data\n", + " if hasattr(true_labels, 'data'):\n", + " true_labels = true_labels.data\n", + "\n", + " # Soften distributions with temperature\n", + " student_soft = self._softmax(student_logits / self.temperature)\n", + " teacher_soft = self._softmax(teacher_logits / self.temperature)\n", + "\n", + " # Soft target loss (KL divergence)\n", + " soft_loss = self._kl_divergence(student_soft, teacher_soft)\n", + "\n", + " # Hard target loss (cross-entropy)\n", + " student_hard = self._softmax(student_logits)\n", + " hard_loss = self._cross_entropy(student_hard, true_labels)\n", + "\n", + " # Combined loss\n", + " total_loss = self.alpha * soft_loss + (1 - self.alpha) * hard_loss\n", + "\n", + " return total_loss\n", + " ### END SOLUTION\n", + "\n", + " def _softmax(self, logits):\n", + " \"\"\"Compute softmax with numerical stability.\"\"\"\n", + " exp_logits = np.exp(logits - np.max(logits, axis=-1, keepdims=True))\n", + " return exp_logits / np.sum(exp_logits, axis=-1, keepdims=True)\n", + "\n", + " def _kl_divergence(self, p, q):\n", + " \"\"\"Compute KL divergence between distributions.\"\"\"\n", + " return np.sum(p * np.log(p / (q + 1e-8) + 1e-8))\n", + "\n", + " def _cross_entropy(self, predictions, labels):\n", + " \"\"\"Compute cross-entropy loss.\"\"\"\n", + " # Simple implementation for integer labels\n", + " if labels.ndim == 1:\n", + " return -np.mean(np.log(predictions[np.arange(len(labels)), labels] + 1e-8))\n", + " else:\n", + " return -np.mean(np.sum(labels * np.log(predictions + 1e-8), axis=1))\n", + "\n", + "def test_unit_knowledge_distillation():\n", + " \"\"\"🔬 Test knowledge distillation functionality.\"\"\"\n", + " print(\"🔬 Unit Test: Knowledge Distillation...\")\n", + "\n", + " # Create teacher and student models\n", + " teacher = Sequential(Linear(10, 20), Linear(20, 5))\n", + " student = Sequential(Linear(10, 5)) # Smaller model\n", + "\n", + " # Initialize knowledge distillation\n", + " kd = KnowledgeDistillation(teacher, student, temperature=3.0, alpha=0.7)\n", + "\n", + " # Create dummy data\n", + " input_data = Tensor(np.random.randn(8, 10)) # Batch of 8\n", + " true_labels = np.array([0, 1, 2, 3, 4, 0, 1, 2]) # Class labels\n", + "\n", + " # Forward passes\n", + " teacher_output = teacher.forward(input_data)\n", + " student_output = student.forward(input_data)\n", + "\n", + " # Calculate distillation loss\n", + " loss = kd.distillation_loss(student_output, teacher_output, true_labels)\n", + "\n", + " # Verify loss is reasonable\n", + " assert isinstance(loss, (float, np.floating)), f\"Loss should be float, got {type(loss)}\"\n", + " assert loss > 0, f\"Loss should be positive, got {loss}\"\n", + " assert not np.isnan(loss), \"Loss should not be NaN\"\n", + "\n", + " print(\"✅ knowledge_distillation works correctly!\")\n", + "\n", + "test_unit_knowledge_distillation()" + ] + }, + { + "cell_type": "markdown", + "id": "bea12725", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 8. Integration: Complete Compression Pipeline\n", + "\n", + "Now let's combine all our compression techniques into a unified system that can apply multiple methods and track their cumulative effects.\n", + "\n", + "### Compression Strategy Design\n", + "\n", + "Real-world compression often combines multiple techniques in sequence, each targeting different types of redundancy:\n", + "\n", + "```\n", + "Multi-Stage Compression Pipeline:\n", + "\n", + "Original Model (100MB, 100% accuracy)\n", + " │\n", + " ↓ Stage 1: Magnitude Pruning (remove 80% of small weights)\n", + "Sparse Model (20MB, 98% accuracy)\n", + " │\n", + " ↓ Stage 2: Structured Pruning (remove 30% of channels)\n", + "Compact Model (14MB, 96% accuracy)\n", + " │\n", + " ↓ Stage 3: Low-Rank Approximation (compress large layers)\n", + "Factorized Model (10MB, 95% accuracy)\n", + " │\n", + " ↓ Stage 4: Knowledge Distillation (train smaller architecture)\n", + "Student Model (5MB, 93% accuracy)\n", + "\n", + "Final Result: 20x size reduction, 7% accuracy loss\n", + "```\n", + "\n", + "### Compression Configuration\n", + "\n", + "Different deployment scenarios require different compression strategies:\n", + "\n", + "```\n", + "Deployment Scenarios and Strategies:\n", + "\n", + "MOBILE APP (Aggressive compression needed):\n", + "┌─────────────────────────────────────────┐\n", + "│ Target: <10MB, <100ms inference │\n", + "│ Strategy: │\n", + "│ • Magnitude pruning: 95% sparsity │\n", + "│ • Structured pruning: 50% channels │\n", + "│ • Knowledge distillation: 10x reduction │\n", + "│ • Quantization: 8-bit weights │\n", + "└─────────────────────────────────────────┘\n", + "\n", + "EDGE DEVICE (Balanced compression):\n", + "┌─────────────────────────────────────────┐\n", + "│ Target: <50MB, <200ms inference │\n", + "│ Strategy: │\n", + "│ • Magnitude pruning: 80% sparsity │\n", + "│ • Structured pruning: 30% channels │\n", + "│ • Low-rank: 50% rank reduction │\n", + "│ • Quantization: 16-bit weights │\n", + "└─────────────────────────────────────────┘\n", + "\n", + "CLOUD SERVICE (Minimal compression):\n", + "┌─────────────────────────────────────────┐\n", + "│ Target: Maintain accuracy, reduce cost │\n", + "│ Strategy: │\n", + "│ • Magnitude pruning: 50% sparsity │\n", + "│ • Structured pruning: 10% channels │\n", + "│ • Dynamic batching optimization │\n", + "│ • Mixed precision inference │\n", + "└─────────────────────────────────────────┘\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68de6767", + "metadata": {}, + "outputs": [], + "source": [ + "def compress_model(model, compression_config):\n", + " \"\"\"\n", + " Apply comprehensive model compression based on configuration.\n", + "\n", + " TODO: Implement complete compression pipeline\n", + "\n", + " APPROACH:\n", + " 1. Apply magnitude pruning if specified\n", + " 2. Apply structured pruning if specified\n", + " 3. Apply low-rank approximation if specified\n", + " 4. Return compression statistics\n", + "\n", + " EXAMPLE:\n", + " >>> config = {\n", + " ... 'magnitude_prune': 0.8,\n", + " ... 'structured_prune': 0.3,\n", + " ... 'low_rank': 0.5\n", + " ... }\n", + " >>> stats = compress_model(model, config)\n", + " >>> print(f\"Final sparsity: {stats['sparsity']:.1f}%\")\n", + " Final sparsity: 85.0%\n", + "\n", + " HINT: Apply techniques sequentially and measure results\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " original_params = sum(p.size for p in model.parameters())\n", + " original_sparsity = measure_sparsity(model)\n", + "\n", + " stats = {\n", + " 'original_params': original_params,\n", + " 'original_sparsity': original_sparsity,\n", + " 'applied_techniques': []\n", + " }\n", + "\n", + " # Apply magnitude pruning\n", + " if 'magnitude_prune' in compression_config:\n", + " sparsity = compression_config['magnitude_prune']\n", + " magnitude_prune(model, sparsity=sparsity)\n", + " stats['applied_techniques'].append(f'magnitude_prune_{sparsity}')\n", + "\n", + " # Apply structured pruning\n", + " if 'structured_prune' in compression_config:\n", + " ratio = compression_config['structured_prune']\n", + " structured_prune(model, prune_ratio=ratio)\n", + " stats['applied_techniques'].append(f'structured_prune_{ratio}')\n", + "\n", + " # Apply low-rank approximation (conceptually - would need architecture changes)\n", + " if 'low_rank' in compression_config:\n", + " ratio = compression_config['low_rank']\n", + " # For demo, we'll just record that it would be applied\n", + " stats['applied_techniques'].append(f'low_rank_{ratio}')\n", + "\n", + " # Final measurements\n", + " final_sparsity = measure_sparsity(model)\n", + " stats['final_sparsity'] = final_sparsity\n", + " stats['sparsity_increase'] = final_sparsity - original_sparsity\n", + "\n", + " return stats\n", + " ### END SOLUTION\n", + "\n", + "def test_unit_compress_model():\n", + " \"\"\"🔬 Test comprehensive model compression.\"\"\"\n", + " print(\"🔬 Unit Test: Compress Model...\")\n", + "\n", + " # Create test model\n", + " model = Sequential(Linear(20, 15), Linear(15, 10), Linear(10, 5))\n", + "\n", + " # Define compression configuration\n", + " config = {\n", + " 'magnitude_prune': 0.7,\n", + " 'structured_prune': 0.2\n", + " }\n", + "\n", + " # Apply compression\n", + " stats = compress_model(model, config)\n", + "\n", + " # Verify statistics\n", + " assert 'original_params' in stats, \"Should track original parameter count\"\n", + " assert 'final_sparsity' in stats, \"Should track final sparsity\"\n", + " assert 'applied_techniques' in stats, \"Should track applied techniques\"\n", + "\n", + " # Verify compression was applied\n", + " assert stats['final_sparsity'] > stats['original_sparsity'], \"Sparsity should increase\"\n", + " assert len(stats['applied_techniques']) == 2, \"Should apply both techniques\"\n", + "\n", + " # Verify model still has reasonable structure\n", + " remaining_params = sum(np.count_nonzero(p.data) for p in model.parameters())\n", + " assert remaining_params > 0, \"Model should retain some parameters\"\n", + "\n", + " print(\"✅ compress_model works correctly!\")\n", + "\n", + "test_unit_compress_model()" + ] + }, + { + "cell_type": "markdown", + "id": "78b4d5fb", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 9. Systems Analysis: Compression Performance and Trade-offs\n", + "\n", + "Understanding how compression techniques affect real-world deployment metrics like storage, memory, speed, and accuracy.\n", + "\n", + "### Compression Effectiveness Analysis\n", + "\n", + "Different techniques excel in different scenarios. Let's measure their effectiveness across various model sizes and architectures." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8025b3f", + "metadata": { + "lines_to_next_cell": 1 + }, + "outputs": [], + "source": [ + "def analyze_compression_ratios():\n", + " \"\"\"📊 Analyze compression ratios for different techniques.\"\"\"\n", + " print(\"📊 Analyzing Compression Ratios...\")\n", + "\n", + " # Create test models of different sizes\n", + " models = {\n", + " 'Small': Sequential(Linear(50, 30), Linear(30, 10)),\n", + " 'Medium': Sequential(Linear(200, 128), Linear(128, 64), Linear(64, 10)),\n", + " 'Large': Sequential(Linear(500, 256), Linear(256, 128), Linear(128, 10))\n", + " }\n", + "\n", + " compression_techniques = [\n", + " ('Magnitude 50%', {'magnitude_prune': 0.5}),\n", + " ('Magnitude 90%', {'magnitude_prune': 0.9}),\n", + " ('Structured 30%', {'structured_prune': 0.3}),\n", + " ('Combined', {'magnitude_prune': 0.8, 'structured_prune': 0.2})\n", + " ]\n", + "\n", + " print(f\"{'Model':<8} {'Technique':<15} {'Original':<10} {'Final':<10} {'Reduction':<10}\")\n", + " print(\"-\" * 65)\n", + "\n", + " for model_name, model in models.items():\n", + " original_params = sum(p.size for p in model.parameters())\n", + "\n", + " for tech_name, config in compression_techniques:\n", + " # Create fresh copy for each test\n", + " test_model = copy.deepcopy(model)\n", + "\n", + " # Apply compression\n", + " stats = compress_model(test_model, config)\n", + "\n", + " # Calculate compression ratio\n", + " remaining_params = sum(np.count_nonzero(p.data) for p in test_model.parameters())\n", + " reduction = (1 - remaining_params / original_params) * 100\n", + "\n", + " print(f\"{model_name:<8} {tech_name:<15} {original_params:<10} {remaining_params:<10} {reduction:<9.1f}%\")\n", + "\n", + " print(\"\\n💡 Key Insights:\")\n", + " print(\"• Magnitude pruning achieves predictable sparsity levels\")\n", + " print(\"• Structured pruning creates hardware-friendly sparsity\")\n", + " print(\"• Combined techniques offer maximum compression\")\n", + " print(\"• Larger models compress better (more redundancy)\")\n", + "\n", + "analyze_compression_ratios()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f29e9dc0", + "metadata": {}, + "outputs": [], + "source": [ + "def analyze_compression_speed():\n", + " \"\"\"📊 Analyze inference speed with different compression levels.\"\"\"\n", + " print(\"📊 Analyzing Compression Speed Impact...\")\n", + "\n", + " # Create test model\n", + " model = Sequential(Linear(512, 256), Linear(256, 128), Linear(128, 10))\n", + " test_input = Tensor(np.random.randn(100, 512)) # Batch of 100\n", + "\n", + " def time_inference(model, input_data, iterations=50):\n", + " \"\"\"Time model inference.\"\"\"\n", + " times = []\n", + " for _ in range(iterations):\n", + " start = time.time()\n", + " _ = model.forward(input_data)\n", + " times.append(time.time() - start)\n", + " return np.mean(times[5:]) # Skip first few for warmup\n", + "\n", + " # Test different compression levels\n", + " compression_levels = [\n", + " ('Original', {}),\n", + " ('Light Pruning', {'magnitude_prune': 0.5}),\n", + " ('Heavy Pruning', {'magnitude_prune': 0.9}),\n", + " ('Structured', {'structured_prune': 0.3}),\n", + " ('Combined', {'magnitude_prune': 0.8, 'structured_prune': 0.2})\n", + " ]\n", + "\n", + " print(f\"{'Compression':<15} {'Sparsity':<10} {'Time (ms)':<12} {'Speedup':<10}\")\n", + " print(\"-\" * 50)\n", + "\n", + " baseline_time = None\n", + "\n", + " for name, config in compression_levels:\n", + " # Create fresh model copy\n", + " test_model = copy.deepcopy(model)\n", + "\n", + " # Apply compression\n", + " if config:\n", + " compress_model(test_model, config)\n", + "\n", + " # Measure performance\n", + " sparsity = measure_sparsity(test_model)\n", + " inference_time = time_inference(test_model, test_input) * 1000 # Convert to ms\n", + "\n", + " if baseline_time is None:\n", + " baseline_time = inference_time\n", + " speedup = 1.0\n", + " else:\n", + " speedup = baseline_time / inference_time\n", + "\n", + " print(f\"{name:<15} {sparsity:<9.1f}% {inference_time:<11.2f} {speedup:<9.2f}x\")\n", + "\n", + " print(\"\\n💡 Speed Insights:\")\n", + " print(\"• Dense matrix operations show minimal speedup from unstructured sparsity\")\n", + " print(\"• Structured sparsity enables better hardware acceleration\")\n", + " print(\"• Real speedups require sparse-optimized libraries (e.g., NVIDIA 2:4 sparsity)\")\n", + " print(\"• Memory bandwidth often more important than parameter count\")\n", + "\n", + "analyze_compression_speed()" + ] + }, + { + "cell_type": "markdown", + "id": "e6c5926b", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 10. Optimization Insights: Production Compression Strategy\n", + "\n", + "Understanding the real-world implications of compression choices and how to design compression strategies for different deployment scenarios.\n", + "\n", + "### Accuracy vs Compression Trade-offs\n", + "\n", + "The fundamental challenge in model compression is balancing three competing objectives: model size, inference speed, and prediction accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "351bffdb", + "metadata": {}, + "outputs": [], + "source": [ + "def analyze_compression_accuracy_tradeoff():\n", + " \"\"\"📊 Analyze accuracy vs compression trade-offs.\"\"\"\n", + " print(\"📊 Analyzing Accuracy vs Compression Trade-offs...\")\n", + "\n", + " # Simulate accuracy degradation (in practice, would need real training/testing)\n", + " def simulate_accuracy_loss(sparsity, technique_type):\n", + " \"\"\"Simulate realistic accuracy loss patterns.\"\"\"\n", + " if technique_type == 'magnitude':\n", + " # Magnitude pruning: gradual degradation\n", + " return max(0, sparsity * 0.3 + np.random.normal(0, 0.05))\n", + " elif technique_type == 'structured':\n", + " # Structured pruning: more aggressive early loss\n", + " return max(0, sparsity * 0.5 + np.random.normal(0, 0.1))\n", + " elif technique_type == 'knowledge_distillation':\n", + " # Knowledge distillation: better preservation\n", + " return max(0, sparsity * 0.1 + np.random.normal(0, 0.02))\n", + " else:\n", + " return sparsity * 0.4\n", + "\n", + " # Test different compression strategies\n", + " strategies = [\n", + " ('Magnitude Only', 'magnitude'),\n", + " ('Structured Only', 'structured'),\n", + " ('Knowledge Distillation', 'knowledge_distillation'),\n", + " ('Combined Approach', 'combined')\n", + " ]\n", + "\n", + " sparsity_levels = np.arange(0.1, 1.0, 0.1)\n", + "\n", + " print(f\"{'Strategy':<20} {'Sparsity':<10} {'Accuracy Loss':<15}\")\n", + " print(\"-\" * 50)\n", + "\n", + " for strategy_name, strategy_type in strategies:\n", + " print(f\"\\n{strategy_name}:\")\n", + " for sparsity in sparsity_levels:\n", + " if strategy_type == 'combined':\n", + " # Combined approach uses multiple techniques\n", + " loss = min(\n", + " simulate_accuracy_loss(sparsity * 0.7, 'magnitude'),\n", + " simulate_accuracy_loss(sparsity * 0.3, 'structured')\n", + " )\n", + " else:\n", + " loss = simulate_accuracy_loss(sparsity, strategy_type)\n", + "\n", + " print(f\"{'':20} {sparsity:<9.1f} {loss:<14.3f}\")\n", + "\n", + " print(\"\\n💡 Trade-off Insights:\")\n", + " print(\"• Knowledge distillation preserves accuracy best at high compression\")\n", + " print(\"• Magnitude pruning offers gradual degradation curve\")\n", + " print(\"• Structured pruning enables hardware acceleration but higher accuracy loss\")\n", + " print(\"• Combined approaches balance multiple objectives\")\n", + " print(\"• Early stopping based on accuracy threshold is crucial\")\n", + "\n", + "analyze_compression_accuracy_tradeoff()" + ] + }, + { + "cell_type": "markdown", + "id": "8a67dffa", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 11. Module Integration Test\n", + "\n", + "Final validation that all compression techniques work together correctly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d51b541", + "metadata": {}, + "outputs": [], + "source": [ + "def test_module():\n", + " \"\"\"\n", + " Comprehensive test of entire compression module functionality.\n", + "\n", + " This final test runs before module summary to ensure:\n", + " - All unit tests pass\n", + " - Functions work together correctly\n", + " - Module is ready for integration with TinyTorch\n", + " \"\"\"\n", + " print(\"🧪 RUNNING MODULE INTEGRATION TEST\")\n", + " print(\"=\" * 50)\n", + "\n", + " # Run all unit tests\n", + " print(\"Running unit tests...\")\n", + " test_unit_measure_sparsity()\n", + " test_unit_magnitude_prune()\n", + " test_unit_structured_prune()\n", + " test_unit_low_rank_approximate()\n", + " test_unit_knowledge_distillation()\n", + " test_unit_compress_model()\n", + "\n", + " print(\"\\nRunning integration scenarios...\")\n", + "\n", + " # Test 1: Complete compression pipeline\n", + " print(\"🔬 Integration Test: Complete compression pipeline...\")\n", + "\n", + " # Create a realistic model\n", + " model = Sequential(\n", + " Linear(784, 512), # Input layer (like MNIST)\n", + " Linear(512, 256), # Hidden layer 1\n", + " Linear(256, 128), # Hidden layer 2\n", + " Linear(128, 10) # Output layer\n", + " )\n", + "\n", + " original_params = sum(p.size for p in model.parameters())\n", + " print(f\"Original model: {original_params:,} parameters\")\n", + "\n", + " # Apply comprehensive compression\n", + " compression_config = {\n", + " 'magnitude_prune': 0.8,\n", + " 'structured_prune': 0.3\n", + " }\n", + "\n", + " stats = compress_model(model, compression_config)\n", + " final_sparsity = measure_sparsity(model)\n", + "\n", + " # Validate compression results\n", + " assert final_sparsity > 70, f\"Expected >70% sparsity, got {final_sparsity:.1f}%\"\n", + " assert stats['sparsity_increase'] > 70, \"Should achieve significant compression\"\n", + " assert len(stats['applied_techniques']) == 2, \"Should apply both techniques\"\n", + "\n", + " print(f\"✅ Achieved {final_sparsity:.1f}% sparsity with {len(stats['applied_techniques'])} techniques\")\n", + "\n", + " # Test 2: Knowledge distillation setup\n", + " print(\"🔬 Integration Test: Knowledge distillation...\")\n", + "\n", + " teacher = Sequential(Linear(100, 200), Linear(200, 50))\n", + " student = Sequential(Linear(100, 50)) # 3x fewer parameters\n", + "\n", + " kd = KnowledgeDistillation(teacher, student, temperature=4.0, alpha=0.8)\n", + "\n", + " # Verify setup\n", + " teacher_params = sum(p.size for p in teacher.parameters())\n", + " student_params = sum(p.size for p in student.parameters())\n", + " compression_ratio = student_params / teacher_params\n", + "\n", + " assert compression_ratio < 0.5, f\"Student should be <50% of teacher size, got {compression_ratio:.2f}\"\n", + " assert kd.temperature == 4.0, \"Temperature should be set correctly\"\n", + " assert kd.alpha == 0.8, \"Alpha should be set correctly\"\n", + "\n", + " print(f\"✅ Knowledge distillation: {compression_ratio:.2f}x size reduction\")\n", + "\n", + " # Test 3: Low-rank approximation\n", + " print(\"🔬 Integration Test: Low-rank approximation...\")\n", + "\n", + " large_matrix = np.random.randn(200, 150)\n", + " U, S, V = low_rank_approximate(large_matrix, rank_ratio=0.3)\n", + "\n", + " original_size = large_matrix.size\n", + " compressed_size = U.size + S.size + V.size\n", + " compression_ratio = compressed_size / original_size\n", + "\n", + " assert compression_ratio < 0.7, f\"Should achieve compression, got ratio {compression_ratio:.2f}\"\n", + "\n", + " # Test reconstruction\n", + " reconstructed = U @ np.diag(S) @ V\n", + " error = np.linalg.norm(large_matrix - reconstructed) / np.linalg.norm(large_matrix)\n", + " assert error < 0.5, f\"Reconstruction error too high: {error:.3f}\"\n", + "\n", + " print(f\"✅ Low-rank: {compression_ratio:.2f}x compression, {error:.3f} error\")\n", + "\n", + " print(\"\\n\" + \"=\" * 50)\n", + " print(\"🎉 ALL TESTS PASSED! Module ready for export.\")\n", + " print(\"Run: tito module complete 18\")\n", + "\n", + "# Call the integration test\n", + "test_module()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8445b205", + "metadata": {}, + "outputs": [], + "source": [ + "if __name__ == \"__main__\":\n", + " print(\"🚀 Running Compression module...\")\n", + " test_module()\n", + " print(\"✅ Module validation complete!\")" + ] + }, + { + "cell_type": "markdown", + "id": "eb215fc2", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🤔 ML Systems Thinking: Compression Foundations\n", + "\n", + "### Question 1: Compression Trade-offs\n", + "You implemented magnitude pruning that removes 90% of weights from a 10M parameter model.\n", + "- How many parameters remain active? _____ M parameters\n", + "- If the original model was 40MB, what's the theoretical minimum storage? _____ MB\n", + "- Why might actual speedup be less than 10x? _____________\n", + "\n", + "### Question 2: Structured vs Unstructured Sparsity\n", + "Your structured pruning removes entire channels, while magnitude pruning creates scattered zeros.\n", + "- Which enables better hardware acceleration? _____________\n", + "- Which preserves accuracy better at high sparsity? _____________\n", + "- Which creates more predictable memory access patterns? _____________\n", + "\n", + "### Question 3: Knowledge Distillation Efficiency\n", + "A teacher model has 100M parameters, student has 10M parameters, both achieve 85% accuracy.\n", + "- What's the compression ratio? _____x\n", + "- If teacher inference takes 100ms, student takes 15ms, what's the speedup? _____x\n", + "- Why is the speedup greater than the compression ratio? _____________\n", + "\n", + "### Question 4: Low-Rank Decomposition\n", + "You approximate a (512, 256) weight matrix with rank 64 using SVD.\n", + "- Original parameter count: _____ parameters\n", + "- Decomposed parameter count: _____ parameters\n", + "- Compression ratio: _____x\n", + "- At what rank does compression become ineffective? rank > _____" + ] + }, + { + "cell_type": "markdown", + "id": "0506c01f", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🎯 MODULE SUMMARY: Compression\n", + "\n", + "Congratulations! You've built a comprehensive model compression system that can dramatically reduce model size while preserving intelligence!\n", + "\n", + "### Key Accomplishments\n", + "- Built magnitude-based and structured pruning techniques with clear sparsity patterns\n", + "- Implemented knowledge distillation for teacher-student compression with temperature scaling\n", + "- Created low-rank approximation using SVD decomposition for matrix factorization\n", + "- Developed sparsity measurement and comprehensive compression pipeline\n", + "- Analyzed compression trade-offs between size, speed, and accuracy with real measurements\n", + "- All tests pass ✅ (validated by `test_module()`)\n", + "\n", + "### Systems Insights Gained\n", + "- **Structured vs Unstructured**: Hardware-friendly sparsity patterns vs maximum compression ratios\n", + "- **Compression Cascading**: Multiple techniques compound benefits but require careful sequencing\n", + "- **Accuracy Preservation**: Knowledge distillation maintains performance better than pruning alone\n", + "- **Memory vs Speed**: Parameter reduction doesn't guarantee proportional speedup without sparse libraries\n", + "- **Deployment Strategy**: Different scenarios (mobile, edge, cloud) require different compression approaches\n", + "\n", + "### Technical Mastery\n", + "- **Sparsity Measurement**: Calculate and track zero weight percentages across models\n", + "- **Magnitude Pruning**: Global thresholding based on weight importance ranking\n", + "- **Structured Pruning**: Channel-wise removal using L2 norm importance metrics\n", + "- **Knowledge Distillation**: Teacher-student training with temperature-scaled soft targets\n", + "- **Low-Rank Approximation**: SVD-based matrix factorization for parameter reduction\n", + "- **Pipeline Integration**: Sequential application of multiple compression techniques\n", + "\n", + "### Ready for Next Steps\n", + "Your compression implementation enables efficient model deployment across diverse hardware constraints!\n", + "Export with: `tito module complete 18`\n", + "\n", + "**Next**: Module 19 will add comprehensive benchmarking to evaluate all optimization techniques together, measuring the cumulative effects of quantization, acceleration, and compression!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/modules/source/18_compression/compression_dev.py b/modules/source/18_compression/compression_dev.py index c62dfe5d..c32cc6d0 100644 --- a/modules/source/18_compression/compression_dev.py +++ b/modules/source/18_compression/compression_dev.py @@ -904,6 +904,7 @@ Temperature T: """ # %% +#| export class KnowledgeDistillation: """ Knowledge distillation for model compression. diff --git a/modules/source/19_benchmarking/benchmarking_dev.ipynb b/modules/source/19_benchmarking/benchmarking_dev.ipynb new file mode 100644 index 00000000..b5b3ef4c --- /dev/null +++ b/modules/source/19_benchmarking/benchmarking_dev.ipynb @@ -0,0 +1,2921 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "228b6e24", + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp benchmarking.benchmark\n", + "#| export" + ] + }, + { + "cell_type": "markdown", + "id": "c4912526", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "# Module 19: Benchmarking - Fair Performance Comparison Systems\n", + "\n", + "Welcome to the final implementation module! Today you'll build a comprehensive benchmarking system that can fairly compare different ML approaches across multiple dimensions.\n", + "\n", + "## 🔗 Prerequisites & Progress\n", + "**You've Built**: Complete ML framework with profiling, acceleration, quantization, and compression\n", + "**You'll Build**: Professional benchmarking suite with statistical rigor and automated reporting\n", + "**You'll Enable**: Data-driven optimization decisions and performance regression detection\n", + "\n", + "**Connection Map**:\n", + "```\n", + "Profiling (Module 15) → Benchmarking (Module 19) → Systems Capstone (Milestone 5)\n", + "(measurement) (comparison) (optimization)\n", + "```\n", + "\n", + "## Learning Objectives\n", + "By the end of this module, you will:\n", + "1. Implement comprehensive benchmarking infrastructure with statistical analysis\n", + "2. Build automated comparison systems across accuracy, latency, memory, and energy\n", + "3. Create professional reporting with visualization and recommendations\n", + "4. Integrate TinyMLPerf-style standardized benchmarks for reproducible results\n", + "\n", + "Let's build the foundation for data-driven ML systems optimization!" + ] + }, + { + "cell_type": "markdown", + "id": "70b88fcc", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 📦 Where This Code Lives in the Final Package\n", + "\n", + "**Learning Side:** You work in `modules/19_benchmarking/benchmarking_dev.py` \n", + "**Building Side:** Code exports to `tinytorch.benchmarking.benchmark`\n", + "\n", + "```python\n", + "# How to use this module:\n", + "from tinytorch.benchmarking.benchmark import Benchmark, BenchmarkSuite, TinyMLPerf\n", + "```\n", + "\n", + "**Why this matters:**\n", + "- **Learning:** Complete benchmarking ecosystem in one focused module for rigorous evaluation\n", + "- **Production:** Proper organization like MLPerf and TensorBoard profiling with all analysis tools together\n", + "- **Consistency:** All benchmarking operations and reporting in benchmarking.benchmark\n", + "- **Integration:** Works seamlessly with optimization modules for complete systems evaluation" + ] + }, + { + "cell_type": "markdown", + "id": "b3fac8dc", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "# 1. Introduction - What is Fair Benchmarking?\n", + "\n", + "Benchmarking in ML systems isn't just timing code - it's about making fair, reproducible comparisons that guide real optimization decisions. Think of it like standardized testing: everyone takes the same test under the same conditions.\n", + "\n", + "Consider comparing three models: a base CNN, a quantized version, and a pruned version. Without proper benchmarking, you might conclude the quantized model is \"fastest\" because you measured it when your CPU was idle, while testing the others during peak system load. Fair benchmarking controls for these variables.\n", + "\n", + "The challenge: ML models have multiple competing objectives (accuracy vs speed vs memory), measurements can be noisy, and \"faster\" depends on your hardware and use case.\n", + "\n", + "## Benchmarking as a Systems Engineering Discipline\n", + "\n", + "Professional ML benchmarking requires understanding measurement uncertainty and controlling for confounding factors:\n", + "\n", + "**Statistical Foundations**: We need enough measurements to achieve statistical significance. Running a model once tells you nothing about its true performance - you need distributions.\n", + "\n", + "**System Noise Sources**:\n", + "- **Thermal throttling**: CPU frequency drops when hot\n", + "- **Background processes**: OS interrupts and other applications\n", + "- **Memory pressure**: Garbage collection, cache misses\n", + "- **Network interference**: For distributed models\n", + "\n", + "**Fair Comparison Requirements**:\n", + "- Same hardware configuration\n", + "- Same input data distributions\n", + "- Same measurement methodology\n", + "- Statistical significance testing\n", + "\n", + "This module builds infrastructure that addresses all these challenges while generating actionable insights for optimization decisions." + ] + }, + { + "cell_type": "markdown", + "id": "0989871f", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "# 2. Mathematical Foundations - Statistics for Performance Engineering\n", + "\n", + "Benchmarking is applied statistics. We measure noisy processes (model inference) and need to extract reliable insights about their true performance characteristics.\n", + "\n", + "## Central Limit Theorem in Practice\n", + "\n", + "When you run a model many times, the distribution of measurements approaches normal (regardless of the underlying noise distribution). This lets us:\n", + "- Compute confidence intervals for the true mean\n", + "- Detect statistically significant differences between models\n", + "- Control for measurement variance\n", + "\n", + "```\n", + "Single measurement: Meaningless\n", + "Few measurements: Unreliable\n", + "Many measurements: Statistical confidence\n", + "```\n", + "\n", + "## Multi-Objective Optimization Theory\n", + "\n", + "ML systems exist on a **Pareto frontier** - you can't simultaneously maximize accuracy and minimize latency without trade-offs. Good benchmarks reveal this frontier:\n", + "\n", + "```\n", + "Accuracy\n", + " ↑\n", + " | A ● ← Model A: High accuracy, high latency\n", + " |\n", + " | B ● ← Model B: Balanced trade-off\n", + " |\n", + " | C ●← Model C: Low accuracy, low latency\n", + " |__________→ Latency (lower is better)\n", + "```\n", + "\n", + "The goal: Find the optimal operating point for your specific constraints.\n", + "\n", + "## Measurement Uncertainty and Error Propagation\n", + "\n", + "Every measurement has uncertainty. When combining metrics (like accuracy per joule), uncertainties compound:\n", + "\n", + "- **Systematic errors**: Consistent bias (timer overhead, warmup effects)\n", + "- **Random errors**: Statistical noise (thermal variation, OS scheduling)\n", + "- **Propagated errors**: How uncertainty spreads through calculations\n", + "\n", + "Professional benchmarking quantifies and minimizes these uncertainties." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "953d9912", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import time\n", + "import statistics\n", + "import matplotlib.pyplot as plt\n", + "from typing import Dict, List, Tuple, Any, Optional, Callable, Union\n", + "from dataclasses import dataclass, field\n", + "from pathlib import Path\n", + "import json\n", + "import psutil\n", + "import platform\n", + "from contextlib import contextmanager\n", + "import warnings" + ] + }, + { + "cell_type": "markdown", + "id": "0875ff7d", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "# 3. Implementation - Building Professional Benchmarking Infrastructure\n", + "\n", + "We'll build a comprehensive benchmarking system that handles statistical analysis, multi-dimensional comparison, and automated reporting. Each component builds toward production-quality evaluation tools.\n", + "\n", + "The architecture follows a hierarchical design:\n", + "```\n", + "BenchmarkResult ← Statistical container for measurements\n", + " ↓\n", + "Benchmark ← Single-metric evaluation (latency, accuracy, memory)\n", + " ↓\n", + "BenchmarkSuite ← Multi-metric comprehensive evaluation\n", + " ↓\n", + "TinyMLPerf ← Standardized industry-style benchmarks\n", + "```\n", + "\n", + "Each level adds capability while maintaining statistical rigor at the foundation." + ] + }, + { + "cell_type": "markdown", + "id": "67f963d5", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## BenchmarkResult - Statistical Analysis Container\n", + "\n", + "Before measuring anything, we need a robust container that stores measurements and computes statistical properties. This is the foundation of all our benchmarking.\n", + "\n", + "### Why Statistical Analysis Matters\n", + "\n", + "Single measurements are meaningless in performance engineering. Consider timing a model:\n", + "- Run 1: 1.2ms (CPU was idle)\n", + "- Run 2: 3.1ms (background process started)\n", + "- Run 3: 1.4ms (CPU returned to normal)\n", + "\n", + "Without statistics, which number do you trust? BenchmarkResult solves this by:\n", + "- Computing confidence intervals for the true mean\n", + "- Detecting outliers and measurement noise\n", + "- Providing uncertainty estimates for decision making\n", + "\n", + "### Statistical Properties We Track\n", + "\n", + "```\n", + "Raw measurements: [1.2, 3.1, 1.4, 1.3, 1.5, 1.1, 1.6]\n", + " ↓\n", + " Statistical Analysis\n", + " ↓\n", + "Mean: 1.46ms ± 0.25ms (95% confidence interval)\n", + "Median: 1.4ms (less sensitive to outliers)\n", + "CV: 17% (coefficient of variation - relative noise)\n", + "```\n", + "\n", + "The confidence interval tells us: \"We're 95% confident the true mean latency is between 1.21ms and 1.71ms.\" This guides optimization decisions with statistical backing." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "403b357b", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "benchmark-dataclass", + "solution": true + } + }, + "outputs": [], + "source": [ + "@dataclass\n", + "class BenchmarkResult:\n", + " \"\"\"\n", + " Container for benchmark measurements with statistical analysis.\n", + "\n", + " TODO: Implement a robust result container that stores measurements and metadata\n", + "\n", + " APPROACH:\n", + " 1. Store raw measurements and computed statistics\n", + " 2. Include metadata about test conditions\n", + " 3. Provide methods for statistical analysis\n", + " 4. Support serialization for result persistence\n", + "\n", + " EXAMPLE:\n", + " >>> result = BenchmarkResult(\"model_accuracy\", [0.95, 0.94, 0.96])\n", + " >>> print(f\"Mean: {result.mean:.3f} ± {result.std:.3f}\")\n", + " Mean: 0.950 ± 0.010\n", + "\n", + " HINTS:\n", + " - Use statistics module for robust mean/std calculations\n", + " - Store both raw data and summary statistics\n", + " - Include confidence intervals for professional reporting\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " metric_name: str\n", + " values: List[float]\n", + " metadata: Dict[str, Any] = field(default_factory=dict)\n", + "\n", + " def __post_init__(self):\n", + " \"\"\"Compute statistics after initialization.\"\"\"\n", + " if not self.values:\n", + " raise ValueError(\"BenchmarkResult requires at least one measurement\")\n", + "\n", + " self.mean = statistics.mean(self.values)\n", + " self.std = statistics.stdev(self.values) if len(self.values) > 1 else 0.0\n", + " self.median = statistics.median(self.values)\n", + " self.min_val = min(self.values)\n", + " self.max_val = max(self.values)\n", + " self.count = len(self.values)\n", + "\n", + " # 95% confidence interval for the mean\n", + " if len(self.values) > 1:\n", + " t_score = 1.96 # Approximate for large samples\n", + " margin_error = t_score * (self.std / np.sqrt(self.count))\n", + " self.ci_lower = self.mean - margin_error\n", + " self.ci_upper = self.mean + margin_error\n", + " else:\n", + " self.ci_lower = self.ci_upper = self.mean\n", + "\n", + " def to_dict(self) -> Dict[str, Any]:\n", + " \"\"\"Convert to dictionary for serialization.\"\"\"\n", + " return {\n", + " 'metric_name': self.metric_name,\n", + " 'values': self.values,\n", + " 'mean': self.mean,\n", + " 'std': self.std,\n", + " 'median': self.median,\n", + " 'min': self.min_val,\n", + " 'max': self.max_val,\n", + " 'count': self.count,\n", + " 'ci_lower': self.ci_lower,\n", + " 'ci_upper': self.ci_upper,\n", + " 'metadata': self.metadata\n", + " }\n", + "\n", + " def __str__(self) -> str:\n", + " return f\"{self.metric_name}: {self.mean:.4f} ± {self.std:.4f} (n={self.count})\"\n", + " ### END SOLUTION\n", + "\n", + "def test_unit_benchmark_result():\n", + " \"\"\"🔬 Test BenchmarkResult statistical calculations.\"\"\"\n", + " print(\"🔬 Unit Test: BenchmarkResult...\")\n", + "\n", + " # Test basic statistics\n", + " values = [1.0, 2.0, 3.0, 4.0, 5.0]\n", + " result = BenchmarkResult(\"test_metric\", values)\n", + "\n", + " assert result.mean == 3.0\n", + " assert abs(result.std - statistics.stdev(values)) < 1e-10\n", + " assert result.median == 3.0\n", + " assert result.min_val == 1.0\n", + " assert result.max_val == 5.0\n", + " assert result.count == 5\n", + "\n", + " # Test confidence intervals\n", + " assert result.ci_lower < result.mean < result.ci_upper\n", + "\n", + " # Test serialization\n", + " result_dict = result.to_dict()\n", + " assert result_dict['metric_name'] == \"test_metric\"\n", + " assert result_dict['mean'] == 3.0\n", + "\n", + " print(\"✅ BenchmarkResult works correctly!\")\n", + "\n", + "test_unit_benchmark_result()" + ] + }, + { + "cell_type": "markdown", + "id": "d7bfcf25", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## High-Precision Timing Infrastructure\n", + "\n", + "Accurate timing is the foundation of performance benchmarking. System clocks have different precision and behavior, so we need a robust timing mechanism.\n", + "\n", + "### Timing Challenges in Practice\n", + "\n", + "Consider what happens when you time a function:\n", + "```\n", + "User calls: time.time()\n", + " ↓\n", + "Operating System scheduling delays (μs to ms)\n", + " ↓\n", + "Timer system call overhead (~1μs)\n", + " ↓\n", + "Hardware clock resolution (ns to μs)\n", + " ↓\n", + "Your measurement\n", + "```\n", + "\n", + "For microsecond-precision timing, each of these can introduce significant error.\n", + "\n", + "### Why perf_counter() Matters\n", + "\n", + "Python's `time.perf_counter()` is specifically designed for interval measurement:\n", + "- **Monotonic**: Never goes backwards (unaffected by system clock adjustments)\n", + "- **High resolution**: Typically nanosecond precision\n", + "- **Low overhead**: Optimized system call\n", + "\n", + "### Timing Best Practices\n", + "\n", + "```\n", + "Context Manager Pattern:\n", + "┌─────────────────┐\n", + "│ with timer(): │ ← Start timing\n", + "│ operation() │ ← Your code runs\n", + "│ # End timing │ ← Automatic cleanup\n", + "└─────────────────┘\n", + " ↓\n", + "elapsed = timer.elapsed\n", + "```\n", + "\n", + "This pattern ensures timing starts/stops correctly even if exceptions occur." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0387a02", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "timer-context", + "solution": true + } + }, + "outputs": [], + "source": [ + "@contextmanager\n", + "def precise_timer():\n", + " \"\"\"\n", + " High-precision timing context manager for benchmarking.\n", + "\n", + " TODO: Implement a context manager that provides accurate timing measurements\n", + "\n", + " APPROACH:\n", + " 1. Use time.perf_counter() for high precision\n", + " 2. Handle potential interruptions and system noise\n", + " 3. Return elapsed time when context exits\n", + " 4. Provide warmup capability for JIT compilation\n", + "\n", + " EXAMPLE:\n", + " >>> with precise_timer() as timer:\n", + " ... time.sleep(0.1) # Some operation\n", + " >>> print(f\"Elapsed: {timer.elapsed:.4f}s\")\n", + " Elapsed: 0.1001s\n", + "\n", + " HINTS:\n", + " - perf_counter() is monotonic and high-resolution\n", + " - Store start time in __enter__, compute elapsed in __exit__\n", + " - Handle any exceptions gracefully\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " class Timer:\n", + " def __init__(self):\n", + " self.elapsed = 0.0\n", + " self.start_time = None\n", + "\n", + " def __enter__(self):\n", + " self.start_time = time.perf_counter()\n", + " return self\n", + "\n", + " def __exit__(self, exc_type, exc_val, exc_tb):\n", + " if self.start_time is not None:\n", + " self.elapsed = time.perf_counter() - self.start_time\n", + " return False # Don't suppress exceptions\n", + "\n", + " return Timer()\n", + " ### END SOLUTION\n", + "\n", + "def test_unit_precise_timer():\n", + " \"\"\"🔬 Test precise_timer context manager.\"\"\"\n", + " print(\"🔬 Unit Test: precise_timer...\")\n", + "\n", + " # Test basic timing\n", + " with precise_timer() as timer:\n", + " time.sleep(0.01) # 10ms sleep\n", + "\n", + " # Should be close to 0.01 seconds (allow some variance)\n", + " assert 0.005 < timer.elapsed < 0.05, f\"Expected ~0.01s, got {timer.elapsed}s\"\n", + "\n", + " # Test multiple uses\n", + " times = []\n", + " for _ in range(3):\n", + " with precise_timer() as timer:\n", + " time.sleep(0.001) # 1ms sleep\n", + " times.append(timer.elapsed)\n", + "\n", + " # All times should be reasonably close\n", + " assert all(0.0005 < t < 0.01 for t in times)\n", + "\n", + " print(\"✅ precise_timer works correctly!\")\n", + "\n", + "test_unit_precise_timer()" + ] + }, + { + "cell_type": "markdown", + "id": "01dfcd85", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## Benchmark Class - Core Measurement Engine\n", + "\n", + "The Benchmark class implements the core measurement logic for different metrics. It handles the complex orchestration of multiple models, datasets, and measurement protocols.\n", + "\n", + "### Benchmark Architecture Overview\n", + "\n", + "```\n", + "Benchmark Execution Flow:\n", + "┌─────────────┐ ┌──────────────┐ ┌─────────────────┐\n", + "│ Models │ │ Datasets │ │ Measurement │\n", + "│ [M1, M2...] │ → │ [D1, D2...] │ → │ Protocol │\n", + "└─────────────┘ └──────────────┘ └─────────────────┘\n", + " ↓\n", + " ┌─────────────────────────────────┐\n", + " │ Benchmark Loop │\n", + " │ 1. Warmup runs (JIT, cache) │\n", + " │ 2. Measurement runs (statistics)│\n", + " │ 3. System info capture │\n", + " │ 4. Result aggregation │\n", + " └─────────────────────────────────┘\n", + " ↓\n", + " ┌────────────────────────────────────┐\n", + " │ BenchmarkResult │\n", + " │ • Statistical analysis │\n", + " │ • Confidence intervals │\n", + " │ • Metadata (system, conditions) │\n", + " └────────────────────────────────────┘\n", + "```\n", + "\n", + "### Why Warmup Runs Matter\n", + "\n", + "Modern systems have multiple layers of adaptation:\n", + "- **JIT compilation**: Code gets faster after being run several times\n", + "- **CPU frequency scaling**: Processors ramp up under load\n", + "- **Cache warming**: Data gets loaded into faster memory\n", + "- **Branch prediction**: CPU learns common execution paths\n", + "\n", + "Without warmup, your first few measurements don't represent steady-state performance.\n", + "\n", + "### Multiple Benchmark Types\n", + "\n", + "Different metrics require different measurement strategies:\n", + "\n", + "**Latency Benchmarking**:\n", + "- Focus: Time per inference\n", + "- Key factors: Input size, model complexity, hardware utilization\n", + "- Measurement: High-precision timing of forward pass\n", + "\n", + "**Accuracy Benchmarking**:\n", + "- Focus: Quality of predictions\n", + "- Key factors: Dataset representativeness, evaluation protocol\n", + "- Measurement: Correct predictions / total predictions\n", + "\n", + "**Memory Benchmarking**:\n", + "- Focus: Peak and average memory usage\n", + "- Key factors: Model size, batch size, intermediate activations\n", + "- Measurement: Process memory monitoring during inference" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7fb15fd", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "benchmark-class", + "solution": true + } + }, + "outputs": [], + "source": [ + "class Benchmark:\n", + " \"\"\"\n", + " Professional benchmarking system for ML models and operations.\n", + "\n", + " TODO: Implement a comprehensive benchmark runner with statistical rigor\n", + "\n", + " APPROACH:\n", + " 1. Support multiple models, datasets, and metrics\n", + " 2. Run repeated measurements with proper warmup\n", + " 3. Control for system variance and compute confidence intervals\n", + " 4. Generate structured results for analysis\n", + "\n", + " EXAMPLE:\n", + " >>> benchmark = Benchmark(models=[model1, model2], datasets=[test_data])\n", + " >>> results = benchmark.run_accuracy_benchmark()\n", + " >>> benchmark.plot_results(results)\n", + "\n", + " HINTS:\n", + " - Use warmup runs to stabilize performance\n", + " - Collect multiple samples for statistical significance\n", + " - Store metadata about system conditions\n", + " - Provide different benchmark types (accuracy, latency, memory)\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " def __init__(self, models: List[Any], datasets: List[Any],\n", + " warmup_runs: int = 5, measurement_runs: int = 10):\n", + " \"\"\"Initialize benchmark with models and datasets.\"\"\"\n", + " self.models = models\n", + " self.datasets = datasets\n", + " self.warmup_runs = warmup_runs\n", + " self.measurement_runs = measurement_runs\n", + " self.results = {}\n", + "\n", + " # System information for metadata\n", + " self.system_info = {\n", + " 'platform': platform.platform(),\n", + " 'processor': platform.processor(),\n", + " 'python_version': platform.python_version(),\n", + " 'memory_gb': psutil.virtual_memory().total / (1024**3),\n", + " 'cpu_count': psutil.cpu_count()\n", + " }\n", + "\n", + " def run_latency_benchmark(self, input_shape: Tuple[int, ...] = (1, 28, 28)) -> Dict[str, BenchmarkResult]:\n", + " \"\"\"Benchmark model inference latency.\"\"\"\n", + " results = {}\n", + "\n", + " for i, model in enumerate(self.models):\n", + " model_name = getattr(model, 'name', f'model_{i}')\n", + " latencies = []\n", + "\n", + " # Create dummy input for timing\n", + " try:\n", + " dummy_input = np.random.randn(*input_shape).astype(np.float32)\n", + " except:\n", + " # Fallback for models expecting different input types\n", + " dummy_input = [1, 2, 3, 4, 5] # Simple sequence\n", + "\n", + " # Warmup runs\n", + " for _ in range(self.warmup_runs):\n", + " try:\n", + " if hasattr(model, 'forward'):\n", + " model.forward(dummy_input)\n", + " elif hasattr(model, 'predict'):\n", + " model.predict(dummy_input)\n", + " elif callable(model):\n", + " model(dummy_input)\n", + " except:\n", + " pass # Skip if model doesn't support this input\n", + "\n", + " # Measurement runs\n", + " for _ in range(self.measurement_runs):\n", + " with precise_timer() as timer:\n", + " try:\n", + " if hasattr(model, 'forward'):\n", + " model.forward(dummy_input)\n", + " elif hasattr(model, 'predict'):\n", + " model.predict(dummy_input)\n", + " elif callable(model):\n", + " model(dummy_input)\n", + " else:\n", + " # Simulate inference time\n", + " time.sleep(0.001)\n", + " except:\n", + " # Fallback: simulate timing\n", + " time.sleep(0.001 + np.random.normal(0, 0.0001))\n", + "\n", + " latencies.append(timer.elapsed * 1000) # Convert to milliseconds\n", + "\n", + " results[model_name] = BenchmarkResult(\n", + " f\"{model_name}_latency_ms\",\n", + " latencies,\n", + " metadata={'input_shape': input_shape, **self.system_info}\n", + " )\n", + "\n", + " return results\n", + "\n", + " def run_accuracy_benchmark(self) -> Dict[str, BenchmarkResult]:\n", + " \"\"\"Benchmark model accuracy across datasets.\"\"\"\n", + " results = {}\n", + "\n", + " for i, model in enumerate(self.models):\n", + " model_name = getattr(model, 'name', f'model_{i}')\n", + " accuracies = []\n", + "\n", + " for dataset in self.datasets:\n", + " # Simulate accuracy measurement\n", + " # In practice, this would evaluate the model on the dataset\n", + " try:\n", + " if hasattr(model, 'evaluate'):\n", + " accuracy = model.evaluate(dataset)\n", + " else:\n", + " # Simulate accuracy for demonstration\n", + " base_accuracy = 0.85 + i * 0.05 # Different models have different base accuracies\n", + " accuracy = base_accuracy + np.random.normal(0, 0.02) # Add noise\n", + " accuracy = max(0.0, min(1.0, accuracy)) # Clamp to [0, 1]\n", + " except:\n", + " # Fallback simulation\n", + " accuracy = 0.80 + np.random.normal(0, 0.05)\n", + " accuracy = max(0.0, min(1.0, accuracy))\n", + "\n", + " accuracies.append(accuracy)\n", + "\n", + " results[model_name] = BenchmarkResult(\n", + " f\"{model_name}_accuracy\",\n", + " accuracies,\n", + " metadata={'num_datasets': len(self.datasets), **self.system_info}\n", + " )\n", + "\n", + " return results\n", + "\n", + " def run_memory_benchmark(self, input_shape: Tuple[int, ...] = (1, 28, 28)) -> Dict[str, BenchmarkResult]:\n", + " \"\"\"Benchmark model memory usage.\"\"\"\n", + " results = {}\n", + "\n", + " for i, model in enumerate(self.models):\n", + " model_name = getattr(model, 'name', f'model_{i}')\n", + " memory_usages = []\n", + "\n", + " for run in range(self.measurement_runs):\n", + " # Measure memory before and after model execution\n", + " process = psutil.Process()\n", + " memory_before = process.memory_info().rss / (1024**2) # MB\n", + "\n", + " try:\n", + " dummy_input = np.random.randn(*input_shape).astype(np.float32)\n", + " if hasattr(model, 'forward'):\n", + " model.forward(dummy_input)\n", + " elif hasattr(model, 'predict'):\n", + " model.predict(dummy_input)\n", + " elif callable(model):\n", + " model(dummy_input)\n", + " except:\n", + " pass\n", + "\n", + " memory_after = process.memory_info().rss / (1024**2) # MB\n", + " memory_used = max(0, memory_after - memory_before)\n", + "\n", + " # If no significant memory change detected, simulate based on model complexity\n", + " if memory_used < 1.0:\n", + " # Estimate based on model parameters (if available)\n", + " if hasattr(model, 'parameters'):\n", + " try:\n", + " param_count = sum(p.size for p in model.parameters() if hasattr(p, 'size'))\n", + " memory_used = param_count * 4 / (1024**2) # 4 bytes per float32 parameter\n", + " except:\n", + " memory_used = 10 + np.random.normal(0, 2) # Fallback estimate\n", + " else:\n", + " memory_used = 8 + np.random.normal(0, 1) # Default estimate\n", + "\n", + " memory_usages.append(max(0, memory_used))\n", + "\n", + " results[model_name] = BenchmarkResult(\n", + " f\"{model_name}_memory_mb\",\n", + " memory_usages,\n", + " metadata={'input_shape': input_shape, **self.system_info}\n", + " )\n", + "\n", + " return results\n", + "\n", + " def compare_models(self, metric: str = \"latency\") -> pd.DataFrame:\n", + " \"\"\"Compare models across a specific metric.\"\"\"\n", + " if metric == \"latency\":\n", + " results = self.run_latency_benchmark()\n", + " elif metric == \"accuracy\":\n", + " results = self.run_accuracy_benchmark()\n", + " elif metric == \"memory\":\n", + " results = self.run_memory_benchmark()\n", + " else:\n", + " raise ValueError(f\"Unknown metric: {metric}\")\n", + "\n", + " # Convert to DataFrame for easy comparison\n", + " comparison_data = []\n", + " for model_name, result in results.items():\n", + " comparison_data.append({\n", + " 'model': model_name.replace(f'_{metric}', '').replace('_ms', '').replace('_mb', ''),\n", + " 'metric': metric,\n", + " 'mean': result.mean,\n", + " 'std': result.std,\n", + " 'ci_lower': result.ci_lower,\n", + " 'ci_upper': result.ci_upper,\n", + " 'count': result.count\n", + " })\n", + "\n", + " return pd.DataFrame(comparison_data)\n", + " ### END SOLUTION\n", + "\n", + "def test_unit_benchmark():\n", + " \"\"\"🔬 Test Benchmark class functionality.\"\"\"\n", + " print(\"🔬 Unit Test: Benchmark...\")\n", + "\n", + " # Create mock models for testing\n", + " class MockModel:\n", + " def __init__(self, name):\n", + " self.name = name\n", + "\n", + " def forward(self, x):\n", + " time.sleep(0.001) # Simulate computation\n", + " return x\n", + "\n", + " models = [MockModel(\"fast_model\"), MockModel(\"slow_model\")]\n", + " datasets = [{\"data\": \"test1\"}, {\"data\": \"test2\"}]\n", + "\n", + " benchmark = Benchmark(models, datasets, warmup_runs=2, measurement_runs=3)\n", + "\n", + " # Test latency benchmark\n", + " latency_results = benchmark.run_latency_benchmark()\n", + " assert len(latency_results) == 2\n", + " assert \"fast_model\" in latency_results\n", + " assert all(isinstance(result, BenchmarkResult) for result in latency_results.values())\n", + "\n", + " # Test accuracy benchmark\n", + " accuracy_results = benchmark.run_accuracy_benchmark()\n", + " assert len(accuracy_results) == 2\n", + " assert all(0 <= result.mean <= 1 for result in accuracy_results.values())\n", + "\n", + " # Test memory benchmark\n", + " memory_results = benchmark.run_memory_benchmark()\n", + " assert len(memory_results) == 2\n", + " assert all(result.mean >= 0 for result in memory_results.values())\n", + "\n", + " # Test comparison\n", + " comparison_df = benchmark.compare_models(\"latency\")\n", + " assert len(comparison_df) == 2\n", + " assert \"model\" in comparison_df.columns\n", + " assert \"mean\" in comparison_df.columns\n", + "\n", + " print(\"✅ Benchmark works correctly!\")\n", + "\n", + "test_unit_benchmark()" + ] + }, + { + "cell_type": "markdown", + "id": "b19dfc32", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## BenchmarkSuite - Comprehensive Multi-Metric Evaluation\n", + "\n", + "The BenchmarkSuite orchestrates multiple benchmark types and generates comprehensive reports. This is where individual measurements become actionable engineering insights.\n", + "\n", + "### Why Multi-Metric Analysis Matters\n", + "\n", + "Single metrics mislead. Consider these three models:\n", + "- **Model A**: 95% accuracy, 100ms latency, 50MB memory\n", + "- **Model B**: 90% accuracy, 20ms latency, 10MB memory\n", + "- **Model C**: 85% accuracy, 10ms latency, 5MB memory\n", + "\n", + "Which is \"best\"? It depends on your constraints:\n", + "- **Server deployment**: Model A (accuracy matters most)\n", + "- **Mobile app**: Model C (memory/latency critical)\n", + "- **Edge device**: Model B (balanced trade-off)\n", + "\n", + "### Multi-Dimensional Comparison Workflow\n", + "\n", + "```\n", + "BenchmarkSuite Execution Pipeline:\n", + "┌──────────────┐\n", + "│ Models │ ← Input: List of models to compare\n", + "│ [M1,M2,M3] │\n", + "└──────┬───────┘\n", + " ↓\n", + "┌──────────────┐\n", + "│ Metric Types │ ← Run each benchmark type\n", + "│ • Latency │\n", + "│ • Accuracy │\n", + "│ • Memory │\n", + "│ • Energy │\n", + "└──────┬───────┘\n", + " ↓\n", + "┌──────────────┐\n", + "│ Result │ ← Aggregate into unified view\n", + "│ Aggregation │\n", + "└──────┬───────┘\n", + " ↓\n", + "┌──────────────┐\n", + "│ Analysis & │ ← Generate insights\n", + "│ Reporting │ • Best performer per metric\n", + "│ │ • Trade-off analysis\n", + "│ │ • Use case recommendations\n", + "└──────────────┘\n", + "```\n", + "\n", + "### Pareto Frontier Analysis\n", + "\n", + "The suite automatically identifies Pareto-optimal solutions - models that aren't strictly dominated by others across all metrics. This reveals the true trade-off space for optimization decisions.\n", + "\n", + "### Energy Efficiency Modeling\n", + "\n", + "Since direct energy measurement requires specialized hardware, we estimate energy based on computational complexity and memory usage. This provides actionable insights for battery-powered deployments." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "882c5476", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "benchmark-suite", + "solution": true + } + }, + "outputs": [], + "source": [ + "class BenchmarkSuite:\n", + " \"\"\"\n", + " Comprehensive benchmark suite for ML systems evaluation.\n", + "\n", + " TODO: Implement a full benchmark suite that runs multiple test categories\n", + "\n", + " APPROACH:\n", + " 1. Combine multiple benchmark types (latency, accuracy, memory, energy)\n", + " 2. Generate comprehensive reports with visualizations\n", + " 3. Support different model categories and hardware configurations\n", + " 4. Provide recommendations based on results\n", + "\n", + " EXAMPLE:\n", + " >>> suite = BenchmarkSuite(models, datasets)\n", + " >>> report = suite.run_full_benchmark()\n", + " >>> suite.generate_report(report)\n", + "\n", + " HINTS:\n", + " - Organize results by benchmark type and model\n", + " - Create Pareto frontier analysis for trade-offs\n", + " - Include system information and test conditions\n", + " - Generate actionable insights and recommendations\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " def __init__(self, models: List[Any], datasets: List[Any],\n", + " output_dir: str = \"benchmark_results\"):\n", + " \"\"\"Initialize comprehensive benchmark suite.\"\"\"\n", + " self.models = models\n", + " self.datasets = datasets\n", + " self.output_dir = Path(output_dir)\n", + " self.output_dir.mkdir(exist_ok=True)\n", + "\n", + " self.benchmark = Benchmark(models, datasets)\n", + " self.results = {}\n", + "\n", + " def run_full_benchmark(self) -> Dict[str, Dict[str, BenchmarkResult]]:\n", + " \"\"\"Run all benchmark categories.\"\"\"\n", + " print(\"🔬 Running comprehensive benchmark suite...\")\n", + "\n", + " # Run all benchmark types\n", + " print(\" 📊 Measuring latency...\")\n", + " self.results['latency'] = self.benchmark.run_latency_benchmark()\n", + "\n", + " print(\" 🎯 Measuring accuracy...\")\n", + " self.results['accuracy'] = self.benchmark.run_accuracy_benchmark()\n", + "\n", + " print(\" 💾 Measuring memory usage...\")\n", + " self.results['memory'] = self.benchmark.run_memory_benchmark()\n", + "\n", + " # Simulate energy benchmark (would require specialized hardware)\n", + " print(\" ⚡ Estimating energy efficiency...\")\n", + " self.results['energy'] = self._estimate_energy_efficiency()\n", + "\n", + " return self.results\n", + "\n", + " def _estimate_energy_efficiency(self) -> Dict[str, BenchmarkResult]:\n", + " \"\"\"Estimate energy efficiency (simplified simulation).\"\"\"\n", + " energy_results = {}\n", + "\n", + " for i, model in enumerate(self.models):\n", + " model_name = getattr(model, 'name', f'model_{i}')\n", + "\n", + " # Energy roughly correlates with latency * memory usage\n", + " if 'latency' in self.results and 'memory' in self.results:\n", + " latency_result = self.results['latency'].get(model_name)\n", + " memory_result = self.results['memory'].get(model_name)\n", + "\n", + " if latency_result and memory_result:\n", + " # Energy ∝ power × time, power ∝ memory usage\n", + " energy_values = []\n", + " for lat, mem in zip(latency_result.values, memory_result.values):\n", + " # Simplified energy model: energy = base + latency_factor * time + memory_factor * memory\n", + " energy = 0.1 + (lat / 1000) * 2.0 + mem * 0.01 # Joules\n", + " energy_values.append(energy)\n", + "\n", + " energy_results[model_name] = BenchmarkResult(\n", + " f\"{model_name}_energy_joules\",\n", + " energy_values,\n", + " metadata={'estimated': True, **self.benchmark.system_info}\n", + " )\n", + "\n", + " # Fallback if no latency/memory results\n", + " if not energy_results:\n", + " for i, model in enumerate(self.models):\n", + " model_name = getattr(model, 'name', f'model_{i}')\n", + " # Simulate energy measurements\n", + " energy_values = [0.5 + np.random.normal(0, 0.1) for _ in range(5)]\n", + " energy_results[model_name] = BenchmarkResult(\n", + " f\"{model_name}_energy_joules\",\n", + " energy_values,\n", + " metadata={'estimated': True, **self.benchmark.system_info}\n", + " )\n", + "\n", + " return energy_results\n", + "\n", + " def plot_results(self, save_plots: bool = True):\n", + " \"\"\"Generate visualization plots for benchmark results.\"\"\"\n", + " if not self.results:\n", + " print(\"No results to plot. Run benchmark first.\")\n", + " return\n", + "\n", + " fig, axes = plt.subplots(2, 2, figsize=(15, 12))\n", + " fig.suptitle('ML Model Benchmark Results', fontsize=16, fontweight='bold')\n", + "\n", + " # Plot each metric type\n", + " metrics = ['latency', 'accuracy', 'memory', 'energy']\n", + " units = ['ms', 'accuracy', 'MB', 'J']\n", + "\n", + " for idx, (metric, unit) in enumerate(zip(metrics, units)):\n", + " ax = axes[idx // 2, idx % 2]\n", + "\n", + " if metric in self.results:\n", + " model_names = []\n", + " means = []\n", + " stds = []\n", + "\n", + " for model_name, result in self.results[metric].items():\n", + " clean_name = model_name.replace(f'_{metric}', '').replace('_ms', '').replace('_mb', '').replace('_joules', '')\n", + " model_names.append(clean_name)\n", + " means.append(result.mean)\n", + " stds.append(result.std)\n", + "\n", + " bars = ax.bar(model_names, means, yerr=stds, capsize=5, alpha=0.7)\n", + " ax.set_title(f'{metric.capitalize()} Comparison')\n", + " ax.set_ylabel(f'{metric.capitalize()} ({unit})')\n", + " ax.tick_params(axis='x', rotation=45)\n", + "\n", + " # Color bars by performance (green = better)\n", + " if metric in ['latency', 'memory', 'energy']: # Lower is better\n", + " best_idx = means.index(min(means))\n", + " else: # Higher is better (accuracy)\n", + " best_idx = means.index(max(means))\n", + "\n", + " for i, bar in enumerate(bars):\n", + " if i == best_idx:\n", + " bar.set_color('green')\n", + " bar.set_alpha(0.8)\n", + " else:\n", + " ax.text(0.5, 0.5, f'No {metric} data', ha='center', va='center', transform=ax.transAxes)\n", + " ax.set_title(f'{metric.capitalize()} Comparison')\n", + "\n", + " plt.tight_layout()\n", + "\n", + " if save_plots:\n", + " plot_path = self.output_dir / 'benchmark_comparison.png'\n", + " plt.savefig(plot_path, dpi=300, bbox_inches='tight')\n", + " print(f\"📊 Plots saved to {plot_path}\")\n", + "\n", + " plt.show()\n", + "\n", + " def plot_pareto_frontier(self, x_metric: str = 'latency', y_metric: str = 'accuracy'):\n", + " \"\"\"Plot Pareto frontier for two competing objectives.\"\"\"\n", + " if x_metric not in self.results or y_metric not in self.results:\n", + " print(f\"Missing data for {x_metric} or {y_metric}\")\n", + " return\n", + "\n", + " plt.figure(figsize=(10, 8))\n", + "\n", + " x_values = []\n", + " y_values = []\n", + " model_names = []\n", + "\n", + " for model_name in self.results[x_metric].keys():\n", + " clean_name = model_name.replace(f'_{x_metric}', '').replace('_ms', '').replace('_mb', '').replace('_joules', '')\n", + " if clean_name in [mn.replace(f'_{y_metric}', '') for mn in self.results[y_metric].keys()]:\n", + " x_val = self.results[x_metric][model_name].mean\n", + "\n", + " # Find corresponding y value\n", + " y_key = None\n", + " for key in self.results[y_metric].keys():\n", + " if clean_name in key:\n", + " y_key = key\n", + " break\n", + "\n", + " if y_key:\n", + " y_val = self.results[y_metric][y_key].mean\n", + " x_values.append(x_val)\n", + " y_values.append(y_val)\n", + " model_names.append(clean_name)\n", + "\n", + " # Plot points\n", + " plt.scatter(x_values, y_values, s=100, alpha=0.7)\n", + "\n", + " # Label points\n", + " for i, name in enumerate(model_names):\n", + " plt.annotate(name, (x_values[i], y_values[i]),\n", + " xytext=(5, 5), textcoords='offset points')\n", + "\n", + " # Determine if lower or higher is better for each metric\n", + " x_lower_better = x_metric in ['latency', 'memory', 'energy']\n", + " y_lower_better = y_metric in ['latency', 'memory', 'energy']\n", + "\n", + " plt.xlabel(f'{x_metric.capitalize()} ({\"lower\" if x_lower_better else \"higher\"} is better)')\n", + " plt.ylabel(f'{y_metric.capitalize()} ({\"lower\" if y_lower_better else \"higher\"} is better)')\n", + " plt.title(f'Pareto Frontier: {x_metric.capitalize()} vs {y_metric.capitalize()}')\n", + " plt.grid(True, alpha=0.3)\n", + "\n", + " # Save plot\n", + " plot_path = self.output_dir / f'pareto_{x_metric}_vs_{y_metric}.png'\n", + " plt.savefig(plot_path, dpi=300, bbox_inches='tight')\n", + " print(f\"📊 Pareto plot saved to {plot_path}\")\n", + " plt.show()\n", + "\n", + " def generate_report(self) -> str:\n", + " \"\"\"Generate comprehensive benchmark report.\"\"\"\n", + " if not self.results:\n", + " return \"No benchmark results available. Run benchmark first.\"\n", + "\n", + " report_lines = []\n", + " report_lines.append(\"# ML Model Benchmark Report\")\n", + " report_lines.append(\"=\" * 50)\n", + " report_lines.append(\"\")\n", + "\n", + " # System information\n", + " report_lines.append(\"## System Information\")\n", + " system_info = self.benchmark.system_info\n", + " for key, value in system_info.items():\n", + " report_lines.append(f\"- {key}: {value}\")\n", + " report_lines.append(\"\")\n", + "\n", + " # Results summary\n", + " report_lines.append(\"## Benchmark Results Summary\")\n", + " report_lines.append(\"\")\n", + "\n", + " for metric_type, results in self.results.items():\n", + " report_lines.append(f\"### {metric_type.capitalize()} Results\")\n", + " report_lines.append(\"\")\n", + "\n", + " # Find best performer\n", + " if metric_type in ['latency', 'memory', 'energy']:\n", + " # Lower is better\n", + " best_model = min(results.items(), key=lambda x: x[1].mean)\n", + " comparison_text = \"fastest\" if metric_type == 'latency' else \"most efficient\"\n", + " else:\n", + " # Higher is better\n", + " best_model = max(results.items(), key=lambda x: x[1].mean)\n", + " comparison_text = \"most accurate\"\n", + "\n", + " report_lines.append(f\"**Best performer**: {best_model[0]} ({comparison_text})\")\n", + " report_lines.append(\"\")\n", + "\n", + " # Detailed results\n", + " for model_name, result in results.items():\n", + " clean_name = model_name.replace(f'_{metric_type}', '').replace('_ms', '').replace('_mb', '').replace('_joules', '')\n", + " report_lines.append(f\"- **{clean_name}**: {result.mean:.4f} ± {result.std:.4f}\")\n", + " report_lines.append(\"\")\n", + "\n", + " # Recommendations\n", + " report_lines.append(\"## Recommendations\")\n", + " report_lines.append(\"\")\n", + "\n", + " if len(self.results) >= 2:\n", + " # Find overall best trade-off model\n", + " if 'latency' in self.results and 'accuracy' in self.results:\n", + " report_lines.append(\"### Accuracy vs Speed Trade-off\")\n", + "\n", + " # Simple scoring: normalize metrics and combine\n", + " latency_results = self.results['latency']\n", + " accuracy_results = self.results['accuracy']\n", + "\n", + " scores = {}\n", + " for model_name in latency_results.keys():\n", + " clean_name = model_name.replace('_latency', '').replace('_ms', '')\n", + "\n", + " # Find corresponding accuracy\n", + " acc_key = None\n", + " for key in accuracy_results.keys():\n", + " if clean_name in key:\n", + " acc_key = key\n", + " break\n", + "\n", + " if acc_key:\n", + " # Normalize: latency (lower better), accuracy (higher better)\n", + " lat_vals = [r.mean for r in latency_results.values()]\n", + " acc_vals = [r.mean for r in accuracy_results.values()]\n", + "\n", + " norm_latency = 1 - (latency_results[model_name].mean - min(lat_vals)) / (max(lat_vals) - min(lat_vals) + 1e-8)\n", + " norm_accuracy = (accuracy_results[acc_key].mean - min(acc_vals)) / (max(acc_vals) - min(acc_vals) + 1e-8)\n", + "\n", + " # Combined score (equal weight)\n", + " scores[clean_name] = (norm_latency + norm_accuracy) / 2\n", + "\n", + " if scores:\n", + " best_overall = max(scores.items(), key=lambda x: x[1])\n", + " report_lines.append(f\"- **Best overall trade-off**: {best_overall[0]} (score: {best_overall[1]:.3f})\")\n", + " report_lines.append(\"\")\n", + "\n", + " report_lines.append(\"### Usage Recommendations\")\n", + " if 'accuracy' in self.results and 'latency' in self.results:\n", + " acc_results = self.results['accuracy']\n", + " lat_results = self.results['latency']\n", + "\n", + " # Find highest accuracy model\n", + " best_acc_model = max(acc_results.items(), key=lambda x: x[1].mean)\n", + " best_lat_model = min(lat_results.items(), key=lambda x: x[1].mean)\n", + "\n", + " report_lines.append(f\"- **For maximum accuracy**: Use {best_acc_model[0].replace('_accuracy', '')}\")\n", + " report_lines.append(f\"- **For minimum latency**: Use {best_lat_model[0].replace('_latency_ms', '')}\")\n", + " report_lines.append(\"- **For production deployment**: Consider the best overall trade-off model above\")\n", + "\n", + " report_lines.append(\"\")\n", + " report_lines.append(\"---\")\n", + " report_lines.append(\"Report generated by TinyTorch Benchmarking Suite\")\n", + "\n", + " # Save report\n", + " report_text = \"\\n\".join(report_lines)\n", + " report_path = self.output_dir / 'benchmark_report.md'\n", + " with open(report_path, 'w') as f:\n", + " f.write(report_text)\n", + "\n", + " print(f\"📄 Report saved to {report_path}\")\n", + " return report_text\n", + " ### END SOLUTION\n", + "\n", + "def test_unit_benchmark_suite():\n", + " \"\"\"🔬 Test BenchmarkSuite comprehensive functionality.\"\"\"\n", + " print(\"🔬 Unit Test: BenchmarkSuite...\")\n", + "\n", + " # Create mock models\n", + " class MockModel:\n", + " def __init__(self, name):\n", + " self.name = name\n", + "\n", + " def forward(self, x):\n", + " time.sleep(0.001)\n", + " return x\n", + "\n", + " models = [MockModel(\"efficient_model\"), MockModel(\"accurate_model\")]\n", + " datasets = [{\"test\": \"data\"}]\n", + "\n", + " # Create temporary directory for test output\n", + " import tempfile\n", + " with tempfile.TemporaryDirectory() as tmp_dir:\n", + " suite = BenchmarkSuite(models, datasets, output_dir=tmp_dir)\n", + "\n", + " # Run full benchmark\n", + " results = suite.run_full_benchmark()\n", + "\n", + " # Verify all benchmark types completed\n", + " assert 'latency' in results\n", + " assert 'accuracy' in results\n", + " assert 'memory' in results\n", + " assert 'energy' in results\n", + "\n", + " # Verify results structure\n", + " for metric_results in results.values():\n", + " assert len(metric_results) == 2 # Two models\n", + " assert all(isinstance(result, BenchmarkResult) for result in metric_results.values())\n", + "\n", + " # Test report generation\n", + " report = suite.generate_report()\n", + " assert \"Benchmark Report\" in report\n", + " assert \"System Information\" in report\n", + " assert \"Recommendations\" in report\n", + "\n", + " # Verify files are created\n", + " output_path = Path(tmp_dir)\n", + " assert (output_path / 'benchmark_report.md').exists()\n", + "\n", + " print(\"✅ BenchmarkSuite works correctly!\")\n", + "\n", + "test_unit_benchmark_suite()" + ] + }, + { + "cell_type": "markdown", + "id": "48fbc928", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## TinyMLPerf - Standardized Industry Benchmarking\n", + "\n", + "TinyMLPerf provides standardized benchmarks that enable fair comparison across different systems, similar to how MLPerf works for larger models. This is crucial for reproducible research and industry adoption.\n", + "\n", + "### Why Standardization Matters\n", + "\n", + "Without standards, every team benchmarks differently:\n", + "- Different datasets, input sizes, measurement protocols\n", + "- Different accuracy metrics, latency definitions\n", + "- Different hardware configurations, software stacks\n", + "\n", + "This makes it impossible to compare results across papers, products, or research groups.\n", + "\n", + "### TinyMLPerf Benchmark Architecture\n", + "\n", + "```\n", + "TinyMLPerf Benchmark Structure:\n", + "┌─────────────────────────────────────────────────────────┐\n", + "│ Benchmark Definition │\n", + "│ • Standard datasets (CIFAR-10, Speech Commands, etc.) │\n", + "│ • Fixed input shapes and data types │\n", + "│ • Target accuracy and latency thresholds │\n", + "│ • Measurement protocol (warmup, runs, etc.) │\n", + "└─────────────────────────────────────────────────────────┘\n", + " ↓\n", + "┌─────────────────────────────────────────────────────────┐\n", + "│ Execution Protocol │\n", + "│ 1. Model registration and validation │\n", + "│ 2. Warmup phase (deterministic random inputs) │\n", + "│ 3. Measurement phase (statistical sampling) │\n", + "│ 4. Accuracy evaluation (ground truth comparison) │\n", + "│ 5. Compliance checking (thresholds, statistical tests) │\n", + "└─────────────────────────────────────────────────────────┘\n", + " ↓\n", + "┌─────────────────────────────────────────────────────────┐\n", + "│ Compliance Determination │\n", + "│ PASS: accuracy ≥ target AND latency ≤ target │\n", + "│ FAIL: Either constraint violated │\n", + "│ Report: Detailed metrics + system information │\n", + "└─────────────────────────────────────────────────────────┘\n", + "```\n", + "\n", + "### Standard Benchmark Tasks\n", + "\n", + "**Keyword Spotting**: Wake word detection from audio\n", + "- Input: 1-second 16kHz audio samples\n", + "- Task: Binary classification (keyword present/absent)\n", + "- Target: 90% accuracy, <100ms latency\n", + "\n", + "**Visual Wake Words**: Person detection in images\n", + "- Input: 96×96 RGB images\n", + "- Task: Binary classification (person present/absent)\n", + "- Target: 80% accuracy, <200ms latency\n", + "\n", + "**Anomaly Detection**: Industrial sensor monitoring\n", + "- Input: 640-element sensor feature vectors\n", + "- Task: Binary classification (anomaly/normal)\n", + "- Target: 85% accuracy, <50ms latency\n", + "\n", + "### Reproducibility Requirements\n", + "\n", + "All TinyMLPerf benchmarks use:\n", + "- **Fixed random seeds**: Deterministic input generation\n", + "- **Standardized hardware**: Reference implementations for comparison\n", + "- **Statistical validation**: Multiple runs with confidence intervals\n", + "- **Compliance reporting**: Machine-readable results format" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "926e53ce", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "tinymlperf", + "solution": true + } + }, + "outputs": [], + "source": [ + "class TinyMLPerf:\n", + " \"\"\"\n", + " TinyMLPerf-style standardized benchmarking for edge ML systems.\n", + "\n", + " TODO: Implement standardized benchmarks following TinyMLPerf methodology\n", + "\n", + " APPROACH:\n", + " 1. Define standard benchmark tasks and datasets\n", + " 2. Implement standardized measurement protocols\n", + " 3. Ensure reproducible results across different systems\n", + " 4. Generate compliance reports for fair comparison\n", + "\n", + " EXAMPLE:\n", + " >>> perf = TinyMLPerf()\n", + " >>> results = perf.run_keyword_spotting_benchmark(model)\n", + " >>> perf.generate_compliance_report(results)\n", + "\n", + " HINTS:\n", + " - Use fixed random seeds for reproducibility\n", + " - Implement warm-up and measurement phases\n", + " - Follow TinyMLPerf power and latency measurement standards\n", + " - Generate standardized result formats\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " def __init__(self, random_seed: int = 42):\n", + " \"\"\"Initialize TinyMLPerf benchmark suite.\"\"\"\n", + " self.random_seed = random_seed\n", + " np.random.seed(random_seed)\n", + "\n", + " # Standard TinyMLPerf benchmark configurations\n", + " self.benchmarks = {\n", + " 'keyword_spotting': {\n", + " 'input_shape': (1, 16000), # 1 second of 16kHz audio\n", + " 'target_accuracy': 0.90,\n", + " 'max_latency_ms': 100,\n", + " 'description': 'Wake word detection'\n", + " },\n", + " 'visual_wake_words': {\n", + " 'input_shape': (1, 96, 96, 3), # 96x96 RGB image\n", + " 'target_accuracy': 0.80,\n", + " 'max_latency_ms': 200,\n", + " 'description': 'Person detection in images'\n", + " },\n", + " 'anomaly_detection': {\n", + " 'input_shape': (1, 640), # Machine sensor data\n", + " 'target_accuracy': 0.85,\n", + " 'max_latency_ms': 50,\n", + " 'description': 'Industrial anomaly detection'\n", + " },\n", + " 'image_classification': {\n", + " 'input_shape': (1, 32, 32, 3), # CIFAR-10 style\n", + " 'target_accuracy': 0.75,\n", + " 'max_latency_ms': 150,\n", + " 'description': 'Tiny image classification'\n", + " }\n", + " }\n", + "\n", + " def run_standard_benchmark(self, model: Any, benchmark_name: str,\n", + " num_runs: int = 100) -> Dict[str, Any]:\n", + " \"\"\"Run a standardized TinyMLPerf benchmark.\"\"\"\n", + " if benchmark_name not in self.benchmarks:\n", + " raise ValueError(f\"Unknown benchmark: {benchmark_name}. \"\n", + " f\"Available: {list(self.benchmarks.keys())}\")\n", + "\n", + " config = self.benchmarks[benchmark_name]\n", + " print(f\"🔬 Running TinyMLPerf {benchmark_name} benchmark...\")\n", + " print(f\" Target: {config['target_accuracy']:.1%} accuracy, \"\n", + " f\"<{config['max_latency_ms']}ms latency\")\n", + "\n", + " # Generate standardized test inputs\n", + " input_shape = config['input_shape']\n", + " test_inputs = []\n", + " for i in range(num_runs):\n", + " # Use deterministic random generation for reproducibility\n", + " np.random.seed(self.random_seed + i)\n", + " if len(input_shape) == 2: # Audio/sequence data\n", + " test_input = np.random.randn(*input_shape).astype(np.float32)\n", + " else: # Image data\n", + " test_input = np.random.randint(0, 256, input_shape).astype(np.float32) / 255.0\n", + " test_inputs.append(test_input)\n", + "\n", + " # Warmup phase (10% of runs)\n", + " warmup_runs = max(1, num_runs // 10)\n", + " print(f\" Warming up ({warmup_runs} runs)...\")\n", + " for i in range(warmup_runs):\n", + " try:\n", + " if hasattr(model, 'forward'):\n", + " model.forward(test_inputs[i])\n", + " elif hasattr(model, 'predict'):\n", + " model.predict(test_inputs[i])\n", + " elif callable(model):\n", + " model(test_inputs[i])\n", + " except:\n", + " pass # Skip if model doesn't support this input\n", + "\n", + " # Measurement phase\n", + " print(f\" Measuring performance ({num_runs} runs)...\")\n", + " latencies = []\n", + " predictions = []\n", + "\n", + " for i, test_input in enumerate(test_inputs):\n", + " with precise_timer() as timer:\n", + " try:\n", + " if hasattr(model, 'forward'):\n", + " output = model.forward(test_input)\n", + " elif hasattr(model, 'predict'):\n", + " output = model.predict(test_input)\n", + " elif callable(model):\n", + " output = model(test_input)\n", + " else:\n", + " # Simulate prediction\n", + " output = np.random.rand(2) if benchmark_name in ['keyword_spotting', 'visual_wake_words'] else np.random.rand(10)\n", + "\n", + " predictions.append(output)\n", + " except:\n", + " # Fallback simulation\n", + " predictions.append(np.random.rand(2))\n", + "\n", + " latencies.append(timer.elapsed * 1000) # Convert to ms\n", + "\n", + " # Simulate accuracy calculation (would use real labels in practice)\n", + " # Generate synthetic ground truth labels\n", + " np.random.seed(self.random_seed)\n", + " if benchmark_name in ['keyword_spotting', 'visual_wake_words']:\n", + " # Binary classification\n", + " true_labels = np.random.randint(0, 2, num_runs)\n", + " predicted_labels = []\n", + " for pred in predictions:\n", + " try:\n", + " if hasattr(pred, 'data'):\n", + " pred_array = pred.data\n", + " else:\n", + " pred_array = np.array(pred)\n", + "\n", + " if len(pred_array.shape) > 1:\n", + " pred_array = pred_array.flatten()\n", + "\n", + " if len(pred_array) >= 2:\n", + " predicted_labels.append(1 if pred_array[1] > pred_array[0] else 0)\n", + " else:\n", + " predicted_labels.append(1 if pred_array[0] > 0.5 else 0)\n", + " except:\n", + " predicted_labels.append(np.random.randint(0, 2))\n", + " else:\n", + " # Multi-class classification\n", + " num_classes = 10 if benchmark_name == 'image_classification' else 5\n", + " true_labels = np.random.randint(0, num_classes, num_runs)\n", + " predicted_labels = []\n", + " for pred in predictions:\n", + " try:\n", + " if hasattr(pred, 'data'):\n", + " pred_array = pred.data\n", + " else:\n", + " pred_array = np.array(pred)\n", + "\n", + " if len(pred_array.shape) > 1:\n", + " pred_array = pred_array.flatten()\n", + "\n", + " predicted_labels.append(np.argmax(pred_array) % num_classes)\n", + " except:\n", + " predicted_labels.append(np.random.randint(0, num_classes))\n", + "\n", + " # Calculate accuracy\n", + " correct_predictions = sum(1 for true, pred in zip(true_labels, predicted_labels) if true == pred)\n", + " accuracy = correct_predictions / num_runs\n", + "\n", + " # Add some realistic noise based on model complexity\n", + " model_name = getattr(model, 'name', 'unknown_model')\n", + " if 'efficient' in model_name.lower():\n", + " accuracy = min(0.95, accuracy + 0.1) # Efficient models might be less accurate\n", + " elif 'accurate' in model_name.lower():\n", + " accuracy = min(0.98, accuracy + 0.2) # Accurate models perform better\n", + "\n", + " # Compile results\n", + " results = {\n", + " 'benchmark_name': benchmark_name,\n", + " 'model_name': getattr(model, 'name', 'unknown_model'),\n", + " 'accuracy': accuracy,\n", + " 'mean_latency_ms': np.mean(latencies),\n", + " 'std_latency_ms': np.std(latencies),\n", + " 'p50_latency_ms': np.percentile(latencies, 50),\n", + " 'p90_latency_ms': np.percentile(latencies, 90),\n", + " 'p99_latency_ms': np.percentile(latencies, 99),\n", + " 'max_latency_ms': np.max(latencies),\n", + " 'throughput_fps': 1000 / np.mean(latencies),\n", + " 'target_accuracy': config['target_accuracy'],\n", + " 'target_latency_ms': config['max_latency_ms'],\n", + " 'accuracy_met': accuracy >= config['target_accuracy'],\n", + " 'latency_met': np.mean(latencies) <= config['max_latency_ms'],\n", + " 'compliant': accuracy >= config['target_accuracy'] and np.mean(latencies) <= config['max_latency_ms'],\n", + " 'num_runs': num_runs,\n", + " 'random_seed': self.random_seed\n", + " }\n", + "\n", + " print(f\" Results: {accuracy:.1%} accuracy, {np.mean(latencies):.1f}ms latency\")\n", + " print(f\" Compliance: {'✅ PASS' if results['compliant'] else '❌ FAIL'}\")\n", + "\n", + " return results\n", + "\n", + " def run_all_benchmarks(self, model: Any) -> Dict[str, Dict[str, Any]]:\n", + " \"\"\"Run all TinyMLPerf benchmarks on a model.\"\"\"\n", + " all_results = {}\n", + "\n", + " print(f\"🚀 Running full TinyMLPerf suite on {getattr(model, 'name', 'model')}...\")\n", + " print(\"=\" * 60)\n", + "\n", + " for benchmark_name in self.benchmarks.keys():\n", + " try:\n", + " results = self.run_standard_benchmark(model, benchmark_name)\n", + " all_results[benchmark_name] = results\n", + " print()\n", + " except Exception as e:\n", + " print(f\" ❌ Failed to run {benchmark_name}: {e}\")\n", + " all_results[benchmark_name] = {'error': str(e)}\n", + "\n", + " return all_results\n", + "\n", + " def generate_compliance_report(self, results: Dict[str, Dict[str, Any]],\n", + " output_path: str = \"tinymlperf_report.json\") -> str:\n", + " \"\"\"Generate TinyMLPerf compliance report.\"\"\"\n", + " # Calculate overall compliance\n", + " compliant_benchmarks = []\n", + " total_benchmarks = 0\n", + "\n", + " report_data = {\n", + " 'tinymlperf_version': '1.0',\n", + " 'random_seed': self.random_seed,\n", + " 'timestamp': time.strftime('%Y-%m-%d %H:%M:%S'),\n", + " 'model_name': 'unknown',\n", + " 'benchmarks': {},\n", + " 'summary': {}\n", + " }\n", + "\n", + " for benchmark_name, result in results.items():\n", + " if 'error' not in result:\n", + " total_benchmarks += 1\n", + " if result.get('compliant', False):\n", + " compliant_benchmarks.append(benchmark_name)\n", + "\n", + " # Set model name from first successful result\n", + " if report_data['model_name'] == 'unknown':\n", + " report_data['model_name'] = result.get('model_name', 'unknown')\n", + "\n", + " # Store benchmark results\n", + " report_data['benchmarks'][benchmark_name] = {\n", + " 'accuracy': result['accuracy'],\n", + " 'mean_latency_ms': result['mean_latency_ms'],\n", + " 'p99_latency_ms': result['p99_latency_ms'],\n", + " 'throughput_fps': result['throughput_fps'],\n", + " 'target_accuracy': result['target_accuracy'],\n", + " 'target_latency_ms': result['target_latency_ms'],\n", + " 'accuracy_met': result['accuracy_met'],\n", + " 'latency_met': result['latency_met'],\n", + " 'compliant': result['compliant']\n", + " }\n", + "\n", + " # Summary statistics\n", + " if total_benchmarks > 0:\n", + " compliance_rate = len(compliant_benchmarks) / total_benchmarks\n", + " report_data['summary'] = {\n", + " 'total_benchmarks': total_benchmarks,\n", + " 'compliant_benchmarks': len(compliant_benchmarks),\n", + " 'compliance_rate': compliance_rate,\n", + " 'overall_compliant': compliance_rate == 1.0,\n", + " 'compliant_benchmark_names': compliant_benchmarks\n", + " }\n", + "\n", + " # Save report\n", + " with open(output_path, 'w') as f:\n", + " json.dump(report_data, f, indent=2)\n", + "\n", + " # Generate human-readable summary\n", + " summary_lines = []\n", + " summary_lines.append(\"# TinyMLPerf Compliance Report\")\n", + " summary_lines.append(\"=\" * 40)\n", + " summary_lines.append(f\"Model: {report_data['model_name']}\")\n", + " summary_lines.append(f\"Date: {report_data['timestamp']}\")\n", + " summary_lines.append(\"\")\n", + "\n", + " if total_benchmarks > 0:\n", + " summary_lines.append(f\"## Overall Result: {'✅ COMPLIANT' if report_data['summary']['overall_compliant'] else '❌ NON-COMPLIANT'}\")\n", + " summary_lines.append(f\"Compliance Rate: {compliance_rate:.1%} ({len(compliant_benchmarks)}/{total_benchmarks})\")\n", + " summary_lines.append(\"\")\n", + "\n", + " summary_lines.append(\"## Benchmark Details:\")\n", + " for benchmark_name, result in report_data['benchmarks'].items():\n", + " status = \"✅ PASS\" if result['compliant'] else \"❌ FAIL\"\n", + " summary_lines.append(f\"- **{benchmark_name}**: {status}\")\n", + " summary_lines.append(f\" - Accuracy: {result['accuracy']:.1%} (target: {result['target_accuracy']:.1%})\")\n", + " summary_lines.append(f\" - Latency: {result['mean_latency_ms']:.1f}ms (target: <{result['target_latency_ms']}ms)\")\n", + " summary_lines.append(\"\")\n", + " else:\n", + " summary_lines.append(\"No successful benchmark runs.\")\n", + "\n", + " summary_text = \"\\n\".join(summary_lines)\n", + "\n", + " # Save human-readable report\n", + " summary_path = output_path.replace('.json', '_summary.md')\n", + " with open(summary_path, 'w') as f:\n", + " f.write(summary_text)\n", + "\n", + " print(f\"📄 TinyMLPerf report saved to {output_path}\")\n", + " print(f\"📄 Summary saved to {summary_path}\")\n", + "\n", + " return summary_text\n", + " ### END SOLUTION\n", + "\n", + "def test_unit_tinymlperf():\n", + " \"\"\"🔬 Test TinyMLPerf standardized benchmarking.\"\"\"\n", + " print(\"🔬 Unit Test: TinyMLPerf...\")\n", + "\n", + " # Create mock model for testing\n", + " class MockModel:\n", + " def __init__(self, name):\n", + " self.name = name\n", + "\n", + " def forward(self, x):\n", + " time.sleep(0.001) # Simulate computation\n", + " # Return appropriate output shape for different benchmarks\n", + " if hasattr(x, 'shape'):\n", + " if len(x.shape) == 2: # Audio/sequence\n", + " return np.random.rand(2) # Binary classification\n", + " else: # Image\n", + " return np.random.rand(10) # Multi-class\n", + " return np.random.rand(2)\n", + "\n", + " model = MockModel(\"test_model\")\n", + " perf = TinyMLPerf(random_seed=42)\n", + "\n", + " # Test individual benchmark\n", + " result = perf.run_standard_benchmark(model, 'keyword_spotting', num_runs=5)\n", + "\n", + " # Verify result structure\n", + " required_keys = ['accuracy', 'mean_latency_ms', 'throughput_fps', 'compliant']\n", + " assert all(key in result for key in required_keys)\n", + " assert 0 <= result['accuracy'] <= 1\n", + " assert result['mean_latency_ms'] > 0\n", + " assert result['throughput_fps'] > 0\n", + "\n", + " # Test full benchmark suite (with fewer runs for speed)\n", + " import tempfile\n", + " with tempfile.TemporaryDirectory() as tmp_dir:\n", + " # Run subset of benchmarks for testing\n", + " subset_results = {}\n", + " for benchmark in ['keyword_spotting', 'image_classification']:\n", + " subset_results[benchmark] = perf.run_standard_benchmark(model, benchmark, num_runs=3)\n", + "\n", + " # Test compliance report generation\n", + " report_path = f\"{tmp_dir}/test_report.json\"\n", + " summary = perf.generate_compliance_report(subset_results, report_path)\n", + "\n", + " # Verify report was created\n", + " assert Path(report_path).exists()\n", + " assert \"TinyMLPerf Compliance Report\" in summary\n", + " assert \"Compliance Rate\" in summary\n", + "\n", + " print(\"✅ TinyMLPerf works correctly!\")\n", + "\n", + "test_unit_tinymlperf()" + ] + }, + { + "cell_type": "markdown", + "id": "f021aeb1", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "# 4. Integration - Building Complete Benchmark Workflows\n", + "\n", + "Now we'll integrate all our benchmarking components into complete workflows that demonstrate professional ML systems evaluation. This integration shows how to combine statistical rigor with practical insights.\n", + "\n", + "The integration layer connects individual measurements into actionable engineering insights. This is where benchmarking becomes a decision-making tool rather than just data collection.\n", + "\n", + "## Workflow Architecture\n", + "\n", + "```\n", + "Integration Workflow Pipeline:\n", + "┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐\n", + "│ Model Variants │ │ Optimization │ │ Use Case │\n", + "│ • Base model │ → │ Techniques │ → │ Analysis │\n", + "│ • Quantized │ │ • Accuracy loss │ │ • Mobile │\n", + "│ • Pruned │ │ • Speed gain │ │ • Server │\n", + "│ • Distilled │ │ • Memory save │ │ • Edge │\n", + "└─────────────────┘ └─────────────────┘ └─────────────────┘\n", + "```\n", + "\n", + "This workflow helps answer questions like:\n", + "- \"Which optimization gives the best accuracy/latency trade-off?\"\n", + "- \"What's the memory budget impact of each technique?\"\n", + "- \"Which model should I deploy for mobile vs server?\"" + ] + }, + { + "cell_type": "markdown", + "id": "0170f7e0", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## Optimization Comparison Engine\n", + "\n", + "Before implementing the comparison function, let's understand what makes optimization comparison challenging and valuable.\n", + "\n", + "### Why Optimization Comparison is Complex\n", + "\n", + "When you optimize a model, you're making trade-offs across multiple dimensions simultaneously:\n", + "\n", + "```\n", + "Optimization Impact Matrix:\n", + " Accuracy Latency Memory Energy\n", + "Quantization -5% +2.1x +2.0x +1.8x\n", + "Pruning -2% +1.4x +3.2x +1.3x\n", + "Knowledge Distill. -8% +1.9x +1.5x +1.7x\n", + "```\n", + "\n", + "The challenge: Which is \"best\"? It depends entirely on your deployment constraints.\n", + "\n", + "### Multi-Objective Decision Framework\n", + "\n", + "Our comparison engine implements a decision framework that:\n", + "\n", + "1. **Measures all dimensions**: Don't optimize in isolation\n", + "2. **Calculates efficiency ratios**: Accuracy per MB, accuracy per ms\n", + "3. **Identifies Pareto frontiers**: Models that aren't dominated in all metrics\n", + "4. **Generates use-case recommendations**: Tailored to specific constraints\n", + "\n", + "### Recommendation Algorithm\n", + "\n", + "```\n", + "For each use case:\n", + "├── Latency-critical (real-time apps)\n", + "│ └── Optimize: min(latency) subject to accuracy > threshold\n", + "├── Memory-constrained (mobile/IoT)\n", + "│ └── Optimize: min(memory) subject to accuracy > threshold\n", + "├── Accuracy-preservation (quality-critical)\n", + "│ └── Optimize: max(accuracy) subject to latency < threshold\n", + "└── Balanced (general deployment)\n", + " └── Optimize: weighted combination of all factors\n", + "```\n", + "\n", + "This principled approach ensures recommendations match real deployment needs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aa163999", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "benchmark-comparison", + "solution": true + } + }, + "outputs": [], + "source": [ + "def compare_optimization_techniques(base_model: Any, optimized_models: List[Any],\n", + " datasets: List[Any]) -> Dict[str, Any]:\n", + " \"\"\"\n", + " Compare base model against various optimization techniques.\n", + "\n", + " TODO: Implement comprehensive comparison of optimization approaches\n", + "\n", + " APPROACH:\n", + " 1. Run benchmarks on base model and all optimized variants\n", + " 2. Calculate improvement ratios and trade-offs\n", + " 3. Generate insights about which optimizations work best\n", + " 4. Create recommendation matrix for different use cases\n", + "\n", + " EXAMPLE:\n", + " >>> models = [base_model, quantized_model, pruned_model, distilled_model]\n", + " >>> results = compare_optimization_techniques(base_model, models[1:], datasets)\n", + " >>> print(results['recommendations'])\n", + "\n", + " HINTS:\n", + " - Compare accuracy retention vs speed/memory improvements\n", + " - Calculate efficiency metrics (accuracy per MB, accuracy per ms)\n", + " - Identify Pareto-optimal solutions\n", + " - Generate actionable recommendations for different scenarios\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " all_models = [base_model] + optimized_models\n", + " suite = BenchmarkSuite(all_models, datasets)\n", + "\n", + " print(\"🔬 Running optimization comparison benchmark...\")\n", + " benchmark_results = suite.run_full_benchmark()\n", + "\n", + " # Extract base model performance for comparison\n", + " base_name = getattr(base_model, 'name', 'model_0')\n", + "\n", + " base_metrics = {}\n", + " for metric_type, results in benchmark_results.items():\n", + " for model_name, result in results.items():\n", + " if base_name in model_name:\n", + " base_metrics[metric_type] = result.mean\n", + " break\n", + "\n", + " # Calculate improvement ratios\n", + " comparison_results = {\n", + " 'base_model': base_name,\n", + " 'base_metrics': base_metrics,\n", + " 'optimized_results': {},\n", + " 'improvements': {},\n", + " 'efficiency_metrics': {},\n", + " 'recommendations': {}\n", + " }\n", + "\n", + " for opt_model in optimized_models:\n", + " opt_name = getattr(opt_model, 'name', f'optimized_model_{len(comparison_results[\"optimized_results\"])}')\n", + "\n", + " # Find results for this optimized model\n", + " opt_metrics = {}\n", + " for metric_type, results in benchmark_results.items():\n", + " for model_name, result in results.items():\n", + " if opt_name in model_name:\n", + " opt_metrics[metric_type] = result.mean\n", + " break\n", + "\n", + " comparison_results['optimized_results'][opt_name] = opt_metrics\n", + "\n", + " # Calculate improvements\n", + " improvements = {}\n", + " for metric_type in ['latency', 'memory', 'energy']:\n", + " if metric_type in base_metrics and metric_type in opt_metrics:\n", + " # For these metrics, lower is better, so improvement = base/optimized\n", + " if opt_metrics[metric_type] > 0:\n", + " improvements[f'{metric_type}_speedup'] = base_metrics[metric_type] / opt_metrics[metric_type]\n", + " else:\n", + " improvements[f'{metric_type}_speedup'] = 1.0\n", + "\n", + " if 'accuracy' in base_metrics and 'accuracy' in opt_metrics:\n", + " # Accuracy retention (higher is better)\n", + " improvements['accuracy_retention'] = opt_metrics['accuracy'] / base_metrics['accuracy']\n", + "\n", + " comparison_results['improvements'][opt_name] = improvements\n", + "\n", + " # Calculate efficiency metrics\n", + " efficiency = {}\n", + " if 'accuracy' in opt_metrics:\n", + " if 'memory' in opt_metrics and opt_metrics['memory'] > 0:\n", + " efficiency['accuracy_per_mb'] = opt_metrics['accuracy'] / opt_metrics['memory']\n", + " if 'latency' in opt_metrics and opt_metrics['latency'] > 0:\n", + " efficiency['accuracy_per_ms'] = opt_metrics['accuracy'] / opt_metrics['latency']\n", + "\n", + " comparison_results['efficiency_metrics'][opt_name] = efficiency\n", + "\n", + " # Generate recommendations based on results\n", + " recommendations = {}\n", + "\n", + " # Find best performers in each category\n", + " best_latency = None\n", + " best_memory = None\n", + " best_accuracy = None\n", + " best_overall = None\n", + "\n", + " best_latency_score = 0\n", + " best_memory_score = 0\n", + " best_accuracy_score = 0\n", + " best_overall_score = 0\n", + "\n", + " for opt_name, improvements in comparison_results['improvements'].items():\n", + " # Latency recommendation\n", + " if 'latency_speedup' in improvements and improvements['latency_speedup'] > best_latency_score:\n", + " best_latency_score = improvements['latency_speedup']\n", + " best_latency = opt_name\n", + "\n", + " # Memory recommendation\n", + " if 'memory_speedup' in improvements and improvements['memory_speedup'] > best_memory_score:\n", + " best_memory_score = improvements['memory_speedup']\n", + " best_memory = opt_name\n", + "\n", + " # Accuracy recommendation\n", + " if 'accuracy_retention' in improvements and improvements['accuracy_retention'] > best_accuracy_score:\n", + " best_accuracy_score = improvements['accuracy_retention']\n", + " best_accuracy = opt_name\n", + "\n", + " # Overall balance (considering all factors)\n", + " overall_score = 0\n", + " count = 0\n", + " for key, value in improvements.items():\n", + " if 'speedup' in key:\n", + " overall_score += min(value, 5.0) # Cap speedup at 5x to avoid outliers\n", + " count += 1\n", + " elif 'retention' in key:\n", + " overall_score += value * 5 # Weight accuracy retention heavily\n", + " count += 1\n", + "\n", + " if count > 0:\n", + " overall_score /= count\n", + " if overall_score > best_overall_score:\n", + " best_overall_score = overall_score\n", + " best_overall = opt_name\n", + "\n", + " recommendations = {\n", + " 'for_latency_critical': {\n", + " 'model': best_latency,\n", + " 'reason': f\"Best latency improvement: {best_latency_score:.2f}x faster\",\n", + " 'use_case': \"Real-time applications, edge devices with strict timing requirements\"\n", + " },\n", + " 'for_memory_constrained': {\n", + " 'model': best_memory,\n", + " 'reason': f\"Best memory reduction: {best_memory_score:.2f}x smaller\",\n", + " 'use_case': \"Mobile devices, IoT sensors, embedded systems\"\n", + " },\n", + " 'for_accuracy_preservation': {\n", + " 'model': best_accuracy,\n", + " 'reason': f\"Best accuracy retention: {best_accuracy_score:.1%} of original\",\n", + " 'use_case': \"Applications where quality cannot be compromised\"\n", + " },\n", + " 'for_balanced_deployment': {\n", + " 'model': best_overall,\n", + " 'reason': f\"Best overall trade-off (score: {best_overall_score:.2f})\",\n", + " 'use_case': \"General production deployment with multiple constraints\"\n", + " }\n", + " }\n", + "\n", + " comparison_results['recommendations'] = recommendations\n", + "\n", + " # Print summary\n", + " print(\"\\n📊 Optimization Comparison Results:\")\n", + " print(\"=\" * 50)\n", + "\n", + " for opt_name, improvements in comparison_results['improvements'].items():\n", + " print(f\"\\n{opt_name}:\")\n", + " for metric, value in improvements.items():\n", + " if 'speedup' in metric:\n", + " print(f\" {metric}: {value:.2f}x improvement\")\n", + " elif 'retention' in metric:\n", + " print(f\" {metric}: {value:.1%}\")\n", + "\n", + " print(\"\\n🎯 Recommendations:\")\n", + " for use_case, rec in recommendations.items():\n", + " if rec['model']:\n", + " print(f\" {use_case}: {rec['model']} - {rec['reason']}\")\n", + "\n", + " return comparison_results\n", + " ### END SOLUTION\n", + "\n", + "def test_unit_optimization_comparison():\n", + " \"\"\"🔬 Test optimization comparison functionality.\"\"\"\n", + " print(\"🔬 Unit Test: compare_optimization_techniques...\")\n", + "\n", + " # Create mock models with different characteristics\n", + " class MockModel:\n", + " def __init__(self, name, latency_factor=1.0, accuracy_factor=1.0, memory_factor=1.0):\n", + " self.name = name\n", + " self.latency_factor = latency_factor\n", + " self.accuracy_factor = accuracy_factor\n", + " self.memory_factor = memory_factor\n", + "\n", + " def forward(self, x):\n", + " time.sleep(0.001 * self.latency_factor)\n", + " return x\n", + "\n", + " # Base model and optimized variants\n", + " base_model = MockModel(\"base_model\", latency_factor=1.0, accuracy_factor=1.0, memory_factor=1.0)\n", + " quantized_model = MockModel(\"quantized_model\", latency_factor=0.7, accuracy_factor=0.95, memory_factor=0.5)\n", + " pruned_model = MockModel(\"pruned_model\", latency_factor=0.8, accuracy_factor=0.98, memory_factor=0.3)\n", + "\n", + " datasets = [{\"test\": \"data\"}]\n", + "\n", + " # Run comparison\n", + " results = compare_optimization_techniques(base_model, [quantized_model, pruned_model], datasets)\n", + "\n", + " # Verify results structure\n", + " assert 'base_model' in results\n", + " assert 'optimized_results' in results\n", + " assert 'improvements' in results\n", + " assert 'recommendations' in results\n", + "\n", + " # Verify improvements were calculated\n", + " assert len(results['improvements']) == 2 # Two optimized models\n", + "\n", + " # Verify recommendations were generated\n", + " recommendations = results['recommendations']\n", + " assert 'for_latency_critical' in recommendations\n", + " assert 'for_memory_constrained' in recommendations\n", + " assert 'for_accuracy_preservation' in recommendations\n", + " assert 'for_balanced_deployment' in recommendations\n", + "\n", + " print(\"✅ compare_optimization_techniques works correctly!\")\n", + "\n", + "test_unit_optimization_comparison()" + ] + }, + { + "cell_type": "markdown", + "id": "2cde2096", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "# 5. Systems Analysis - Performance Engineering Insights\n", + "\n", + "Let's analyze how our benchmarking system behaves under different conditions and reveal insights about measurement accuracy, system variability, and scalability patterns.\n", + "\n", + "This analysis section demonstrates a key principle: **benchmark the benchmarking system itself**. Understanding how your measurement tools behave is crucial for interpreting results correctly.\n", + "\n", + "## Why Analyze Measurement Systems?\n", + "\n", + "Consider two scenarios:\n", + "- **Scenario A**: Your measurements show Model B is 10% faster than Model A\n", + "- **Scenario B**: Your measurements show Model B is 10% faster, but measurement uncertainty is ±15%\n", + "\n", + "In Scenario A, you might deploy Model B. In Scenario B, the difference isn't statistically significant - you can't trust the comparison.\n", + "\n", + "Professional benchmarking requires understanding and quantifying measurement uncertainty." + ] + }, + { + "cell_type": "markdown", + "id": "e4e0e4ae", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## Measurement Variance Analysis\n", + "\n", + "Understanding measurement variance is fundamental to statistical significance. This analysis reveals how sample size affects measurement reliability and helps determine optimal benchmark configurations.\n", + "\n", + "### Statistical Significance in Practice\n", + "\n", + "When you measure a model's latency multiple times, you get a distribution of values. The key insight: **more measurements reduce uncertainty about the true mean, but with diminishing returns**.\n", + "\n", + "```\n", + "Measurement Variance Relationship:\n", + "Standard Error = σ / √n\n", + "\n", + "Where:\n", + "- σ = underlying measurement noise\n", + "- n = number of samples\n", + "- Standard Error = uncertainty in the estimated mean\n", + "\n", + "Doubling samples reduces uncertainty by √2 ≈ 1.41x\n", + "10x samples reduces uncertainty by √10 ≈ 3.16x\n", + "```\n", + "\n", + "### Variance Sources in ML Benchmarking\n", + "\n", + "**System-Level Variance**:\n", + "- CPU frequency scaling (thermal throttling)\n", + "- Background processes (OS scheduling)\n", + "- Memory pressure (garbage collection)\n", + "- Network traffic (for distributed models)\n", + "\n", + "**Algorithm-Level Variance**:\n", + "- Input-dependent computation paths\n", + "- Random initialization effects\n", + "- Numerical precision variations\n", + "\n", + "**Measurement-Level Variance**:\n", + "- Timer resolution and overhead\n", + "- Function call overhead\n", + "- Memory allocation patterns\n", + "\n", + "This analysis quantifies these effects and determines optimal measurement protocols." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "731af32a", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "analyze-measurement-variance", + "solution": true + } + }, + "outputs": [], + "source": [ + "def analyze_measurement_variance():\n", + " \"\"\"📊 Analyze how measurement variance affects benchmark reliability.\"\"\"\n", + " print(\"📊 Analyzing measurement variance and statistical significance...\")\n", + "\n", + " # Create a simple test model for consistent analysis\n", + " class TestModel:\n", + " def __init__(self, base_latency=0.001):\n", + " self.base_latency = base_latency\n", + " self.name = \"test_model\"\n", + "\n", + " def forward(self, x):\n", + " # Add realistic variance sources\n", + " system_noise = np.random.normal(0, 0.0001) # System noise\n", + " thermal_variance = np.random.normal(0, 0.00005) # CPU frequency variation\n", + " time.sleep(max(0, self.base_latency + system_noise + thermal_variance))\n", + " return x\n", + "\n", + " model = TestModel()\n", + "\n", + " # Test different numbers of measurement runs\n", + " run_counts = [3, 5, 10, 20, 50, 100]\n", + " variance_results = []\n", + "\n", + " for num_runs in run_counts:\n", + " benchmark = Benchmark([model], [{\"data\": \"test\"}],\n", + " warmup_runs=2, measurement_runs=num_runs)\n", + "\n", + " # Run multiple benchmark sessions to see variance between sessions\n", + " session_means = []\n", + " session_stds = []\n", + "\n", + " for session in range(5): # 5 different benchmark sessions\n", + " results = benchmark.run_latency_benchmark()\n", + " result = list(results.values())[0]\n", + " session_means.append(result.mean)\n", + " session_stds.append(result.std)\n", + "\n", + " # Calculate variance across sessions\n", + " mean_of_means = np.mean(session_means)\n", + " std_of_means = np.std(session_means)\n", + " mean_of_stds = np.mean(session_stds)\n", + "\n", + " variance_results.append({\n", + " 'num_runs': num_runs,\n", + " 'mean_latency': mean_of_means,\n", + " 'std_between_sessions': std_of_means,\n", + " 'mean_std_within_session': mean_of_stds,\n", + " 'coefficient_of_variation': std_of_means / mean_of_means if mean_of_means > 0 else 0\n", + " })\n", + "\n", + " # Plot results\n", + " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n", + "\n", + " # Plot 1: Standard deviation vs number of runs\n", + " num_runs_list = [r['num_runs'] for r in variance_results]\n", + " between_session_std = [r['std_between_sessions'] * 1000 for r in variance_results] # Convert to ms\n", + " within_session_std = [r['mean_std_within_session'] * 1000 for r in variance_results]\n", + "\n", + " ax1.plot(num_runs_list, between_session_std, 'o-', label='Between Sessions', linewidth=2)\n", + " ax1.plot(num_runs_list, within_session_std, 's-', label='Within Session', linewidth=2)\n", + " ax1.set_xlabel('Number of Measurement Runs')\n", + " ax1.set_ylabel('Standard Deviation (ms)')\n", + " ax1.set_title('Measurement Variance vs Sample Size')\n", + " ax1.legend()\n", + " ax1.grid(True, alpha=0.3)\n", + " ax1.set_xscale('log')\n", + "\n", + " # Plot 2: Coefficient of variation\n", + " cv_values = [r['coefficient_of_variation'] * 100 for r in variance_results]\n", + " ax2.plot(num_runs_list, cv_values, 'o-', color='red', linewidth=2)\n", + " ax2.set_xlabel('Number of Measurement Runs')\n", + " ax2.set_ylabel('Coefficient of Variation (%)')\n", + " ax2.set_title('Measurement Reliability vs Sample Size')\n", + " ax2.grid(True, alpha=0.3)\n", + " ax2.set_xscale('log')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # Key insights\n", + " print(\"\\n💡 Measurement Variance Analysis:\")\n", + " print(f\"With 10 runs: CV = {variance_results[2]['coefficient_of_variation']:.1%}\")\n", + " print(f\"With 50 runs: CV = {variance_results[4]['coefficient_of_variation']:.1%}\")\n", + " print(f\"With 100 runs: CV = {variance_results[5]['coefficient_of_variation']:.1%}\")\n", + "\n", + " if variance_results[4]['coefficient_of_variation'] < 0.05:\n", + " print(\"🚀 50+ runs provide stable measurements (CV < 5%)\")\n", + " else:\n", + " print(\"⚠️ High variance detected - consider longer warmup or controlled environment\")\n", + "\n", + "analyze_measurement_variance()" + ] + }, + { + "cell_type": "markdown", + "id": "def9859a", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## Benchmark Scaling Analysis\n", + "\n", + "Understanding how benchmark overhead scales with model complexity helps optimize measurement protocols and interpret results correctly.\n", + "\n", + "### Why Benchmark Overhead Matters\n", + "\n", + "Every measurement tool adds overhead. For benchmarking to be meaningful, this overhead must be:\n", + "1. **Consistent**: Same overhead across different models\n", + "2. **Minimal**: Small compared to what you're measuring\n", + "3. **Predictable**: Understood so you can account for it\n", + "\n", + "### Overhead Analysis Framework\n", + "\n", + "```\n", + "Total Measured Time = True Model Time + Benchmark Overhead\n", + "\n", + "Benchmark Overhead includes:\n", + "├── Framework setup (model loading, input preparation)\n", + "├── Timing infrastructure (context managers, precision counters)\n", + "├── Result collection (statistics, metadata gathering)\n", + "└── System interactions (memory allocation, Python overhead)\n", + "```\n", + "\n", + "### Scaling Behavior Patterns\n", + "\n", + "**Good Scaling**: Overhead decreases as percentage of total time\n", + "- Simple models: 20% overhead (still usable)\n", + "- Complex models: 2% overhead (negligible)\n", + "\n", + "**Bad Scaling**: Overhead increases with model complexity\n", + "- Indicates benchmark framework bottlenecks\n", + "- Makes results unreliable for optimization decisions\n", + "\n", + "**Optimal Configuration**: Overhead < 5% for target model complexity range\n", + "\n", + "This analysis identifies the optimal benchmark configuration for different model types and deployment scenarios." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63b65aa4", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "analyze-scaling-behavior", + "solution": true + } + }, + "outputs": [], + "source": [ + "def analyze_scaling_behavior():\n", + " \"\"\"📊 Analyze how benchmark overhead scales with model and input complexity.\"\"\"\n", + " print(\"📊 Analyzing benchmark overhead and scaling behavior...\")\n", + "\n", + " # Create models with different computational complexity\n", + " class ScalingTestModel:\n", + " def __init__(self, complexity_factor, name):\n", + " self.complexity_factor = complexity_factor\n", + " self.name = name\n", + "\n", + " def forward(self, x):\n", + " # Simulate computational work proportional to complexity\n", + " base_time = 0.001 # 1ms base\n", + " compute_time = base_time * self.complexity_factor\n", + "\n", + " # Simulate actual computation with matrix operations\n", + " if hasattr(x, 'shape'):\n", + " size = np.prod(x.shape)\n", + " else:\n", + " size = len(x) if hasattr(x, '__len__') else 100\n", + "\n", + " # Simulate memory allocation and computation\n", + " temp_data = np.random.randn(int(size * self.complexity_factor))\n", + " _ = np.sum(temp_data * temp_data) # Some computation\n", + "\n", + " time.sleep(compute_time)\n", + " return x\n", + "\n", + " # Models with different complexity\n", + " models = [\n", + " ScalingTestModel(1, \"simple_model\"),\n", + " ScalingTestModel(5, \"medium_model\"),\n", + " ScalingTestModel(20, \"complex_model\"),\n", + " ScalingTestModel(100, \"very_complex_model\")\n", + " ]\n", + "\n", + " # Test different input sizes\n", + " input_sizes = [(1, 28, 28), (1, 64, 64), (1, 128, 128), (1, 256, 256)]\n", + "\n", + " scaling_results = []\n", + "\n", + " for input_shape in input_sizes:\n", + " print(f\"Testing input shape: {input_shape}\")\n", + "\n", + " for model in models:\n", + " # Measure pure model time (without benchmark overhead)\n", + " dummy_input = np.random.randn(*input_shape).astype(np.float32)\n", + "\n", + " pure_times = []\n", + " for _ in range(10):\n", + " with precise_timer() as timer:\n", + " model.forward(dummy_input)\n", + " pure_times.append(timer.elapsed * 1000)\n", + "\n", + " pure_mean = np.mean(pure_times)\n", + "\n", + " # Measure with benchmark framework\n", + " benchmark = Benchmark([model], [{\"data\": \"test\"}],\n", + " warmup_runs=3, measurement_runs=10)\n", + "\n", + " bench_results = benchmark.run_latency_benchmark(input_shape)\n", + " bench_mean = list(bench_results.values())[0].mean\n", + "\n", + " # Calculate overhead\n", + " overhead_ms = bench_mean - pure_mean\n", + " overhead_percent = (overhead_ms / pure_mean) * 100 if pure_mean > 0 else 0\n", + "\n", + " scaling_results.append({\n", + " 'input_size': np.prod(input_shape),\n", + " 'model_complexity': model.complexity_factor,\n", + " 'model_name': model.name,\n", + " 'pure_latency_ms': pure_mean,\n", + " 'benchmark_latency_ms': bench_mean,\n", + " 'overhead_ms': overhead_ms,\n", + " 'overhead_percent': overhead_percent\n", + " })\n", + "\n", + " # Create DataFrame for analysis\n", + " df = pd.DataFrame(scaling_results)\n", + "\n", + " # Plot results\n", + " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n", + "\n", + " # Plot 1: Overhead vs model complexity\n", + " for input_size in [784, 4096, 16384, 65536]: # Representative sizes\n", + " subset = df[df['input_size'] == input_size]\n", + " if not subset.empty:\n", + " ax1.plot(subset['model_complexity'], subset['overhead_percent'],\n", + " 'o-', label=f'Input size: {input_size}', linewidth=2)\n", + "\n", + " ax1.set_xlabel('Model Complexity Factor')\n", + " ax1.set_ylabel('Benchmark Overhead (%)')\n", + " ax1.set_title('Benchmark Overhead vs Model Complexity')\n", + " ax1.legend()\n", + " ax1.grid(True, alpha=0.3)\n", + " ax1.set_xscale('log')\n", + "\n", + " # Plot 2: Absolute overhead vs input size\n", + " for complexity in [1, 5, 20, 100]:\n", + " subset = df[df['model_complexity'] == complexity]\n", + " if not subset.empty:\n", + " ax2.plot(subset['input_size'], subset['overhead_ms'],\n", + " 'o-', label=f'Complexity: {complexity}x', linewidth=2)\n", + "\n", + " ax2.set_xlabel('Input Size (elements)')\n", + " ax2.set_ylabel('Benchmark Overhead (ms)')\n", + " ax2.set_title('Benchmark Overhead vs Input Size')\n", + " ax2.legend()\n", + " ax2.grid(True, alpha=0.3)\n", + " ax2.set_xscale('log')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # Analysis insights\n", + " print(\"\\n💡 Scaling Behavior Analysis:\")\n", + "\n", + " # Find overhead patterns\n", + " high_complexity_overhead = df[df['model_complexity'] >= 20]['overhead_percent'].mean()\n", + " low_complexity_overhead = df[df['model_complexity'] <= 5]['overhead_percent'].mean()\n", + "\n", + " print(f\"Low complexity models: {low_complexity_overhead:.1f}% overhead\")\n", + " print(f\"High complexity models: {high_complexity_overhead:.1f}% overhead\")\n", + "\n", + " if high_complexity_overhead < 5:\n", + " print(\"🚀 Benchmark overhead is negligible for complex models\")\n", + " elif low_complexity_overhead > 20:\n", + " print(\"⚠️ High overhead for simple models - consider optimization\")\n", + " else:\n", + " print(\"✅ Benchmark scaling is appropriate for intended use cases\")\n", + "\n", + "analyze_scaling_behavior()" + ] + }, + { + "cell_type": "markdown", + "id": "ed0612d5", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "# 6. Optimization Insights - Trade-offs and Production Patterns\n", + "\n", + "Understanding the real-world implications of benchmarking decisions and how to optimize the measurement process itself for different use cases.\n", + "\n", + "This section addresses a meta-question: **How do you optimize the optimization process?** Different use cases need different measurement trade-offs.\n", + "\n", + "## Benchmarking Configuration Optimization\n", + "\n", + "Professional ML teams face a fundamental trade-off in benchmarking:\n", + "- **More accurate measurements** require more time and resources\n", + "- **Faster measurements** enable more iteration but with less precision\n", + "- **Different development phases** need different measurement fidelity\n", + "\n", + "The goal: Find the minimum measurement overhead that provides sufficient confidence for decision-making." + ] + }, + { + "cell_type": "markdown", + "id": "25d834e0", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## Optimal Benchmark Configuration Analysis\n", + "\n", + "This analysis helps determine the right benchmark configuration for different development scenarios. It's a practical application of statistics to engineering workflow optimization.\n", + "\n", + "### The Measurement Fidelity Spectrum\n", + "\n", + "```\n", + "Development Phase Accuracy Need Speed Need Optimal Config\n", + "─────────────────────────────────────────────────────────────────────\n", + "Rapid prototyping Low High Fast (5 runs)\n", + "Feature development Medium Medium Standard (20 runs)\n", + "Performance optimization High Low Accurate (50 runs)\n", + "Production validation Very High Very Low Research (100+ runs)\n", + "Regression testing Medium High Automated (15 runs)\n", + "```\n", + "\n", + "### Multi-Objective Optimization for Benchmarking\n", + "\n", + "We optimize across three competing objectives:\n", + "1. **Accuracy**: How close to the true performance value\n", + "2. **Precision**: How consistent are repeated measurements\n", + "3. **Speed**: How quickly we get results\n", + "\n", + "```\n", + "Benchmark Configuration Optimization:\n", + "minimize: w₁×(accuracy_error) + w₂×(precision_error) + w₃×(time_cost)\n", + "subject to: measurement_runs ≥ min_statistical_power\n", + " total_time ≤ max_allowed_time\n", + "\n", + "Where weights w₁, w₂, w₃ depend on use case\n", + "```\n", + "\n", + "This analysis empirically determines optimal configurations for different scenarios." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3841a3e9", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "benchmark-optimization", + "solution": true + } + }, + "outputs": [], + "source": [ + "def optimize_benchmark_configuration():\n", + " \"\"\"📊 Find optimal benchmark configuration for different accuracy vs speed needs.\"\"\"\n", + " print(\"📊 Optimizing benchmark configuration for different use cases...\")\n", + "\n", + " # Test model for configuration optimization\n", + " class ConfigTestModel:\n", + " def __init__(self):\n", + " self.name = \"config_test_model\"\n", + "\n", + " def forward(self, x):\n", + " # Consistent baseline with small variance\n", + " time.sleep(0.002 + np.random.normal(0, 0.0001))\n", + " return x\n", + "\n", + " model = ConfigTestModel()\n", + "\n", + " # Test different configuration combinations\n", + " configurations = [\n", + " {'warmup': 1, 'runs': 5, 'name': 'fast'},\n", + " {'warmup': 3, 'runs': 10, 'name': 'standard'},\n", + " {'warmup': 5, 'runs': 20, 'name': 'accurate'},\n", + " {'warmup': 10, 'runs': 50, 'name': 'precise'},\n", + " {'warmup': 15, 'runs': 100, 'name': 'research'}\n", + " ]\n", + "\n", + " config_results = []\n", + "\n", + " # Ground truth: run very long benchmark to get \"true\" value\n", + " true_benchmark = Benchmark([model], [{\"data\": \"test\"}],\n", + " warmup_runs=20, measurement_runs=200)\n", + " true_results = true_benchmark.run_latency_benchmark()\n", + " true_latency = list(true_results.values())[0].mean\n", + "\n", + " print(f\"Ground truth latency: {true_latency:.4f}s\")\n", + "\n", + " for config in configurations:\n", + " print(f\"\\nTesting {config['name']} configuration...\")\n", + "\n", + " # Run multiple trials with this configuration\n", + " trial_results = []\n", + " total_time_spent = []\n", + "\n", + " for trial in range(8): # 8 trials per configuration\n", + " start_time = time.time()\n", + "\n", + " benchmark = Benchmark([model], [{\"data\": \"test\"}],\n", + " warmup_runs=config['warmup'],\n", + " measurement_runs=config['runs'])\n", + "\n", + " results = benchmark.run_latency_benchmark()\n", + " measured_latency = list(results.values())[0].mean\n", + "\n", + " end_time = time.time()\n", + "\n", + " trial_results.append(measured_latency)\n", + " total_time_spent.append(end_time - start_time)\n", + "\n", + " # Calculate accuracy and efficiency metrics\n", + " trial_mean = np.mean(trial_results)\n", + " trial_std = np.std(trial_results)\n", + " accuracy_error = abs(trial_mean - true_latency) / true_latency * 100\n", + " precision_cv = trial_std / trial_mean * 100 if trial_mean > 0 else 0\n", + " avg_benchmark_time = np.mean(total_time_spent)\n", + "\n", + " config_results.append({\n", + " 'name': config['name'],\n", + " 'warmup_runs': config['warmup'],\n", + " 'measurement_runs': config['runs'],\n", + " 'total_runs': config['warmup'] + config['runs'],\n", + " 'accuracy_error_percent': accuracy_error,\n", + " 'precision_cv_percent': precision_cv,\n", + " 'benchmark_time_s': avg_benchmark_time,\n", + " 'efficiency_score': 100 / (accuracy_error + precision_cv + avg_benchmark_time * 10) # Combined score\n", + " })\n", + "\n", + " # Create comparison DataFrame\n", + " df = pd.DataFrame(config_results)\n", + "\n", + " # Visualize trade-offs\n", + " fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 12))\n", + "\n", + " # Plot 1: Accuracy vs Speed\n", + " ax1.scatter(df['benchmark_time_s'], df['accuracy_error_percent'],\n", + " s=100, alpha=0.7, c=df['total_runs'], cmap='viridis')\n", + " for i, name in enumerate(df['name']):\n", + " ax1.annotate(name, (df['benchmark_time_s'].iloc[i], df['accuracy_error_percent'].iloc[i]),\n", + " xytext=(5, 5), textcoords='offset points')\n", + " ax1.set_xlabel('Benchmark Time (seconds)')\n", + " ax1.set_ylabel('Accuracy Error (%)')\n", + " ax1.set_title('Accuracy vs Speed Trade-off')\n", + " ax1.grid(True, alpha=0.3)\n", + "\n", + " # Plot 2: Precision vs Speed\n", + " ax2.scatter(df['benchmark_time_s'], df['precision_cv_percent'],\n", + " s=100, alpha=0.7, c=df['total_runs'], cmap='viridis')\n", + " for i, name in enumerate(df['name']):\n", + " ax2.annotate(name, (df['benchmark_time_s'].iloc[i], df['precision_cv_percent'].iloc[i]),\n", + " xytext=(5, 5), textcoords='offset points')\n", + " ax2.set_xlabel('Benchmark Time (seconds)')\n", + " ax2.set_ylabel('Precision CV (%)')\n", + " ax2.set_title('Precision vs Speed Trade-off')\n", + " ax2.grid(True, alpha=0.3)\n", + "\n", + " # Plot 3: Efficiency comparison\n", + " ax3.bar(df['name'], df['efficiency_score'], alpha=0.7)\n", + " ax3.set_ylabel('Efficiency Score (higher = better)')\n", + " ax3.set_title('Overall Benchmark Efficiency')\n", + " ax3.tick_params(axis='x', rotation=45)\n", + "\n", + " # Plot 4: Configuration breakdown\n", + " width = 0.35\n", + " x = np.arange(len(df))\n", + " ax4.bar(x - width/2, df['warmup_runs'], width, label='Warmup Runs', alpha=0.7)\n", + " ax4.bar(x + width/2, df['measurement_runs'], width, label='Measurement Runs', alpha=0.7)\n", + " ax4.set_xlabel('Configuration')\n", + " ax4.set_ylabel('Number of Runs')\n", + " ax4.set_title('Configuration Breakdown')\n", + " ax4.set_xticks(x)\n", + " ax4.set_xticklabels(df['name'])\n", + " ax4.legend()\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # Generate recommendations\n", + " print(\"\\n💡 Benchmark Configuration Recommendations:\")\n", + "\n", + " # Find best configurations for different use cases\n", + " best_fast = df.loc[df['benchmark_time_s'].idxmin()]\n", + " best_accurate = df.loc[df['accuracy_error_percent'].idxmin()]\n", + " best_precise = df.loc[df['precision_cv_percent'].idxmin()]\n", + " best_balanced = df.loc[df['efficiency_score'].idxmax()]\n", + "\n", + " print(f\"🚀 Fastest: {best_fast['name']} - {best_fast['benchmark_time_s']:.1f}s, {best_fast['accuracy_error_percent']:.1f}% error\")\n", + " print(f\"🎯 Most Accurate: {best_accurate['name']} - {best_accurate['accuracy_error_percent']:.1f}% error\")\n", + " print(f\"📊 Most Precise: {best_precise['name']} - {best_precise['precision_cv_percent']:.1f}% CV\")\n", + " print(f\"⚖️ Best Balanced: {best_balanced['name']} - efficiency score {best_balanced['efficiency_score']:.1f}\")\n", + "\n", + " print(\"\\n🎯 Use Case Recommendations:\")\n", + " print(\"- Development/debugging: Use 'fast' config for quick feedback\")\n", + " print(\"- CI/CD pipelines: Use 'standard' config for reasonable accuracy/speed balance\")\n", + " print(\"- Performance optimization: Use 'accurate' config for reliable comparisons\")\n", + " print(\"- Research papers: Use 'precise' or 'research' config for publication-quality results\")\n", + "\n", + "optimize_benchmark_configuration()" + ] + }, + { + "cell_type": "markdown", + "id": "dd36c977", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "# 7. Module Integration Test\n", + "\n", + "Final validation that our complete benchmarking system works correctly and integrates properly with all TinyTorch components.\n", + "\n", + "This comprehensive test validates the entire benchmarking ecosystem and ensures it's ready for production use in the final capstone project." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cbbfb62c", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test-module", + "locked": true, + "points": 10 + } + }, + "outputs": [], + "source": [ + "def test_module():\n", + " \"\"\"\n", + " Comprehensive test of entire benchmarking module functionality.\n", + "\n", + " This final test runs before module summary to ensure:\n", + " - All benchmarking components work together correctly\n", + " - Statistical analysis provides reliable results\n", + " - Integration with optimization modules functions properly\n", + " - Professional reporting generates actionable insights\n", + " \"\"\"\n", + " print(\"🧪 RUNNING MODULE INTEGRATION TEST\")\n", + " print(\"=\" * 50)\n", + "\n", + " # Run all unit tests\n", + " print(\"Running unit tests...\")\n", + " test_unit_benchmark_result()\n", + " test_unit_precise_timer()\n", + " test_unit_benchmark()\n", + " test_unit_benchmark_suite()\n", + " test_unit_tinymlperf()\n", + " test_unit_optimization_comparison()\n", + "\n", + " print(\"\\nRunning integration scenarios...\")\n", + "\n", + " # Test realistic benchmarking workflow\n", + " print(\"🔬 Integration Test: Complete benchmarking workflow...\")\n", + "\n", + " # Create realistic test models\n", + " class RealisticModel:\n", + " def __init__(self, name, characteristics):\n", + " self.name = name\n", + " self.characteristics = characteristics\n", + "\n", + " def forward(self, x):\n", + " # Simulate different model behaviors\n", + " base_time = self.characteristics.get('base_latency', 0.001)\n", + " variance = self.characteristics.get('variance', 0.0001)\n", + " memory_factor = self.characteristics.get('memory_factor', 1.0)\n", + "\n", + " # Simulate realistic computation\n", + " time.sleep(max(0, base_time + np.random.normal(0, variance)))\n", + "\n", + " # Simulate memory usage\n", + " if hasattr(x, 'shape'):\n", + " temp_size = int(np.prod(x.shape) * memory_factor)\n", + " temp_data = np.random.randn(temp_size)\n", + " _ = np.sum(temp_data) # Use the data\n", + "\n", + " return x\n", + "\n", + " def evaluate(self, dataset):\n", + " # Simulate evaluation\n", + " base_acc = self.characteristics.get('base_accuracy', 0.85)\n", + " return base_acc + np.random.normal(0, 0.02)\n", + "\n", + " def parameters(self):\n", + " # Simulate parameter count\n", + " param_count = self.characteristics.get('param_count', 1000000)\n", + " return [np.random.randn(param_count)]\n", + "\n", + " # Create test model suite\n", + " models = [\n", + " RealisticModel(\"efficient_model\", {\n", + " 'base_latency': 0.001,\n", + " 'base_accuracy': 0.82,\n", + " 'memory_factor': 0.5,\n", + " 'param_count': 500000\n", + " }),\n", + " RealisticModel(\"accurate_model\", {\n", + " 'base_latency': 0.003,\n", + " 'base_accuracy': 0.95,\n", + " 'memory_factor': 2.0,\n", + " 'param_count': 2000000\n", + " }),\n", + " RealisticModel(\"balanced_model\", {\n", + " 'base_latency': 0.002,\n", + " 'base_accuracy': 0.88,\n", + " 'memory_factor': 1.0,\n", + " 'param_count': 1000000\n", + " })\n", + " ]\n", + "\n", + " datasets = [{\"test_data\": f\"dataset_{i}\"} for i in range(3)]\n", + "\n", + " # Test 1: Comprehensive benchmark suite\n", + " print(\" Testing comprehensive benchmark suite...\")\n", + " suite = BenchmarkSuite(models, datasets)\n", + " results = suite.run_full_benchmark()\n", + "\n", + " assert 'latency' in results\n", + " assert 'accuracy' in results\n", + " assert 'memory' in results\n", + " assert 'energy' in results\n", + "\n", + " # Verify all models were tested\n", + " for result_type in results.values():\n", + " assert len(result_type) == len(models)\n", + "\n", + " # Test 2: Statistical analysis\n", + " print(\" Testing statistical analysis...\")\n", + " for result_type, model_results in results.items():\n", + " for model_name, result in model_results.items():\n", + " assert isinstance(result, BenchmarkResult)\n", + " assert result.count > 0\n", + " assert result.std >= 0\n", + " assert result.ci_lower <= result.mean <= result.ci_upper\n", + "\n", + " # Test 3: Report generation\n", + " print(\" Testing report generation...\")\n", + " report = suite.generate_report()\n", + " assert \"Benchmark Report\" in report\n", + " assert \"System Information\" in report\n", + " assert \"Recommendations\" in report\n", + "\n", + " # Test 4: TinyMLPerf compliance\n", + " print(\" Testing TinyMLPerf compliance...\")\n", + " perf = TinyMLPerf(random_seed=42)\n", + " perf_results = perf.run_standard_benchmark(models[0], 'keyword_spotting', num_runs=5)\n", + "\n", + " required_keys = ['accuracy', 'mean_latency_ms', 'compliant', 'target_accuracy']\n", + " assert all(key in perf_results for key in required_keys)\n", + " assert 0 <= perf_results['accuracy'] <= 1\n", + " assert perf_results['mean_latency_ms'] > 0\n", + "\n", + " # Test 5: Optimization comparison\n", + " print(\" Testing optimization comparison...\")\n", + " comparison_results = compare_optimization_techniques(\n", + " models[0], models[1:], datasets[:1]\n", + " )\n", + "\n", + " assert 'base_model' in comparison_results\n", + " assert 'improvements' in comparison_results\n", + " assert 'recommendations' in comparison_results\n", + " assert len(comparison_results['improvements']) == 2\n", + "\n", + " # Test 6: Cross-platform compatibility\n", + " print(\" Testing cross-platform compatibility...\")\n", + " system_info = {\n", + " 'platform': platform.platform(),\n", + " 'processor': platform.processor(),\n", + " 'python_version': platform.python_version()\n", + " }\n", + "\n", + " # Verify system information is captured\n", + " benchmark = Benchmark(models[:1], datasets[:1])\n", + " assert all(key in benchmark.system_info for key in system_info.keys())\n", + "\n", + " print(\"✅ End-to-end benchmarking workflow works!\")\n", + "\n", + " print(\"\\n\" + \"=\" * 50)\n", + " print(\"🎉 ALL TESTS PASSED! Module ready for export.\")\n", + " print(\"Run: tito module complete 19\")\n", + "\n", + "test_module()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2fb3540", + "metadata": {}, + "outputs": [], + "source": [ + "if __name__ == \"__main__\":\n", + " print(\"🚀 Running Benchmarking module...\")\n", + " test_module()\n", + " print(\"✅ Module validation complete!\")" + ] + }, + { + "cell_type": "markdown", + "id": "939236c8", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🤔 ML Systems Thinking: Benchmarking and Performance Engineering\n", + "\n", + "### Question 1: Statistical Confidence in Measurements\n", + "You implemented BenchmarkResult with confidence intervals for measurements.\n", + "If you run 20 trials and get mean latency 5.2ms with std dev 0.8ms:\n", + "- What's the 95% confidence interval for the true mean? [_____ ms, _____ ms]\n", + "- How many more trials would you need to halve the confidence interval width? _____ total trials\n", + "\n", + "### Question 2: Measurement Overhead Analysis\n", + "Your precise_timer context manager has microsecond precision, but models run for milliseconds.\n", + "For a model that takes 1ms to execute:\n", + "- If timer overhead is 10μs, what's the relative error? _____%\n", + "- At what model latency does timer overhead become negligible (<1%)? _____ ms\n", + "\n", + "### Question 3: Benchmark Configuration Trade-offs\n", + "Your optimize_benchmark_configuration() function tested different warmup/measurement combinations.\n", + "For a CI/CD pipeline that runs 100 benchmarks per day:\n", + "- Fast config (3s each): _____ minutes total daily\n", + "- Accurate config (15s each): _____ minutes total daily\n", + "- What's the key trade-off you're making? [accuracy/precision/development velocity]\n", + "\n", + "### Question 4: TinyMLPerf Compliance Metrics\n", + "You implemented TinyMLPerf-style standardized benchmarks with target thresholds.\n", + "If a model achieves 89% accuracy (target: 90%) and 120ms latency (target: <100ms):\n", + "- Is it compliant? [Yes/No] _____\n", + "- Which constraint is more critical for edge deployment? [accuracy/latency]\n", + "- How would you prioritize optimization? [accuracy first/latency first/balanced]\n", + "\n", + "### Question 5: Optimization Comparison Analysis\n", + "Your compare_optimization_techniques() generates recommendations for different use cases.\n", + "Given three optimized models:\n", + "- Quantized: 0.8× memory, 2× speed, 0.95× accuracy\n", + "- Pruned: 0.3× memory, 1.5× speed, 0.98× accuracy\n", + "- Distilled: 0.6× memory, 1.8× speed, 0.92× accuracy\n", + "\n", + "For a mobile app with 50MB model size limit and <100ms latency requirement:\n", + "- Which optimization offers best memory reduction? _____\n", + "- Which balances all constraints best? _____\n", + "- What's the key insight about optimization trade-offs? [no free lunch/specialization wins/measurement guides decisions]" + ] + }, + { + "cell_type": "markdown", + "id": "d3301207", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🎯 MODULE SUMMARY: Benchmarking\n", + "\n", + "Congratulations! You've built a professional benchmarking system that rivals industry-standard evaluation frameworks!\n", + "\n", + "### Key Accomplishments\n", + "- Built comprehensive benchmarking infrastructure with BenchmarkResult, Benchmark, and BenchmarkSuite classes\n", + "- Implemented statistical rigor with confidence intervals, variance analysis, and measurement optimization\n", + "- Created TinyMLPerf-style standardized benchmarks for reproducible cross-system comparison\n", + "- Developed optimization comparison workflows that generate actionable recommendations\n", + "- All tests pass ✅ (validated by `test_module()`)\n", + "\n", + "### Systems Engineering Insights Gained\n", + "- **Measurement Science**: Statistical significance requires proper sample sizes and variance control\n", + "- **Benchmark Design**: Standardized protocols enable fair comparison across different systems\n", + "- **Trade-off Analysis**: Pareto frontiers reveal optimization opportunities and constraints\n", + "- **Production Integration**: Automated reporting transforms measurements into engineering decisions\n", + "\n", + "### Ready for Systems Capstone\n", + "Your benchmarking implementation enables the final milestone: a comprehensive systems evaluation comparing CNN vs TinyGPT with quantization, pruning, and performance analysis. This is where all 19 modules come together!\n", + "\n", + "Export with: `tito module complete 19`\n", + "\n", + "**Next**: Milestone 5 (Systems Capstone) will demonstrate the complete ML systems engineering workflow!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/modules/source/19_benchmarking/benchmarking_dev.py b/modules/source/19_benchmarking/benchmarking_dev.py index 78161dd0..5f94fba9 100644 --- a/modules/source/19_benchmarking/benchmarking_dev.py +++ b/modules/source/19_benchmarking/benchmarking_dev.py @@ -480,6 +480,7 @@ Different metrics require different measurement strategies: """ # %% nbgrader={"grade": false, "grade_id": "benchmark-class", "solution": true} +#| export class Benchmark: """ Professional benchmarking system for ML models and operations. @@ -788,6 +789,7 @@ Since direct energy measurement requires specialized hardware, we estimate energ """ # %% nbgrader={"grade": false, "grade_id": "benchmark-suite", "solution": true} +#| export class BenchmarkSuite: """ Comprehensive benchmark suite for ML systems evaluation. @@ -1222,6 +1224,7 @@ All TinyMLPerf benchmarks use: """ # %% nbgrader={"grade": false, "grade_id": "tinymlperf", "solution": true} +#| export class TinyMLPerf: """ TinyMLPerf-style standardized benchmarking for edge ML systems. diff --git a/modules/source/20_capstone/capstone_dev.ipynb b/modules/source/20_capstone/capstone_dev.ipynb new file mode 100644 index 00000000..2109bbc2 --- /dev/null +++ b/modules/source/20_capstone/capstone_dev.ipynb @@ -0,0 +1,2287 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1c02cf30", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "# Module 20: Capstone - Building TinyGPT End-to-End\n", + "\n", + "Welcome to the capstone project of TinyTorch! You've built an entire ML framework from scratch across 19 modules. Now it's time to put it all together and build something amazing: **TinyGPT** - a complete transformer-based language model.\n", + "\n", + "## 🔗 Prerequisites & Progress\n", + "**You've Built**: The complete TinyTorch framework with 19 specialized modules\n", + "**You'll Build**: A complete end-to-end ML system demonstrating production capabilities\n", + "**You'll Enable**: Understanding of how modern AI systems work from tensor to text generation\n", + "\n", + "**Connection Map**:\n", + "```\n", + "Modules 01-19 → Capstone Integration → Complete TinyGPT System\n", + "(Foundation) (Systems Thinking) (Real AI Application)\n", + "```\n", + "\n", + "## Learning Objectives\n", + "By the end of this capstone, you will:\n", + "1. **Integrate** all TinyTorch modules into a cohesive system\n", + "2. **Build** a complete TinyGPT model with training and inference\n", + "3. **Optimize** the system with quantization, pruning, and acceleration\n", + "4. **Benchmark** performance against accuracy trade-offs\n", + "5. **Demonstrate** end-to-end ML systems engineering\n", + "\n", + "This capstone represents the culmination of your journey from basic tensors to a complete AI system!" + ] + }, + { + "cell_type": "markdown", + "id": "ba68ded0", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 📦 Where This Code Lives in the Final Package\n", + "\n", + "**Learning Side:** You work in `modules/20_capstone/capstone_dev.py` \n", + "**Building Side:** Code exports to `tinytorch.applications.tinygpt`\n", + "\n", + "```python\n", + "# How to use this module:\n", + "from tinytorch.applications.tinygpt import TinyGPT, FullPipeline\n", + "```\n", + "\n", + "**Why this matters:**\n", + "- **Learning:** Complete ML system integrating all previous learning into real application\n", + "- **Production:** Demonstrates how framework components compose into deployable systems\n", + "- **Consistency:** Shows the power of modular design and clean abstractions\n", + "- **Integration:** Validates that our 19-module journey builds something meaningful" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f758fd43", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "exports", + "solution": true + } + }, + "outputs": [], + "source": [ + "#| default_exp applications.tinygpt\n", + "#| export" + ] + }, + { + "cell_type": "markdown", + "id": "c6850420", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🔮 Introduction: From Building Blocks to Intelligence\n", + "\n", + "Over the past 19 modules, you've built the complete infrastructure for modern ML:\n", + "\n", + "**Foundation (Modules 01-04):** Tensors, activations, layers, and losses\n", + "**Training (Modules 05-07):** Automatic differentiation, optimizers, and training loops\n", + "**Architecture (Modules 08-09):** Spatial processing and data loading\n", + "**Language (Modules 10-14):** Text processing, embeddings, attention, transformers, and KV caching\n", + "**Optimization (Modules 15-19):** Profiling, acceleration, quantization, compression, and benchmarking\n", + "\n", + "Now we integrate everything into **TinyGPT** - a complete language model that demonstrates the power of your framework.\n", + "\n", + "```\n", + "Your Journey:\n", + " Tensor Ops → Neural Networks → Training → Transformers → Optimization → TinyGPT\n", + " (Module 01) (Modules 02-07) (Mod 08-09) (Mod 10-14) (Mod 15-19) (Module 20)\n", + "```\n", + "\n", + "This isn't just a demo - it's a production-ready system that showcases everything you've learned about ML systems engineering." + ] + }, + { + "cell_type": "markdown", + "id": "470a2c0a", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 📊 Systems Architecture: The Complete ML Pipeline\n", + "\n", + "This capstone demonstrates how all 19 modules integrate into a complete ML system. Let's visualize the full architecture and understand how each component contributes to the final TinyGPT system.\n", + "\n", + "### Complete TinyGPT System Architecture\n", + "\n", + "```\n", + " 🏗️ TINYGPT COMPLETE SYSTEM ARCHITECTURE 🏗️\n", + "\n", + "┌─────────────────────────────────────────────────────────────────────────────────────┐\n", + "│ DATA PIPELINE │\n", + "├─────────────────────────────────────────────────────────────────────────────────────┤\n", + "│ Raw Text → Tokenizer → DataLoader → Training Loop │\n", + "│ \"Hello AI\" [72,101,..] Batches(32) Loss/Gradients │\n", + "│ (Module 10) (Module 10) (Module 08) (Modules 05-07) │\n", + "└─────────────────────────────────────────────────────────────────────────────────────┘\n", + " │\n", + " ▼\n", + "┌─────────────────────────────────────────────────────────────────────────────────────┐\n", + "│ MODEL ARCHITECTURE │\n", + "├─────────────────────────────────────────────────────────────────────────────────────┤\n", + "│ │\n", + "│ Token IDs → [Embeddings] → [Positional] → [Dropout] → [Transformer Blocks] → Output │\n", + "│ (Module 11) (Module 11) (Module 03) (Module 13) │\n", + "│ │\n", + "│ Transformer Block Details: │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ Input → [LayerNorm] → [MultiHeadAttention] → [Residual] → [LayerNorm] │ │\n", + "│ │ (Module 03) (Module 12) (Module 01) (Module 03) │ │\n", + "│ │ ↓ │ │\n", + "│ │ [MLP] ← [Residual] ← [GELU] ← [Linear] ← [Linear] │ │\n", + "│ │ (Module 03) (Module 01) (Module 02) (Module 03) │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "└─────────────────────────────────────────────────────────────────────────────────────┘\n", + " │\n", + " ▼\n", + "┌─────────────────────────────────────────────────────────────────────────────────────┐\n", + "│ GENERATION PIPELINE │\n", + "├─────────────────────────────────────────────────────────────────────────────────────┤\n", + "│ Model Output → [Sampling] → [Token Selection] → [Decoding] → Generated Text │\n", + "│ (Temperature) (Greedy/Random) (Module 10) │\n", + "│ │\n", + "│ With KV Caching (Module 14): │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ Cache Keys/Values → Only Process New Token → O(n) vs O(n²) Complexity │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "└─────────────────────────────────────────────────────────────────────────────────────┘\n", + " │\n", + " ▼\n", + "┌─────────────────────────────────────────────────────────────────────────────────────┐\n", + "│ OPTIMIZATION PIPELINE │\n", + "├─────────────────────────────────────────────────────────────────────────────────────┤\n", + "│ Base Model → [Profiling] → [Quantization] → [Pruning] → [Benchmarking] → Optimized │\n", + "│ (Module 15) (Module 17) (Module 18) (Module 19) │\n", + "│ │\n", + "│ Memory Reduction Pipeline: │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ FP32 (4 bytes) → INT8 (1 byte) → 90% Pruning → 40× Memory Reduction │ │\n", + "│ │ 200MB → 50MB → 5MB → Final Size │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "└─────────────────────────────────────────────────────────────────────────────────────┘\n", + "```\n", + "\n", + "### Memory Footprint Analysis for Different Model Sizes\n", + "\n", + "```\n", + "TinyGPT Model Sizes and Memory Requirements:\n", + "\n", + "┌──────────────┬────────────────┬─────────────────┬─────────────────┬─────────────────┐\n", + "│ Model Size │ Parameters │ Inference (MB) │ Training (MB) │ Quantized (MB) │\n", + "├──────────────┼────────────────┼─────────────────┼─────────────────┼─────────────────┤\n", + "│ TinyGPT-1M │ 1,000,000 │ 4.0 │ 12.0 │ 1.0 │\n", + "│ TinyGPT-13M │ 13,000,000 │ 52.0 │ 156.0 │ 13.0 │\n", + "│ TinyGPT-50M │ 50,000,000 │ 200.0 │ 600.0 │ 50.0 │\n", + "│ TinyGPT-100M │ 100,000,000 │ 400.0 │ 1200.0 │ 100.0 │\n", + "└──────────────┴────────────────┴─────────────────┴─────────────────┴─────────────────┘\n", + "\n", + "Memory Breakdown:\n", + "• Inference = Parameters × 4 bytes (FP32)\n", + "• Training = Parameters × 12 bytes (params + gradients + optimizer states)\n", + "• Quantized = Parameters × 1 byte (INT8)\n", + "```\n", + "\n", + "### Critical Systems Properties\n", + "\n", + "**Computational Complexity:**\n", + "- **Attention Mechanism**: O(n² × d) where n=sequence_length, d=embed_dim\n", + "- **MLP Layers**: O(n × d²) per layer\n", + "- **Generation**: O(n²) without KV cache, O(n) with KV cache\n", + "\n", + "**Memory Scaling:**\n", + "- **Linear with batch size**: memory = base_memory × batch_size\n", + "- **Quadratic with sequence length**: attention memory ∝ seq_len²\n", + "- **Linear with model depth**: memory ∝ num_layers\n", + "\n", + "**Performance Characteristics:**\n", + "- **Training throughput**: ~100-1000 tokens/second (depending on model size)\n", + "- **Inference latency**: ~1-10ms per token (depending on hardware)\n", + "- **Memory efficiency**: 4× improvement with quantization, 10× with pruning" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a2fa5c74", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "imports", + "solution": true + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import time\n", + "import json\n", + "from pathlib import Path\n", + "from typing import Dict, List, Tuple, Optional, Any\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Import all TinyTorch modules (representing 19 modules of work!)\n", + "### BEGIN SOLUTION\n", + "# Module 01: Tensor foundation\n", + "from tinytorch.core.tensor import Tensor\n", + "\n", + "# Module 02: Activations\n", + "from tinytorch.core.activations import ReLU, GELU, Sigmoid\n", + "\n", + "# Module 03: Layers\n", + "from tinytorch.core.layers import Linear, Sequential, Dropout\n", + "\n", + "# Module 04: Losses\n", + "from tinytorch.core.losses import CrossEntropyLoss\n", + "\n", + "# Module 05: Autograd (enhances Tensor)\n", + "from tinytorch.core.autograd import Function\n", + "\n", + "# Module 06: Optimizers\n", + "from tinytorch.core.optimizers import AdamW, SGD\n", + "\n", + "# Module 07: Training\n", + "from tinytorch.core.training import Trainer, CosineSchedule\n", + "\n", + "# Module 08: DataLoader\n", + "from tinytorch.data.loader import DataLoader, TensorDataset\n", + "\n", + "# Module 09: Spatial (for potential CNN comparisons)\n", + "from tinytorch.core.spatial import Conv2d, MaxPool2d\n", + "\n", + "# Module 10: Tokenization\n", + "from tinytorch.text.tokenization import CharTokenizer\n", + "\n", + "# Module 11: Embeddings\n", + "from tinytorch.text.embeddings import Embedding, PositionalEncoding\n", + "\n", + "# Module 12: Attention\n", + "from tinytorch.core.attention import MultiHeadAttention, scaled_dot_product_attention\n", + "\n", + "# Module 13: Transformers\n", + "from tinytorch.models.transformer import GPT, TransformerBlock\n", + "\n", + "# Module 14: KV Caching\n", + "from tinytorch.generation.kv_cache import KVCache\n", + "\n", + "# Module 15: Profiling\n", + "from tinytorch.profiling.profiler import Profiler\n", + "\n", + "# Module 16: Acceleration\n", + "from tinytorch.optimization.acceleration import MixedPrecisionTrainer\n", + "\n", + "# Module 17: Quantization\n", + "from tinytorch.optimization.quantization import quantize_model, QuantizedLinear\n", + "\n", + "# Module 18: Compression\n", + "from tinytorch.optimization.compression import magnitude_prune, structured_prune\n", + "\n", + "# Module 19: Benchmarking\n", + "from tinytorch.benchmarking.benchmark import Benchmark\n", + "### END SOLUTION\n", + "\n", + "print(\"🎉 Successfully imported all 19 TinyTorch modules!\")\n", + "print(\"📦 Framework Status: COMPLETE\")" + ] + }, + { + "cell_type": "markdown", + "id": "2d6fa877", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 🏗️ Stage 1: Core TinyGPT Architecture\n", + "\n", + "We'll build TinyGPT in three systematic stages, each demonstrating different aspects of ML systems engineering:\n", + "\n", + "### What We're Building: Complete Transformer Architecture\n", + "\n", + "The TinyGPT architecture integrates every component you've built across 19 modules into a cohesive system. Here's how all the pieces fit together:\n", + "\n", + "```\n", + " 🧠 TINYGPT ARCHITECTURE BREAKDOWN 🧠\n", + "\n", + "┌─────────────────────────────────────────────────────────────────────────────────────┐\n", + "│ INPUT PROCESSING │\n", + "├─────────────────────────────────────────────────────────────────────────────────────┤\n", + "│ Token IDs (integers) │\n", + "│ │ │\n", + "│ ▼ │\n", + "│ [Token Embedding] ──────────────── Maps vocab_size → embed_dim │\n", + "│ (Module 11) ╲ │\n", + "│ │ ╲ │\n", + "│ ▼ ╲─→ [Element-wise Addition] ──────► Dense Vectors │\n", + "│ [Positional Encoding] ──╱ (Module 01) │\n", + "│ (Module 11) ╱ │\n", + "│ ╱ │\n", + "│ │ ╱ │\n", + "│ ▼ ╱ │\n", + "│ [Dropout] ────────╱ ←──────────────── Regularization (Module 03) │\n", + "└─────────────────────────────────────────────────────────────────────────────────────┘\n", + " │\n", + " ▼\n", + "┌─────────────────────────────────────────────────────────────────────────────────────┐\n", + "│ TRANSFORMER PROCESSING │\n", + "├─────────────────────────────────────────────────────────────────────────────────────┤\n", + "│ │\n", + "│ For each of num_layers (typically 4-12): │\n", + "│ │\n", + "│ ┌───────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ TRANSFORMER BLOCK │ │\n", + "│ │ │ │\n", + "│ │ Input Vectors (batch, seq_len, embed_dim) │ │\n", + "│ │ │ │ │\n", + "│ │ ▼ │ │\n", + "│ │ ┌─────────────┐ ┌──────────────────────────────────────────────┐ │ │\n", + "│ │ │ Layer Norm │──▶│ Multi-Head Self-Attention (Module 12) │ │ │\n", + "│ │ │ (Module 03) │ │ │ │ │\n", + "│ │ └─────────────┘ │ • Query, Key, Value projections │ │ │\n", + "│ │ │ • Scaled dot-product attention │ │ │\n", + "│ │ │ • Multi-head parallel processing │ │ │\n", + "│ │ │ • Output projection │ │ │\n", + "│ │ └──────────────────────────────────────────────┘ │ │\n", + "│ │ │ │ │\n", + "│ │ ▼ │ │\n", + "│ │ ┌─────────────────────────────────────────┐ │ │\n", + "│ │ ┌─────────────┐ │ Residual Connection (Module 01) │ │ │\n", + "│ │ │ │◄──┤ output = input + attention(input) │ │ │\n", + "│ │ │ │ └─────────────────────────────────────────┘ │ │\n", + "│ │ │ │ │ │\n", + "│ │ │ ▼ │ │\n", + "│ │ │ ┌─────────────┐ ┌──────────────────────────────────────┐ │ │\n", + "│ │ │ │ Layer Norm │──▶│ Feed-Forward Network (MLP) │ │ │\n", + "│ │ │ │ (Module 03) │ │ │ │ │\n", + "│ │ │ └─────────────┘ │ • Linear: embed_dim → 4×embed_dim │ │ │\n", + "│ │ │ │ • GELU Activation (Module 02) │ │ │\n", + "│ │ │ │ • Linear: 4×embed_dim → embed_dim │ │ │\n", + "│ │ │ │ • Dropout │ │ │\n", + "│ │ │ └──────────────────────────────────────┘ │ │\n", + "│ │ │ │ │ │\n", + "│ │ │ ▼ │ │\n", + "│ │ │ ┌─────────────────────────────────────────┐ │ │\n", + "│ │ └─────────────────────────│ Residual Connection (Module 01) │ │ │\n", + "│ │ │ output = input + mlp(input) │ │ │\n", + "│ │ └─────────────────────────────────────────┘ │ │\n", + "│ └───────────────────────────────────────────────────────────────────────────┘ │\n", + "│ │ │\n", + "│ ▼ │\n", + "│ Next Transformer Block │\n", + "└─────────────────────────────────────────────────────────────────────────────────────┘\n", + " │\n", + " ▼\n", + "┌─────────────────────────────────────────────────────────────────────────────────────┐\n", + "│ OUTPUT PROCESSING │\n", + "├─────────────────────────────────────────────────────────────────────────────────────┤\n", + "│ Final Hidden States (batch, seq_len, embed_dim) │\n", + "│ │ │\n", + "│ ▼ │\n", + "│ [Output Linear Layer] ──────► Logits (batch, seq_len, vocab_size) │\n", + "│ (Module 03) │\n", + "│ │ │\n", + "│ ▼ │\n", + "│ [Softmax + Sampling] ──────► Next Token Predictions │\n", + "│ │\n", + "└─────────────────────────────────────────────────────────────────────────────────────┘\n", + "```\n", + "\n", + "### Systems Focus: Parameter Distribution and Memory Impact\n", + "\n", + "Understanding where parameters live in TinyGPT is crucial for optimization:\n", + "\n", + "```\n", + "Parameter Distribution in TinyGPT (embed_dim=128, vocab_size=1000, 4 layers):\n", + "\n", + "┌─────────────────────┬─────────────────┬─────────────────┬─────────────────┐\n", + "│ Component │ Parameter Count │ Memory (MB) │ % of Total │\n", + "├─────────────────────┼─────────────────┼─────────────────┼─────────────────┤\n", + "│ Token Embeddings │ 128,000 │ 0.5 │ 15% │\n", + "│ Positional Encoding │ 32,768 │ 0.1 │ 4% │\n", + "│ Attention Layers │ 262,144 │ 1.0 │ 31% │\n", + "│ MLP Layers │ 393,216 │ 1.5 │ 46% │\n", + "│ Layer Norms │ 2,048 │ 0.01 │ 0.2% │\n", + "│ Output Projection │ 128,000 │ 0.5 │ 15% │\n", + "├─────────────────────┼─────────────────┼─────────────────┼─────────────────┤\n", + "│ TOTAL │ 946,176 │ 3.6 │ 100% │\n", + "└─────────────────────┴─────────────────┴─────────────────┴─────────────────┘\n", + "\n", + "Key Insights:\n", + "• MLP layers dominate parameter count (46% of total)\n", + "• Attention layers are second largest (31% of total)\n", + "• Embedding tables scale with vocabulary size\n", + "• Memory scales linearly with embed_dim²\n", + "```\n", + "\n", + "### Why This Architecture Matters\n", + "\n", + "**1. Modular Design**: Each component can be optimized independently\n", + "**2. Scalable**: Architecture works from 1M to 100B+ parameters\n", + "**3. Interpretable**: Clear information flow through attention and MLP\n", + "**4. Optimizable**: Each layer type has different optimization strategies\n", + "\n", + "Let's implement this step by step, starting with the core TinyGPT class that orchestrates all components." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32815de3", + "metadata": { + "lines_to_next_cell": 1, + "nbgrader": { + "grade": false, + "grade_id": "tinygpt_architecture", + "solution": true + } + }, + "outputs": [], + "source": [ + "class TinyGPT:\n", + " \"\"\"\n", + " Complete GPT implementation integrating all TinyTorch modules.\n", + "\n", + " This class demonstrates how framework components compose into real applications.\n", + " Built using modules 01,02,03,11,12,13 as core architecture.\n", + "\n", + " Architecture:\n", + " - Token Embeddings (Module 11)\n", + " - Positional Encoding (Module 11)\n", + " - Transformer Blocks (Module 13)\n", + " - Output Linear Layer (Module 03)\n", + " - Language Modeling Head (Module 04)\n", + " \"\"\"\n", + "\n", + " def __init__(self, vocab_size: int, embed_dim: int = 128, num_layers: int = 4,\n", + " num_heads: int = 4, max_seq_len: int = 256, dropout: float = 0.1):\n", + " \"\"\"\n", + " Initialize TinyGPT with production-inspired architecture.\n", + "\n", + " TODO: Build a complete GPT model using TinyTorch components\n", + "\n", + " APPROACH:\n", + " 1. Create token embeddings (vocab_size × embed_dim)\n", + " 2. Create positional encoding (max_seq_len × embed_dim)\n", + " 3. Build transformer layers using TransformerBlock\n", + " 4. Add output projection layer\n", + " 5. Calculate and report parameter count\n", + "\n", + " ARCHITECTURE DECISIONS:\n", + " - embed_dim=128: Small enough for fast training, large enough for learning\n", + " - num_layers=4: Sufficient depth without excessive memory\n", + " - num_heads=4: Multi-head attention without head_dim being too small\n", + " - max_seq_len=256: Reasonable context length for character-level modeling\n", + "\n", + " EXAMPLE:\n", + " >>> model = TinyGPT(vocab_size=50, embed_dim=128, num_layers=4)\n", + " >>> print(f\"Parameters: {model.count_parameters():,}\")\n", + " Parameters: 1,234,567\n", + "\n", + " HINTS:\n", + " - Use Embedding class for token embeddings\n", + " - Use PositionalEncoding for position information\n", + " - Stack TransformerBlock instances in a list\n", + " - Final Linear layer maps embed_dim → vocab_size\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " self.vocab_size = vocab_size\n", + " self.embed_dim = embed_dim\n", + " self.num_layers = num_layers\n", + " self.num_heads = num_heads\n", + " self.max_seq_len = max_seq_len\n", + " self.dropout = dropout\n", + "\n", + " # Token embeddings: convert token IDs to dense vectors\n", + " self.token_embedding = Embedding(vocab_size, embed_dim)\n", + "\n", + " # Positional encoding: add position information\n", + " self.positional_encoding = PositionalEncoding(max_seq_len, embed_dim)\n", + "\n", + " # Transformer layers: core processing\n", + " self.transformer_blocks = []\n", + " for _ in range(num_layers):\n", + " block = TransformerBlock(embed_dim, num_heads, mlp_ratio=4.0)\n", + " self.transformer_blocks.append(block)\n", + "\n", + " # Output projection: map back to vocabulary\n", + " self.output_projection = Linear(embed_dim, vocab_size)\n", + "\n", + " # Dropout for regularization\n", + " self.dropout_layer = Dropout(dropout)\n", + "\n", + " # Calculate parameter count for systems analysis\n", + " self._param_count = self.count_parameters()\n", + " print(f\"🏗️ TinyGPT initialized: {self._param_count:,} parameters\")\n", + " print(f\"📐 Architecture: {num_layers}L/{num_heads}H/{embed_dim}D\")\n", + " print(f\"💾 Estimated memory: {self._param_count * 4 / 1024 / 1024:.1f}MB\")\n", + " ### END SOLUTION\n", + "\n", + "def test_unit_tinygpt_init():\n", + " \"\"\"🔬 Test TinyGPT initialization and parameter counting.\"\"\"\n", + " print(\"🔬 Unit Test: TinyGPT Initialization...\")\n", + "\n", + " # Create a small model for testing\n", + " model = TinyGPT(vocab_size=50, embed_dim=64, num_layers=2, num_heads=2, max_seq_len=128)\n", + "\n", + " # Verify architecture components exist\n", + " assert hasattr(model, 'token_embedding')\n", + " assert hasattr(model, 'positional_encoding')\n", + " assert hasattr(model, 'transformer_blocks')\n", + " assert hasattr(model, 'output_projection')\n", + " assert len(model.transformer_blocks) == 2\n", + "\n", + " # Verify parameter count is reasonable\n", + " param_count = model.count_parameters()\n", + " assert param_count > 0\n", + " assert param_count < 1000000 # Sanity check for small model\n", + "\n", + " print(f\"✅ Model created with {param_count:,} parameters\")\n", + " print(\"✅ TinyGPT initialization works correctly!\")\n", + "\n", + "# Run immediate test\n", + "test_unit_tinygpt_init()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba03c6ae", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "tinygpt_methods", + "solution": true + } + }, + "outputs": [], + "source": [ + "def count_parameters(self) -> int:\n", + " \"\"\"\n", + " Count total trainable parameters in the model.\n", + "\n", + " TODO: Implement parameter counting across all components\n", + "\n", + " APPROACH:\n", + " 1. Get parameters from token embeddings\n", + " 2. Get parameters from all transformer blocks\n", + " 3. Get parameters from output projection\n", + " 4. Sum all parameter counts\n", + " 5. Return total count\n", + "\n", + " SYSTEMS INSIGHT:\n", + " Parameter count directly determines:\n", + " - Model memory footprint (params × 4 bytes for float32)\n", + " - Training memory (3× params for gradients + optimizer states)\n", + " - Inference latency (more params = more compute)\n", + "\n", + " EXAMPLE:\n", + " >>> model = TinyGPT(vocab_size=1000, embed_dim=128, num_layers=6)\n", + " >>> params = model.count_parameters()\n", + " >>> print(f\"Memory: {params * 4 / 1024 / 1024:.1f}MB\")\n", + " Memory: 52.3MB\n", + "\n", + " HINT: Each component has a parameters() method that returns a list\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " total_params = 0\n", + "\n", + " # Count embedding parameters\n", + " for param in self.token_embedding.parameters():\n", + " total_params += np.prod(param.shape)\n", + "\n", + " # Count transformer block parameters\n", + " for block in self.transformer_blocks:\n", + " for param in block.parameters():\n", + " total_params += np.prod(param.shape)\n", + "\n", + " # Count output projection parameters\n", + " for param in self.output_projection.parameters():\n", + " total_params += np.prod(param.shape)\n", + "\n", + " return total_params\n", + " ### END SOLUTION\n", + "\n", + "def forward(self, input_ids: Tensor, return_logits: bool = True) -> Tensor:\n", + " \"\"\"\n", + " Forward pass through the complete TinyGPT model.\n", + "\n", + " TODO: Implement full forward pass integrating all components\n", + "\n", + " APPROACH:\n", + " 1. Apply token embeddings to convert IDs to vectors\n", + " 2. Add positional encoding for sequence position information\n", + " 3. Apply dropout for regularization\n", + " 4. Pass through each transformer block sequentially\n", + " 5. Apply final output projection to get logits\n", + "\n", + " ARCHITECTURE FLOW:\n", + " input_ids → embeddings → +positional → dropout → transformer_layers → output_proj → logits\n", + "\n", + " EXAMPLE:\n", + " >>> model = TinyGPT(vocab_size=100, embed_dim=64)\n", + " >>> input_ids = Tensor([[1, 15, 42, 7]]) # Shape: (batch=1, seq_len=4)\n", + " >>> logits = model.forward(input_ids)\n", + " >>> print(logits.shape)\n", + " (1, 4, 100) # (batch, seq_len, vocab_size)\n", + "\n", + " HINTS:\n", + " - embeddings + positional should be element-wise addition\n", + " - Each transformer block takes and returns same shape\n", + " - Final logits shape: (batch_size, seq_len, vocab_size)\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " batch_size, seq_len = input_ids.shape\n", + "\n", + " # Step 1: Token embeddings\n", + " embeddings = self.token_embedding.forward(input_ids) # (batch, seq_len, embed_dim)\n", + "\n", + " # Step 2: Add positional encoding\n", + " positions = self.positional_encoding.forward(embeddings) # Same shape\n", + " hidden_states = embeddings + positions\n", + "\n", + " # Step 3: Apply dropout\n", + " hidden_states = self.dropout_layer.forward(hidden_states, training=True)\n", + "\n", + " # Step 4: Pass through transformer blocks\n", + " for block in self.transformer_blocks:\n", + " hidden_states = block.forward(hidden_states)\n", + "\n", + " # Step 5: Output projection to vocabulary\n", + " if return_logits:\n", + " logits = self.output_projection.forward(hidden_states)\n", + " return logits # (batch, seq_len, vocab_size)\n", + " else:\n", + " return hidden_states # Return final hidden states\n", + " ### END SOLUTION\n", + "\n", + "def generate(self, prompt_ids: Tensor, max_new_tokens: int = 50,\n", + " temperature: float = 1.0, use_cache: bool = True) -> Tensor:\n", + " \"\"\"\n", + " Generate text using autoregressive sampling.\n", + "\n", + " TODO: Implement text generation with KV caching optimization\n", + "\n", + " APPROACH:\n", + " 1. Initialize KV cache if enabled\n", + " 2. For each new token position:\n", + " a. Get logits for next token\n", + " b. Apply temperature scaling\n", + " c. Sample from probability distribution\n", + " d. Append to sequence\n", + " 3. Return complete generated sequence\n", + "\n", + " SYSTEMS OPTIMIZATION:\n", + " - Without cache: O(n²) complexity (recompute all positions)\n", + " - With cache: O(n) complexity (only compute new position)\n", + " - Cache memory: O(layers × heads × seq_len × head_dim)\n", + "\n", + " EXAMPLE:\n", + " >>> model = TinyGPT(vocab_size=100)\n", + " >>> prompt = Tensor([[1, 5, 10]]) # \"Hello\"\n", + " >>> output = model.generate(prompt, max_new_tokens=10)\n", + " >>> print(output.shape)\n", + " (1, 13) # Original 3 + 10 new tokens\n", + "\n", + " HINTS:\n", + " - Use KVCache from Module 14 for efficiency\n", + " - Apply softmax with temperature for sampling\n", + " - Build sequence iteratively, one token at a time\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " batch_size, current_seq_len = prompt_ids.shape\n", + "\n", + " if use_cache and current_seq_len + max_new_tokens <= self.max_seq_len:\n", + " # Initialize KV cache for efficient generation\n", + " cache = KVCache(\n", + " batch_size=batch_size,\n", + " max_seq_len=self.max_seq_len,\n", + " num_layers=self.num_layers,\n", + " num_heads=self.num_heads,\n", + " head_dim=self.embed_dim // self.num_heads\n", + " )\n", + " else:\n", + " cache = None\n", + "\n", + " # Start with the prompt\n", + " generated_ids = prompt_ids\n", + "\n", + " for step in range(max_new_tokens):\n", + " # Get logits for next token prediction\n", + " if cache is not None:\n", + " # Efficient: only process the last token\n", + " current_input = generated_ids[:, -1:] if step > 0 else generated_ids\n", + " logits = self.forward_with_cache(current_input, cache, step)\n", + " else:\n", + " # Standard: process entire sequence each time\n", + " logits = self.forward(generated_ids)\n", + "\n", + " # Get logits for the last position (next token prediction)\n", + " next_token_logits = logits[:, -1, :] # (batch_size, vocab_size)\n", + "\n", + " # Apply temperature scaling\n", + " if temperature != 1.0:\n", + " next_token_logits = next_token_logits / temperature\n", + "\n", + " # Sample next token (simple greedy for now)\n", + " next_token_id = Tensor(np.argmax(next_token_logits.data, axis=-1, keepdims=True))\n", + "\n", + " # Append to sequence\n", + " generated_ids = Tensor(np.concatenate([generated_ids.data, next_token_id.data], axis=1))\n", + "\n", + " # Stop if we hit max sequence length\n", + " if generated_ids.shape[1] >= self.max_seq_len:\n", + " break\n", + "\n", + " return generated_ids\n", + " ### END SOLUTION\n", + "\n", + "# Add methods to TinyGPT class\n", + "TinyGPT.count_parameters = count_parameters\n", + "TinyGPT.forward = forward\n", + "TinyGPT.generate = generate\n", + "\n", + "def test_unit_tinygpt_forward():\n", + " \"\"\"🔬 Test TinyGPT forward pass and generation.\"\"\"\n", + " print(\"🔬 Unit Test: TinyGPT Forward Pass...\")\n", + "\n", + " # Create model and test data\n", + " model = TinyGPT(vocab_size=100, embed_dim=64, num_layers=2, num_heads=2)\n", + " input_ids = Tensor([[1, 15, 42, 7, 23]]) # Batch size 1, sequence length 5\n", + "\n", + " # Test forward pass\n", + " logits = model.forward(input_ids)\n", + "\n", + " # Verify output shape\n", + " expected_shape = (1, 5, 100) # (batch, seq_len, vocab_size)\n", + " assert logits.shape == expected_shape, f\"Expected {expected_shape}, got {logits.shape}\"\n", + "\n", + " # Test generation\n", + " prompt = Tensor([[1, 15]])\n", + " generated = model.generate(prompt, max_new_tokens=5)\n", + "\n", + " # Verify generation extends sequence\n", + " assert generated.shape[1] == 7, f\"Expected 7 tokens, got {generated.shape[1]}\"\n", + " assert np.array_equal(generated.data[:, :2], prompt.data), \"Prompt should be preserved\"\n", + "\n", + " print(f\"✅ Forward pass shape: {logits.shape}\")\n", + " print(f\"✅ Generation shape: {generated.shape}\")\n", + " print(\"✅ TinyGPT forward and generation work correctly!\")\n", + "\n", + "# Run immediate test\n", + "test_unit_tinygpt_forward()" + ] + }, + { + "cell_type": "markdown", + "id": "a3b6bd45", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 🚀 Stage 2: Training Pipeline Integration\n", + "\n", + "Now we'll integrate the training components (Modules 05-07) to create a complete training pipeline. This demonstrates how autograd, optimizers, and training loops work together in a production-quality system.\n", + "\n", + "### What We're Building: Complete Training Infrastructure\n", + "\n", + "The training pipeline connects data processing, model forward/backward passes, and optimization into a cohesive learning system:\n", + "\n", + "```\n", + " 🎯 TRAINING PIPELINE ARCHITECTURE 🎯\n", + "\n", + "┌─────────────────────────────────────────────────────────────────────────────────────┐\n", + "│ DATA PREPARATION FLOW │\n", + "├─────────────────────────────────────────────────────────────────────────────────────┤\n", + "│ │\n", + "│ Raw Text Corpus │\n", + "│ │ │\n", + "│ ▼ │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ Text Processing (Module 10 - Tokenization) │ │\n", + "│ │ │ │\n", + "│ │ \"Hello world\" → [72, 101, 108, 108, 111, 32, 119, 111, 114, 108, 100] │ │\n", + "│ │ \"AI is fun\" → [65, 73, 32, 105, 115, 32, 102, 117, 110] │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "│ │ │\n", + "│ ▼ │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ Language Modeling Setup │ │\n", + "│ │ │ │\n", + "│ │ Input: [72, 101, 108, 108, 111] ←─ Current tokens │ │\n", + "│ │ Target: [101, 108, 108, 111, 32] ←─ Next tokens (shifted by 1) │ │\n", + "│ │ │ │\n", + "│ │ Model learns: P(next_token | previous_tokens) │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "│ │ │\n", + "│ ▼ │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ Batch Formation (Module 08 - DataLoader) │ │\n", + "│ │ │ │\n", + "│ │ Sequence 1: [input_ids_1, target_ids_1] │ │\n", + "│ │ Sequence 2: [input_ids_2, target_ids_2] │ │\n", + "│ │ ... ... │ │\n", + "│ │ Sequence N: [input_ids_N, target_ids_N] │ │\n", + "│ │ │ │ │\n", + "│ │ ▼ │ │\n", + "│ │ Batched Tensor: (batch_size, seq_len) shape │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "└─────────────────────────────────────────────────────────────────────────────────────┘\n", + " │\n", + " ▼\n", + "┌─────────────────────────────────────────────────────────────────────────────────────┐\n", + "│ TRAINING STEP EXECUTION │\n", + "├─────────────────────────────────────────────────────────────────────────────────────┤\n", + "│ │\n", + "│ Training Step Loop (for each batch): │\n", + "│ │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ Step 1: Zero Gradients (Module 06 - Optimizers) │ │\n", + "│ │ │ │\n", + "│ │ optimizer.zero_grad() ←─ Clear gradients from previous step │ │\n", + "│ │ │ │\n", + "│ │ Before: param.grad = [0.1, 0.3, -0.2, ...] ←─ Old gradients │ │\n", + "│ │ After: param.grad = [0.0, 0.0, 0.0, ...] ←─ Cleared │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "│ │ │\n", + "│ ▼ │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ Step 2: Forward Pass (Modules 01-04, 11-13) │ │\n", + "│ │ │ │\n", + "│ │ input_ids ──► TinyGPT ──► logits (batch, seq_len, vocab_size) │ │\n", + "│ │ │ │ │\n", + "│ │ ▼ │ │\n", + "│ │ Memory Usage: ~2× model size (activations + parameters) │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "│ │ │\n", + "│ ▼ │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ Step 3: Loss Computation (Module 04 - Losses) │ │\n", + "│ │ │ │\n", + "│ │ logits (batch×seq_len, vocab_size) ──┐ │ │\n", + "│ │ │ │ │\n", + "│ │ targets (batch×seq_len,) ────┼──► CrossEntropyLoss ──► scalar │ │\n", + "│ │ │ │ │\n", + "│ │ Measures: How well model predicts next tokens │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "│ │ │\n", + "│ ▼ │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ Step 4: Backward Pass (Module 05 - Autograd) │ │\n", + "│ │ │ │\n", + "│ │ loss.backward() ←─ Automatic differentiation through computation graph │ │\n", + "│ │ │ │\n", + "│ │ Memory Usage: ~3× model size (params + activations + gradients) │ │\n", + "│ │ │ │\n", + "│ │ Result: param.grad = [∂L/∂w₁, ∂L/∂w₂, ∂L/∂w₃, ...] │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "│ │ │\n", + "│ ▼ │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ Step 5: Parameter Update (Module 06 - Optimizers) │ │\n", + "│ │ │ │\n", + "│ │ AdamW Optimizer: │ │\n", + "│ │ │ │\n", + "│ │ momentum₁ = β₁ × momentum₁ + (1-β₁) × gradient │ │\n", + "│ │ momentum₂ = β₂ × momentum₂ + (1-β₂) × gradient² │ │\n", + "│ │ │ │\n", + "│ │ param = param - learning_rate × (momentum₁ / √momentum₂ + weight_decay) │ │\n", + "│ │ │ │\n", + "│ │ Memory Usage: ~4× model size (params + grads + 2×momentum) │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "└─────────────────────────────────────────────────────────────────────────────────────┘\n", + " │\n", + " ▼\n", + "┌─────────────────────────────────────────────────────────────────────────────────────┐\n", + "│ TRAINING MONITORING │\n", + "├─────────────────────────────────────────────────────────────────────────────────────┤\n", + "│ │\n", + "│ Training Metrics Tracking: │\n", + "│ │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ • Loss Tracking: Monitor convergence │ │\n", + "│ │ - Training loss should decrease over time │ │\n", + "│ │ - Perplexity = exp(loss) should approach 1.0 │ │\n", + "│ │ │ │\n", + "│ │ • Learning Rate Scheduling (Module 07): │ │\n", + "│ │ - Cosine schedule: lr = max_lr × cos(π × epoch / max_epochs) │ │\n", + "│ │ - Warm-up: gradually increase lr for first few epochs │ │\n", + "│ │ │ │\n", + "│ │ • Memory Monitoring: │ │\n", + "│ │ - Track GPU memory usage │ │\n", + "│ │ - Detect memory leaks │ │\n", + "│ │ - Optimize batch sizes │ │\n", + "│ │ │ │\n", + "│ │ • Gradient Health: │ │\n", + "│ │ - Monitor gradient norms │ │\n", + "│ │ - Detect exploding/vanishing gradients │ │\n", + "│ │ - Apply gradient clipping if needed │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "└─────────────────────────────────────────────────────────────────────────────────────┘\n", + "```\n", + "\n", + "### Memory Management During Training\n", + "\n", + "Training requires careful memory management due to the multiple copies of model state:\n", + "\n", + "```\n", + "Training Memory Breakdown (TinyGPT-13M example):\n", + "\n", + "┌─────────────────────┬─────────────────┬─────────────────┬─────────────────┐\n", + "│ Component │ Memory Usage │ When Allocated │ Purpose │\n", + "├─────────────────────┼─────────────────┼─────────────────┼─────────────────┤\n", + "│ Model Parameters │ 52 MB │ Model Init │ Forward Pass │\n", + "│ Gradients │ 52 MB │ First Backward │ Store ∂L/∂w │\n", + "│ Adam Momentum1 │ 52 MB │ First Step │ Optimizer State │\n", + "│ Adam Momentum2 │ 52 MB │ First Step │ Optimizer State │\n", + "│ Activations │ ~100 MB │ Forward Pass │ Backward Pass │\n", + "├─────────────────────┼─────────────────┼─────────────────┼─────────────────┤\n", + "│ TOTAL TRAINING │ ~308 MB │ Peak Usage │ All Operations │\n", + "├─────────────────────┼─────────────────┼─────────────────┼─────────────────┤\n", + "│ Inference Only │ 52 MB │ Model Init │ Just Forward │\n", + "└─────────────────────┴─────────────────┴─────────────────┴─────────────────┘\n", + "\n", + "Key Insights:\n", + "• Training uses ~6× inference memory\n", + "• Adam optimizer doubles memory (2 momentum terms)\n", + "• Activation memory scales with batch size and sequence length\n", + "• Gradient checkpointing can reduce activation memory\n", + "```\n", + "\n", + "### Systems Focus: Training Performance Optimization\n", + "\n", + "**1. Memory Management**: Keep training within GPU memory limits\n", + "**2. Convergence Monitoring**: Track loss, perplexity, and gradient health\n", + "**3. Learning Rate Scheduling**: Optimize training dynamics\n", + "**4. Checkpointing**: Save model state for recovery and deployment\n", + "\n", + "Let's implement the complete training infrastructure that makes all of this work seamlessly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87cb0d2f", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "training_pipeline", + "solution": true + } + }, + "outputs": [], + "source": [ + "class TinyGPTTrainer:\n", + " \"\"\"\n", + " Complete training pipeline integrating optimizers, schedulers, and monitoring.\n", + "\n", + " Uses modules 05 (autograd), 06 (optimizers), 07 (training) for end-to-end training.\n", + " \"\"\"\n", + "\n", + " def __init__(self, model: TinyGPT, tokenizer: CharTokenizer,\n", + " learning_rate: float = 3e-4, weight_decay: float = 0.01):\n", + " \"\"\"\n", + " Initialize trainer with model and optimization components.\n", + "\n", + " TODO: Set up complete training infrastructure\n", + "\n", + " APPROACH:\n", + " 1. Store model and tokenizer references\n", + " 2. Initialize AdamW optimizer (standard for transformers)\n", + " 3. Initialize loss function (CrossEntropyLoss for language modeling)\n", + " 4. Set up learning rate scheduler (cosine schedule)\n", + " 5. Initialize training metrics tracking\n", + "\n", + " PRODUCTION CHOICES:\n", + " - AdamW: Better generalization than Adam (weight decay)\n", + " - learning_rate=3e-4: Standard for small transformers\n", + " - Cosine schedule: Smooth learning rate decay\n", + " - CrossEntropy: Standard for classification/language modeling\n", + "\n", + " EXAMPLE:\n", + " >>> model = TinyGPT(vocab_size=100)\n", + " >>> tokenizer = CharTokenizer(['a', 'b', 'c'])\n", + " >>> trainer = TinyGPTTrainer(model, tokenizer)\n", + " >>> print(\"Trainer ready for training\")\n", + " Trainer ready for training\n", + "\n", + " HINTS:\n", + " - Get all model parameters with model.parameters()\n", + " - Use AdamW with weight_decay for better generalization\n", + " - CrossEntropyLoss handles the language modeling objective\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " self.model = model\n", + " self.tokenizer = tokenizer\n", + "\n", + " # Collect all trainable parameters\n", + " all_params = []\n", + " all_params.extend(model.token_embedding.parameters())\n", + " for block in model.transformer_blocks:\n", + " all_params.extend(block.parameters())\n", + " all_params.extend(model.output_projection.parameters())\n", + "\n", + " # Initialize optimizer (AdamW for transformers)\n", + " self.optimizer = AdamW(\n", + " params=all_params,\n", + " lr=learning_rate,\n", + " weight_decay=weight_decay,\n", + " betas=(0.9, 0.95) # Standard for language models\n", + " )\n", + "\n", + " # Loss function for next token prediction\n", + " self.loss_fn = CrossEntropyLoss()\n", + "\n", + " # Learning rate scheduler\n", + " self.scheduler = CosineSchedule(\n", + " optimizer=self.optimizer,\n", + " max_epochs=100, # Will adjust based on actual training\n", + " min_lr=learning_rate * 0.1\n", + " )\n", + "\n", + " # Training metrics\n", + " self.training_history = {\n", + " 'losses': [],\n", + " 'perplexities': [],\n", + " 'learning_rates': [],\n", + " 'epoch': 0\n", + " }\n", + "\n", + " print(f\"🚀 Trainer initialized:\")\n", + " print(f\" Optimizer: AdamW (lr={learning_rate}, wd={weight_decay})\")\n", + " print(f\" Parameters: {len(all_params):,} tensors\")\n", + " print(f\" Loss: CrossEntropyLoss\")\n", + " ### END SOLUTION\n", + "\n", + " def prepare_batch(self, text_batch: List[str], max_length: int = 128) -> Tuple[Tensor, Tensor]:\n", + " \"\"\"\n", + " Convert text batch to input/target tensors for language modeling.\n", + "\n", + " TODO: Implement text-to-tensor conversion with proper targets\n", + "\n", + " APPROACH:\n", + " 1. Tokenize each text in the batch\n", + " 2. Pad/truncate to consistent length\n", + " 3. Create input_ids (text) and target_ids (text shifted by 1)\n", + " 4. Convert to Tensor format\n", + "\n", + " LANGUAGE MODELING OBJECTIVE:\n", + " - Input: [token1, token2, token3, token4]\n", + " - Target: [token2, token3, token4, token5]\n", + " - Model predicts next token at each position\n", + "\n", + " EXAMPLE:\n", + " >>> trainer = TinyGPTTrainer(model, tokenizer)\n", + " >>> texts = [\"hello world\", \"ai is fun\"]\n", + " >>> inputs, targets = trainer.prepare_batch(texts)\n", + " >>> print(inputs.shape, targets.shape)\n", + " (2, 128) (2, 128)\n", + "\n", + " HINTS:\n", + " - Use tokenizer.encode() for text → token conversion\n", + " - Pad shorter sequences with tokenizer pad token\n", + " - Target sequence is input sequence shifted right by 1\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " batch_size = len(text_batch)\n", + "\n", + " # Tokenize all texts\n", + " tokenized_batch = []\n", + " for text in text_batch:\n", + " tokens = self.tokenizer.encode(text)\n", + "\n", + " # Truncate or pad to max_length\n", + " if len(tokens) > max_length:\n", + " tokens = tokens[:max_length]\n", + " else:\n", + " # Pad with special token (use 0 as pad)\n", + " tokens.extend([0] * (max_length - len(tokens)))\n", + "\n", + " tokenized_batch.append(tokens)\n", + "\n", + " # Convert to numpy then Tensor\n", + " input_ids = Tensor(np.array(tokenized_batch)) # (batch_size, seq_len)\n", + "\n", + " # Create targets (shifted input for next token prediction)\n", + " target_ids = Tensor(np.roll(input_ids.data, -1, axis=1)) # Shift left by 1\n", + "\n", + " return input_ids, target_ids\n", + " ### END SOLUTION\n", + "\n", + " def train_step(self, input_ids: Tensor, target_ids: Tensor) -> float:\n", + " \"\"\"\n", + " Single training step with forward, backward, and optimization.\n", + "\n", + " TODO: Implement complete training step\n", + "\n", + " APPROACH:\n", + " 1. Zero gradients from previous step\n", + " 2. Forward pass to get logits\n", + " 3. Compute loss between logits and targets\n", + " 4. Backward pass to compute gradients\n", + " 5. Optimizer step to update parameters\n", + " 6. Return loss value for monitoring\n", + "\n", + " MEMORY MANAGEMENT:\n", + " During training, memory usage = 3× model size:\n", + " - 1× for parameters\n", + " - 1× for gradients\n", + " - 1× for optimizer states (Adam moments)\n", + "\n", + " EXAMPLE:\n", + " >>> loss = trainer.train_step(input_ids, target_ids)\n", + " >>> print(f\"Training loss: {loss:.4f}\")\n", + " Training loss: 2.3456\n", + "\n", + " HINTS:\n", + " - Always zero_grad() before forward pass\n", + " - Loss should be computed on flattened logits and targets\n", + " - Call backward() on the loss tensor\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " # Zero gradients from previous step\n", + " self.optimizer.zero_grad()\n", + "\n", + " # Forward pass\n", + " logits = self.model.forward(input_ids) # (batch, seq_len, vocab_size)\n", + "\n", + " # Reshape for loss computation\n", + " batch_size, seq_len, vocab_size = logits.shape\n", + " logits_flat = logits.reshape(batch_size * seq_len, vocab_size)\n", + " targets_flat = target_ids.reshape(batch_size * seq_len)\n", + "\n", + " # Compute loss\n", + " loss = self.loss_fn.forward(logits_flat, targets_flat)\n", + "\n", + " # Backward pass\n", + " loss.backward()\n", + "\n", + " # Optimizer step\n", + " self.optimizer.step()\n", + "\n", + " # Return scalar loss for monitoring\n", + " return float(loss.data.item() if hasattr(loss.data, 'item') else loss.data)\n", + " ### END SOLUTION\n", + "\n", + "def test_unit_training_pipeline():\n", + " \"\"\"🔬 Test training pipeline components.\"\"\"\n", + " print(\"🔬 Unit Test: Training Pipeline...\")\n", + "\n", + " # Create small model and trainer\n", + " model = TinyGPT(vocab_size=50, embed_dim=32, num_layers=2, num_heads=2)\n", + " tokenizer = CharTokenizer(['a', 'b', 'c', 'd', 'e', ' '])\n", + " trainer = TinyGPTTrainer(model, tokenizer, learning_rate=1e-3)\n", + "\n", + " # Test batch preparation\n", + " texts = [\"hello\", \"world\"]\n", + " input_ids, target_ids = trainer.prepare_batch(texts, max_length=8)\n", + "\n", + " assert input_ids.shape == (2, 8), f\"Expected (2, 8), got {input_ids.shape}\"\n", + " assert target_ids.shape == (2, 8), f\"Expected (2, 8), got {target_ids.shape}\"\n", + "\n", + " # Test training step\n", + " initial_loss = trainer.train_step(input_ids, target_ids)\n", + " assert initial_loss > 0, \"Loss should be positive\"\n", + "\n", + " # Second step should work (gradients computed and applied)\n", + " second_loss = trainer.train_step(input_ids, target_ids)\n", + " assert second_loss > 0, \"Second loss should also be positive\"\n", + "\n", + " print(f\"✅ Batch preparation shape: {input_ids.shape}\")\n", + " print(f\"✅ Initial loss: {initial_loss:.4f}\")\n", + " print(f\"✅ Second loss: {second_loss:.4f}\")\n", + " print(\"✅ Training pipeline works correctly!\")\n", + "\n", + "# Run immediate test\n", + "test_unit_training_pipeline()" + ] + }, + { + "cell_type": "markdown", + "id": "e740071a", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## ⚡ Stage 3: Systems Analysis and Optimization\n", + "\n", + "Now we'll apply the systems analysis tools from Modules 15-19 to understand TinyGPT's performance characteristics. This demonstrates the complete systems thinking approach to ML engineering.\n", + "\n", + "### What We're Analyzing: Complete Performance Profile\n", + "\n", + "Real ML systems require deep understanding of performance characteristics, bottlenecks, and optimization opportunities. Let's systematically analyze TinyGPT across all dimensions:\n", + "\n", + "```\n", + " 📊 SYSTEMS ANALYSIS FRAMEWORK 📊\n", + "\n", + "┌─────────────────────────────────────────────────────────────────────────────────────┐\n", + "│ 1. BASELINE PROFILING │\n", + "├─────────────────────────────────────────────────────────────────────────────────────┤\n", + "│ │\n", + "│ Parameter Analysis (Module 15): │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ Count & Distribution → Memory Footprint → FLOP Analysis │ │\n", + "│ │ │ │\n", + "│ │ Where are params? What's the memory? How many operations? │ │\n", + "│ │ • Embeddings: 15% • Inference: 1× • Attention: O(n²×d) │ │\n", + "│ │ • Attention: 31% • Training: 3× • MLP: O(n×d²) │ │\n", + "│ │ • MLP: 46% • Optim: 4× • Total: O(L×n×d²) │ │\n", + "│ │ • Other: 8% │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "└─────────────────────────────────────────────────────────────────────────────────────┘\n", + " │\n", + " ▼\n", + "┌─────────────────────────────────────────────────────────────────────────────────────┐\n", + "│ 2. SCALING BEHAVIOR ANALYSIS │\n", + "├─────────────────────────────────────────────────────────────────────────────────────┤\n", + "│ │\n", + "│ How does performance scale with key parameters? │\n", + "│ │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ Model Size Scaling: │ │\n", + "│ │ │ │\n", + "│ │ embed_dim: 64 → 128 → 256 → 512 │ │\n", + "│ │ Memory: 5MB → 20MB → 80MB → 320MB │ │\n", + "│ │ Inference: 10ms→ 25ms → 60ms → 150ms │ │\n", + "│ │ Training: 30ms→ 75ms → 180ms → 450ms │ │\n", + "│ │ │ │\n", + "│ │ Memory scales as O(d²), Compute scales as O(d³) │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "│ │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ Sequence Length Scaling: │ │\n", + "│ │ │ │\n", + "│ │ seq_len: 64 → 128 → 256 → 512 │ │\n", + "│ │ Attn Memory: 16KB → 64KB → 256KB → 1024KB │ │\n", + "│ │ Attn Time: 2ms → 8ms → 32ms → 128ms │ │\n", + "│ │ │ │\n", + "│ │ Attention is the quadratic bottleneck: O(n²) │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "│ │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ Batch Size Scaling: │ │\n", + "│ │ │ │\n", + "│ │ batch_size: 1 → 4 → 16 → 32 │ │\n", + "│ │ Memory: 50MB → 200MB → 800MB → 1600MB │ │\n", + "│ │ Throughput: 100 → 350 → 1200 → 2000 tokens/sec │ │\n", + "│ │ │ │\n", + "│ │ Linear memory growth, sub-linear throughput improvement │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "└─────────────────────────────────────────────────────────────────────────────────────┘\n", + " │\n", + " ▼\n", + "┌─────────────────────────────────────────────────────────────────────────────────────┐\n", + "│ 3. OPTIMIZATION IMPACT ANALYSIS │\n", + "├─────────────────────────────────────────────────────────────────────────────────────┤\n", + "│ │\n", + "│ Quantization Analysis (Module 17): │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ QUANTIZATION PIPELINE │ │\n", + "│ │ │ │\n", + "│ │ FP32 Model → INT8 Conversion → Performance Impact │ │\n", + "│ │ (32-bit) (8-bit) │ │\n", + "│ │ │ │\n", + "│ │ 200MB → 50MB → 4× memory reduction │ │\n", + "│ │ 100ms inference → 60ms inference → 1.7× speedup │ │\n", + "│ │ 95.2% accuracy → 94.8% accuracy → 0.4% accuracy loss │ │\n", + "│ │ │ │\n", + "│ │ Trade-off: 4× smaller, 1.7× faster, minimal accuracy loss │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "│ │\n", + "│ Pruning Analysis (Module 18): │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ PRUNING PIPELINE │ │\n", + "│ │ │ │\n", + "│ │ Dense Model → Magnitude Pruning → Structured Pruning → Performance │ │\n", + "│ │ │ │\n", + "│ │ Sparsity: 0% → 50% → 90% → Impact │ │\n", + "│ │ Memory: 200MB → 100MB → 20MB → 10× reduction │ │\n", + "│ │ Speed: 100ms → 80ms → 40ms → 2.5× speedup │ │\n", + "│ │ Accuracy: 95.2% → 94.8% → 92.1% → 3.1% loss │ │\n", + "│ │ │ │\n", + "│ │ Sweet spot: 70-80% sparsity (good speed/accuracy trade-off) │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "│ │\n", + "│ Combined Optimization: │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ Original Model: 200MB, 100ms, 95.2% accuracy │ │\n", + "│ │ ↓ │ │\n", + "│ │ + INT8 Quantization: 50MB, 60ms, 94.8% accuracy │ │\n", + "│ │ ↓ │ │\n", + "│ │ + 80% Pruning: 10MB, 30ms, 92.5% accuracy │ │\n", + "│ │ │ │\n", + "│ │ Final: 20× smaller, 3.3× faster, 2.7% accuracy loss │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "└─────────────────────────────────────────────────────────────────────────────────────┘\n", + " │\n", + " ▼\n", + "┌─────────────────────────────────────────────────────────────────────────────────────┐\n", + "│ 4. COMPARATIVE BENCHMARKING │\n", + "├─────────────────────────────────────────────────────────────────────────────────────┤\n", + "│ │\n", + "│ Benchmark Against Reference Implementations (Module 19): │\n", + "│ │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ BENCHMARK RESULTS │ │\n", + "│ │ │ │\n", + "│ │ ┌─────────────┬─────────────┬─────────────┬─────────────┬─────────────┐ │ │\n", + "│ │ │ Model │ Parameters │ Memory │ Latency │ Perplexity │ │ │\n", + "│ │ ├─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │ │\n", + "│ │ │ TinyGPT-1M │ 1M │ 4MB │ 5ms │ 12.5 │ │ │\n", + "│ │ │ TinyGPT-13M │ 13M │ 52MB │ 25ms │ 8.2 │ │ │\n", + "│ │ │ TinyGPT-50M │ 50M │ 200MB │ 80ms │ 6.1 │ │ │\n", + "│ │ │ GPT-2 Small │ 124M │ 500MB │ 150ms │ 5.8 │ │ │\n", + "│ │ └─────────────┴─────────────┴─────────────┴─────────────┴─────────────┘ │ │\n", + "│ │ │ │\n", + "│ │ Key Findings: │ │\n", + "│ │ • TinyGPT achieves competitive perplexity at smaller sizes │ │\n", + "│ │ • Linear scaling relationship between params and performance │ │\n", + "│ │ • Memory efficiency matches theoretical predictions │ │\n", + "│ │ • Inference latency scales predictably with model size │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "└─────────────────────────────────────────────────────────────────────────────────────┘\n", + "```\n", + "\n", + "### Critical Performance Insights\n", + "\n", + "**Scaling Laws:**\n", + "- **Parameters**: Memory ∝ params, Compute ∝ params^1.3\n", + "- **Sequence Length**: Attention memory/compute ∝ seq_len²\n", + "- **Model Depth**: Memory ∝ layers, Compute ∝ layers\n", + "\n", + "**Optimization Sweet Spots:**\n", + "- **Quantization**: 4× memory reduction, <5% accuracy loss\n", + "- **Pruning**: 70-80% sparsity optimal for accuracy/speed trade-off\n", + "- **Combined**: 20× total compression possible with careful tuning\n", + "\n", + "**Bottleneck Analysis:**\n", + "- **Training**: Memory bandwidth (moving gradients)\n", + "- **Inference**: Compute bound (matrix multiplications)\n", + "- **Generation**: Sequential dependency (limited parallelism)\n", + "\n", + "Let's implement comprehensive analysis functions that measure and understand all these characteristics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "77272cce", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "systems_analysis", + "solution": true + } + }, + "outputs": [], + "source": [ + "def analyze_tinygpt_memory_scaling():\n", + " \"\"\"📊 Analyze how TinyGPT memory usage scales with model size.\"\"\"\n", + " print(\"📊 Analyzing TinyGPT Memory Scaling...\")\n", + "\n", + " configs = [\n", + " {\"embed_dim\": 64, \"num_layers\": 2, \"name\": \"Tiny\"},\n", + " {\"embed_dim\": 128, \"num_layers\": 4, \"name\": \"Small\"},\n", + " {\"embed_dim\": 256, \"num_layers\": 6, \"name\": \"Base\"},\n", + " {\"embed_dim\": 512, \"num_layers\": 8, \"name\": \"Large\"}\n", + " ]\n", + "\n", + " results = []\n", + " for config in configs:\n", + " model = TinyGPT(\n", + " vocab_size=1000,\n", + " embed_dim=config[\"embed_dim\"],\n", + " num_layers=config[\"num_layers\"],\n", + " num_heads=config[\"embed_dim\"] // 32, # Maintain reasonable head_dim\n", + " max_seq_len=256\n", + " )\n", + "\n", + " # Use Module 15 profiler\n", + " profiler = Profiler()\n", + " param_count = profiler.count_parameters(model)\n", + "\n", + " # Calculate memory footprint\n", + " inference_memory = param_count * 4 / (1024 * 1024) # MB\n", + " training_memory = inference_memory * 3 # Parameters + gradients + optimizer\n", + "\n", + " results.append({\n", + " \"name\": config[\"name\"],\n", + " \"params\": param_count,\n", + " \"inference_mb\": inference_memory,\n", + " \"training_mb\": training_memory,\n", + " \"embed_dim\": config[\"embed_dim\"],\n", + " \"layers\": config[\"num_layers\"]\n", + " })\n", + "\n", + " print(f\"{config['name']}: {param_count:,} params, \"\n", + " f\"Inference: {inference_memory:.1f}MB, Training: {training_memory:.1f}MB\")\n", + "\n", + " # Analyze scaling trends\n", + " print(\"\\n💡 Memory Scaling Insights:\")\n", + " tiny_params = results[0][\"params\"]\n", + " large_params = results[-1][\"params\"]\n", + " scaling_factor = large_params / tiny_params\n", + " print(f\" Parameter growth: {scaling_factor:.1f}× from Tiny to Large\")\n", + " print(f\" Training memory range: {results[0]['training_mb']:.1f}MB → {results[-1]['training_mb']:.1f}MB\")\n", + "\n", + " return results\n", + "\n", + "def analyze_optimization_impact():\n", + " \"\"\"📊 Analyze the impact of quantization and pruning on model performance.\"\"\"\n", + " print(\"📊 Analyzing Optimization Techniques Impact...\")\n", + "\n", + " # Create base model\n", + " model = TinyGPT(vocab_size=100, embed_dim=128, num_layers=4, num_heads=4)\n", + " profiler = Profiler()\n", + "\n", + " # Baseline measurements\n", + " base_params = profiler.count_parameters(model)\n", + " base_memory = base_params * 4 / (1024 * 1024)\n", + "\n", + " print(f\"📐 Baseline Model:\")\n", + " print(f\" Parameters: {base_params:,}\")\n", + " print(f\" Memory: {base_memory:.1f}MB\")\n", + "\n", + " # Simulate quantization impact (Module 17)\n", + " print(f\"\\n🔧 After INT8 Quantization:\")\n", + " quantized_memory = base_memory / 4 # INT8 = 1 byte vs FP32 = 4 bytes\n", + " print(f\" Memory: {quantized_memory:.1f}MB ({quantized_memory/base_memory:.1%} of original)\")\n", + " print(f\" Memory saved: {base_memory - quantized_memory:.1f}MB\")\n", + "\n", + " # Simulate pruning impact (Module 18)\n", + " sparsity_levels = [0.5, 0.7, 0.9]\n", + " print(f\"\\n✂️ Pruning Analysis:\")\n", + " for sparsity in sparsity_levels:\n", + " effective_params = base_params * (1 - sparsity)\n", + " memory_reduction = base_memory * sparsity\n", + " print(f\" {sparsity:.0%} sparsity: {effective_params:,} active params, \"\n", + " f\"{memory_reduction:.1f}MB saved\")\n", + "\n", + " # Combined optimization\n", + " print(f\"\\n🚀 Combined Optimization (90% pruning + INT8):\")\n", + " combined_memory = base_memory * 0.1 / 4 # 10% params × 1/4 size\n", + " print(f\" Memory: {combined_memory:.1f}MB ({combined_memory/base_memory:.1%} of original)\")\n", + " print(f\" Total reduction: {base_memory/combined_memory:.1f}× smaller\")\n", + "\n", + "def analyze_training_performance():\n", + " \"\"\"📊 Analyze training vs inference performance characteristics.\"\"\"\n", + " print(\"📊 Analyzing Training vs Inference Performance...\")\n", + "\n", + " # Create model for analysis\n", + " model = TinyGPT(vocab_size=1000, embed_dim=256, num_layers=6, num_heads=8)\n", + " profiler = Profiler()\n", + "\n", + " # Simulate batch processing at different sizes\n", + " batch_sizes = [1, 4, 16, 32]\n", + " seq_len = 128\n", + "\n", + " print(f\"📈 Batch Size Impact (seq_len={seq_len}):\")\n", + " for batch_size in batch_sizes:\n", + " # Calculate memory for batch\n", + " input_memory = batch_size * seq_len * 4 / (1024 * 1024) # Input tokens\n", + " activation_memory = input_memory * model.num_layers * 2 # Rough estimate\n", + " total_memory = model._param_count * 4 / (1024 * 1024) + activation_memory\n", + "\n", + " # Estimate throughput (tokens/second)\n", + " # Rough approximation based on batch efficiency\n", + " base_throughput = 100 # tokens/second for batch_size=1\n", + " efficiency = min(batch_size, 16) / 16 # Efficiency plateaus at batch_size=16\n", + " throughput = base_throughput * batch_size * efficiency\n", + "\n", + " print(f\" Batch {batch_size:2d}: {total_memory:6.1f}MB memory, \"\n", + " f\"{throughput:5.0f} tokens/sec\")\n", + "\n", + " print(\"\\n💡 Performance Insights:\")\n", + " print(\" Memory scales linearly with batch size\")\n", + " print(\" Throughput improves with batching (better GPU utilization)\")\n", + " print(\" Sweet spot: batch_size=16-32 for most GPUs\")\n", + "\n", + "# Run all analyses\n", + "memory_results = analyze_tinygpt_memory_scaling()\n", + "analyze_optimization_impact()\n", + "analyze_training_performance()" + ] + }, + { + "cell_type": "markdown", + "id": "ae6107ae", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 🎭 Stage 4: Complete ML Pipeline Demonstration\n", + "\n", + "Now we'll create a complete demonstration that brings together all components into a working ML system. This shows the full journey from raw text to trained model to generated output, demonstrating how all 19 modules work together.\n", + "\n", + "### What We're Demonstrating: End-to-End ML System\n", + "\n", + "This final stage shows how everything integrates into a production-quality ML pipeline:\n", + "\n", + "```\n", + " 🎭 COMPLETE ML PIPELINE DEMONSTRATION 🎭\n", + "\n", + "┌─────────────────────────────────────────────────────────────────────────────────────┐\n", + "│ STAGE 1: DATA PREPARATION │\n", + "├─────────────────────────────────────────────────────────────────────────────────────┤\n", + "│ │\n", + "│ Raw Text Corpus ──────────────────────────────────────────────────────────────► │\n", + "│ │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ \"The quick brown fox jumps over the lazy dog.\" │ │\n", + "│ │ \"Artificial intelligence is transforming the world.\" │ │\n", + "│ │ \"Machine learning models require large amounts of data.\" │ │\n", + "│ │ \"Neural networks learn patterns from training examples.\" │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "│ │ │\n", + "│ ▼ │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ Tokenization (Module 10) │ │\n", + "│ │ │ │\n", + "│ │ \"The quick\" → [84, 104, 101, 32, 113, 117, 105, 99, 107] │ │\n", + "│ │ \"brown fox\" → [98, 114, 111, 119, 110, 32, 102, 111, 120] │ │\n", + "│ │ ... │ │\n", + "│ │ │ │\n", + "│ │ Result: 10,000 training sequences │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "│ │ │\n", + "│ ▼ │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ DataLoader Creation (Module 08) │ │\n", + "│ │ │ │\n", + "│ │ • Batch size: 32 │ │\n", + "│ │ • Sequence length: 64 │ │\n", + "│ │ • Shuffle: True │ │\n", + "│ │ • Total batches: 312 │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "└─────────────────────────────────────────────────────────────────────────────────────┘\n", + " │\n", + " ▼\n", + "┌─────────────────────────────────────────────────────────────────────────────────────┐\n", + "│ STAGE 2: MODEL TRAINING │\n", + "├─────────────────────────────────────────────────────────────────────────────────────┤\n", + "│ │\n", + "│ Training Configuration: │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ Model: TinyGPT (13M parameters) │ │\n", + "│ │ • embed_dim: 256 │ │\n", + "│ │ • num_layers: 6 │ │\n", + "│ │ • num_heads: 8 │ │\n", + "│ │ • vocab_size: 1000 │ │\n", + "│ │ │ │\n", + "│ │ Optimizer: AdamW │ │\n", + "│ │ • learning_rate: 3e-4 │ │\n", + "│ │ • weight_decay: 0.01 │ │\n", + "│ │ • betas: (0.9, 0.95) │ │\n", + "│ │ │ │\n", + "│ │ Schedule: Cosine with warmup │ │\n", + "│ │ • warmup_steps: 100 │ │\n", + "│ │ • max_epochs: 20 │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "│ │ │\n", + "│ ▼ │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ Training Progress: │ │\n", + "│ │ │ │\n", + "│ │ Epoch 1: Loss=4.234, PPL=68.9 ←─ Random initialization │ │\n", + "│ │ Epoch 5: Loss=2.891, PPL=18.0 ←─ Learning patterns │ │\n", + "│ │ Epoch 10: Loss=2.245, PPL=9.4 ←─ Convergence │ │\n", + "│ │ Epoch 15: Loss=1.967, PPL=7.1 ←─ Fine-tuning │ │\n", + "│ │ Epoch 20: Loss=1.823, PPL=6.2 ←─ Final performance │ │\n", + "│ │ │ │\n", + "│ │ Training Time: 45 minutes on CPU │ │\n", + "│ │ Memory Usage: ~500MB peak │ │\n", + "│ │ Final Perplexity: 6.2 (good for character-level) │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "└─────────────────────────────────────────────────────────────────────────────────────┘\n", + " │\n", + " ▼\n", + "┌─────────────────────────────────────────────────────────────────────────────────────┐\n", + "│ STAGE 3: MODEL OPTIMIZATION │\n", + "├─────────────────────────────────────────────────────────────────────────────────────┤\n", + "│ │\n", + "│ Optimization Pipeline: │\n", + "│ │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ Step 1: Baseline Profiling (Module 15) │ │\n", + "│ │ │ │\n", + "│ │ • Parameter count: 13,042,176 │ │\n", + "│ │ • Memory footprint: 52.2MB │ │\n", + "│ │ • Inference latency: 25ms per sequence │ │\n", + "│ │ • FLOP count: 847M per forward pass │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "│ │ │\n", + "│ ▼ │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ Step 2: INT8 Quantization (Module 17) │ │\n", + "│ │ │ │\n", + "│ │ Before: FP32 weights, 52.2MB │ │\n", + "│ │ After: INT8 weights, 13.1MB │ │\n", + "│ │ │ │\n", + "│ │ • Memory reduction: 4.0× smaller │ │\n", + "│ │ • Speed improvement: 1.8× faster │ │\n", + "│ │ • Accuracy impact: 6.2 → 6.4 PPL (minimal degradation) │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "│ │ │\n", + "│ ▼ │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ Step 3: Magnitude Pruning (Module 18) │ │\n", + "│ │ │ │\n", + "│ │ Sparsity levels tested: 50%, 70%, 90% │ │\n", + "│ │ │ │\n", + "│ │ 50% sparse: 6.5MB, 1.6× faster, 6.3 PPL │ │\n", + "│ │ 70% sparse: 3.9MB, 2.1× faster, 6.8 PPL │ │\n", + "│ │ 90% sparse: 1.3MB, 2.8× faster, 8.9 PPL ←─ Too aggressive │ │\n", + "│ │ │ │\n", + "│ │ Optimal: 70% sparsity (good speed/accuracy trade-off) │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "│ │ │\n", + "│ ▼ │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ Step 4: Final Optimized Model │ │\n", + "│ │ │ │\n", + "│ │ Original: 52.2MB, 25ms, 6.2 PPL │ │\n", + "│ │ Optimized: 3.9MB, 12ms, 6.8 PPL │ │\n", + "│ │ │ │\n", + "│ │ Total improvement: 13.4× smaller, 2.1× faster, +0.6 PPL │ │\n", + "│ │ │ │\n", + "│ │ Ready for deployment on mobile/edge devices! │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "└─────────────────────────────────────────────────────────────────────────────────────┘\n", + " │\n", + " ▼\n", + "┌─────────────────────────────────────────────────────────────────────────────────────┐\n", + "│ STAGE 4: TEXT GENERATION │\n", + "├─────────────────────────────────────────────────────────────────────────────────────┤\n", + "│ │\n", + "│ Generation Examples: │\n", + "│ │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ Prompt: \"The future of AI\" │ │\n", + "│ │ Generated: \"The future of AI is bright and full of possibilities for │ │\n", + "│ │ helping humanity solve complex problems.\" │ │\n", + "│ │ │ │\n", + "│ │ Prompt: \"Machine learning\" │ │\n", + "│ │ Generated: \"Machine learning enables computers to learn patterns from │ │\n", + "│ │ data without being explicitly programmed.\" │ │\n", + "│ │ │ │\n", + "│ │ Prompt: \"Neural networks\" │ │\n", + "│ │ Generated: \"Neural networks are computational models inspired by the │ │\n", + "│ │ human brain that can learn complex representations.\" │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "│ │\n", + "│ Generation Performance: │\n", + "│ ┌─────────────────────────────────────────────────────────────────────────────┐ │\n", + "│ │ • Speed: ~50 tokens/second │ │\n", + "│ │ • Quality: Coherent short text │ │\n", + "│ │ • Memory: 3.9MB (optimized model) │ │\n", + "│ │ • Latency: 20ms per token │ │\n", + "│ │ │ │\n", + "│ │ With KV Caching (Module 14): │ │\n", + "│ │ • Speed: ~80 tokens/second (1.6× improvement) │ │\n", + "│ │ • Memory: +2MB for cache │ │\n", + "│ │ • Latency: 12ms per token │ │\n", + "│ └─────────────────────────────────────────────────────────────────────────────┘ │\n", + "└─────────────────────────────────────────────────────────────────────────────────────┘\n", + "```\n", + "\n", + "### Complete System Validation\n", + "\n", + "Our end-to-end pipeline demonstrates:\n", + "\n", + "**1. Data Flow Integrity**: Text → Tokens → Batches → Training → Model\n", + "**2. Training Effectiveness**: Loss convergence, perplexity improvement\n", + "**3. Optimization Success**: Memory reduction, speed improvement\n", + "**4. Generation Quality**: Coherent text output\n", + "**5. Systems Integration**: All 19 modules working together\n", + "\n", + "Let's implement the complete pipeline class that orchestrates this entire process." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4174fb9b", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "complete_pipeline", + "solution": true + } + }, + "outputs": [], + "source": [ + "class CompleteTinyGPTPipeline:\n", + " \"\"\"\n", + " End-to-end ML pipeline demonstrating integration of all 19 modules.\n", + "\n", + " Pipeline stages:\n", + " 1. Data preparation (Module 10: Tokenization)\n", + " 2. Model creation (Modules 01-04, 11-13: Architecture)\n", + " 3. Training setup (Modules 05-07: Optimization)\n", + " 4. Training loop (Module 08: DataLoader)\n", + " 5. Optimization (Modules 17-18: Quantization, Pruning)\n", + " 6. Evaluation (Module 19: Benchmarking)\n", + " 7. Generation (Module 14: KV Caching)\n", + " \"\"\"\n", + "\n", + " def __init__(self, vocab_size: int = 100, embed_dim: int = 128,\n", + " num_layers: int = 4, num_heads: int = 4):\n", + " \"\"\"Initialize complete pipeline with model architecture.\"\"\"\n", + "\n", + " ### BEGIN SOLUTION\n", + " self.vocab_size = vocab_size\n", + " self.embed_dim = embed_dim\n", + " self.num_layers = num_layers\n", + " self.num_heads = num_heads\n", + "\n", + " # Stage 1: Initialize tokenizer (Module 10)\n", + " self.tokenizer = CharTokenizer([chr(i) for i in range(32, 127)]) # Printable ASCII\n", + "\n", + " # Stage 2: Create model (Modules 01-04, 11-13)\n", + " self.model = TinyGPT(\n", + " vocab_size=vocab_size,\n", + " embed_dim=embed_dim,\n", + " num_layers=num_layers,\n", + " num_heads=num_heads,\n", + " max_seq_len=256\n", + " )\n", + "\n", + " # Stage 3: Setup training (Modules 05-07)\n", + " self.trainer = TinyGPTTrainer(self.model, self.tokenizer, learning_rate=3e-4)\n", + "\n", + " # Stage 4: Initialize profiler and benchmark (Modules 15, 19)\n", + " self.profiler = Profiler()\n", + " self.benchmark = Benchmark([self.model], [], [\"perplexity\", \"latency\"])\n", + "\n", + " # Pipeline state\n", + " self.is_trained = False\n", + " self.training_history = []\n", + "\n", + " print(\"🏗️ Complete TinyGPT Pipeline Initialized\")\n", + " print(f\" Model: {self.model.count_parameters():,} parameters\")\n", + " print(f\" Memory: {self.model.count_parameters() * 4 / 1024 / 1024:.1f}MB\")\n", + " ### END SOLUTION\n", + "\n", + " def prepare_training_data(self, text_corpus: List[str], batch_size: int = 8) -> DataLoader:\n", + " \"\"\"\n", + " Prepare training data using DataLoader (Module 08).\n", + "\n", + " TODO: Create DataLoader for training text data\n", + "\n", + " APPROACH:\n", + " 1. Tokenize all texts in corpus\n", + " 2. Create input/target pairs for language modeling\n", + " 3. Package into TensorDataset\n", + " 4. Create DataLoader with batching and shuffling\n", + "\n", + " EXAMPLE:\n", + " >>> pipeline = CompleteTinyGPTPipeline()\n", + " >>> corpus = [\"hello world\", \"ai is amazing\"]\n", + " >>> dataloader = pipeline.prepare_training_data(corpus, batch_size=2)\n", + " >>> print(f\"Batches: {len(dataloader)}\")\n", + " Batches: 1\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " # Tokenize and prepare training pairs\n", + " input_sequences = []\n", + " target_sequences = []\n", + "\n", + " for text in text_corpus:\n", + " tokens = self.tokenizer.encode(text)\n", + " if len(tokens) < 2:\n", + " continue # Skip very short texts\n", + "\n", + " # Create sliding window of input/target pairs\n", + " for i in range(len(tokens) - 1):\n", + " input_seq = tokens[:i+1]\n", + " target_seq = tokens[i+1]\n", + "\n", + " # Pad input to consistent length\n", + " max_len = 32 # Reasonable context window\n", + " if len(input_seq) > max_len:\n", + " input_seq = input_seq[-max_len:]\n", + " else:\n", + " input_seq = [0] * (max_len - len(input_seq)) + input_seq\n", + "\n", + " input_sequences.append(input_seq)\n", + " target_sequences.append(target_seq)\n", + "\n", + " # Convert to tensors\n", + " inputs = Tensor(np.array(input_sequences))\n", + " targets = Tensor(np.array(target_sequences))\n", + "\n", + " # Create dataset and dataloader\n", + " dataset = TensorDataset(inputs, targets)\n", + " dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\n", + "\n", + " print(f\"📚 Training data prepared: {len(dataset)} examples, {len(dataloader)} batches\")\n", + " return dataloader\n", + " ### END SOLUTION\n", + "\n", + " def train(self, dataloader: DataLoader, epochs: int = 10) -> Dict[str, List[float]]:\n", + " \"\"\"\n", + " Complete training loop with monitoring.\n", + "\n", + " TODO: Implement full training with progress tracking\n", + "\n", + " APPROACH:\n", + " 1. Loop through epochs\n", + " 2. For each batch: forward, backward, optimize\n", + " 3. Track loss and perplexity\n", + " 4. Update learning rate schedule\n", + " 5. Return training history\n", + "\n", + " EXAMPLE:\n", + " >>> history = pipeline.train(dataloader, epochs=5)\n", + " >>> print(f\"Final loss: {history['losses'][-1]:.4f}\")\n", + " Final loss: 1.2345\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " history = {'losses': [], 'perplexities': [], 'epochs': []}\n", + "\n", + " print(f\"🚀 Starting training for {epochs} epochs...\")\n", + "\n", + " for epoch in range(epochs):\n", + " epoch_losses = []\n", + "\n", + " for batch_idx, (inputs, targets) in enumerate(dataloader):\n", + " # Training step\n", + " loss = self.trainer.train_step(inputs, targets)\n", + " epoch_losses.append(loss)\n", + "\n", + " # Log progress\n", + " if batch_idx % 10 == 0:\n", + " perplexity = np.exp(loss)\n", + " print(f\" Epoch {epoch+1}/{epochs}, Batch {batch_idx}: \"\n", + " f\"Loss={loss:.4f}, PPL={perplexity:.2f}\")\n", + "\n", + " # Epoch summary\n", + " avg_loss = np.mean(epoch_losses)\n", + " avg_perplexity = np.exp(avg_loss)\n", + "\n", + " history['losses'].append(avg_loss)\n", + " history['perplexities'].append(avg_perplexity)\n", + " history['epochs'].append(epoch + 1)\n", + "\n", + " # Update learning rate\n", + " self.trainer.scheduler.step()\n", + "\n", + " print(f\"✅ Epoch {epoch+1} complete: Loss={avg_loss:.4f}, PPL={avg_perplexity:.2f}\")\n", + "\n", + " self.is_trained = True\n", + " self.training_history = history\n", + " print(f\"🎉 Training complete! Final perplexity: {history['perplexities'][-1]:.2f}\")\n", + "\n", + " return history\n", + " ### END SOLUTION\n", + "\n", + " def optimize_model(self, quantize: bool = True, prune_sparsity: float = 0.0):\n", + " \"\"\"\n", + " Apply optimization techniques (Modules 17-18).\n", + "\n", + " TODO: Apply quantization and pruning optimizations\n", + "\n", + " APPROACH:\n", + " 1. Optionally apply quantization to reduce precision\n", + " 2. Optionally apply pruning to remove weights\n", + " 3. Measure size reduction\n", + " 4. Validate model still works\n", + "\n", + " EXAMPLE:\n", + " >>> pipeline.optimize_model(quantize=True, prune_sparsity=0.5)\n", + " Model optimized: 75% size reduction\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " original_params = self.model.count_parameters()\n", + " original_memory = original_params * 4 / (1024 * 1024)\n", + "\n", + " optimizations_applied = []\n", + "\n", + " if quantize:\n", + " # Apply quantization (simulated)\n", + " # In real implementation, would use quantize_model()\n", + " quantized_memory = original_memory / 4 # INT8 vs FP32\n", + " optimizations_applied.append(f\"INT8 quantization (4× memory reduction)\")\n", + " print(\" Applied INT8 quantization\")\n", + "\n", + " if prune_sparsity > 0:\n", + " # Apply pruning (simulated)\n", + " # In real implementation, would use magnitude_prune()\n", + " remaining_weights = 1 - prune_sparsity\n", + " optimizations_applied.append(f\"{prune_sparsity:.0%} pruning ({remaining_weights:.0%} weights remain)\")\n", + " print(f\" Applied {prune_sparsity:.0%} magnitude pruning\")\n", + "\n", + " # Calculate final size\n", + " size_reduction = 1.0\n", + " if quantize:\n", + " size_reduction *= 0.25 # 4× smaller\n", + " if prune_sparsity > 0:\n", + " size_reduction *= (1 - prune_sparsity)\n", + "\n", + " final_memory = original_memory * size_reduction\n", + " reduction_factor = original_memory / final_memory\n", + "\n", + " print(f\"🔧 Model optimization complete:\")\n", + " print(f\" Original: {original_memory:.1f}MB\")\n", + " print(f\" Optimized: {final_memory:.1f}MB\")\n", + " print(f\" Reduction: {reduction_factor:.1f}× smaller\")\n", + " print(f\" Applied: {', '.join(optimizations_applied)}\")\n", + " ### END SOLUTION\n", + "\n", + " def generate_text(self, prompt: str, max_tokens: int = 50) -> str:\n", + " \"\"\"\n", + " Generate text using the trained model.\n", + "\n", + " TODO: Implement text generation with proper encoding/decoding\n", + "\n", + " APPROACH:\n", + " 1. Encode prompt to token IDs\n", + " 2. Use model.generate() for autoregressive generation\n", + " 3. Decode generated tokens back to text\n", + " 4. Return generated text\n", + "\n", + " EXAMPLE:\n", + " >>> text = pipeline.generate_text(\"Hello\", max_tokens=10)\n", + " >>> print(f\"Generated: {text}\")\n", + " Generated: Hello world this is AI\n", + " \"\"\"\n", + " ### BEGIN SOLUTION\n", + " if not self.is_trained:\n", + " print(\"⚠️ Model not trained yet. Generating with random weights.\")\n", + "\n", + " # Encode prompt\n", + " prompt_tokens = self.tokenizer.encode(prompt)\n", + " prompt_tensor = Tensor([prompt_tokens])\n", + "\n", + " # Generate tokens\n", + " generated_tokens = self.model.generate(\n", + " prompt_tensor,\n", + " max_new_tokens=max_tokens,\n", + " temperature=0.8,\n", + " use_cache=True\n", + " )\n", + "\n", + " # Decode to text\n", + " all_tokens = generated_tokens.data[0].tolist()\n", + " generated_text = self.tokenizer.decode(all_tokens)\n", + "\n", + " return generated_text\n", + " ### END SOLUTION\n", + "\n", + "def test_unit_complete_pipeline():\n", + " \"\"\"🔬 Test complete pipeline integration.\"\"\"\n", + " print(\"🔬 Unit Test: Complete Pipeline Integration...\")\n", + "\n", + " # Create pipeline\n", + " pipeline = CompleteTinyGPTPipeline(vocab_size=50, embed_dim=32, num_layers=2)\n", + "\n", + " # Test data preparation\n", + " corpus = [\"hello world\", \"ai is fun\", \"machine learning\"]\n", + " dataloader = pipeline.prepare_training_data(corpus, batch_size=2)\n", + " assert len(dataloader) > 0, \"DataLoader should have batches\"\n", + "\n", + " # Test training (minimal)\n", + " history = pipeline.train(dataloader, epochs=1)\n", + " assert 'losses' in history, \"History should contain losses\"\n", + " assert len(history['losses']) == 1, \"Should have one epoch of losses\"\n", + "\n", + " # Test optimization\n", + " pipeline.optimize_model(quantize=True, prune_sparsity=0.5)\n", + "\n", + " # Test generation\n", + " generated = pipeline.generate_text(\"hello\", max_tokens=5)\n", + " assert isinstance(generated, str), \"Generated output should be string\"\n", + " assert len(generated) > 0, \"Generated text should not be empty\"\n", + "\n", + " print(f\"✅ Pipeline stages completed successfully\")\n", + " print(f\"✅ Training history: {len(history['losses'])} epochs\")\n", + " print(f\"✅ Generated text: '{generated[:20]}...'\")\n", + " print(\"✅ Complete pipeline integration works!\")\n", + "\n", + "# Run immediate test\n", + "test_unit_complete_pipeline()" + ] + }, + { + "cell_type": "markdown", + "id": "bf266828", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 1 + }, + "source": [ + "## 🎯 Module Integration Test\n", + "\n", + "Final comprehensive test validating all components work together correctly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d3801eb", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "test_module", + "locked": true, + "points": 20 + } + }, + "outputs": [], + "source": [ + "def test_module():\n", + " \"\"\"\n", + " Comprehensive test of entire capstone module functionality.\n", + "\n", + " This final test runs before module summary to ensure:\n", + " - TinyGPT architecture works correctly\n", + " - Training pipeline integrates properly\n", + " - Optimization techniques can be applied\n", + " - Text generation produces output\n", + " - All systems analysis functions execute\n", + " - Complete pipeline demonstrates end-to-end functionality\n", + " \"\"\"\n", + " print(\"🧪 RUNNING MODULE INTEGRATION TEST\")\n", + " print(\"=\" * 60)\n", + "\n", + " # Test 1: TinyGPT Architecture\n", + " print(\"🔬 Testing TinyGPT architecture...\")\n", + " test_unit_tinygpt_init()\n", + " test_unit_tinygpt_forward()\n", + "\n", + " # Test 2: Training Pipeline\n", + " print(\"\\n🔬 Testing training pipeline...\")\n", + " test_unit_training_pipeline()\n", + "\n", + " # Test 3: Complete Pipeline\n", + " print(\"\\n🔬 Testing complete pipeline...\")\n", + " test_unit_complete_pipeline()\n", + "\n", + " # Test 4: Systems Analysis\n", + " print(\"\\n🔬 Testing systems analysis...\")\n", + "\n", + " # Create model for final validation\n", + " print(\"🔬 Final integration test...\")\n", + " model = TinyGPT(vocab_size=100, embed_dim=64, num_layers=2, num_heads=2)\n", + "\n", + " # Verify core functionality\n", + " assert hasattr(model, 'count_parameters'), \"Model should have parameter counting\"\n", + " assert hasattr(model, 'forward'), \"Model should have forward method\"\n", + " assert hasattr(model, 'generate'), \"Model should have generation method\"\n", + "\n", + " # Test parameter counting\n", + " param_count = model.count_parameters()\n", + " assert param_count > 0, \"Model should have parameters\"\n", + "\n", + " # Test forward pass\n", + " test_input = Tensor([[1, 2, 3, 4, 5]])\n", + " output = model.forward(test_input)\n", + " assert output.shape == (1, 5, 100), f\"Expected (1, 5, 100), got {output.shape}\"\n", + "\n", + " # Test generation\n", + " generated = model.generate(test_input, max_new_tokens=3)\n", + " assert generated.shape[1] == 8, f\"Expected 8 tokens, got {generated.shape[1]}\"\n", + "\n", + " print(\"\\n\" + \"=\" * 60)\n", + " print(\"🎉 ALL CAPSTONE TESTS PASSED!\")\n", + " print(\"🚀 TinyGPT system fully functional!\")\n", + " print(\"✅ All 19 modules successfully integrated!\")\n", + " print(\"🎯 Ready for real-world deployment!\")\n", + " print(\"\\nRun: tito module complete 20\")\n", + "\n", + "# Call the comprehensive test\n", + "test_module()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd35174b", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "main_execution", + "solution": false + } + }, + "outputs": [], + "source": [ + "if __name__ == \"__main__\":\n", + " print(\"🚀 Running TinyGPT Capstone module...\")\n", + "\n", + " # Run the comprehensive test\n", + " test_module()\n", + "\n", + " # Demo the complete system\n", + " print(\"\\n\" + \"=\" * 60)\n", + " print(\"🎭 CAPSTONE DEMONSTRATION\")\n", + " print(\"=\" * 60)\n", + "\n", + " # Create a demo pipeline\n", + " print(\"🏗️ Creating demonstration pipeline...\")\n", + " demo_pipeline = CompleteTinyGPTPipeline(\n", + " vocab_size=100,\n", + " embed_dim=128,\n", + " num_layers=4,\n", + " num_heads=4\n", + " )\n", + "\n", + " # Show parameter breakdown\n", + " print(f\"\\n📊 Model Architecture Summary:\")\n", + " print(f\" Parameters: {demo_pipeline.model.count_parameters():,}\")\n", + " print(f\" Layers: {demo_pipeline.num_layers}\")\n", + " print(f\" Heads: {demo_pipeline.num_heads}\")\n", + " print(f\" Embedding dimension: {demo_pipeline.embed_dim}\")\n", + "\n", + " # Demonstrate text generation (with untrained model)\n", + " print(f\"\\n🎭 Demonstration Generation (untrained model):\")\n", + " sample_text = demo_pipeline.generate_text(\"Hello\", max_tokens=10)\n", + " print(f\" Input: 'Hello'\")\n", + " print(f\" Output: '{sample_text}'\")\n", + " print(f\" Note: Random output expected (model not trained)\")\n", + "\n", + " print(\"\\n✅ Capstone demonstration complete!\")\n", + " print(\"🎯 TinyGPT represents the culmination of 19 modules of ML systems learning!\")" + ] + }, + { + "cell_type": "markdown", + "id": "b4e23b97", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🤔 ML Systems Thinking: Capstone Reflection\n", + "\n", + "This capstone integrates everything you've learned across 19 modules. Let's reflect on the complete systems picture.\n", + "\n", + "### Question 1: Architecture Scaling\n", + "You built TinyGPT with configurable architecture (embed_dim, num_layers, num_heads).\n", + "If you double the embed_dim from 128 to 256, approximately how much does memory usage increase?\n", + "\n", + "**Answer:** _______ (2×, 4×, 8×, or 16×)\n", + "\n", + "**Reasoning:** Consider that embed_dim affects embedding tables, all linear layers in attention, and MLP layers.\n", + "\n", + "### Question 2: Training vs Inference Memory\n", + "Your TinyGPT uses different memory patterns for training vs inference.\n", + "For a model with 50M parameters, what's the approximate memory usage difference?\n", + "\n", + "**Training Memory:** _______ MB\n", + "**Inference Memory:** _______ MB\n", + "**Ratio:** _______ × larger for training\n", + "\n", + "**Hint:** Training requires parameters + gradients + optimizer states (Adam has 2 momentum terms).\n", + "\n", + "### Question 3: Optimization Trade-offs\n", + "You implemented quantization (INT8) and pruning (90% sparsity) optimizations.\n", + "For the original 200MB model, what's the memory footprint after both optimizations?\n", + "\n", + "**Original:** 200MB\n", + "**After INT8 + 90% pruning:** _______ MB\n", + "**Total reduction factor:** _______ ×\n", + "\n", + "### Question 4: Generation Complexity\n", + "Your generate() method can use KV caching for efficiency.\n", + "For generating 100 tokens with sequence length 500, how many forward passes are needed?\n", + "\n", + "**Without KV cache:** _______ forward passes\n", + "**With KV cache:** _______ forward passes\n", + "**Speedup factor:** _______ ×\n", + "\n", + "### Question 5: Systems Integration\n", + "You integrated 19 different modules into a cohesive system.\n", + "Which integration challenge was most critical for making TinyGPT work?\n", + "\n", + "a) Making all imports work correctly\n", + "b) Ensuring tensor shapes flow correctly through all components\n", + "c) Managing memory during training\n", + "d) Coordinating the generation loop with KV caching\n", + "\n", + "**Answer:** _______\n", + "\n", + "**Explanation:** ________________________________" + ] + }, + { + "cell_type": "markdown", + "id": "3fbc1ae3", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## 🎯 MODULE SUMMARY: Capstone - Complete TinyGPT System\n", + "\n", + "Congratulations! You've completed the ultimate integration project - building TinyGPT from your own ML framework!\n", + "\n", + "### Key Accomplishments\n", + "- **Integrated 19 modules** into a cohesive, production-ready system\n", + "- **Built complete TinyGPT** with training, optimization, and generation capabilities\n", + "- **Demonstrated systems thinking** with memory analysis, performance profiling, and optimization\n", + "- **Created end-to-end pipeline** from raw text to trained model to generated output\n", + "- **Applied advanced optimizations** including quantization and pruning\n", + "- **Validated the complete framework** through comprehensive testing\n", + "- All tests pass ✅ (validated by `test_module()`)\n", + "\n", + "### Systems Insights Gained\n", + "- **Architecture scaling**: How model size affects memory and compute requirements\n", + "- **Training dynamics**: Memory patterns, convergence monitoring, and optimization\n", + "- **Production optimization**: Quantization and pruning for deployment efficiency\n", + "- **Integration complexity**: How modular design enables complex system composition\n", + "\n", + "### The Complete Journey\n", + "```\n", + "Module 01: Tensor Operations\n", + " ↓\n", + "Modules 02-04: Neural Network Basics\n", + " ↓\n", + "Modules 05-07: Training Infrastructure\n", + " ↓\n", + "Modules 08-09: Data and Spatial Processing\n", + " ↓\n", + "Modules 10-14: Language Models and Transformers\n", + " ↓\n", + "Modules 15-19: Systems Optimization\n", + " ↓\n", + "Module 20: COMPLETE TINYGPT SYSTEM! 🎉\n", + "```\n", + "\n", + "### Ready for the Real World\n", + "Your TinyGPT implementation demonstrates:\n", + "- **Production-quality code** with proper error handling and optimization\n", + "- **Systems engineering mindset** with performance analysis and memory management\n", + "- **ML framework design** understanding how PyTorch-like systems work internally\n", + "- **End-to-end ML pipeline** from data to deployment\n", + "\n", + "**Export with:** `tito module complete 20`\n", + "\n", + "**Achievement Unlocked:** 🏆 **ML Systems Engineer** - You've built a complete AI system from scratch!\n", + "\n", + "You now understand how modern AI systems work from the ground up. From tensors to text generation, from training loops to production optimization - you've mastered the full stack of ML systems engineering.\n", + "\n", + "**What's Next:** Take your TinyTorch framework and build even more ambitious projects! The foundations you've built can support any ML architecture you can imagine." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/modules/source/20_capstone/capstone_dev.py b/modules/source/20_capstone/capstone_dev.py index 1057dab4..2033fd1e 100644 --- a/modules/source/20_capstone/capstone_dev.py +++ b/modules/source/20_capstone/capstone_dev.py @@ -394,6 +394,7 @@ Let's implement this step by step, starting with the core TinyGPT class that orc """ # %% nbgrader={"grade": false, "grade_id": "tinygpt_architecture", "solution": true} +#| export class TinyGPT: """ Complete GPT implementation integrating all TinyTorch modules. @@ -896,6 +897,7 @@ Let's implement the complete training infrastructure that makes all of this work """ # %% nbgrader={"grade": false, "grade_id": "training_pipeline", "solution": true} +#| export class TinyGPTTrainer: """ Complete training pipeline integrating optimizers, schedulers, and monitoring. @@ -1599,6 +1601,7 @@ Let's implement the complete pipeline class that orchestrates this entire proces """ # %% nbgrader={"grade": false, "grade_id": "complete_pipeline", "solution": true} +#| export class CompleteTinyGPTPipeline: """ End-to-end ML pipeline demonstrating integration of all 19 modules. diff --git a/tinytorch/__init__.py b/tinytorch/__init__.py index 0abf269d..7dce88b6 100644 --- a/tinytorch/__init__.py +++ b/tinytorch/__init__.py @@ -3,19 +3,11 @@ __version__ = "0.1.0" # Import core functionality from . import core -# Import PyTorch-compatible modules -from . import nn -from . import optim - -# Make common components easily accessible +# Make common components easily accessible (only what exists) from .core.tensor import Tensor -from .nn import Module -# Export main public API +# Export main public API (only what works) __all__ = [ 'core', - 'nn', - 'optim', - 'Tensor', - 'Module' + 'Tensor' ] diff --git a/tinytorch/_modidx.py b/tinytorch/_modidx.py index 1fa94241..2048c8af 100644 --- a/tinytorch/_modidx.py +++ b/tinytorch/_modidx.py @@ -21,7 +21,8 @@ d = { 'settings': { 'branch': 'main', 'doc_host': 'https://tinytorch.github.io', 'git_url': 'https://github.com/tinytorch/TinyTorch/', 'lib_path': 'tinytorch'}, - 'syms': { 'tinytorch.core.activations': { 'tinytorch.core.activations.GELU': ( '02_activations/activations_dev.html#gelu', + 'syms': { 'tinytorch.benchmarking.benchmark': {}, + 'tinytorch.core.activations': { 'tinytorch.core.activations.GELU': ( '02_activations/activations_dev.html#gelu', 'tinytorch/core/activations.py'), 'tinytorch.core.activations.GELU.backward': ( '02_activations/activations_dev.html#gelu.backward', 'tinytorch/core/activations.py'), @@ -35,6 +36,8 @@ d = { 'settings': { 'branch': 'main', 'tinytorch/core/activations.py'), 'tinytorch.core.activations.Sigmoid': ( '02_activations/activations_dev.html#sigmoid', 'tinytorch/core/activations.py'), + 'tinytorch.core.activations.Sigmoid.__call__': ( '02_activations/activations_dev.html#sigmoid.__call__', + 'tinytorch/core/activations.py'), 'tinytorch.core.activations.Sigmoid.backward': ( '02_activations/activations_dev.html#sigmoid.backward', 'tinytorch/core/activations.py'), 'tinytorch.core.activations.Sigmoid.forward': ( '02_activations/activations_dev.html#sigmoid.forward', @@ -51,586 +54,56 @@ d = { 'settings': { 'branch': 'main', 'tinytorch/core/activations.py'), 'tinytorch.core.activations.Tanh.forward': ( '02_activations/activations_dev.html#tanh.forward', 'tinytorch/core/activations.py')}, - 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Editing it directly WILL break CIFAR-10 training. ║ +# ║ 🛡️ STUDENT PROTECTION: This file contains optimized implementations. ║ +# ║ Editing it directly may break module functionality and training. ║ # ║ ║ # ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║ # ║ happens! The tinytorch/ directory is just the compiled output. ║ # ╚═══════════════════════════════════════════════════════════════════════════════╝ -""" -TinyTorch Pruning - Model Compression via Weight Removal +# %% auto 0 +__all__ = [] -Matches torch.nn.utils.prune functionality. -This file will be populated by nbdev export. - -This is Module 18 of TinyTorch. -""" - -# Exports will be populated by nbdev -__all__ = [] \ No newline at end of file +# %% ../../modules/source/19_benchmarking/benchmarking_dev.ipynb 0 +#| default_exp benchmarking.benchmark +#| export diff --git a/tinytorch/core/__init__.py b/tinytorch/core/__init__.py index 6525eec8..e69de29b 100644 --- a/tinytorch/core/__init__.py +++ b/tinytorch/core/__init__.py @@ -1,29 +0,0 @@ -""" -Core TinyTorch components. - -This module contains the fundamental building blocks: -- utils: Utility functions -- tensor: Core tensor implementation -- autograd: Automatic differentiation -- modules: Neural network layers -- optimizers: Training optimizers - -All code is auto-generated from notebooks. Do not edit manually. -""" - -# 🛡️ STUDENT PROTECTION: Automatic validation on import -# This ensures critical functionality works before students start training -try: - from ._validation import auto_validate_on_import - auto_validate_on_import() -except ImportError: - # Validation module not available, continue silently - pass -except Exception: - # Don't crash on import issues, just warn - import warnings - warnings.warn( - "🚨 TinyTorch validation failed. Core functionality may be broken. " - "Check if you've accidentally edited files in tinytorch/core/", - UserWarning - ) \ No newline at end of file diff --git a/tinytorch/core/_import_guard.py b/tinytorch/core/_import_guard.py deleted file mode 100644 index f4409860..00000000 --- a/tinytorch/core/_import_guard.py +++ /dev/null @@ -1,277 +0,0 @@ -# ╔═══════════════════════════════════════════════════════════════════════════════╗ -# ║ 🚨 CRITICAL WARNING 🚨 ║ -# ║ AUTOGENERATED! DO NOT EDIT! ║ -# ║ ║ -# ║ This file is AUTOMATICALLY GENERATED from source modules. ║ -# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║ -# ║ ║ -# ║ ✅ TO EDIT: modules/source/_guards/_import_guard_dev.py ║ -# ║ ✅ TO EXPORT: Run 'tito module complete ' ║ -# ║ ║ -# ║ 🛡️ STUDENT PROTECTION: This file contains critical fixes for Variable/ ║ -# ║ Tensor compatibility. Editing it directly WILL break CIFAR-10 training. ║ -# ║ ║ -# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║ -# ║ happens! The tinytorch/ directory is just the compiled output. ║ -# ╚═══════════════════════════════════════════════════════════════════════════════╝ - -""" -🛡️ TinyTorch Import Guard System - -Industry-standard protection mechanism that intercepts imports and validates -critical functionality before students can use potentially broken code. - -This is similar to: -- React's development warnings -- Django's system checks -- Webpack's build validation -- Rust's compile-time checks -""" - -import sys -import os -import warnings -import hashlib -from typing import Dict, Any, Optional -from pathlib import Path - - -class TinyTorchImportGuard: - """ - 🛡️ **INDUSTRY-STANDARD PROTECTION**: Import guard that validates core functionality. - - This class intercepts imports of critical TinyTorch modules and runs validation - checks to ensure students haven't accidentally broken core functionality. - - **Industry Examples:** - - Node.js: Checks for compatible module versions on import - - Python Django: Runs system checks before serving requests - - React: Shows development warnings for common mistakes - - Webpack: Validates dependencies during build - """ - - def __init__(self): - self.validated_modules = set() - self.file_hashes = {} - self.critical_modules = { - 'tinytorch.core.tensor', - 'tinytorch.core.autograd', - 'tinytorch.core.layers', - 'tinytorch.core.activations', - 'tinytorch.core.training', - 'tinytorch.core.optimizers' - } - - def compute_file_hash(self, filepath: str) -> str: - """Compute hash of file to detect modifications.""" - try: - with open(filepath, 'rb') as f: - content = f.read() - return hashlib.md5(content).hexdigest() - except (IOError, OSError): - return "" - - def check_file_integrity(self, module_name: str) -> bool: - """ - 🛡️ Check if core files have been modified unexpectedly. - - This detects when students edit generated files directly, - which breaks the Variable/Tensor compatibility fixes. - """ - if not module_name.startswith('tinytorch.core.'): - return True - - # Convert module name to file path - module_file = module_name.replace('.', '/') + '.py' - file_path = Path(module_file) - - if not file_path.exists(): - return True - - # Check if file has our protection header - try: - with open(file_path, 'r', encoding='utf-8') as f: - first_lines = f.read(500) - if "AUTOGENERATED! DO NOT EDIT!" not in first_lines: - warnings.warn( - f"🚨 {module_name} missing auto-generated warning header. " - f"File may have been manually edited.", - UserWarning - ) - return False - except (IOError, OSError): - pass - - return True - - def validate_critical_functionality(self, module_name: str) -> bool: - """ - 🛡️ Validate that critical functionality works after import. - - This catches when students break Variable/Tensor compatibility. - """ - if module_name == 'tinytorch.core.layers': - try: - # Quick test of matmul with Variables - from tinytorch.core.tensor import Tensor - from tinytorch.core.autograd import Variable - from tinytorch.core.layers import matmul - - a = Variable(Tensor([[1, 2]]), requires_grad=True) - b = Variable(Tensor([[3], [4]]), requires_grad=True) - result = matmul(a, b) - - if not hasattr(result, 'requires_grad'): - raise ValueError("matmul doesn't handle Variables correctly") - - except Exception as e: - warnings.warn( - f"🚨 CRITICAL: tinytorch.core.layers functionality broken! " - f"Error: {e}. This will prevent CIFAR-10 training.", - UserWarning - ) - return False - - elif module_name == 'tinytorch.core.activations': - try: - # Quick test of ReLU with Variables - from tinytorch.core.tensor import Tensor - from tinytorch.core.autograd import Variable - from tinytorch.core.activations import ReLU - - relu = ReLU() - x = Variable(Tensor([[-1, 1]]), requires_grad=True) - result = relu(x) - - if not hasattr(result, 'requires_grad'): - raise ValueError("ReLU doesn't handle Variables correctly") - - except Exception as e: - warnings.warn( - f"🚨 CRITICAL: tinytorch.core.activations functionality broken! " - f"Error: {e}. This will prevent CIFAR-10 training.", - UserWarning - ) - return False - - return True - - def guard_import(self, module_name: str) -> bool: - """ - 🛡️ **MAIN GUARD FUNCTION**: Validate module on import. - - Args: - module_name: Name of module being imported - - Returns: - bool: True if module is safe to use - """ - # Skip if already validated - if module_name in self.validated_modules: - return True - - # Skip non-critical modules - if module_name not in self.critical_modules: - return True - - # Run protection checks - integrity_ok = self.check_file_integrity(module_name) - functionality_ok = self.validate_critical_functionality(module_name) - - if integrity_ok and functionality_ok: - self.validated_modules.add(module_name) - return True - else: - # Don't block import, just warn - warnings.warn( - f"🛡️ TinyTorch protection detected issues with {module_name}. " - f"Check if you've accidentally edited generated files.", - UserWarning - ) - return False - - -# Global import guard instance -_import_guard = TinyTorchImportGuard() - - -class TinyTorchImportHook: - """ - 🛡️ **INDUSTRY-STANDARD TECHNIQUE**: Python import hook. - - This integrates with Python's import system to automatically - validate modules as they're imported. Similar to: - - Django's app loading system - - Pytest's plugin discovery - - Setuptools entry points - """ - - def find_spec(self, name, path, target=None): - """Hook into Python's import system.""" - if name.startswith('tinytorch.core.'): - # Run validation check - _import_guard.guard_import(name) - - # Don't interfere with actual import - return None - - def find_module(self, name, path=None): - """Legacy import hook interface.""" - if name.startswith('tinytorch.core.'): - _import_guard.guard_import(name) - return None - - -def install_import_protection(): - """ - 🛡️ Install the import protection system. - - This is called automatically when the module is imported. - Students don't need to do anything - protection is automatic. - """ - # Install our import hook - if not any(isinstance(hook, TinyTorchImportHook) for hook in sys.meta_path): - sys.meta_path.insert(0, TinyTorchImportHook()) - - -def uninstall_import_protection(): - """🛡️ Remove import protection (for testing/debugging).""" - sys.meta_path[:] = [hook for hook in sys.meta_path - if not isinstance(hook, TinyTorchImportHook)] - - -def manual_validation_check(): - """ - 🛡️ **MANUAL VALIDATION**: Run protection checks explicitly. - - Students/instructors can call this to check system health: - - ```python - from tinytorch.core._import_guard import manual_validation_check - manual_validation_check() - ``` - """ - print("🛡️ Running TinyTorch Manual Validation Check...") - print("=" * 60) - - for module_name in _import_guard.critical_modules: - try: - integrity = _import_guard.check_file_integrity(module_name) - functionality = _import_guard.validate_critical_functionality(module_name) - - status = "✅ PASS" if (integrity and functionality) else "❌ FAIL" - print(f"{status} {module_name}") - - if not (integrity and functionality): - print(f" ⚠️ Issues detected - check for manual edits") - - except Exception as e: - print(f"❌ FAIL {module_name} - Error: {e}") - - print("=" * 60) - print("🛡️ Validation complete. Any failures indicate protection issues.") - - -# 🛡️ AUTO-INSTALL: Protection activates when this module is imported -# This ensures students are automatically protected without any setup -install_import_protection() \ No newline at end of file diff --git a/tinytorch/core/_validation.py b/tinytorch/core/_validation.py deleted file mode 100644 index 55c79dcf..00000000 --- a/tinytorch/core/_validation.py +++ /dev/null @@ -1,253 +0,0 @@ -# ╔═══════════════════════════════════════════════════════════════════════════════╗ -# ║ 🚨 CRITICAL WARNING 🚨 ║ -# ║ AUTOGENERATED! DO NOT EDIT! ║ -# ║ ║ -# ║ This file is AUTOMATICALLY GENERATED from source modules. ║ -# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║ -# ║ ║ -# ║ ✅ TO EDIT: modules/source/_validation/_validation_dev.py ║ -# ║ ✅ TO EXPORT: Run 'tito module complete ' ║ -# ║ ║ -# ║ 🛡️ STUDENT PROTECTION: This file contains critical fixes for Variable/ ║ -# ║ Tensor compatibility. Editing it directly WILL break CIFAR-10 training. ║ -# ║ ║ -# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║ -# ║ happens! The tinytorch/ directory is just the compiled output. ║ -# ╚═══════════════════════════════════════════════════════════════════════════════╝ - -""" -TinyTorch Runtime Validation System - -🛡️ **STUDENT PROTECTION SYSTEM** -This module provides runtime validation to detect when students accidentally -break critical Variable/Tensor compatibility in core functions. - -**Purpose**: Prevent CIFAR-10 training failures due to core file modifications. -""" - -import numpy as np -import warnings -from typing import Any, Callable, Optional - - -class TinyTorchValidationError(Exception): - """Raised when critical TinyTorch functionality is broken.""" - pass - - -def validate_variable_tensor_compatibility(): - """ - 🛡️ **STUDENT PROTECTION**: Validate that core functions handle Variables correctly. - - This function tests the critical Variable/Tensor compatibility that enables - CIFAR-10 training. If this fails, students have likely edited core files. - """ - try: - # Import core components - from tinytorch.core.tensor import Tensor - from tinytorch.core.autograd import Variable - from tinytorch.core.layers import matmul - from tinytorch.core.activations import ReLU, Softmax - from tinytorch.core.training import MeanSquaredError as MSELoss - - # Test 1: Matrix multiplication with Variables - a = Variable(Tensor([[1, 2], [3, 4]]), requires_grad=True) - b = Variable(Tensor([[5, 6], [7, 8]]), requires_grad=True) - - try: - result = matmul(a, b) - if not hasattr(result, 'requires_grad'): - raise TinyTorchValidationError("matmul doesn't return Variables properly") - except Exception as e: - raise TinyTorchValidationError(f"Matrix multiplication with Variables failed: {e}") - - # Test 2: ReLU with Variables - relu = ReLU() - x = Variable(Tensor([[-1, 0, 1]]), requires_grad=True) - - try: - relu_result = relu(x) - if not hasattr(relu_result, 'requires_grad'): - raise TinyTorchValidationError("ReLU doesn't return Variables properly") - except Exception as e: - raise TinyTorchValidationError(f"ReLU with Variables failed: {e}") - - # Test 3: Softmax with Variables - softmax = Softmax() - x = Variable(Tensor([[1, 2, 3]]), requires_grad=True) - - try: - softmax_result = softmax(x) - if not hasattr(softmax_result, 'requires_grad'): - raise TinyTorchValidationError("Softmax doesn't return Variables properly") - # Check if it's a valid probability distribution - prob_sum = np.sum(softmax_result.data.data) - if not np.isclose(prob_sum, 1.0, atol=1e-6): - raise TinyTorchValidationError("Softmax doesn't produce valid probabilities") - except Exception as e: - raise TinyTorchValidationError(f"Softmax with Variables failed: {e}") - - # Test 4: Loss function with Variables - loss_fn = MSELoss() - pred = Variable(Tensor([[0.1, 0.2, 0.7]]), requires_grad=True) - true = Variable(Tensor([[0.0, 0.0, 1.0]]), requires_grad=False) - - try: - loss = loss_fn(pred, true) - # Handle Variable/Tensor data access properly - if hasattr(loss.data, 'data'): - loss_value = float(loss.data.data) - elif hasattr(loss.data, '_data'): - loss_value = float(loss.data._data) - else: - loss_value = float(loss.data) - if not isinstance(loss_value, (int, float)) or np.isnan(loss_value): - raise TinyTorchValidationError("Loss function doesn't return valid scalar") - except Exception as e: - raise TinyTorchValidationError(f"Loss function with Variables failed: {e}") - - return True - - except ImportError as e: - raise TinyTorchValidationError(f"Core modules not available: {e}") - - -def validate_training_pipeline(): - """ - 🛡️ **STUDENT PROTECTION**: Validate complete training pipeline works. - - Tests the full forward pass that CIFAR-10 training requires. - """ - try: - from tinytorch.core.tensor import Tensor - from tinytorch.core.autograd import Variable - from tinytorch.core.layers import Dense - from tinytorch.core.activations import ReLU, Softmax - from tinytorch.core.training import MeanSquaredError as MSELoss - from tinytorch.core.optimizers import Adam - - # Create a mini neural network - fc1 = Dense(10, 5) - relu = ReLU() - fc2 = Dense(5, 3) - softmax = Softmax() - - # Make it trainable - fc1.weights = Variable(fc1.weights.data, requires_grad=True) - fc1.bias = Variable(fc1.bias.data, requires_grad=True) - fc2.weights = Variable(fc2.weights.data, requires_grad=True) - fc2.bias = Variable(fc2.bias.data, requires_grad=True) - - # Test forward pass - x = Variable(Tensor(np.random.randn(2, 10)), requires_grad=False) - h1 = fc1(x) - h1_act = relu(h1) - h2 = fc2(h1_act) - output = softmax(h2) - - # Test loss computation - target = Variable(Tensor(np.random.randn(2, 3)), requires_grad=False) - loss_fn = MSELoss() - loss = loss_fn(output, target) - - # Test optimizer - optimizer = Adam([fc1.weights, fc1.bias, fc2.weights, fc2.bias], learning_rate=0.001) - - # Validate shapes are preserved - original_bias_shape = fc1.bias.data.shape - optimizer.step() # This should not corrupt shapes - - if fc1.bias.data.shape != original_bias_shape: - raise TinyTorchValidationError(f"Bias shape corrupted: {original_bias_shape} -> {fc1.bias.data.shape}") - - return True - - except Exception as e: - raise TinyTorchValidationError(f"Training pipeline validation failed: {e}") - - -def run_student_protection_checks(verbose: bool = False): - """ - 🛡️ **MAIN PROTECTION FUNCTION**: Run all validation checks. - - This function should be called before CIFAR-10 training to ensure - students haven't accidentally broken core functionality. - - Args: - verbose: If True, print detailed validation results - - Returns: - bool: True if all checks pass - - Raises: - TinyTorchValidationError: If any critical functionality is broken - """ - checks = [ - ("Variable/Tensor Compatibility", validate_variable_tensor_compatibility), - ("Training Pipeline", validate_training_pipeline), - ] - - if verbose: - print("🛡️ Running TinyTorch Student Protection Checks...") - print("=" * 60) - - for check_name, check_func in checks: - try: - check_func() - if verbose: - print(f"✅ {check_name}: PASSED") - except TinyTorchValidationError as e: - error_msg = f""" -🚨 CRITICAL ERROR: {check_name} validation failed! - -{e} - -🛡️ STUDENT PROTECTION TRIGGERED: -This error suggests that core TinyTorch files have been accidentally modified. - -📋 TO FIX: -1. Check if you've edited any files in tinytorch/core/ directory -2. Those files are auto-generated and should NOT be edited directly -3. Make changes in modules/source/ instead -4. Run 'tito module complete ' to regenerate core files - -⚠️ CIFAR-10 training will FAIL until this is fixed! -""" - if verbose: - print(f"❌ {check_name}: FAILED") - print(error_msg) - raise TinyTorchValidationError(error_msg) - - if verbose: - print("=" * 60) - print("🎉 All protection checks passed! CIFAR-10 training should work.") - - return True - - -def auto_validate_on_import(): - """ - 🛡️ **AUTOMATIC PROTECTION**: Run validation when core modules are imported. - - This provides automatic protection without requiring students to - remember to run validation checks. - """ - try: - run_student_protection_checks(verbose=False) - except TinyTorchValidationError: - # Only warn on import, don't crash - warnings.warn( - "🚨 TinyTorch core functionality may be broken. " - "Run 'from tinytorch.core._validation import run_student_protection_checks; " - "run_student_protection_checks(verbose=True)' for details.", - UserWarning - ) - - -# Run automatic validation when this module is imported -# This provides silent protection for students -try: - auto_validate_on_import() -except Exception: - # Don't crash on import, just warn - pass \ No newline at end of file diff --git a/tinytorch/core/activations.py b/tinytorch/core/activations.py index d25bc57f..7ea930a0 100644 --- a/tinytorch/core/activations.py +++ b/tinytorch/core/activations.py @@ -17,21 +17,18 @@ # %% auto 0 __all__ = ['Sigmoid', 'ReLU', 'Tanh', 'GELU', 'Softmax'] -# %% ../../modules/source/02_activations/activations_dev.ipynb 2 +# %% ../../modules/source/02_activations/activations_dev.ipynb 3 import numpy as np from typing import Optional import sys import os -# Import our Tensor class - try from package first, then from local module -try: - from tinytorch.core.tensor import Tensor -except ImportError: - # For development, import from local tensor module - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '01_tensor')) - from tensor_dev import Tensor -# %% ../../modules/source/02_activations/activations_dev.ipynb 7 +# Import will be in export cell + +# %% ../../modules/source/02_activations/activations_dev.ipynb 8 +from .tensor import Tensor + class Sigmoid: """ Sigmoid activation: σ(x) = 1/(1 + e^(-x)) @@ -66,11 +63,15 @@ class Sigmoid: return Tensor(result) ### END SOLUTION + def __call__(self, x: Tensor) -> Tensor: + """Allows the activation to be called like a function.""" + return self.forward(x) + def backward(self, grad: Tensor) -> Tensor: """Compute gradient (implemented in Module 05).""" pass # Will implement backward pass in Module 05 -# %% ../../modules/source/02_activations/activations_dev.ipynb 11 +# %% ../../modules/source/02_activations/activations_dev.ipynb 12 class ReLU: """ ReLU activation: f(x) = max(0, x) @@ -108,7 +109,7 @@ class ReLU: """Compute gradient (implemented in Module 05).""" pass # Will implement backward pass in Module 05 -# %% ../../modules/source/02_activations/activations_dev.ipynb 15 +# %% ../../modules/source/02_activations/activations_dev.ipynb 16 class Tanh: """ Tanh activation: f(x) = (e^x - e^(-x))/(e^x + e^(-x)) @@ -146,7 +147,7 @@ class Tanh: """Compute gradient (implemented in Module 05).""" pass # Will implement backward pass in Module 05 -# %% ../../modules/source/02_activations/activations_dev.ipynb 19 +# %% ../../modules/source/02_activations/activations_dev.ipynb 20 class GELU: """ GELU activation: f(x) = x * Φ(x) ≈ x * Sigmoid(1.702 * x) @@ -189,7 +190,7 @@ class GELU: """Compute gradient (implemented in Module 05).""" pass # Will implement backward pass in Module 05 -# %% ../../modules/source/02_activations/activations_dev.ipynb 23 +# %% ../../modules/source/02_activations/activations_dev.ipynb 24 class Softmax: """ Softmax activation: f(x_i) = e^(x_i) / Σ(e^(x_j)) diff --git a/tinytorch/core/attention.py b/tinytorch/core/attention.py deleted file mode 100644 index 4d8d6547..00000000 --- a/tinytorch/core/attention.py +++ /dev/null @@ -1,618 +0,0 @@ -# ╔═══════════════════════════════════════════════════════════════════════════════╗ -# ║ 🚨 CRITICAL WARNING 🚨 ║ -# ║ AUTOGENERATED! DO NOT EDIT! ║ -# ║ ║ -# ║ This file is AUTOMATICALLY GENERATED from source modules. ║ -# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║ -# ║ ║ -# ║ ✅ TO EDIT: modules/source/07_attention/attention_dev.py ║ -# ║ ✅ TO EXPORT: Run 'tito module complete ' ║ -# ║ ║ -# ║ 🛡️ STUDENT PROTECTION: This file contains optimized implementations. ║ -# ║ Editing it directly may break module functionality and training. ║ -# ║ ║ -# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║ -# ║ happens! The tinytorch/ directory is just the compiled output. ║ -# ╚═══════════════════════════════════════════════════════════════════════════════╝ - -# %% auto 0 -__all__ = ['scaled_dot_product_attention', 'SelfAttention', 'create_causal_mask', 'create_padding_mask', - 'create_bidirectional_mask', 'AttentionEfficiencyProfiler'] - -# %% ../../modules/source/12_attention/attention_dev.ipynb 1 -import numpy as np -import math -import sys -import os -from typing import List, Union, Optional, Tuple -import matplotlib.pyplot as plt - -# Import our building blocks - try package first, then local modules -try: - from tinytorch.core.tensor import Tensor -except ImportError: - # For development, import from local modules - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '02_tensor')) - from tensor_dev import Tensor - -# %% ../../modules/source/12_attention/attention_dev.ipynb 7 -def scaled_dot_product_attention(Q: Tensor, K: Tensor, V: Tensor, - mask: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]: - """ - Scaled Dot-Product Attention - The foundation of all transformer models. - - This is the exact mechanism used in GPT, BERT, and all modern language models. - - TODO: Implement the core attention mechanism. - - STEP-BY-STEP IMPLEMENTATION: - 1. Get d_k (dimension of keys) from Q.shape[-1] - 2. Compute attention scores: Q @ K^T (matrix multiplication) - 3. Scale by √d_k: scores / sqrt(d_k) - 4. Apply mask if provided: set masked positions to -1e9 - 5. Apply softmax to get attention weights (probabilities) - 6. Apply attention weights to values: weights @ V - 7. Return (output, attention_weights) - - MATHEMATICAL OPERATION: - Attention(Q,K,V) = softmax(QK^T/√d_k)V - - IMPLEMENTATION HINTS: - - Use np.matmul() for matrix multiplication - - Use np.swapaxes(K, -2, -1) to transpose last two dimensions - - Use math.sqrt() for square root - - Use np.where() for masking: np.where(mask == 0, -1e9, scores) - - Implement softmax manually: exp(x) / sum(exp(x)) - - Use keepdims=True for broadcasting - - LEARNING CONNECTIONS: - - This exact function powers ChatGPT, BERT, GPT-4 - - The scaling prevents gradient vanishing in deep networks - - Masking enables causal (GPT) and bidirectional (BERT) models - - Attention weights are interpretable - you can visualize them! - - Args: - Q: Query tensor of shape (..., seq_len_q, d_k) - K: Key tensor of shape (..., seq_len_k, d_k) - V: Value tensor of shape (..., seq_len_v, d_v) - mask: Optional mask tensor of shape (..., seq_len_q, seq_len_k) - - Returns: - output: Attention output tensor (..., seq_len_q, d_v) - attention_weights: Attention probabilities tensor (..., seq_len_q, seq_len_k) - """ - ### BEGIN SOLUTION - # Get the dimension for scaling - d_k = Q.shape[-1] - - # Step 1: Compute attention scores (QK^T) - # This measures similarity between each query and each key - scores_data = np.matmul(Q.data, np.swapaxes(K.data, -2, -1)) - - # Step 2: Scale by √d_k to prevent exploding gradients - scores_data = scores_data / math.sqrt(d_k) - - # Step 3: Apply mask if provided (for padding or causality) - if mask is not None: - # Replace masked positions with large negative values - # This makes softmax output ~0 for these positions - scores_data = np.where(mask.data == 0, -1e9, scores_data) - - # Step 4: Apply softmax to get attention probabilities - # Each row sums to 1, representing where to focus attention - # Using numerically stable softmax - scores_max = np.max(scores_data, axis=-1, keepdims=True) - scores_exp = np.exp(scores_data - scores_max) - attention_weights_data = scores_exp / np.sum(scores_exp, axis=-1, keepdims=True) - - # Step 5: Apply attention weights to values - output_data = np.matmul(attention_weights_data, V.data) - - return Tensor(output_data), Tensor(attention_weights_data) - ### END SOLUTION - -# %% ../../modules/source/12_attention/attention_dev.ipynb 11 -class SelfAttention: - """ - Self-Attention wrapper - Convenience class for self-attention where Q=K=V. - - This is the most common use case in transformer models where each position - attends to all positions in the same sequence. - """ - - def __init__(self, d_model: int): - """ - Initialize Self-Attention. - - TODO: Store the model dimension for this self-attention layer. - - STEP-BY-STEP IMPLEMENTATION: - 1. Store d_model as an instance variable (self.d_model) - 2. Print initialization message for debugging - - EXAMPLE USAGE: - ```python - self_attn = SelfAttention(d_model=64) - output, weights = self_attn(input_sequence) - ``` - - IMPLEMENTATION HINTS: - - Simply store d_model parameter: self.d_model = d_model - - Print message: print(f"🔧 SelfAttention: d_model={d_model}") - - LEARNING CONNECTIONS: - - This is like nn.MultiheadAttention in PyTorch (but simpler) - - Used in every transformer layer for self-attention - - Foundation for understanding GPT, BERT architectures - - Args: - d_model: Model dimension - """ - ### BEGIN SOLUTION - self.d_model = d_model - print(f"🔧 SelfAttention: d_model={d_model}") - ### END SOLUTION - - def forward(self, x: Tensor, mask: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]: - """ - Forward pass of self-attention. - - TODO: Apply self-attention where Q=K=V=x. - - STEP-BY-STEP IMPLEMENTATION: - 1. Call scaled_dot_product_attention with Q=K=V=x - 2. Pass the mask parameter through - 3. Return the output and attention weights - - EXAMPLE USAGE: - ```python - x = Tensor(np.random.randn(seq_len, d_model)) # Input sequence - output, weights = self_attn.forward(x) - # weights[i,j] = how much position i attends to position j - ``` - - IMPLEMENTATION HINTS: - - Use the function you implemented above - - Self-attention means: Q = K = V = x - - Return: scaled_dot_product_attention(x, x, x, mask) - - LEARNING CONNECTIONS: - - This is how transformers process sequences - - Each position can attend to any other position - - Enables understanding of long-range dependencies - - Args: - x: Input tensor (..., seq_len, d_model) - mask: Optional attention mask - - Returns: - output: Self-attention output (..., seq_len, d_model) - attention_weights: Attention weights - """ - ### BEGIN SOLUTION - # Self-attention: Q = K = V = x - return scaled_dot_product_attention(x, x, x, mask) - ### END SOLUTION - - def __call__(self, x: Tensor, mask: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]: - """Make the class callable.""" - return self.forward(x, mask) - -# %% ../../modules/source/12_attention/attention_dev.ipynb 15 -def create_causal_mask(seq_len: int) -> np.ndarray: - """ - Create a causal (lower triangular) mask for autoregressive models. - - Used in models like GPT where each position can only attend to - previous positions, not future ones. - - TODO: Create a lower triangular matrix of ones. - - STEP-BY-STEP IMPLEMENTATION: - 1. Use np.tril() to create lower triangular matrix - 2. Create matrix of ones with shape (seq_len, seq_len) - 3. Return the lower triangular part - - EXAMPLE USAGE: - ```python - mask = create_causal_mask(4) - # mask = [[1, 0, 0, 0], - # [1, 1, 0, 0], - # [1, 1, 1, 0], - # [1, 1, 1, 1]] - ``` - - IMPLEMENTATION HINTS: - - Use np.ones((seq_len, seq_len)) to create matrix of ones - - Use np.tril() to get lower triangular part - - Or combine: np.tril(np.ones((seq_len, seq_len))) - - LEARNING CONNECTIONS: - - Used in GPT for autoregressive generation - - Prevents looking into the future during training - - Essential for language modeling tasks - - Args: - seq_len: Sequence length - - Returns: - mask: Causal mask (seq_len, seq_len) with 1s for allowed positions, 0s for blocked - """ - ### BEGIN SOLUTION - return np.tril(np.ones((seq_len, seq_len))) - ### END SOLUTION - -#| export -def create_padding_mask(lengths: List[int], max_length: int) -> np.ndarray: - """ - Create padding mask for variable-length sequences. - - TODO: Create mask that ignores padding tokens. - - STEP-BY-STEP IMPLEMENTATION: - 1. Initialize zero array with shape (batch_size, max_length, max_length) - 2. For each sequence in the batch, set valid positions to 1 - 3. Valid positions are [:length, :length] for each sequence - 4. Return the mask array - - EXAMPLE USAGE: - ```python - lengths = [3, 2, 4] # Actual sequence lengths - mask = create_padding_mask(lengths, max_length=4) - # For sequence 0 (length=3): positions [0,1,2] can attend to [0,1,2] - # For sequence 1 (length=2): positions [0,1] can attend to [0,1] - ``` - - IMPLEMENTATION HINTS: - - batch_size = len(lengths) - - Use np.zeros((batch_size, max_length, max_length)) - - Loop through lengths: for i, length in enumerate(lengths) - - Set valid region: mask[i, :length, :length] = 1 - - LEARNING CONNECTIONS: - - Used when sequences have different lengths - - Prevents attention to padding tokens - - Essential for efficient batch processing - - Args: - lengths: List of actual sequence lengths - max_length: Maximum sequence length (padded length) - - Returns: - mask: Padding mask (batch_size, max_length, max_length) - """ - ### BEGIN SOLUTION - batch_size = len(lengths) - mask = np.zeros((batch_size, max_length, max_length)) - - for i, length in enumerate(lengths): - mask[i, :length, :length] = 1 - - return mask - ### END SOLUTION - -#| export -def create_bidirectional_mask(seq_len: int) -> np.ndarray: - """ - Create a bidirectional mask where all positions can attend to all positions. - - Used in models like BERT for bidirectional context understanding. - - TODO: Create a matrix of all ones. - - STEP-BY-STEP IMPLEMENTATION: - 1. Use np.ones() to create matrix of all ones - 2. Shape should be (seq_len, seq_len) - 3. Return the matrix - - EXAMPLE USAGE: - ```python - mask = create_bidirectional_mask(3) - # mask = [[1, 1, 1], - # [1, 1, 1], - # [1, 1, 1]] - ``` - - IMPLEMENTATION HINTS: - - Very simple: np.ones((seq_len, seq_len)) - - All positions can attend to all positions - - LEARNING CONNECTIONS: - - Used in BERT for bidirectional understanding - - Allows looking at past and future context - - Good for understanding tasks, not generation - - Args: - seq_len: Sequence length - - Returns: - mask: All-ones mask (seq_len, seq_len) - """ - ### BEGIN SOLUTION - return np.ones((seq_len, seq_len)) - ### END SOLUTION - -# %% ../../modules/source/12_attention/attention_dev.ipynb 29 -import time -from collections import defaultdict - -class AttentionEfficiencyProfiler: - """ - Production Attention Mechanism Performance Analysis and Optimization - - Analyzes attention mechanism efficiency, memory patterns, and scaling - challenges for production transformer systems. - """ - - def __init__(self): - """Initialize attention efficiency profiler.""" - self.profiling_data = defaultdict(list) - self.scaling_analysis = defaultdict(list) - self.optimization_insights = [] - - def profile_attention_scaling(self, sequence_lengths=[64, 128, 256, 512]): - """ - Profile attention mechanism scaling with sequence length. - - TODO: Implement attention scaling analysis. - - APPROACH: - 1. Measure attention computation time for different sequence lengths - 2. Analyze memory usage scaling patterns - 3. Calculate computational complexity (FLOPs vs sequence length) - 4. Identify quadratic scaling bottlenecks - 5. Generate optimization recommendations for production deployment - - EXAMPLE: - profiler = AttentionEfficiencyProfiler() - scaling_analysis = profiler.profile_attention_scaling([64, 128, 256]) - print(f"Attention scaling factor: {scaling_analysis['quadratic_factor']:.2f}") - - HINTS: - - Create test tensors for different sequence lengths - - Measure both computation time and memory usage - - Calculate theoretical FLOPs: seq_len^2 * d_model for attention - - Compare empirical vs theoretical scaling - - Focus on production-relevant sequence lengths - """ - ### BEGIN SOLUTION - print("🔧 Profiling Attention Mechanism Scaling...") - - results = {} - d_model = 64 # Model dimension for testing - - for seq_len in sequence_lengths: - print(f" Testing sequence length: {seq_len}") - - # Create test tensors for attention computation - # Q, K, V have shape (seq_len, d_model) - query = Tensor(np.random.randn(seq_len, d_model)) - key = Tensor(np.random.randn(seq_len, d_model)) - value = Tensor(np.random.randn(seq_len, d_model)) - - # Measure attention computation time - iterations = 5 - start_time = time.time() - - for _ in range(iterations): - try: - # Simulate scaled dot-product attention - # attention_scores = query @ key.T / sqrt(d_model) - scores = query.data @ key.data.T / math.sqrt(d_model) - - # Softmax (simplified) - exp_scores = np.exp(scores - np.max(scores, axis=-1, keepdims=True)) - attention_weights = exp_scores / np.sum(exp_scores, axis=-1, keepdims=True) - - # Apply attention to values - output = attention_weights @ value.data - - except Exception as e: - # Fallback computation for testing - output = np.random.randn(seq_len, d_model) - - end_time = time.time() - avg_time = (end_time - start_time) / iterations - - # Calculate computational metrics - # Attention complexity: O(seq_len² * d_model) - theoretical_flops = seq_len * seq_len * d_model # QK^T - theoretical_flops += seq_len * seq_len # Softmax - theoretical_flops += seq_len * seq_len * d_model # Attention @ V - - # Memory analysis - query_memory = query.data.nbytes / (1024 * 1024) # MB - key_memory = key.data.nbytes / (1024 * 1024) - value_memory = value.data.nbytes / (1024 * 1024) - - # Attention matrix memory (most critical) - attention_matrix_memory = (seq_len * seq_len * 4) / (1024 * 1024) # MB, float32 - - total_memory = query_memory + key_memory + value_memory + attention_matrix_memory - - # Calculate efficiency metrics - flops_per_second = theoretical_flops / avg_time if avg_time > 0 else 0 - memory_bandwidth = total_memory / avg_time if avg_time > 0 else 0 - - result = { - 'sequence_length': seq_len, - 'time_ms': avg_time * 1000, - 'theoretical_flops': theoretical_flops, - 'flops_per_second': flops_per_second, - 'query_memory_mb': query_memory, - 'attention_matrix_memory_mb': attention_matrix_memory, - 'total_memory_mb': total_memory, - 'memory_bandwidth_mbs': memory_bandwidth - } - - results[seq_len] = result - - print(f" Time: {avg_time*1000:.3f}ms, Memory: {total_memory:.2f}MB") - - # Analyze scaling patterns - scaling_analysis = self._analyze_attention_scaling(results) - - # Store profiling data - self.profiling_data['attention_scaling'] = results - self.scaling_analysis = scaling_analysis - - return { - 'detailed_results': results, - 'scaling_analysis': scaling_analysis, - 'optimization_recommendations': self._generate_attention_optimizations(results) - } - ### END SOLUTION - - def _analyze_attention_scaling(self, results): - """Analyze attention scaling patterns and identify bottlenecks.""" - analysis = {} - - # Extract metrics for analysis - seq_lengths = sorted(results.keys()) - times = [results[seq_len]['time_ms'] for seq_len in seq_lengths] - memories = [results[seq_len]['total_memory_mb'] for seq_len in seq_lengths] - attention_memories = [results[seq_len]['attention_matrix_memory_mb'] for seq_len in seq_lengths] - - # Calculate scaling factors - if len(seq_lengths) >= 2: - small_seq = seq_lengths[0] - large_seq = seq_lengths[-1] - - seq_ratio = large_seq / small_seq - time_ratio = results[large_seq]['time_ms'] / results[small_seq]['time_ms'] - memory_ratio = results[large_seq]['total_memory_mb'] / results[small_seq]['total_memory_mb'] - attention_memory_ratio = results[large_seq]['attention_matrix_memory_mb'] / results[small_seq]['attention_matrix_memory_mb'] - - # Theoretical quadratic scaling - theoretical_quadratic = seq_ratio ** 2 - - analysis['sequence_scaling'] = { - 'sequence_ratio': seq_ratio, - 'time_scaling_factor': time_ratio, - 'memory_scaling_factor': memory_ratio, - 'attention_memory_scaling': attention_memory_ratio, - 'theoretical_quadratic': theoretical_quadratic, - 'time_vs_quadratic_ratio': time_ratio / theoretical_quadratic - } - - # Identify bottlenecks - if time_ratio > theoretical_quadratic * 1.2: - analysis['primary_bottleneck'] = 'computation' - analysis['bottleneck_reason'] = 'Time scaling worse than O(n^2) - computational bottleneck' - elif attention_memory_ratio > seq_ratio * 1.5: - analysis['primary_bottleneck'] = 'memory' - analysis['bottleneck_reason'] = 'Attention matrix memory scaling limiting performance' - else: - analysis['primary_bottleneck'] = 'balanced' - analysis['bottleneck_reason'] = 'Scaling follows expected O(n^2) pattern' - - # Memory breakdown analysis - total_memory_peak = max(memories) - attention_memory_peak = max(attention_memories) - attention_memory_percentage = (attention_memory_peak / total_memory_peak) * 100 - - analysis['memory_breakdown'] = { - 'peak_total_memory_mb': total_memory_peak, - 'peak_attention_memory_mb': attention_memory_peak, - 'attention_memory_percentage': attention_memory_percentage - } - - return analysis - - def _generate_attention_optimizations(self, results): - """Generate attention optimization recommendations.""" - recommendations = [] - - # Analyze sequence length limitations - max_seq_len = max(results.keys()) - peak_memory = max(result['total_memory_mb'] for result in results.values()) - - if peak_memory > 100: # > 100MB for attention - recommendations.append("💾 High memory usage detected") - recommendations.append("🔧 Consider: Gradient checkpointing, attention chunking") - - if max_seq_len >= 512: - recommendations.append("⚡ Long sequence processing detected") - recommendations.append("🔧 Consider: Sparse attention patterns, sliding window attention") - - # Memory efficiency recommendations - attention_memory_ratios = [r['attention_matrix_memory_mb'] / r['total_memory_mb'] - for r in results.values()] - avg_attention_ratio = sum(attention_memory_ratios) / len(attention_memory_ratios) - - if avg_attention_ratio > 0.6: # Attention matrix dominates memory - recommendations.append("📊 Attention matrix dominates memory usage") - recommendations.append("🔧 Consider: Flash Attention, memory-efficient attention") - - # Computational efficiency - scaling_analysis = self.scaling_analysis - if scaling_analysis and 'sequence_scaling' in scaling_analysis: - time_vs_quad = scaling_analysis['sequence_scaling']['time_vs_quadratic_ratio'] - if time_vs_quad > 1.5: - recommendations.append("🐌 Computational scaling worse than O(n^2)") - recommendations.append("🔧 Consider: Optimized GEMM operations, tensor cores") - - # Production deployment recommendations - recommendations.append("🏭 Production optimizations:") - recommendations.append(" • KV-cache for autoregressive generation") - recommendations.append(" • Mixed precision (fp16) for memory reduction") - recommendations.append(" • Attention kernel fusion for GPU efficiency") - - return recommendations - - def analyze_multi_head_efficiency(self, num_heads_range=[1, 2, 4, 8], seq_len=128, d_model=512): - """ - Analyze multi-head attention efficiency patterns. - - This function is PROVIDED to demonstrate multi-head scaling. - Students use it to understand parallelization trade-offs. - """ - print("🔍 MULTI-HEAD ATTENTION EFFICIENCY ANALYSIS") - print("=" * 50) - - d_k = d_model // max(num_heads_range) # Head dimension - - multi_head_results = [] - - for num_heads in num_heads_range: - head_dim = d_model // num_heads - - # Simulate multi-head computation - total_params = num_heads * (3 * d_model * head_dim) # Q, K, V projections - - # Memory for all heads - # Each head processes (seq_len, head_dim) - single_head_attention_memory = (seq_len * seq_len * 4) / (1024 * 1024) # MB - total_attention_memory = num_heads * single_head_attention_memory - - # Computational load per head is reduced - flops_per_head = seq_len * seq_len * head_dim - total_flops = num_heads * flops_per_head - - # Parallelization efficiency (simplified model) - parallelization_efficiency = min(1.0, num_heads / 8.0) # Assumes 8-way parallelism - effective_compute_time = total_flops / (num_heads * parallelization_efficiency) - - result = { - 'num_heads': num_heads, - 'head_dimension': head_dim, - 'total_parameters': total_params, - 'attention_memory_mb': total_attention_memory, - 'total_flops': total_flops, - 'parallelization_efficiency': parallelization_efficiency, - 'effective_compute_time': effective_compute_time - } - multi_head_results.append(result) - - print(f" {num_heads} heads: {head_dim}d each, {total_attention_memory:.1f}MB, {parallelization_efficiency:.2f} parallel efficiency") - - # Analyze optimal configuration - best_efficiency = max(multi_head_results, key=lambda x: x['parallelization_efficiency']) - memory_efficient = min(multi_head_results, key=lambda x: x['attention_memory_mb']) - - print(f"\n📈 Multi-Head Analysis:") - print(f" Best parallelization: {best_efficiency['num_heads']} heads") - print(f" Most memory efficient: {memory_efficient['num_heads']} heads") - print(f" Trade-off: More heads = better parallelism but higher memory") - - return multi_head_results diff --git a/tinytorch/core/autograd.py b/tinytorch/core/autograd.py deleted file mode 100644 index 60dbd5e3..00000000 --- a/tinytorch/core/autograd.py +++ /dev/null @@ -1,756 +0,0 @@ -# ╔═══════════════════════════════════════════════════════════════════════════════╗ -# ║ 🚨 CRITICAL WARNING 🚨 ║ -# ║ AUTOGENERATED! DO NOT EDIT! ║ -# ║ ║ -# ║ This file is AUTOMATICALLY GENERATED from source modules. ║ -# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║ -# ║ ║ -# ║ ✅ TO EDIT: modules/source/09_autograd/autograd_dev.py ║ -# ║ ✅ TO EXPORT: Run 'tito module complete ' ║ -# ║ ║ -# ║ 🛡️ STUDENT PROTECTION: This file contains optimized implementations. ║ -# ║ Editing it directly may break module functionality and training. ║ -# ║ ║ -# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║ -# ║ happens! The tinytorch/ directory is just the compiled output. ║ -# ╚═══════════════════════════════════════════════════════════════════════════════╝ - -# %% auto 0 -__all__ = ['Variable', 'add', 'multiply', 'subtract', 'AutogradSystemsProfiler'] - -# %% ../../modules/source/08_autograd/autograd_dev.ipynb 1 -import numpy as np -import sys -from typing import Union, List, Tuple, Optional, Any, Callable -from collections import defaultdict - -# Import our existing components -try: - from tinytorch.core.tensor import Tensor -except ImportError: - # For development, import from local modules - import os - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '01_tensor')) - from tensor_dev import Tensor - -# %% ../../modules/source/08_autograd/autograd_dev.ipynb 7 -class Variable: - """ - Variable: Tensor wrapper with automatic differentiation capabilities. - - The fundamental class for gradient computation in TinyTorch. - Wraps Tensor objects and tracks computational history for backpropagation. - """ - - def __init__(self, data: Union[Tensor, np.ndarray, list, float, int], - requires_grad: bool = True, grad_fn: Optional[Callable] = None): - """ - Create a Variable with gradient tracking. - - TODO: Implement Variable initialization with gradient tracking. - - STEP-BY-STEP IMPLEMENTATION: - 1. Convert data to Tensor if it is not already a Tensor - 2. Store the tensor data in self.data - 3. Set gradient tracking flag (requires_grad) - 4. Initialize gradient to None (will be computed during backward pass) - 5. Store the gradient function for backward pass - 6. Track if this is a leaf node (no grad_fn means it is a leaf) - - EXAMPLE USAGE: - ```python - # Create leaf variables (input data) - x = Variable(5.0, requires_grad=True) - y = Variable([1, 2, 3], requires_grad=True) - - # Create intermediate variables (results of operations) - z = x + y # Has grad_fn for addition - ``` - - IMPLEMENTATION HINTS: - - Use isinstance(data, Tensor) to check type - - Convert with Tensor(data) if needed - - Store requires_grad, grad_fn flags - - Initialize self.grad = None - - Leaf nodes have grad_fn = None - - Set self.is_leaf = (grad_fn is None) - - LEARNING CONNECTIONS: - - This is like torch.Tensor with requires_grad=True - - Forms the basis for all neural network training - - Each Variable is a node in the computational graph - - Enables automatic gradient computation - """ - ### BEGIN SOLUTION - # Convert data to Tensor if needed - if isinstance(data, Tensor): - self.data = data - else: - self.data = Tensor(data) - - # Set gradient tracking - self.requires_grad = requires_grad - self.grad = None # Will be initialized when needed - self.grad_fn = grad_fn - self.is_leaf = grad_fn is None - - # For computational graph - self._backward_hooks = [] - ### END SOLUTION - - @property - def shape(self) -> Tuple[int, ...]: - """Get the shape of the underlying tensor.""" - return self.data.shape - - @property - def size(self) -> int: - """Get the total number of elements.""" - return self.data.size - - def __repr__(self) -> str: - """String representation of the Variable.""" - grad_str = f", grad_fn={self.grad_fn.__name__}" if self.grad_fn else "" - return f"Variable({self.data.data.tolist()}, requires_grad={self.requires_grad}{grad_str})" - - def backward(self, gradient: Optional['Variable'] = None) -> None: - """ - Compute gradients using backpropagation. - - TODO: Implement backward pass for gradient computation. - - STEP-BY-STEP IMPLEMENTATION: - 1. If gradient is None, create gradient of ones (for scalar outputs) - 2. If this Variable requires gradients, accumulate the gradient - 3. If this Variable has a grad_fn, call it to propagate gradients - 4. The grad_fn will recursively call backward on input Variables - - EXAMPLE USAGE: - ```python - x = Variable(2.0, requires_grad=True) - y = Variable(3.0, requires_grad=True) - z = add(x, y) # z = 5.0 - z.backward() - print(x.grad) # 1.0 (∂z/∂x = 1) - print(y.grad) # 1.0 (∂z/∂y = 1) - ``` - - IMPLEMENTATION HINTS: - - If gradient is None: gradient = Variable(np.ones_like(self.data.data)) - - If self.requires_grad: accumulate gradient into self.grad - - If self.grad_fn: call self.grad_fn(gradient) - - Handle gradient accumulation (add to existing gradient) - - LEARNING CONNECTIONS: - - This implements the chain rule of calculus - - Gradients flow backward through the computational graph - - Each operation contributes its local gradient - - Enables training of any differentiable function - """ - ### BEGIN SOLUTION - if gradient is None: - gradient = Variable(np.ones_like(self.data.data)) - - if self.requires_grad: - if self.grad is None: - self.grad = gradient - else: - # Accumulate gradients - self.grad = Variable(self.grad.data.data + gradient.data.data) - - if self.grad_fn is not None: - self.grad_fn(gradient) - ### END SOLUTION - - def zero_grad(self) -> None: - """Reset gradients to zero.""" - self.grad = None - - def __add__(self, other: Union['Variable', float, int]) -> 'Variable': - """Addition operator: self + other""" - return add(self, other) - - def __mul__(self, other: Union['Variable', float, int]) -> 'Variable': - """Multiplication operator: self * other""" - return multiply(self, other) - - def __sub__(self, other: Union['Variable', float, int]) -> 'Variable': - """Subtraction operator: self - other""" - return subtract(self, other) - - def __truediv__(self, other: Union['Variable', float, int]) -> 'Variable': - """Division operator: self / other""" - return divide(self, other) - -# %% ../../modules/source/08_autograd/autograd_dev.ipynb 11 -def add(a: Union[Variable, float, int], b: Union[Variable, float, int]) -> Variable: - """ - Addition operation with gradient tracking: a + b - - TODO: Implement addition with automatic differentiation. - - STEP-BY-STEP IMPLEMENTATION: - 1. Convert inputs to Variables if they are scalars - 2. Compute forward pass: result = a.data + b.data - 3. Create gradient function that implements: ∂(a+b)/∂a = 1, ∂(a+b)/∂b = 1 - 4. Return new Variable with result and gradient function - - MATHEMATICAL FOUNDATION: - - Forward: z = x + y - - Backward: ∂z/∂x = 1, ∂z/∂y = 1 - - Chain rule: ∂L/∂x = ∂L/∂z · ∂z/∂x = ∂L/∂z · 1 = ∂L/∂z - - EXAMPLE USAGE: - ```python - x = Variable(2.0, requires_grad=True) - y = Variable(3.0, requires_grad=True) - z = add(x, y) # z = 5.0 - z.backward() - print(x.grad) # 1.0 (∂z/∂x = 1) - print(y.grad) # 1.0 (∂z/∂y = 1) - ``` - - IMPLEMENTATION HINTS: - - Convert scalars: if isinstance(a, (int, float)): a = Variable(a, requires_grad=False) - - Forward pass: result_data = a.data + b.data - - Backward function: def grad_fn(grad_output): if a.requires_grad: a.backward(grad_output) - - Return: Variable(result_data, grad_fn=grad_fn) - - Only propagate gradients to Variables that require them - - LEARNING CONNECTIONS: - - This is like torch.add() with autograd - - Addition distributes gradients equally to both inputs - - Forms the basis for bias addition in neural networks - - Chain rule propagates gradients through the graph - """ - ### BEGIN SOLUTION - # Convert scalars to Variables - if isinstance(a, (int, float)): - a = Variable(a, requires_grad=False) - if isinstance(b, (int, float)): - b = Variable(b, requires_grad=False) - - # Forward pass - result_data = a.data + b.data - - # Backward function - def grad_fn(grad_output): - # Addition distributes gradients equally, but must handle broadcasting - if a.requires_grad: - # Get gradient data - if hasattr(grad_output.data, 'data'): - grad_data = grad_output.data.data - else: - grad_data = grad_output.data - - # Check if we need to sum over broadcasted dimensions - a_shape = a.data.shape if hasattr(a.data, 'shape') else () - if grad_data.shape != a_shape: - # Sum over the broadcasted dimensions - # For bias: (batch_size, features) -> (features,) - if len(grad_data.shape) == 2 and len(a_shape) == 1: - grad_for_a = Variable(Tensor(np.sum(grad_data, axis=0))) - else: - # Handle other broadcasting cases - grad_for_a = grad_output - else: - grad_for_a = grad_output - - a.backward(grad_for_a) - - if b.requires_grad: - # Get gradient data - if hasattr(grad_output.data, 'data'): - grad_data = grad_output.data.data - else: - grad_data = grad_output.data - - # Check if we need to sum over broadcasted dimensions - b_shape = b.data.shape if hasattr(b.data, 'shape') else () - if grad_data.shape != b_shape: - # Sum over the broadcasted dimensions - # For bias: (batch_size, features) -> (features,) - if len(grad_data.shape) == 2 and len(b_shape) == 1: - grad_for_b = Variable(Tensor(np.sum(grad_data, axis=0))) - else: - # Handle other broadcasting cases - grad_for_b = grad_output - else: - grad_for_b = grad_output - - b.backward(grad_for_b) - - # Return new Variable with gradient function - requires_grad = a.requires_grad or b.requires_grad - return Variable(result_data, requires_grad=requires_grad, grad_fn=grad_fn) - ### END SOLUTION - -# %% ../../modules/source/08_autograd/autograd_dev.ipynb 15 -def multiply(a: Union[Variable, float, int], b: Union[Variable, float, int]) -> Variable: - """ - Multiplication operation with gradient tracking: a * b - - TODO: Implement multiplication with automatic differentiation. - - STEP-BY-STEP IMPLEMENTATION: - 1. Convert inputs to Variables if they are scalars - 2. Compute forward pass: result = a.data * b.data - 3. Create gradient function implementing product rule: ∂(a*b)/∂a = b, ∂(a*b)/∂b = a - 4. Return new Variable with result and gradient function - - MATHEMATICAL FOUNDATION: - - Forward: z = x * y - - Backward: ∂z/∂x = y, ∂z/∂y = x - - Chain rule: ∂L/∂x = ∂L/∂z · y, ∂L/∂y = ∂L/∂z · x - - EXAMPLE USAGE: - ```python - x = Variable(2.0, requires_grad=True) - y = Variable(3.0, requires_grad=True) - z = multiply(x, y) # z = 6.0 - z.backward() - print(x.grad) # 3.0 (∂z/∂x = y) - print(y.grad) # 2.0 (∂z/∂y = x) - ``` - - IMPLEMENTATION HINTS: - - Convert scalars to Variables (same as addition) - - Forward pass: result_data = a.data * b.data - - Backward function: multiply incoming gradient by the other variable - - For a: a.backward(grad_output * b.data) - - For b: b.backward(grad_output * a.data) - - LEARNING CONNECTIONS: - - This is like torch.mul() with autograd - - Product rule is fundamental to backpropagation - - Used in weight updates and attention mechanisms - - Each input's gradient depends on the other input's value - """ - ### BEGIN SOLUTION - # Convert scalars to Variables - if isinstance(a, (int, float)): - a = Variable(a, requires_grad=False) - if isinstance(b, (int, float)): - b = Variable(b, requires_grad=False) - - # Forward pass - result_data = a.data * b.data - - # Backward function - def grad_fn(grad_output): - # Product rule: d(xy)/dx = y, d(xy)/dy = x - if a.requires_grad: - a.backward(Variable(grad_output.data.data * b.data.data)) - if b.requires_grad: - b.backward(Variable(grad_output.data.data * a.data.data)) - - # Return new Variable with gradient function - requires_grad = a.requires_grad or b.requires_grad - return Variable(result_data, requires_grad=requires_grad, grad_fn=grad_fn) - ### END SOLUTION - -# %% ../../modules/source/08_autograd/autograd_dev.ipynb 18 -def subtract(a: Union[Variable, float, int], b: Union[Variable, float, int]) -> Variable: - """ - Subtraction operation with gradient tracking. - - Args: - a: First operand (minuend) - b: Second operand (subtrahend) - - Returns: - Variable with difference and gradient function - - TODO: Implement subtraction with gradient computation. - - APPROACH: - 1. Convert inputs to Variables if needed - 2. Compute forward pass: result = a - b - 3. Create gradient function with correct signs - 4. Return Variable with result and grad_fn - - MATHEMATICAL RULE: - If z = x - y, then dz/dx = 1, dz/dy = -1 - - EXAMPLE: - x = Variable(5.0), y = Variable(3.0) - z = subtract(x, y) # z.data = 2.0 - z.backward() # x.grad = 1.0, y.grad = -1.0 - - HINTS: - - Forward pass is straightforward: a - b - - Gradient for a is positive, for b is negative - - Remember to negate the gradient for b - """ - ### BEGIN SOLUTION - # Convert to Variables if needed - if not isinstance(a, Variable): - a = Variable(a, requires_grad=False) - if not isinstance(b, Variable): - b = Variable(b, requires_grad=False) - - # Forward pass - result_data = a.data - b.data - - # Create gradient function - def grad_fn(grad_output): - # Subtraction rule: d(x-y)/dx = 1, d(x-y)/dy = -1 - if a.requires_grad: - a.backward(grad_output) - if b.requires_grad: - b_grad = Variable(-grad_output.data.data) - b.backward(b_grad) - - # Determine if result requires gradients - requires_grad = a.requires_grad or b.requires_grad - - return Variable(result_data, requires_grad=requires_grad, grad_fn=grad_fn) - ### END SOLUTION - -# %% ../../modules/source/08_autograd/autograd_dev.ipynb 25 -import time -import gc -from collections import defaultdict, deque - -class AutogradSystemsProfiler: - """ - Production Autograd System Performance Analysis and Optimization - - Analyzes computational graph efficiency, memory patterns, and optimization - opportunities for production automatic differentiation systems. - """ - - def __init__(self): - """Initialize autograd systems profiler.""" - self.profiling_data = defaultdict(list) - self.graph_analysis = defaultdict(list) - self.optimization_strategies = [] - - def profile_computational_graph_depth(self, max_depth=10, operations_per_level=5): - """ - Profile computational graph performance vs depth. - - TODO: Implement computational graph depth analysis. - - APPROACH: - 1. Create computational graphs of increasing depth - 2. Measure forward and backward pass timing - 3. Analyze memory usage patterns during gradient computation - 4. Identify memory accumulation and gradient flow bottlenecks - 5. Generate graph optimization recommendations - - EXAMPLE: - profiler = AutogradSystemsProfiler() - graph_analysis = profiler.profile_computational_graph_depth(max_depth=8) - print(f"Memory scaling factor: {graph_analysis['memory_scaling_factor']:.2f}") - - HINTS: - - Build graphs by chaining operations: x -> op1 -> op2 -> ... -> loss - - Measure both forward and backward pass timing separately - - Track memory usage throughout the computation - - Monitor gradient accumulation patterns - - Focus on production-relevant graph depths - """ - ### BEGIN SOLUTION - print("🔧 Profiling Computational Graph Depth Impact...") - - results = {} - - for depth in range(1, max_depth + 1): - print(f" Testing graph depth: {depth}") - - # Create a computational graph of specified depth - # Each level adds more operations to test scaling - - # Start with input variable - try: - # Use Variable if available, otherwise simulate - x = Variable(np.random.randn(100, 100), requires_grad=True) - except: - # Fallback for testing - simulate Variable with Tensor - x = Tensor(np.random.randn(100, 100)) - - # Build computational graph of specified depth - current_var = x - operations = [] - - for level in range(depth): - # Add multiple operations per level to increase complexity - for op_idx in range(operations_per_level): - try: - # Simulate various operations - if op_idx % 4 == 0: - current_var = current_var * 0.9 # Scale operation - elif op_idx % 4 == 1: - current_var = current_var + 0.1 # Add operation - elif op_idx % 4 == 2: - # Matrix multiplication (most expensive) - weight = Tensor(np.random.randn(100, 100)) - if hasattr(current_var, 'data'): - current_var = Tensor(current_var.data @ weight.data) - else: - current_var = current_var @ weight - else: - # Activation-like operation - if hasattr(current_var, 'data'): - current_var = Tensor(np.maximum(0, current_var.data)) - else: - current_var = current_var # Skip for simplicity - - operations.append(f"level_{level}_op_{op_idx}") - except: - # Fallback for testing - current_var = Tensor(np.random.randn(100, 100)) - operations.append(f"level_{level}_op_{op_idx}_fallback") - - # Add final loss computation - try: - if hasattr(current_var, 'data'): - loss = Tensor(np.sum(current_var.data ** 2)) - else: - loss = np.sum(current_var ** 2) - except: - loss = Tensor(np.array([1.0])) - - # Measure forward pass timing - forward_iterations = 3 - forward_start = time.time() - - for _ in range(forward_iterations): - # Simulate forward pass computation - temp_x = x - for level in range(depth): - for op_idx in range(operations_per_level): - if op_idx % 4 == 0: - temp_x = temp_x * 0.9 - elif op_idx % 4 == 1: - temp_x = temp_x + 0.1 - # Skip expensive ops for timing - - forward_end = time.time() - avg_forward_time = (forward_end - forward_start) / forward_iterations - - # Measure backward pass timing (simulated) - # In real implementation, this would be loss.backward() - backward_start = time.time() - - # Simulate gradient computation through the graph - for _ in range(forward_iterations): - # Simulate backpropagation through all operations - gradient_accumulation = 0 - for level in range(depth): - for op_idx in range(operations_per_level): - # Simulate gradient computation - gradient_accumulation += level * op_idx * 0.001 - - backward_end = time.time() - avg_backward_time = (backward_end - backward_start) / forward_iterations - - # Memory analysis - try: - if hasattr(x, 'data'): - base_memory = x.data.nbytes / (1024 * 1024) # MB - if hasattr(current_var, 'data'): - result_memory = current_var.data.nbytes / (1024 * 1024) - else: - result_memory = base_memory - else: - base_memory = x.nbytes / (1024 * 1024) if hasattr(x, 'nbytes') else 1.0 - result_memory = base_memory - except: - base_memory = 1.0 - result_memory = 1.0 - - # Estimate gradient memory (in production, each operation stores gradients) - estimated_gradient_memory = depth * operations_per_level * base_memory * 0.5 - total_memory = base_memory + result_memory + estimated_gradient_memory - - # Calculate efficiency metrics - total_operations = depth * operations_per_level - total_time = avg_forward_time + avg_backward_time - operations_per_second = total_operations / total_time if total_time > 0 else 0 - - result = { - 'graph_depth': depth, - 'total_operations': total_operations, - 'forward_time_ms': avg_forward_time * 1000, - 'backward_time_ms': avg_backward_time * 1000, - 'total_time_ms': total_time * 1000, - 'base_memory_mb': base_memory, - 'estimated_gradient_memory_mb': estimated_gradient_memory, - 'total_memory_mb': total_memory, - 'operations_per_second': operations_per_second, - 'memory_per_operation': total_memory / total_operations if total_operations > 0 else 0 - } - - results[depth] = result - - print(f" Forward: {avg_forward_time*1000:.3f}ms, Backward: {avg_backward_time*1000:.3f}ms, Memory: {total_memory:.2f}MB") - - # Analyze scaling patterns - graph_analysis = self._analyze_graph_scaling(results) - - # Store profiling data - self.profiling_data['graph_depth_analysis'] = results - self.graph_analysis = graph_analysis - - return { - 'detailed_results': results, - 'graph_analysis': graph_analysis, - 'optimization_strategies': self._generate_graph_optimizations(results) - } - ### END SOLUTION - - def _analyze_graph_scaling(self, results): - """Analyze computational graph scaling patterns.""" - analysis = {} - - # Extract metrics for scaling analysis - depths = sorted(results.keys()) - forward_times = [results[d]['forward_time_ms'] for d in depths] - backward_times = [results[d]['backward_time_ms'] for d in depths] - total_times = [results[d]['total_time_ms'] for d in depths] - memory_usage = [results[d]['total_memory_mb'] for d in depths] - - # Calculate scaling factors - if len(depths) >= 2: - shallow = depths[0] - deep = depths[-1] - - depth_ratio = deep / shallow - forward_time_ratio = results[deep]['forward_time_ms'] / results[shallow]['forward_time_ms'] - backward_time_ratio = results[deep]['backward_time_ms'] / results[shallow]['backward_time_ms'] - memory_ratio = results[deep]['total_memory_mb'] / results[shallow]['total_memory_mb'] - - analysis['scaling_metrics'] = { - 'depth_ratio': depth_ratio, - 'forward_time_scaling': forward_time_ratio, - 'backward_time_scaling': backward_time_ratio, - 'memory_scaling': memory_ratio, - 'theoretical_linear': depth_ratio # Expected linear scaling - } - - # Identify bottlenecks - if backward_time_ratio > forward_time_ratio * 1.5: - analysis['primary_bottleneck'] = 'backward_pass' - analysis['bottleneck_reason'] = 'Gradient computation scaling worse than forward pass' - elif memory_ratio > depth_ratio * 1.5: - analysis['primary_bottleneck'] = 'memory' - analysis['bottleneck_reason'] = 'Memory usage scaling faster than linear' - else: - analysis['primary_bottleneck'] = 'balanced' - analysis['bottleneck_reason'] = 'Forward and backward passes scaling proportionally' - - # Backward/Forward ratio analysis - backward_forward_ratios = [ - results[d]['backward_time_ms'] / max(results[d]['forward_time_ms'], 0.001) - for d in depths - ] - avg_backward_forward_ratio = sum(backward_forward_ratios) / len(backward_forward_ratios) - - analysis['efficiency_metrics'] = { - 'avg_backward_forward_ratio': avg_backward_forward_ratio, - 'peak_memory_mb': max(memory_usage), - 'memory_efficiency_trend': 'increasing' if memory_usage[-1] > memory_usage[0] * 2 else 'stable' - } - - return analysis - - def _generate_graph_optimizations(self, results): - """Generate computational graph optimization strategies.""" - strategies = [] - - # Analyze memory growth patterns - peak_memory = max(result['total_memory_mb'] for result in results.values()) - - if peak_memory > 50: # > 50MB memory usage - strategies.append("💾 High memory usage detected in computational graph") - strategies.append("🔧 Strategy: Gradient checkpointing for deep graphs") - strategies.append("🔧 Strategy: In-place operations where mathematically valid") - - # Analyze computational efficiency - graph_analysis = self.graph_analysis - if graph_analysis and 'scaling_metrics' in graph_analysis: - backward_scaling = graph_analysis['scaling_metrics']['backward_time_scaling'] - if backward_scaling > 2.0: - strategies.append("🐌 Backward pass scaling poorly with graph depth") - strategies.append("🔧 Strategy: Kernel fusion for backward operations") - strategies.append("🔧 Strategy: Parallel gradient computation") - - # Memory vs computation trade-offs - if graph_analysis and 'efficiency_metrics' in graph_analysis: - backward_forward_ratio = graph_analysis['efficiency_metrics']['avg_backward_forward_ratio'] - if backward_forward_ratio > 3.0: - strategies.append("⚖️ Backward pass significantly slower than forward") - strategies.append("🔧 Strategy: Optimize gradient computation with sparse gradients") - strategies.append("🔧 Strategy: Use mixed precision to reduce memory bandwidth") - - # Production optimization recommendations - strategies.append("🏭 Production graph optimizations:") - strategies.append(" • Graph compilation and optimization (TorchScript, XLA)") - strategies.append(" • Operator fusion to minimize intermediate allocations") - strategies.append(" • Dynamic shape optimization for variable input sizes") - strategies.append(" • Gradient accumulation for large effective batch sizes") - - return strategies - - def analyze_memory_checkpointing_trade_offs(self, checkpoint_frequencies=[1, 2, 4, 8]): - """ - Analyze memory vs computation trade-offs with gradient checkpointing. - - This function is PROVIDED to demonstrate checkpointing analysis. - Students use it to understand memory optimization strategies. - """ - print("🔍 GRADIENT CHECKPOINTING ANALYSIS") - print("=" * 45) - - base_graph_depth = 12 - base_memory_per_layer = 10 # MB per layer - base_computation_time = 5 # ms per layer - - checkpointing_results = [] - - for freq in checkpoint_frequencies: - # Calculate memory savings - # Without checkpointing: store all intermediate activations - no_checkpoint_memory = base_graph_depth * base_memory_per_layer - - # With checkpointing: only store every freq-th activation - checkpointed_memory = (base_graph_depth // freq + 1) * base_memory_per_layer - memory_savings = no_checkpoint_memory - checkpointed_memory - memory_reduction_pct = (memory_savings / no_checkpoint_memory) * 100 - - # Calculate recomputation overhead - # Need to recompute (freq-1) layers for each checkpoint - recomputation_layers = base_graph_depth * (freq - 1) / freq - recomputation_time = recomputation_layers * base_computation_time - - # Total training time = forward + backward + recomputation - base_training_time = base_graph_depth * base_computation_time * 2 # forward + backward - total_training_time = base_training_time + recomputation_time - time_overhead_pct = (recomputation_time / base_training_time) * 100 - - result = { - 'checkpoint_frequency': freq, - 'memory_mb': checkpointed_memory, - 'memory_reduction_pct': memory_reduction_pct, - 'recomputation_time_ms': recomputation_time, - 'time_overhead_pct': time_overhead_pct, - 'memory_time_ratio': memory_reduction_pct / max(time_overhead_pct, 1) - } - checkpointing_results.append(result) - - print(f" Checkpoint every {freq} layers:") - print(f" Memory: {checkpointed_memory:.0f}MB ({memory_reduction_pct:.1f}% reduction)") - print(f" Time overhead: {time_overhead_pct:.1f}%") - print(f" Efficiency ratio: {result['memory_time_ratio']:.2f}") - - # Find optimal trade-off - optimal = max(checkpointing_results, key=lambda x: x['memory_time_ratio']) - - print(f"\n📈 Checkpointing Analysis:") - print(f" Optimal frequency: Every {optimal['checkpoint_frequency']} layers") - print(f" Best trade-off: {optimal['memory_reduction_pct']:.1f}% memory reduction") - print(f" Cost: {optimal['time_overhead_pct']:.1f}% time overhead") - - return checkpointing_results diff --git a/tinytorch/core/benchmarking.py b/tinytorch/core/benchmarking.py deleted file mode 100644 index 54c5c08c..00000000 --- a/tinytorch/core/benchmarking.py +++ /dev/null @@ -1,1206 +0,0 @@ -# ╔═══════════════════════════════════════════════════════════════════════════════╗ -# ║ 🚨 CRITICAL WARNING 🚨 ║ -# ║ AUTOGENERATED! DO NOT EDIT! ║ -# ║ ║ -# ║ This file is AUTOMATICALLY GENERATED from source modules. ║ -# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║ -# ║ ║ -# ║ ✅ TO EDIT: modules/source/14_benchmarking/benchmarking_dev.py ║ -# ║ ✅ TO EXPORT: Run 'tito module complete ' ║ -# ║ ║ -# ║ 🛡️ STUDENT PROTECTION: This file contains optimized implementations. ║ -# ║ Editing it directly may break module functionality and training. ║ -# ║ ║ -# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║ -# ║ happens! The tinytorch/ directory is just the compiled output. ║ -# ╚═══════════════════════════════════════════════════════════════════════════════╝ - -# %% auto 0 -__all__ = ['BenchmarkScenario', 'BenchmarkResult', 'BenchmarkScenarios', 'StatisticalValidation', 'StatisticalValidator', - 'TinyTorchPerf', 'PerformanceReporter', 'plot_benchmark_results', 'ProductionBenchmarkingProfiler'] - -# %% ../../modules/source/temp_holding/14_benchmarking/benchmarking_dev.ipynb 1 -import numpy as np -import matplotlib.pyplot as plt -import time -import statistics -import math -from typing import Dict, List, Tuple, Optional, Any, Callable -from enum import Enum -from dataclasses import dataclass -import os -import sys - -# Import our TinyTorch dependencies -try: - from tinytorch.core.tensor import Tensor - from tinytorch.core.networks import Sequential - from tinytorch.core.layers import Dense - from tinytorch.core.activations import ReLU, Softmax - from tinytorch.core.dataloader import DataLoader -except ImportError: - # For development, import from local modules - parent_dirs = [ - os.path.join(os.path.dirname(__file__), '..', '01_tensor'), - os.path.join(os.path.dirname(__file__), '..', '03_layers'), - os.path.join(os.path.dirname(__file__), '..', '02_activations'), - os.path.join(os.path.dirname(__file__), '..', '04_networks'), - os.path.join(os.path.dirname(__file__), '..', '06_dataloader') - ] - for path in parent_dirs: - if path not in sys.path: - sys.path.append(path) - - try: - from tensor_dev import Tensor - from networks_dev import Sequential - from layers_dev import Dense - from activations_dev import ReLU, Softmax - from dataloader_dev import DataLoader - except ImportError: - # Fallback for missing modules - print("⚠️ Some TinyTorch modules not available - using minimal implementations") - -# %% ../../modules/source/temp_holding/14_benchmarking/benchmarking_dev.ipynb 8 -class BenchmarkScenario(Enum): - """Standard benchmark scenarios from MLPerf""" - SINGLE_STREAM = "single_stream" - SERVER = "server" - OFFLINE = "offline" - -@dataclass -class BenchmarkResult: - """Results from a benchmark run""" - scenario: BenchmarkScenario - latencies: List[float] # All latency measurements in seconds - throughput: float # Samples per second - accuracy: float # Model accuracy (0-1) - metadata: Optional[Dict[str, Any]] = None - -#| export -class BenchmarkScenarios: - """ - Implements the three standard MLPerf benchmark scenarios. - - TODO: Implement the three benchmark scenarios following MLPerf patterns. - - STEP-BY-STEP IMPLEMENTATION: - 1. Single-Stream: Send queries one at a time, measure latency - 2. Server: Send queries following Poisson distribution, measure QPS - 3. Offline: Send all queries at once, measure total throughput - - IMPLEMENTATION APPROACH: - 1. Each scenario should run the model multiple times - 2. Collect latency measurements for each run - 3. Calculate appropriate metrics for each scenario - 4. Return BenchmarkResult with all measurements - - LEARNING CONNECTIONS: - - **MLPerf Standards**: Industry-standard benchmarking methodology used by Google, NVIDIA, etc. - - **Performance Scenarios**: Different deployment patterns require different measurement approaches - - **Production Validation**: Benchmarking validates model performance before deployment - - **Resource Planning**: Results guide infrastructure scaling and capacity planning - - EXAMPLE USAGE: - scenarios = BenchmarkScenarios() - result = scenarios.single_stream(model, dataset, num_queries=1000) - print(f"90th percentile latency: {result.latencies[int(0.9 * len(result.latencies))]} seconds") - """ - - def __init__(self): - self.results = [] - - def single_stream(self, model: Callable, dataset: List, num_queries: int = 1000) -> BenchmarkResult: - """ - Run single-stream benchmark scenario. - - TODO: Implement single-stream benchmarking. - - STEP-BY-STEP IMPLEMENTATION: - 1. Initialize empty list for latencies - 2. For each query (up to num_queries): - a. Get next sample from dataset (cycle if needed) - b. Record start time - c. Run model on sample - d. Record end time - e. Calculate latency = end - start - f. Add latency to list - 3. Calculate throughput = num_queries / total_time - 4. Calculate accuracy if possible - 5. Return BenchmarkResult with SINGLE_STREAM scenario - - LEARNING CONNECTIONS: - - **Mobile/Edge Deployment**: Single-stream simulates user-facing applications - - **Tail Latency**: 90th/95th percentiles matter more than averages for user experience - - **Interactive Systems**: Chatbots, recommendation engines use single-stream patterns - - **SLA Validation**: Ensures models meet response time requirements - - HINTS: - - Use time.perf_counter() for precise timing - - Use dataset[i % len(dataset)] to cycle through samples - - Sort latencies for percentile calculations - """ - ### BEGIN SOLUTION - latencies = [] - correct_predictions = 0 - total_start_time = time.perf_counter() - - for i in range(num_queries): - # Get sample (cycle through dataset) - sample = dataset[i % len(dataset)] - - # Time the inference - start_time = time.perf_counter() - result = model(sample) - end_time = time.perf_counter() - - latency = end_time - start_time - latencies.append(latency) - - # Simple accuracy calculation (if possible) - if hasattr(sample, 'target') and hasattr(result, 'data'): - predicted = np.argmax(result.data) - if predicted == sample.target: - correct_predictions += 1 - - total_time = time.perf_counter() - total_start_time - throughput = num_queries / total_time - accuracy = correct_predictions / num_queries if num_queries > 0 else 0.0 - - return BenchmarkResult( - scenario=BenchmarkScenario.SINGLE_STREAM, - latencies=sorted(latencies), - throughput=throughput, - accuracy=accuracy, - metadata={"num_queries": num_queries} - ) - ### END SOLUTION - raise NotImplementedError("Student implementation required") - - def server(self, model: Callable, dataset: List, target_qps: float = 10.0, - duration: float = 60.0) -> BenchmarkResult: - """ - Run server benchmark scenario with Poisson-distributed queries. - - TODO: Implement server benchmarking. - - STEP-BY-STEP IMPLEMENTATION: - 1. Calculate inter-arrival time = 1.0 / target_qps - 2. Run for specified duration: - a. Wait for next query arrival (Poisson distribution) - b. Get sample from dataset - c. Record start time - d. Run model - e. Record end time and latency - 3. Calculate actual QPS = total_queries / duration - 4. Return results - - LEARNING CONNECTIONS: - - **Web Services**: Server scenario simulates API endpoints handling concurrent requests - - **Load Testing**: Validates system behavior under realistic traffic patterns - - **Scalability Analysis**: Tests how well models handle increasing load - - **Production Deployment**: Critical for microservices and web-scale applications - - HINTS: - - Use np.random.exponential(inter_arrival_time) for Poisson - - Track both query arrival times and completion times - - Server scenario cares about sustained throughput - """ - ### BEGIN SOLUTION - latencies = [] - inter_arrival_time = 1.0 / target_qps - start_time = time.perf_counter() - current_time = start_time - query_count = 0 - - while (current_time - start_time) < duration: - # Wait for next query (Poisson distribution) - wait_time = np.random.exponential(inter_arrival_time) - # Use minimal delay for fast testing - if wait_time > 0.0001: # Only sleep for very long waits - time.sleep(min(wait_time, 0.0001)) - - # Get sample - sample = dataset[query_count % len(dataset)] - - # Time the inference - query_start = time.perf_counter() - result = model(sample) - query_end = time.perf_counter() - - latency = query_end - query_start - latencies.append(latency) - - query_count += 1 - current_time = time.perf_counter() - - actual_duration = current_time - start_time - actual_qps = query_count / actual_duration - - return BenchmarkResult( - scenario=BenchmarkScenario.SERVER, - latencies=sorted(latencies), - throughput=actual_qps, - accuracy=0.0, # Would need labels for accuracy - metadata={"target_qps": target_qps, "actual_qps": actual_qps, "duration": actual_duration} - ) - ### END SOLUTION - raise NotImplementedError("Student implementation required") - - def offline(self, model: Callable, dataset: List, batch_size: int = 32) -> BenchmarkResult: - """ - Run offline benchmark scenario with batch processing. - - TODO: Implement offline benchmarking. - - STEP-BY-STEP IMPLEMENTATION: - 1. Group dataset into batches of batch_size - 2. For each batch: - a. Record start time - b. Run model on entire batch - c. Record end time - d. Calculate batch latency - 3. Calculate total throughput = total_samples / total_time - 4. Return results - - LEARNING CONNECTIONS: - - **Batch Processing**: Offline scenario simulates data pipeline and ETL workloads - - **Throughput Optimization**: Maximizes processing efficiency for large datasets - - **Data Center Workloads**: Common in recommendation systems and analytics pipelines - - **Cost Optimization**: High throughput reduces compute costs per sample - - HINTS: - - Process data in batches for efficiency - - Measure total time for all batches - - Offline cares about maximum throughput - """ - ### BEGIN SOLUTION - latencies = [] - total_samples = len(dataset) - total_start_time = time.perf_counter() - - for batch_start in range(0, total_samples, batch_size): - batch_end = min(batch_start + batch_size, total_samples) - batch = dataset[batch_start:batch_end] - - # Time the batch inference - batch_start_time = time.perf_counter() - for sample in batch: - result = model(sample) - batch_end_time = time.perf_counter() - - batch_latency = batch_end_time - batch_start_time - latencies.append(batch_latency) - - total_time = time.perf_counter() - total_start_time - throughput = total_samples / total_time - - return BenchmarkResult( - scenario=BenchmarkScenario.OFFLINE, - latencies=latencies, - throughput=throughput, - accuracy=0.0, # Would need labels for accuracy - metadata={"batch_size": batch_size, "total_samples": total_samples} - ) - ### END SOLUTION - raise NotImplementedError("Student implementation required") - -# %% ../../modules/source/temp_holding/14_benchmarking/benchmarking_dev.ipynb 12 -@dataclass -class StatisticalValidation: - """Results from statistical validation""" - is_significant: bool - p_value: float - effect_size: float - confidence_interval: Tuple[float, float] - recommendation: str - -#| export -class StatisticalValidator: - """ - Validates benchmark results using proper statistical methods. - - TODO: Implement statistical validation for benchmark results. - - STEP-BY-STEP IMPLEMENTATION: - 1. Null hypothesis: No difference between models - 2. T-test: Compare means of two groups - 3. P-value: Probability of seeing this difference by chance - 4. Effect size: Magnitude of the difference - 5. Confidence interval: Range of likely true values - - IMPLEMENTATION APPROACH: - 1. Calculate basic statistics (mean, std, n) - 2. Perform t-test to get p-value - 3. Calculate effect size (Cohen's d) - 4. Calculate confidence interval - 5. Provide clear recommendation - - LEARNING CONNECTIONS: - - **Scientific Rigor**: Ensures performance claims are statistically valid - - **A/B Testing**: Foundation for production model comparison and rollout decisions - - **Research Validation**: Required for academic papers and technical reports - - **Business Decisions**: Statistical significance guides investment in new models - """ - - def __init__(self, confidence_level: float = 0.95): - self.confidence_level = confidence_level - self.alpha = 1 - confidence_level - - def validate_comparison(self, results_a: List[float], results_b: List[float]) -> StatisticalValidation: - """ - Compare two sets of benchmark results statistically. - - TODO: Implement statistical comparison. - - STEP-BY-STEP: - 1. Calculate basic statistics for both groups - 2. Perform two-sample t-test - 3. Calculate effect size (Cohen's d) - 4. Calculate confidence interval for the difference - 5. Generate recommendation based on results - - HINTS: - - Use scipy.stats.ttest_ind for t-test (or implement manually) - - Cohen's d = (mean_a - mean_b) / pooled_std - - CI = difference ± (critical_value * standard_error) - """ - ### BEGIN SOLUTION - import math - - # Basic statistics - mean_a = statistics.mean(results_a) - mean_b = statistics.mean(results_b) - std_a = statistics.stdev(results_a) - std_b = statistics.stdev(results_b) - n_a = len(results_a) - n_b = len(results_b) - - # Two-sample t-test (simplified) - pooled_std = math.sqrt(((n_a - 1) * std_a**2 + (n_b - 1) * std_b**2) / (n_a + n_b - 2)) - standard_error = pooled_std * math.sqrt(1/n_a + 1/n_b) - - if standard_error == 0: - t_stat = 0 - p_value = 1.0 - else: - t_stat = (mean_a - mean_b) / standard_error - # Simplified p-value calculation (assuming normal distribution) - p_value = 2 * (1 - abs(t_stat) / (abs(t_stat) + math.sqrt(n_a + n_b - 2))) - - # Effect size (Cohen's d) - effect_size = (mean_a - mean_b) / pooled_std if pooled_std > 0 else 0 - - # Confidence interval for difference - difference = mean_a - mean_b - critical_value = 1.96 # Approximate for 95% CI - margin_of_error = critical_value * standard_error - ci_lower = difference - margin_of_error - ci_upper = difference + margin_of_error - - # Determine significance - is_significant = p_value < self.alpha - - # Generate recommendation - if is_significant: - if effect_size > 0.8: - recommendation = "Large significant difference - strong evidence for improvement" - elif effect_size > 0.5: - recommendation = "Medium significant difference - good evidence for improvement" - else: - recommendation = "Small significant difference - weak evidence for improvement" - else: - recommendation = "No significant difference - insufficient evidence for improvement" - - return StatisticalValidation( - is_significant=is_significant, - p_value=p_value, - effect_size=effect_size, - confidence_interval=(ci_lower, ci_upper), - recommendation=recommendation - ) - ### END SOLUTION - raise NotImplementedError("Student implementation required") - - def validate_benchmark_result(self, result: BenchmarkResult, - min_samples: int = 100) -> StatisticalValidation: - """ - Validate that a benchmark result has sufficient statistical power. - - TODO: Implement validation for single benchmark result. - - STEP-BY-STEP: - 1. Check if we have enough samples - 2. Calculate confidence interval for the metric - 3. Check for common pitfalls (outliers, etc.) - 4. Provide recommendations - """ - ### BEGIN SOLUTION - latencies = result.latencies - n = len(latencies) - - if n < min_samples: - return StatisticalValidation( - is_significant=False, - p_value=1.0, - effect_size=0.0, - confidence_interval=(0.0, 0.0), - recommendation=f"Insufficient samples: {n} < {min_samples}. Need more data." - ) - - # Calculate confidence interval for mean latency - mean_latency = statistics.mean(latencies) - std_latency = statistics.stdev(latencies) - standard_error = std_latency / math.sqrt(n) - - critical_value = 1.96 # 95% CI - margin_of_error = critical_value * standard_error - ci_lower = mean_latency - margin_of_error - ci_upper = mean_latency + margin_of_error - - # Check for outliers (simple check) - q1 = latencies[int(0.25 * n)] - q3 = latencies[int(0.75 * n)] - iqr = q3 - q1 - outlier_threshold = q3 + 1.5 * iqr - outliers = [l for l in latencies if l > outlier_threshold] - - if len(outliers) > 0.1 * n: # More than 10% outliers - recommendation = f"Warning: {len(outliers)} outliers detected. Results may be unreliable." - else: - recommendation = "Benchmark result appears statistically valid." - - return StatisticalValidation( - is_significant=True, - p_value=0.0, # Not applicable for single result - effect_size=std_latency / mean_latency, # Coefficient of variation - confidence_interval=(ci_lower, ci_upper), - recommendation=recommendation - ) - ### END SOLUTION - raise NotImplementedError("Student implementation required") - -# %% ../../modules/source/temp_holding/14_benchmarking/benchmarking_dev.ipynb 16 -class TinyTorchPerf: - """ - Complete MLPerf-inspired benchmarking framework for TinyTorch. - - TODO: Implement the complete benchmarking framework. - - STEP-BY-STEP IMPLEMENTATION: - 1. Combines all benchmark scenarios - 2. Integrates statistical validation - 3. Provides easy-to-use API - 4. Generates professional reports - - IMPLEMENTATION APPROACH: - 1. Initialize with model and dataset - 2. Provide methods for each scenario - 3. Include statistical validation - 4. Generate comprehensive reports - - LEARNING CONNECTIONS: - - **MLPerf Integration**: Follows industry-standard benchmarking patterns - - **Production Deployment**: Validates models before production rollout - - **Performance Engineering**: Identifies bottlenecks and optimization opportunities - - **Framework Design**: Demonstrates how to build reusable ML tools - """ - - def __init__(self): - self.scenarios = BenchmarkScenarios() - self.validator = StatisticalValidator() - self.model = None - self.dataset = None - self.results = {} - - def set_model(self, model: Callable): - """Set the model to benchmark.""" - self.model = model - - def set_dataset(self, dataset: List): - """Set the dataset for benchmarking.""" - self.dataset = dataset - - def run_single_stream(self, num_queries: int = 1000) -> BenchmarkResult: - """ - Run single-stream benchmark. - - TODO: Implement single-stream benchmark with validation. - - STEP-BY-STEP: - 1. Check that model and dataset are set - 2. Run single-stream scenario - 3. Validate results statistically - 4. Store results - 5. Return result - """ - ### BEGIN SOLUTION - if self.model is None or self.dataset is None: - raise ValueError("Model and dataset must be set before running benchmarks") - - result = self.scenarios.single_stream(self.model, self.dataset, num_queries) - validation = self.validator.validate_benchmark_result(result) - - self.results['single_stream'] = { - 'result': result, - 'validation': validation - } - - return result - ### END SOLUTION - raise NotImplementedError("Student implementation required") - - def run_server(self, target_qps: float = 10.0, duration: float = 60.0) -> BenchmarkResult: - """ - Run server benchmark. - - TODO: Implement server benchmark with validation. - """ - ### BEGIN SOLUTION - if self.model is None or self.dataset is None: - raise ValueError("Model and dataset must be set before running benchmarks") - - result = self.scenarios.server(self.model, self.dataset, target_qps, duration) - validation = self.validator.validate_benchmark_result(result) - - self.results['server'] = { - 'result': result, - 'validation': validation - } - - return result - ### END SOLUTION - raise NotImplementedError("Student implementation required") - - def run_offline(self, batch_size: int = 32) -> BenchmarkResult: - """ - Run offline benchmark. - - TODO: Implement offline benchmark with validation. - """ - ### BEGIN SOLUTION - if self.model is None or self.dataset is None: - raise ValueError("Model and dataset must be set before running benchmarks") - - result = self.scenarios.offline(self.model, self.dataset, batch_size) - validation = self.validator.validate_benchmark_result(result) - - self.results['offline'] = { - 'result': result, - 'validation': validation - } - - return result - ### END SOLUTION - raise NotImplementedError("Student implementation required") - - def run_all_scenarios(self, quick_test: bool = False) -> Dict[str, BenchmarkResult]: - """ - Run all benchmark scenarios. - - TODO: Implement comprehensive benchmarking. - """ - ### BEGIN SOLUTION - if quick_test: - # Quick test with very small parameters for fast testing - single_result = self.run_single_stream(num_queries=5) - server_result = self.run_server(target_qps=20.0, duration=0.2) - offline_result = self.run_offline(batch_size=3) - else: - # Full benchmarking - single_result = self.run_single_stream(num_queries=1000) - server_result = self.run_server(target_qps=10.0, duration=60.0) - offline_result = self.run_offline(batch_size=32) - - return { - 'single_stream': single_result, - 'server': server_result, - 'offline': offline_result - } - ### END SOLUTION - raise NotImplementedError("Student implementation required") - - def compare_models(self, model_a: Callable, model_b: Callable, - scenario: str = 'single_stream') -> StatisticalValidation: - """ - Compare two models statistically. - - TODO: Implement model comparison. - """ - ### BEGIN SOLUTION - # Run both models on the same scenario - self.set_model(model_a) - if scenario == 'single_stream': - result_a = self.run_single_stream(num_queries=100) - elif scenario == 'server': - result_a = self.run_server(target_qps=5.0, duration=10.0) - else: # offline - result_a = self.run_offline(batch_size=16) - - self.set_model(model_b) - if scenario == 'single_stream': - result_b = self.run_single_stream(num_queries=100) - elif scenario == 'server': - result_b = self.run_server(target_qps=5.0, duration=10.0) - else: # offline - result_b = self.run_offline(batch_size=16) - - # Compare latencies - return self.validator.validate_comparison(result_a.latencies, result_b.latencies) - ### END SOLUTION - raise NotImplementedError("Student implementation required") - - def generate_report(self) -> str: - """ - Generate a comprehensive benchmark report. - - TODO: Implement professional report generation. - """ - ### BEGIN SOLUTION - report = "# TinyTorch Benchmark Report\n\n" - - for scenario_name, scenario_data in self.results.items(): - result = scenario_data['result'] - validation = scenario_data['validation'] - - report += f"## {scenario_name.replace('_', ' ').title()} Scenario\n\n" - report += f"- **Throughput**: {result.throughput:.2f} samples/second\n" - report += f"- **Mean Latency**: {statistics.mean(result.latencies)*1000:.2f} ms\n" - report += f"- **90th Percentile**: {result.latencies[int(0.9*len(result.latencies))]*1000:.2f} ms\n" - report += f"- **95th Percentile**: {result.latencies[int(0.95*len(result.latencies))]*1000:.2f} ms\n" - report += f"- **Statistical Validation**: {validation.recommendation}\n\n" - - return report - ### END SOLUTION - raise NotImplementedError("Student implementation required") - -# %% ../../modules/source/temp_holding/14_benchmarking/benchmarking_dev.ipynb 20 -class PerformanceReporter: - """ - Generates professional performance reports for ML projects. - - TODO: Implement professional report generation. - - UNDERSTANDING PROFESSIONAL REPORTS: - 1. Executive summary with key metrics - 2. Detailed methodology section - 3. Statistical validation results - 4. Comparison with baselines - 5. Recommendations for improvement - """ - - def __init__(self): - self.reports = [] - - def generate_project_report(self, benchmark_results: Dict[str, BenchmarkResult], - model_name: str = "TinyTorch Model") -> str: - """ - Generate a professional performance report for ML projects. - - TODO: Implement project report generation. - - STEP-BY-STEP: - 1. Create executive summary - 2. Add methodology section - 3. Present detailed results - 4. Include statistical validation - 5. Add recommendations - """ - ### BEGIN SOLUTION - report = f"""# {model_name} Performance Report - -## Executive Summary - -This report presents comprehensive performance benchmarking results for {model_name} using MLPerf-inspired methodology. The evaluation covers three standard scenarios: single-stream (latency), server (throughput), and offline (batch processing). - -### Key Findings -""" - - # Add key metrics - for scenario_name, result in benchmark_results.items(): - mean_latency = statistics.mean(result.latencies) * 1000 - p90_latency = result.latencies[int(0.9 * len(result.latencies))] * 1000 - - report += f"- **{scenario_name.replace('_', ' ').title()}**: {result.throughput:.2f} samples/sec, " - report += f"{mean_latency:.2f}ms mean latency, {p90_latency:.2f}ms 90th percentile\n" - - report += """ -## Methodology - -### Benchmark Framework -- **Architecture**: MLPerf-inspired four-component system -- **Scenarios**: Single-stream, server, and offline evaluation -- **Statistical Validation**: Multiple runs with confidence intervals -- **Metrics**: Latency distribution, throughput, accuracy - -### Test Environment -- **Hardware**: Standard development machine -- **Software**: TinyTorch framework -- **Dataset**: Standardized evaluation dataset -- **Validation**: Statistical significance testing - -## Detailed Results - -""" - - # Add detailed results for each scenario - for scenario_name, result in benchmark_results.items(): - report += f"### {scenario_name.replace('_', ' ').title()} Scenario\n\n" - - latencies_ms = [l * 1000 for l in result.latencies] - - report += f"- **Sample Count**: {len(result.latencies)}\n" - report += f"- **Mean Latency**: {statistics.mean(latencies_ms):.2f} ms\n" - report += f"- **Median Latency**: {statistics.median(latencies_ms):.2f} ms\n" - report += f"- **90th Percentile**: {latencies_ms[int(0.9 * len(latencies_ms))]:.2f} ms\n" - report += f"- **95th Percentile**: {latencies_ms[int(0.95 * len(latencies_ms))]:.2f} ms\n" - report += f"- **Standard Deviation**: {statistics.stdev(latencies_ms):.2f} ms\n" - report += f"- **Throughput**: {result.throughput:.2f} samples/second\n" - - if result.accuracy > 0: - report += f"- **Accuracy**: {result.accuracy:.4f}\n" - - report += "\n" - - report += """## Statistical Validation - -All results include proper statistical validation: -- Multiple independent runs for reliability -- Confidence intervals for key metrics -- Outlier detection and handling -- Significance testing for comparisons - -## Recommendations - -Based on the benchmark results: -1. **Performance Characteristics**: Model shows consistent performance across scenarios -2. **Optimization Opportunities**: Focus on reducing tail latency for production deployment -3. **Scalability**: Server scenario results indicate good potential for production scaling -4. **Further Testing**: Consider testing with larger datasets and different hardware configurations - -## Conclusion - -This comprehensive benchmarking demonstrates {model_name}'s performance characteristics using industry-standard methodology. The results provide a solid foundation for production deployment decisions and further optimization efforts. -""" - - return report - ### END SOLUTION - raise NotImplementedError("Student implementation required") - - def save_report(self, report: str, filename: str = "benchmark_report.md"): - """Save report to file.""" - with open(filename, 'w') as f: - f.write(report) - print(f"📄 Report saved to {filename}") - -def plot_benchmark_results(benchmark_results: Dict[str, BenchmarkResult]): - """Visualize benchmark results.""" - - # Create visualizations - fig, axes = plt.subplots(1, 3, figsize=(18, 5)) - - # Latency distribution for single-stream - if 'single_stream' in benchmark_results: - axes[0].hist(benchmark_results['single_stream'].latencies, bins=50, color='skyblue') - axes[0].set_title("Single-Stream Latency Distribution") - axes[0].set_xlabel("Latency (s)") - axes[0].set_ylabel("Frequency") - - # Server scenario latency - if 'server' in benchmark_results: - axes[1].plot(benchmark_results['server'].latencies, marker='o', linestyle='-', color='salmon') - axes[1].set_title("Server Scenario Latency Over Time") - axes[1].set_xlabel("Query Index") - axes[1].set_ylabel("Latency (s)") - - # Offline scenario throughput - if 'offline' in benchmark_results: - offline_result = benchmark_results['offline'] - throughput = len(offline_result.latencies) / sum(offline_result.latencies) - axes[2].bar(['Throughput'], [throughput], color='lightgreen') - axes[2].set_title("Offline Scenario Throughput") - axes[2].set_ylabel("Samples per second") - - plt.tight_layout() - plt.show() - -# %% ../../modules/source/temp_holding/14_benchmarking/benchmarking_dev.ipynb 29 -class ProductionBenchmarkingProfiler: - """ - Advanced production-grade benchmarking profiler for ML systems. - - This class implements comprehensive performance analysis patterns used in - production ML systems, including end-to-end latency analysis, resource - monitoring, A/B testing frameworks, and production monitoring integration. - - TODO: Implement production-grade profiling capabilities. - - STEP-BY-STEP IMPLEMENTATION: - 1. End-to-end pipeline analysis (not just model inference) - 2. Resource utilization monitoring (CPU, memory, bandwidth) - 3. Statistical A/B testing frameworks - 4. Production monitoring and alerting integration - 5. Performance regression detection - 6. Load testing and capacity planning - - LEARNING CONNECTIONS: - - **Production ML Systems**: Real-world profiling for deployment optimization - - **Performance Engineering**: Systematic approach to identifying and fixing bottlenecks - - **A/B Testing**: Statistical frameworks for safe model rollouts - - **Cost Optimization**: Understanding resource usage for efficient cloud deployment - """ - - def __init__(self, enable_monitoring: bool = True): - self.enable_monitoring = enable_monitoring - self.baseline_metrics = {} - self.production_metrics = [] - self.ab_test_results = {} - self.resource_usage = [] - - def profile_end_to_end_pipeline(self, model: Callable, dataset: List, - preprocessing_fn: Optional[Callable] = None, - postprocessing_fn: Optional[Callable] = None) -> Dict[str, float]: - """ - Profile the complete ML pipeline including preprocessing and postprocessing. - - TODO: Implement end-to-end pipeline profiling. - - IMPLEMENTATION STEPS: - 1. Profile data loading and preprocessing time - 2. Profile model inference time - 3. Profile postprocessing and output formatting time - 4. Measure total memory usage throughout pipeline - 5. Calculate end-to-end latency distribution - 6. Identify bottlenecks in the pipeline - - HINTS: - - Use context managers for timing different stages - - Track memory usage with sys.getsizeof or psutil - - Measure both CPU and wall-clock time - - Consider batch vs single-sample processing differences - """ - ### BEGIN SOLUTION - import time - import sys - - pipeline_metrics = { - 'preprocessing_time': [], - 'inference_time': [], - 'postprocessing_time': [], - 'memory_usage': [], - 'end_to_end_latency': [] - } - - for sample in dataset[:100]: # Profile first 100 samples - start_time = time.perf_counter() - - # Preprocessing stage - preprocess_start = time.perf_counter() - if preprocessing_fn: - processed_sample = preprocessing_fn(sample) - else: - processed_sample = sample - preprocess_end = time.perf_counter() - pipeline_metrics['preprocessing_time'].append(preprocess_end - preprocess_start) - - # Inference stage - inference_start = time.perf_counter() - model_output = model(processed_sample) - inference_end = time.perf_counter() - pipeline_metrics['inference_time'].append(inference_end - inference_start) - - # Postprocessing stage - postprocess_start = time.perf_counter() - if postprocessing_fn: - final_output = postprocessing_fn(model_output) - else: - final_output = model_output - postprocess_end = time.perf_counter() - pipeline_metrics['postprocessing_time'].append(postprocess_end - postprocess_start) - - end_time = time.perf_counter() - pipeline_metrics['end_to_end_latency'].append(end_time - start_time) - - # Memory usage estimation - memory_usage = sys.getsizeof(processed_sample) + sys.getsizeof(model_output) + sys.getsizeof(final_output) - pipeline_metrics['memory_usage'].append(memory_usage) - - # Calculate summary statistics - summary_metrics = {} - for metric_name, values in pipeline_metrics.items(): - summary_metrics[f'{metric_name}_mean'] = statistics.mean(values) - summary_metrics[f'{metric_name}_p95'] = values[int(0.95 * len(values))] if values else 0 - summary_metrics[f'{metric_name}_max'] = max(values) if values else 0 - - return summary_metrics - ### END SOLUTION - raise NotImplementedError("Student implementation required") - - def monitor_resource_utilization(self, duration: float = 60.0) -> Dict[str, List[float]]: - """ - Monitor system resource utilization during model execution. - - TODO: Implement resource monitoring. - - IMPLEMENTATION STEPS: - 1. Sample CPU usage over time - 2. Track memory consumption patterns - 3. Monitor bandwidth utilization (if applicable) - 4. Record resource usage spikes and patterns - 5. Correlate resource usage with performance - - STUDENT IMPLEMENTATION CHALLENGE (75% level): - You need to implement the resource monitoring logic. - Consider how you would track CPU, memory, and other resources - during model execution in a production environment. - """ - ### BEGIN SOLUTION - import time - import os - - resource_metrics = { - 'cpu_usage': [], - 'memory_usage': [], - 'timestamp': [] - } - - start_time = time.perf_counter() - - while (time.perf_counter() - start_time) < duration: - current_time = time.perf_counter() - start_time - - # Simple CPU usage estimation (in real production, use psutil) - # This is a placeholder implementation - cpu_usage = 50 + 30 * np.random.rand() # Simulated CPU usage - - # Memory usage estimation - memory_usage = 1024 + 512 * np.random.rand() # Simulated memory in MB - - resource_metrics['cpu_usage'].append(cpu_usage) - resource_metrics['memory_usage'].append(memory_usage) - resource_metrics['timestamp'].append(current_time) - - time.sleep(0.1) # Sample every 100ms - - return resource_metrics - ### END SOLUTION - raise NotImplementedError("Student implementation required") - - def setup_ab_testing_framework(self, model_a: Callable, model_b: Callable, - traffic_split: float = 0.5) -> Dict[str, Any]: - """ - Set up A/B testing framework for comparing model versions in production. - - TODO: Implement A/B testing framework. - - IMPLEMENTATION STEPS: - 1. Implement traffic splitting logic - 2. Track metrics for both model versions - 3. Implement statistical significance testing - 4. Monitor for performance regressions - 5. Provide recommendations for rollout - - STUDENT IMPLEMENTATION CHALLENGE (75% level): - Implement a production-ready A/B testing framework that can - safely compare two model versions with proper statistical validation. - """ - ### BEGIN SOLUTION - ab_test_config = { - 'model_a': model_a, - 'model_b': model_b, - 'traffic_split': traffic_split, - 'metrics_a': {'latencies': [], 'accuracies': [], 'errors': 0}, - 'metrics_b': {'latencies': [], 'accuracies': [], 'errors': 0}, - 'total_requests': 0, - 'requests_a': 0, - 'requests_b': 0 - } - - return ab_test_config - ### END SOLUTION - raise NotImplementedError("Student implementation required") - - def run_ab_test(self, ab_config: Dict[str, Any], dataset: List, - num_samples: int = 1000) -> Dict[str, Any]: - """ - Execute A/B test with statistical validation. - - TODO: Implement A/B test execution. - - STUDENT IMPLEMENTATION CHALLENGE (75% level): - Execute the A/B test, collect metrics, and provide statistical - analysis of the results with confidence intervals. - """ - ### BEGIN SOLUTION - import time - - model_a = ab_config['model_a'] - model_b = ab_config['model_b'] - traffic_split = ab_config['traffic_split'] - - for i in range(num_samples): - sample = dataset[i % len(dataset)] - - # Route traffic based on split - if np.random.rand() < traffic_split: - # Route to model A - start_time = time.perf_counter() - try: - result = model_a(sample) - latency = time.perf_counter() - start_time - ab_config['metrics_a']['latencies'].append(latency) - ab_config['requests_a'] += 1 - except Exception: - ab_config['metrics_a']['errors'] += 1 - else: - # Route to model B - start_time = time.perf_counter() - try: - result = model_b(sample) - latency = time.perf_counter() - start_time - ab_config['metrics_b']['latencies'].append(latency) - ab_config['requests_b'] += 1 - except Exception: - ab_config['metrics_b']['errors'] += 1 - - ab_config['total_requests'] += 1 - - # Calculate test results - latencies_a = ab_config['metrics_a']['latencies'] - latencies_b = ab_config['metrics_b']['latencies'] - - if latencies_a and latencies_b: - # Statistical comparison - validator = StatisticalValidator() - statistical_result = validator.validate_comparison(latencies_a, latencies_b) - - results = { - 'model_a_performance': { - 'mean_latency': statistics.mean(latencies_a), - 'p95_latency': latencies_a[int(0.95 * len(latencies_a))], - 'error_rate': ab_config['metrics_a']['errors'] / ab_config['requests_a'] if ab_config['requests_a'] > 0 else 0 - }, - 'model_b_performance': { - 'mean_latency': statistics.mean(latencies_b), - 'p95_latency': latencies_b[int(0.95 * len(latencies_b))], - 'error_rate': ab_config['metrics_b']['errors'] / ab_config['requests_b'] if ab_config['requests_b'] > 0 else 0 - }, - 'statistical_analysis': statistical_result, - 'recommendation': self._generate_ab_recommendation(statistical_result) - } - else: - results = {'error': 'Insufficient data for comparison'} - - return results - ### END SOLUTION - raise NotImplementedError("Student implementation required") - - def _generate_ab_recommendation(self, statistical_result: StatisticalValidation) -> str: - """ - Generate production rollout recommendation based on A/B test results. - - STUDENT IMPLEMENTATION CHALLENGE (75% level): - Based on the statistical results, provide a clear recommendation - for production rollout decisions. - """ - ### BEGIN SOLUTION - if not statistical_result.is_significant: - return "No significant difference detected. Consider longer test duration or larger sample size." - - if statistical_result.effect_size < 0: - return "Model B shows worse performance. Do not proceed with rollout." - elif statistical_result.effect_size > 0.2: - return "Model B shows significant improvement. Proceed with gradual rollout." - else: - return "Model B shows marginal improvement. Consider business impact before rollout." - ### END SOLUTION - raise NotImplementedError("Student implementation required") - - def detect_performance_regression(self, current_metrics: Dict[str, float], - baseline_metrics: Dict[str, float], - threshold: float = 0.1) -> Dict[str, Any]: - """ - Detect performance regressions compared to baseline. - - TODO: Implement regression detection. - - STUDENT IMPLEMENTATION CHALLENGE (75% level): - Implement automated detection of performance regressions - with configurable thresholds and alerting. - """ - ### BEGIN SOLUTION - regressions = [] - improvements = [] - - for metric_name, current_value in current_metrics.items(): - if metric_name in baseline_metrics: - baseline_value = baseline_metrics[metric_name] - if baseline_value > 0: # Avoid division by zero - change_percent = (current_value - baseline_value) / baseline_value - - if change_percent > threshold: - regressions.append({ - 'metric': metric_name, - 'baseline': baseline_value, - 'current': current_value, - 'change_percent': change_percent * 100 - }) - elif change_percent < -threshold: - improvements.append({ - 'metric': metric_name, - 'baseline': baseline_value, - 'current': current_value, - 'change_percent': abs(change_percent) * 100 - }) - - return { - 'regressions': regressions, - 'improvements': improvements, - 'alert_level': 'HIGH' if regressions else 'LOW', - 'recommendation': 'Review deployment' if regressions else 'Performance stable' - } - ### END SOLUTION - raise NotImplementedError("Student implementation required") - - def generate_capacity_planning_report(self, current_load: Dict[str, float], - projected_growth: float = 1.5) -> str: - """ - Generate capacity planning report for scaling production systems. - - STUDENT IMPLEMENTATION CHALLENGE (75% level): - Create a comprehensive capacity planning analysis that helps - engineering teams plan for growth and resource allocation. - """ - ### BEGIN SOLUTION - report = f"""# Capacity Planning Report - -## Current System Load -- **Average CPU Usage**: {current_load.get('cpu_usage', 0):.1f}% -- **Memory Usage**: {current_load.get('memory_usage', 0):.1f} MB -- **Request Rate**: {current_load.get('request_rate', 0):.1f} req/sec -- **Average Latency**: {current_load.get('latency', 0):.2f} ms - -## Projected Requirements (Growth Factor: {projected_growth}x) -- **Projected CPU Usage**: {current_load.get('cpu_usage', 0) * projected_growth:.1f}% -- **Projected Memory**: {current_load.get('memory_usage', 0) * projected_growth:.1f} MB -- **Projected Request Rate**: {current_load.get('request_rate', 0) * projected_growth:.1f} req/sec - -## Scaling Recommendations -""" - - cpu_projected = current_load.get('cpu_usage', 0) * projected_growth - memory_projected = current_load.get('memory_usage', 0) * projected_growth - - if cpu_projected > 80: - report += "- **CPU Scaling**: Consider adding more compute instances\n" - if memory_projected > 8000: # 8GB threshold - report += "- **Memory Scaling**: Consider upgrading to higher memory instances\n" - - report += "\n## Infrastructure Recommendations\n" - report += "- Monitor performance metrics continuously\n" - report += "- Set up auto-scaling policies\n" - report += "- Plan for peak load scenarios\n" - - return report - ### END SOLUTION - raise NotImplementedError("Student implementation required") diff --git a/tinytorch/core/cnn.py b/tinytorch/core/cnn.py deleted file mode 100644 index 78a81306..00000000 --- a/tinytorch/core/cnn.py +++ /dev/null @@ -1,229 +0,0 @@ -# ╔═══════════════════════════════════════════════════════════════════════════════╗ -# ║ 🚨 CRITICAL WARNING 🚨 ║ -# ║ AUTOGENERATED! DO NOT EDIT! ║ -# ║ ║ -# ║ This file is AUTOMATICALLY GENERATED from source modules. ║ -# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║ -# ║ ║ -# ║ ✅ TO EDIT: modules/source/XX_cnn/cnn_dev.py ║ -# ║ ✅ TO EXPORT: Run 'tito module complete ' ║ -# ║ ║ -# ║ 🛡️ STUDENT PROTECTION: This file contains optimized implementations. ║ -# ║ Editing it directly may break module functionality and training. ║ -# ║ ║ -# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║ -# ║ happens! The tinytorch/ directory is just the compiled output. ║ -# ╚═══════════════════════════════════════════════════════════════════════════════╝ - -# %% auto 0 -__all__ = ['conv2d_naive', 'Conv2D', 'flatten'] - -# %% ../../modules/source/06_spatial/spatial_dev.ipynb 1 -import numpy as np -import os -import sys -from typing import List, Tuple, Optional -import matplotlib.pyplot as plt - -# Import from the main package - try package first, then local modules -try: - from tinytorch.core.tensor import Tensor - from tinytorch.core.layers import Dense - from tinytorch.core.activations import ReLU -except ImportError: - # For development, import from local modules - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '01_tensor')) - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '02_activations')) - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '03_layers')) - from tensor_dev import Tensor - from activations_dev import ReLU - from layers_dev import Dense - -# %% ../../modules/source/06_spatial/spatial_dev.ipynb 2 -def _should_show_plots(): - """Check if we should show plots (disable during testing)""" - # Check multiple conditions that indicate we're in test mode - is_pytest = ( - 'pytest' in sys.modules or - 'test' in sys.argv or - os.environ.get('PYTEST_CURRENT_TEST') is not None or - any('test' in arg for arg in sys.argv) or - any('pytest' in arg for arg in sys.argv) - ) - - # Show plots in development mode (when not in test mode) - return not is_pytest - -# %% ../../modules/source/06_spatial/spatial_dev.ipynb 6 -def conv2d_naive(input: np.ndarray, kernel: np.ndarray) -> np.ndarray: - """ - Naive 2D convolution (single channel, no stride, no padding). - - Args: - input: 2D input array (H, W) - kernel: 2D filter (kH, kW) - Returns: - 2D output array (H-kH+1, W-kW+1) - - TODO: Implement the sliding window convolution using for-loops. - - APPROACH: - 1. Get input dimensions: H, W = input.shape - 2. Get kernel dimensions: kH, kW = kernel.shape - 3. Calculate output dimensions: out_H = H - kH + 1, out_W = W - kW + 1 - 4. Create output array: np.zeros((out_H, out_W)) - 5. Use nested loops to slide the kernel: - - i loop: output rows (0 to out_H-1) - - j loop: output columns (0 to out_W-1) - - di loop: kernel rows (0 to kH-1) - - dj loop: kernel columns (0 to kW-1) - 6. For each (i,j), compute: output[i,j] += input[i+di, j+dj] * kernel[di, dj] - - EXAMPLE: - Input: [[1, 2, 3], Kernel: [[1, 0], - [4, 5, 6], [0, -1]] - [7, 8, 9]] - - Output[0,0] = 1*1 + 2*0 + 4*0 + 5*(-1) = 1 - 5 = -4 - Output[0,1] = 2*1 + 3*0 + 5*0 + 6*(-1) = 2 - 6 = -4 - Output[1,0] = 4*1 + 5*0 + 7*0 + 8*(-1) = 4 - 8 = -4 - Output[1,1] = 5*1 + 6*0 + 8*0 + 9*(-1) = 5 - 9 = -4 - - HINTS: - - Start with output = np.zeros((out_H, out_W)) - - Use four nested loops: for i in range(out_H): for j in range(out_W): for di in range(kH): for dj in range(kW): - - Accumulate the sum: output[i,j] += input[i+di, j+dj] * kernel[di, dj] - """ - ### BEGIN SOLUTION - # Get input and kernel dimensions - H, W = input.shape - kH, kW = kernel.shape - - # Calculate output dimensions - out_H, out_W = H - kH + 1, W - kW + 1 - - # Initialize output array - output = np.zeros((out_H, out_W), dtype=input.dtype) - - # Sliding window convolution with four nested loops - for i in range(out_H): - for j in range(out_W): - for di in range(kH): - for dj in range(kW): - output[i, j] += input[i + di, j + dj] * kernel[di, dj] - - return output - ### END SOLUTION - -# %% ../../modules/source/06_spatial/spatial_dev.ipynb 10 -class Conv2D: - """ - 2D Convolutional Layer (single channel, single filter, no stride/pad). - - A learnable convolutional layer that applies a kernel to detect spatial patterns. - Perfect for building the foundation of convolutional neural networks. - """ - - def __init__(self, kernel_size: Tuple[int, int]): - """ - Initialize Conv2D layer with random kernel. - - Args: - kernel_size: (kH, kW) - size of the convolution kernel - - TODO: Initialize a random kernel with small values. - - APPROACH: - 1. Store kernel_size as instance variable - 2. Initialize random kernel with small values - 3. Use proper initialization for stable training - - EXAMPLE: - Conv2D((2, 2)) creates: - - kernel: shape (2, 2) with small random values - - HINTS: - - Store kernel_size as self.kernel_size - - Initialize kernel: np.random.randn(kH, kW) * 0.1 (small values) - - Convert to float32 for consistency - """ - ### BEGIN SOLUTION - # Store kernel size - self.kernel_size = kernel_size - kH, kW = kernel_size - - # Initialize random kernel with small values - self.kernel = np.random.randn(kH, kW).astype(np.float32) * 0.1 - ### END SOLUTION - - def forward(self, x): - """ - Forward pass: apply convolution to input tensor. - - Args: - x: Input tensor (2D for simplicity) - - Returns: - Output tensor after convolution - - TODO: Implement forward pass using conv2d_naive function. - - APPROACH: - 1. Extract numpy array from input tensor - 2. Apply conv2d_naive with stored kernel - 3. Return result wrapped in Tensor - - EXAMPLE: - x = Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # shape (3, 3) - layer = Conv2D((2, 2)) - y = layer(x) # shape (2, 2) - - HINTS: - - Use x.data to get numpy array - - Use conv2d_naive(x.data, self.kernel) - - Return Tensor(result) to wrap the result - """ - ### BEGIN SOLUTION - # Apply convolution using naive implementation - result = conv2d_naive(x.data, self.kernel) - return type(x)(result) - ### END SOLUTION - - def __call__(self, x): - """Make layer callable: layer(x) same as layer.forward(x)""" - return self.forward(x) - -# %% ../../modules/source/06_spatial/spatial_dev.ipynb 14 -def flatten(x): - """ - Flatten a 2D tensor to 1D (for connecting to Dense layers). - - Args: - x: Input tensor to flatten - - Returns: - Flattened tensor with batch dimension preserved - - TODO: Implement flattening operation. - - APPROACH: - 1. Get the numpy array from the tensor - 2. Use .flatten() to convert to 1D - 3. Add batch dimension with [None, :] - 4. Return Tensor wrapped around the result - - EXAMPLE: - Input: Tensor([[1, 2], [3, 4]]) # shape (2, 2) - Output: Tensor([[1, 2, 3, 4]]) # shape (1, 4) - - HINTS: - - Use x.data.flatten() to get 1D array - - Add batch dimension: result[None, :] - - Return Tensor(result) - """ - ### BEGIN SOLUTION - # Flatten the tensor and add batch dimension - flattened = x.data.flatten() - result = flattened[None, :] # Add batch dimension - return type(x)(result) - ### END SOLUTION diff --git a/tinytorch/core/compression.py b/tinytorch/core/compression.py deleted file mode 100644 index 893d17e7..00000000 --- a/tinytorch/core/compression.py +++ /dev/null @@ -1,1187 +0,0 @@ -# ╔═══════════════════════════════════════════════════════════════════════════════╗ -# ║ 🚨 CRITICAL WARNING 🚨 ║ -# ║ AUTOGENERATED! DO NOT EDIT! ║ -# ║ ║ -# ║ This file is AUTOMATICALLY GENERATED from source modules. ║ -# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║ -# ║ ║ -# ║ ✅ TO EDIT: modules/source/12_compression/compression_dev.py ║ -# ║ ✅ TO EXPORT: Run 'tito module complete ' ║ -# ║ ║ -# ║ 🛡️ STUDENT PROTECTION: This file contains optimized implementations. ║ -# ║ Editing it directly may break module functionality and training. ║ -# ║ ║ -# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║ -# ║ happens! The tinytorch/ directory is just the compiled output. ║ -# ╚═══════════════════════════════════════════════════════════════════════════════╝ - -# %% auto 0 -__all__ = ['setup_import_paths', 'CompressionMetrics', 'prune_weights_by_magnitude', 'calculate_sparsity', - 'quantize_layer_weights', 'DistillationLoss', 'compute_neuron_importance', 'prune_layer_neurons', - 'CompressionSystemsProfiler', 'compare_compression_techniques'] - -# %% ../../modules/source/temp_holding/16_regularization/regularization_dev.ipynb 1 -import numpy as np -import sys -import os -from typing import List, Dict, Any, Optional, Union, Tuple - -# Helper function to set up import paths -def setup_import_paths(): - """Set up import paths for development modules.""" - import sys - import os - - # Add module directories to path - base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) - module_dirs = [ - '01_tensor', '02_activations', '03_layers', '04_networks', - '05_cnn', '06_dataloader', '07_autograd', '08_optimizers', '09_training' - ] - - for module_dir in module_dirs: - sys.path.append(os.path.join(base_dir, module_dir)) - -# Set up paths -setup_import_paths() - -# Import all the building blocks we need -try: - from tinytorch.core.tensor import Tensor - from tinytorch.core.layers import Dense - from tinytorch.core.networks import Sequential - from tinytorch.core.training import CrossEntropyLoss, Trainer -except ImportError: - # For development, create mock classes or import from local modules - try: - from tensor_dev import Tensor - from layers_dev import Dense - from networks_dev import Sequential - from training_dev import CrossEntropyLoss, Trainer - except ImportError: - # Create minimal mock classes for development - class Tensor: - def __init__(self, data): - self.data = np.array(data) - self.shape = self.data.shape - - def __str__(self): - return f"Tensor({self.data})" - - class Dense: - def __init__(self, input_size, output_size): - self.input_size = input_size - self.output_size = output_size - self.weights = Tensor(np.random.randn(input_size, output_size) * 0.1) - self.bias = Tensor(np.zeros(output_size)) - - def __str__(self): - return f"Dense({self.input_size}, {self.output_size})" - - class Sequential: - def __init__(self, layers=None): - self.layers = layers or [] - - class CrossEntropyLoss: - def __init__(self): - pass - - class Trainer: - def __init__(self, model, optimizer, loss_function): - self.model = model - self.optimizer = optimizer - self.loss_function = loss_function - -# %% ../../modules/source/temp_holding/16_regularization/regularization_dev.ipynb 7 -class CompressionMetrics: - """ - Utilities for measuring model size, sparsity, and compression efficiency. - - This class provides tools to analyze neural network models and understand - their memory footprint, parameter distribution, and compression potential. - """ - - def __init__(self): - """Initialize compression metrics analyzer.""" - pass - - def count_parameters(self, model: Sequential) -> Dict[str, int]: - """ - Count parameters in a neural network model. - - Args: - model: Sequential model to analyze - - Returns: - Dictionary with parameter counts per layer and total - - TODO: Implement parameter counting for neural network analysis. - - STEP-BY-STEP IMPLEMENTATION: - 1. Initialize counters for different parameter types - 2. Iterate through each layer in the model - 3. Count weights and biases for each layer - 4. Calculate total parameters across all layers - 5. Return detailed breakdown dictionary - - EXAMPLE OUTPUT: - { - 'layer_0_weights': 100352, - 'layer_0_bias': 128, - 'layer_1_weights': 8192, - 'layer_1_bias': 64, - 'layer_2_weights': 640, - 'layer_2_bias': 10, - 'total_parameters': 109386, - 'total_weights': 109184, - 'total_bias': 202 - } - - IMPLEMENTATION HINTS: - - Use hasattr() to check if layer has weights/bias attributes - - Weight matrices have shape (input_size, output_size) - - Bias vectors have shape (output_size,) - - Use np.prod() to calculate total elements from shape - - Track layer index for detailed reporting - - LEARNING CONNECTIONS: - - This is like `model.numel()` in PyTorch - - Understanding where parameters are concentrated - - Foundation for compression target selection - """ - ### BEGIN SOLUTION - param_counts = {} - total_params = 0 - total_weights = 0 - total_bias = 0 - - for i, layer in enumerate(model.layers): - # Count weights if layer has them - if hasattr(layer, 'weights') and layer.weights is not None: - # Handle different weight formats - if hasattr(layer.weights, 'shape'): - weight_count = np.prod(layer.weights.shape) - else: - weight_count = np.prod(layer.weights.data.shape) - - param_counts[f'layer_{i}_weights'] = weight_count - total_weights += weight_count - total_params += weight_count - - # Count bias if layer has them - if hasattr(layer, 'bias') and layer.bias is not None: - # Handle different bias formats - if hasattr(layer.bias, 'shape'): - bias_count = np.prod(layer.bias.shape) - else: - bias_count = np.prod(layer.bias.data.shape) - - param_counts[f'layer_{i}_bias'] = bias_count - total_bias += bias_count - total_params += bias_count - - # Add summary statistics - param_counts['total_parameters'] = total_params - param_counts['total_weights'] = total_weights - param_counts['total_bias'] = total_bias - - return param_counts - ### END SOLUTION - - def calculate_model_size(self, model: Sequential, dtype: str = 'float32') -> Dict[str, Any]: - """ - Calculate memory footprint of a neural network model. - - Args: - model: Sequential model to analyze - dtype: Data type for size calculation ('float32', 'float16', 'int8') - - Returns: - Dictionary with size information in different units - """ - # Get parameter count - param_info = self.count_parameters(model) - total_params = param_info['total_parameters'] - - # Determine bytes per parameter - bytes_per_param = { - 'float32': 4, - 'float16': 2, - 'int8': 1 - }.get(dtype, 4) - - # Calculate sizes - total_bytes = total_params * bytes_per_param - size_kb = total_bytes / 1024 - size_mb = size_kb / 1024 - - return { - 'total_parameters': total_params, - 'bytes_per_parameter': bytes_per_param, - 'total_bytes': total_bytes, - 'size_kb': round(size_kb, 2), - 'size_mb': round(size_mb, 2), - 'dtype': dtype - } - -# %% ../../modules/source/temp_holding/16_regularization/regularization_dev.ipynb 11 -def prune_weights_by_magnitude(layer: Dense, pruning_ratio: float = 0.5) -> Tuple[Dense, Dict[str, Any]]: - """ - Prune weights in a Dense layer by magnitude. - - Args: - layer: Dense layer to prune - pruning_ratio: Fraction of weights to remove (0.0 to 1.0) - - Returns: - Tuple of (pruned_layer, pruning_info) - - TODO: Implement magnitude-based weight pruning. - - STEP-BY-STEP IMPLEMENTATION: - 1. Get weight matrix from layer - 2. Calculate absolute values (magnitudes) - 3. Find threshold using percentile - 4. Create binary mask for weights above threshold - 5. Apply mask to weights (set small weights to zero) - 6. Update layer weights and return pruning statistics - - EXAMPLE USAGE: - ```python - layer = Dense(784, 128) - pruned_layer, info = prune_weights_by_magnitude(layer, pruning_ratio=0.3) - print(f"Pruned {info['weights_removed']} weights, sparsity: {info['sparsity']:.2f}") - ``` - - IMPLEMENTATION HINTS: - - Use np.percentile() with pruning_ratio * 100 for threshold - - Create mask with np.abs(weights) > threshold - - Apply mask by element-wise multiplication - - Count zeros to calculate sparsity - - Return original layer (modified) and statistics - - LEARNING CONNECTIONS: - - This is the foundation of network pruning - - Magnitude pruning is simplest but effective - - Sparsity = fraction of weights that are zero - - Threshold selection affects accuracy vs compression trade-off - """ - ### BEGIN SOLUTION - # Get current weights and ensure they're numpy arrays - weights = layer.weights.data - if not isinstance(weights, np.ndarray): - weights = np.array(weights) - - original_weights = weights.copy() - - # Calculate magnitudes and threshold - magnitudes = np.abs(weights) - threshold = np.percentile(magnitudes, pruning_ratio * 100) - - # Create mask and apply pruning - mask = magnitudes > threshold - pruned_weights = weights * mask - - # Update layer weights by creating a new Tensor - layer.weights = Tensor(pruned_weights) - - # Calculate pruning statistics - total_weights = weights.size - zero_weights = np.sum(pruned_weights == 0) - weights_removed = zero_weights - np.sum(original_weights == 0) - sparsity = zero_weights / total_weights - - pruning_info = { - 'pruning_ratio': pruning_ratio, - 'threshold': float(threshold), - 'total_weights': total_weights, - 'weights_removed': weights_removed, - 'remaining_weights': total_weights - zero_weights, - 'sparsity': float(sparsity), - 'compression_ratio': 1 / (1 - sparsity) if sparsity < 1 else float('inf') - } - - return layer, pruning_info - ### END SOLUTION - -# %% ../../modules/source/temp_holding/16_regularization/regularization_dev.ipynb 12 -def calculate_sparsity(layer: Dense) -> float: - """ - Calculate sparsity (fraction of zero weights) in a Dense layer. - - Args: - layer: Dense layer to analyze - - Returns: - Sparsity as float between 0.0 and 1.0 - - TODO: Implement sparsity calculation. - - STEP-BY-STEP IMPLEMENTATION: - 1. Get weight matrix from layer - 2. Count total number of weights - 3. Count number of zero weights - 4. Calculate sparsity = zero_weights / total_weights - 5. Return as float - - EXAMPLE USAGE: - ```python - layer = Dense(100, 50) - sparsity = calculate_sparsity(layer) - print(f"Layer sparsity: {sparsity:.2%}") - ``` - - IMPLEMENTATION HINTS: - - Use np.sum() with condition to count zeros - - Use .size attribute for total elements - - Return 0.0 if no weights (edge case) - - Sparsity of 0.0 = dense, 1.0 = completely sparse - - LEARNING CONNECTIONS: - - Sparsity is key metric for compression - - Higher sparsity = more compression - - Sparsity patterns affect hardware efficiency - """ - ### BEGIN SOLUTION - if not hasattr(layer, 'weights') or layer.weights is None: - return 0.0 - - weights = layer.weights.data - if not isinstance(weights, np.ndarray): - weights = np.array(weights) - - total_weights = weights.size - zero_weights = np.sum(weights == 0) - - return zero_weights / total_weights if total_weights > 0 else 0.0 - ### END SOLUTION - -# %% ../../modules/source/temp_holding/16_regularization/regularization_dev.ipynb 16 -def quantize_layer_weights(layer: Dense, bits: int = 8) -> Tuple[Dense, Dict[str, Any]]: - """ - Quantize layer weights to reduce precision. - - Args: - layer: Dense layer to quantize - bits: Number of bits for quantization (8, 16, etc.) - - Returns: - Tuple of (quantized_layer, quantization_info) - - TODO: Implement weight quantization for memory efficiency. - - STEP-BY-STEP IMPLEMENTATION: - 1. Get weight matrix from layer - 2. Find min and max values for quantization range - 3. Calculate scale factor: (max - min) / (2^bits - 1) - 4. Quantize: round((weights - min) / scale) - 5. Dequantize back to float: quantized * scale + min - 6. Update layer weights and return statistics - - EXAMPLE USAGE: - ```python - layer = Dense(784, 128) - quantized_layer, info = quantize_layer_weights(layer, bits=8) - print(f"Memory reduction: {info['memory_reduction']:.1f}x") - ``` - - IMPLEMENTATION HINTS: - - Use np.min() and np.max() to find weight range - - Clamp quantized values to valid range [0, 2^bits-1] - - Store original dtype for memory calculation - - Calculate theoretical memory savings - - LEARNING CONNECTIONS: - - This is how mobile AI frameworks work - - Hardware accelerators optimize for INT8 - - Precision-performance trade-off is key - """ - ### BEGIN SOLUTION - # Get current weights and ensure they're numpy arrays - weights = layer.weights.data - if not isinstance(weights, np.ndarray): - weights = np.array(weights) - - original_weights = weights.copy() - original_dtype = weights.dtype - - # Find min and max for quantization range - w_min, w_max = np.min(weights), np.max(weights) - - # Calculate scale factor - scale = (w_max - w_min) / (2**bits - 1) - - # Quantize weights - quantized = np.round((weights - w_min) / scale) - quantized = np.clip(quantized, 0, 2**bits - 1) # Clamp to valid range - - # Dequantize back to float (simulation of quantized inference) - dequantized = quantized * scale + w_min - - # Update layer weights - layer.weights = Tensor(dequantized.astype(np.float32)) - - # Calculate quantization statistics - total_weights = weights.size - original_bytes = total_weights * 4 # FP32 = 4 bytes - quantized_bytes = total_weights * (bits // 8) # bits/8 bytes per weight - memory_reduction = original_bytes / quantized_bytes if quantized_bytes > 0 else 1.0 - - # Calculate quantization error - mse_error = np.mean((original_weights - dequantized) ** 2) - max_error = np.max(np.abs(original_weights - dequantized)) - - quantization_info = { - 'bits': bits, - 'scale': float(scale), - 'min_val': float(w_min), - 'max_val': float(w_max), - 'total_weights': total_weights, - 'original_bytes': original_bytes, - 'quantized_bytes': quantized_bytes, - 'memory_reduction': float(memory_reduction), - 'mse_error': float(mse_error), - 'max_error': float(max_error), - 'original_dtype': str(original_dtype) - } - - return layer, quantization_info - ### END SOLUTION - -# %% ../../modules/source/temp_holding/16_regularization/regularization_dev.ipynb 20 -class DistillationLoss: - """ - Combined loss function for knowledge distillation. - - This loss combines standard classification loss (hard targets) with - distillation loss (soft targets from teacher) for training compact models. - """ - - def __init__(self, temperature: float = 3.0, alpha: float = 0.5): - """ - Initialize distillation loss. - - Args: - temperature: Temperature for softening probability distributions - alpha: Weight for hard loss (1-alpha for soft loss) - """ - self.temperature = temperature - self.alpha = alpha - self.ce_loss = CrossEntropyLoss() - - def __call__(self, student_logits: np.ndarray, teacher_logits: np.ndarray, - true_labels: np.ndarray) -> float: - """ - Calculate combined distillation loss. - - Args: - student_logits: Raw outputs from student model - teacher_logits: Raw outputs from teacher model - true_labels: Ground truth labels - - Returns: - Combined loss value - - TODO: Implement knowledge distillation loss function. - - STEP-BY-STEP IMPLEMENTATION: - 1. Calculate hard loss using standard cross-entropy - 2. Apply temperature scaling to both logits - 3. Calculate soft targets from teacher logits - 4. Calculate soft loss between student and teacher distributions - 5. Combine hard and soft losses with alpha weighting - 6. Return total loss - - EXAMPLE USAGE: - ```python - distill_loss = DistillationLoss(temperature=3.0, alpha=0.5) - loss = distill_loss(student_out, teacher_out, labels) - ``` - - IMPLEMENTATION HINTS: - - Use temperature scaling before softmax: logits / temperature - - Implement stable softmax to avoid numerical issues - - Scale soft loss by temperature^2 (standard practice) - - Ensure proper normalization for both losses - - LEARNING CONNECTIONS: - - This is how DistilBERT was trained - - Temperature controls knowledge transfer richness - - Alpha balances accuracy vs compression - """ - ### BEGIN SOLUTION - # Convert inputs to numpy arrays if needed - if not isinstance(student_logits, np.ndarray): - student_logits = np.array(student_logits) - if not isinstance(teacher_logits, np.ndarray): - teacher_logits = np.array(teacher_logits) - if not isinstance(true_labels, np.ndarray): - true_labels = np.array(true_labels) - - # Hard loss: standard classification loss - hard_loss = self._cross_entropy_loss(student_logits, true_labels) - - # Soft loss: distillation from teacher - # Apply temperature scaling - teacher_soft = self._softmax(teacher_logits / self.temperature) - student_soft = self._softmax(student_logits / self.temperature) - - # Calculate soft loss (KL divergence) - soft_loss = -np.mean(np.sum(teacher_soft * np.log(student_soft + 1e-10), axis=-1)) - - # Scale soft loss by temperature^2 (standard practice) - soft_loss *= (self.temperature ** 2) - - # Combine losses - total_loss = self.alpha * hard_loss + (1 - self.alpha) * soft_loss - - return float(total_loss) - ### END SOLUTION - - def _softmax(self, logits: np.ndarray) -> np.ndarray: - """Numerically stable softmax.""" - # Subtract max for numerical stability - exp_logits = np.exp(logits - np.max(logits, axis=-1, keepdims=True)) - return exp_logits / np.sum(exp_logits, axis=-1, keepdims=True) - - def _cross_entropy_loss(self, logits: np.ndarray, labels: np.ndarray) -> float: - """Simple cross-entropy loss implementation.""" - # Convert labels to one-hot if needed - if labels.ndim == 1: - num_classes = logits.shape[-1] - one_hot = np.zeros((labels.shape[0], num_classes)) - one_hot[np.arange(labels.shape[0]), labels] = 1 - labels = one_hot - - # Apply softmax and calculate cross-entropy - probs = self._softmax(logits) - return -np.mean(np.sum(labels * np.log(probs + 1e-10), axis=-1)) - -# %% ../../modules/source/temp_holding/16_regularization/regularization_dev.ipynb 24 -def compute_neuron_importance(layer: Dense, method: str = 'weight_magnitude') -> np.ndarray: - """ - Compute importance scores for each neuron in a Dense layer. - - Args: - layer: Dense layer to analyze - method: Importance computation method - - Returns: - Array of importance scores for each output neuron - - TODO: Implement neuron importance calculation. - - STEP-BY-STEP IMPLEMENTATION: - 1. Get weight matrix from layer - 2. Choose importance metric based on method - 3. Calculate per-neuron importance scores - 4. Return array of scores (one per output neuron) - - AVAILABLE METHODS: - - 'weight_magnitude': Sum of absolute weights per neuron - - 'weight_variance': Variance of weights per neuron - - 'random': Random importance (for baseline comparison) - - IMPLEMENTATION HINTS: - - Weights shape is (input_size, output_size) - - Each column represents one output neuron - - Use axis=0 for operations across input dimensions - - Higher scores = more important neurons - - LEARNING CONNECTIONS: - - This is how neural architecture search works - - Different metrics capture different aspects of importance - - Importance ranking is crucial for effective pruning - """ - ### BEGIN SOLUTION - # Get weights and ensure they're numpy arrays - weights = layer.weights.data - if not isinstance(weights, np.ndarray): - weights = np.array(weights) - - if method == 'weight_magnitude': - # Sum of absolute weights per neuron (column) - importance = np.sum(np.abs(weights), axis=0) - - elif method == 'weight_variance': - # Variance of weights per neuron (column) - importance = np.var(weights, axis=0) - - elif method == 'random': - # Random importance for baseline comparison - importance = np.random.rand(weights.shape[1]) - - else: - raise ValueError(f"Unknown importance method: {method}") - - return importance - ### END SOLUTION - -# %% ../../modules/source/temp_holding/16_regularization/regularization_dev.ipynb 25 -def prune_layer_neurons(layer: Dense, keep_ratio: float = 0.7, - importance_method: str = 'weight_magnitude') -> Tuple[Dense, Dict[str, Any]]: - """ - Remove least important neurons from a Dense layer. - - Args: - layer: Dense layer to prune - keep_ratio: Fraction of neurons to keep (0.0 to 1.0) - importance_method: Method for computing neuron importance - - Returns: - Tuple of (pruned_layer, pruning_info) - - TODO: Implement structured neuron pruning. - - STEP-BY-STEP IMPLEMENTATION: - 1. Compute importance scores for all neurons - 2. Determine how many neurons to keep - 3. Select indices of most important neurons - 4. Create new layer with reduced dimensions - 5. Copy weights and biases for selected neurons - 6. Return pruned layer and statistics - - EXAMPLE USAGE: - ```python - layer = Dense(784, 128) - pruned_layer, info = prune_layer_neurons(layer, keep_ratio=0.75) - print(f"Reduced from {info['original_neurons']} to {info['remaining_neurons']} neurons") - ``` - - IMPLEMENTATION HINTS: - - Use np.argsort() to rank neurons by importance - - Take the top keep_count neurons: indices[-keep_count:] - - Create new layer with reduced output size - - Copy both weights and bias for selected neurons - - Track original and new sizes for statistics - - LEARNING CONNECTIONS: - - This is actual model architecture modification - - Hardware gets real speedup from smaller matrices - - Must consider cascade effects on next layers - """ - ### BEGIN SOLUTION - # Compute neuron importance - importance_scores = compute_neuron_importance(layer, importance_method) - - # Determine how many neurons to keep - original_neurons = layer.output_size - keep_count = max(1, int(original_neurons * keep_ratio)) # Keep at least 1 neuron - - # Select most important neurons - sorted_indices = np.argsort(importance_scores) - keep_indices = sorted_indices[-keep_count:] # Take top keep_count neurons - keep_indices = np.sort(keep_indices) # Sort for consistent ordering - - # Get current weights and biases - weights = layer.weights.data - if not isinstance(weights, np.ndarray): - weights = np.array(weights) - - bias = layer.bias.data if layer.bias is not None else None - if bias is not None and not isinstance(bias, np.ndarray): - bias = np.array(bias) - - # Create new layer with reduced dimensions - pruned_layer = Dense(layer.input_size, keep_count) - - # Copy weights for selected neurons - pruned_weights = weights[:, keep_indices] - pruned_layer.weights = Tensor(np.ascontiguousarray(pruned_weights)) - - # Copy bias for selected neurons - if bias is not None: - pruned_bias = bias[keep_indices] - pruned_layer.bias = Tensor(np.ascontiguousarray(pruned_bias)) - - # Calculate pruning statistics - neurons_removed = original_neurons - keep_count - compression_ratio = original_neurons / keep_count if keep_count > 0 else float('inf') - - # Calculate parameter reduction - original_params = layer.input_size * original_neurons + (original_neurons if bias is not None else 0) - new_params = layer.input_size * keep_count + (keep_count if bias is not None else 0) - param_reduction = (original_params - new_params) / original_params - - pruning_info = { - 'keep_ratio': keep_ratio, - 'importance_method': importance_method, - 'original_neurons': original_neurons, - 'remaining_neurons': keep_count, - 'neurons_removed': neurons_removed, - 'compression_ratio': float(compression_ratio), - 'original_params': original_params, - 'new_params': new_params, - 'param_reduction': float(param_reduction), - 'keep_indices': keep_indices.tolist() - } - - return pruned_layer, pruning_info - ### END SOLUTION - -# %% ../../modules/source/temp_holding/16_regularization/regularization_dev.ipynb 29 -class CompressionSystemsProfiler: - """ - Advanced profiling system for analyzing compression techniques in production environments. - - This profiler provides 65% implementation level analysis of compression techniques, - focusing on production deployment scenarios including quantization impact analysis, - inference speedup measurements, and hardware-specific optimizations. - """ - - def __init__(self): - """Initialize the compression systems profiler.""" - self.metrics = CompressionMetrics() - self.compression_history = [] - - def analyze_quantization_impact(self, model: Sequential, target_bits: List[int] = [32, 16, 8, 4]) -> Dict[str, Any]: - """ - Analyze quantization impact across different bit widths for production deployment. - - Args: - model: Sequential model to analyze - target_bits: List of bit widths to test - - Returns: - Comprehensive quantization analysis including accuracy vs compression tradeoffs - - TODO: Implement advanced quantization impact analysis (65% implementation level). - - STEP-BY-STEP IMPLEMENTATION: - 1. Create model copies for each bit width - 2. Apply quantization with different bit widths - 3. Measure memory reduction and inference implications - 4. Calculate theoretical speedup for different hardware - 5. Analyze accuracy degradation patterns - 6. Generate production deployment recommendations - - PRODUCTION PATTERNS TO ANALYZE: - - Mobile deployment (ARM processors, limited memory) - - Edge inference (TPUs, power constraints) - - Cloud serving (GPU acceleration, batch processing) - - Real-time systems (latency requirements) - - IMPLEMENTATION HINTS: - - Model different hardware characteristics - - Consider memory bandwidth limitations - - Include power consumption estimates - - Analyze batch vs single inference patterns - - LEARNING CONNECTIONS: - - This mirrors TensorFlow Lite quantization analysis - - Production systems need this kind of comprehensive analysis - - Hardware-aware compression is crucial for deployment - """ - ### BEGIN SOLUTION - results = { - 'quantization_analysis': {}, - 'hardware_recommendations': {}, - 'deployment_scenarios': {} - } - - baseline_size = self.metrics.calculate_model_size(model, dtype='float32') - baseline_params = self.metrics.count_parameters(model)['total_parameters'] - - for bits in target_bits: - # Create model copy for quantization - test_model = Sequential([Dense(layer.input_size, layer.output_size) for layer in model.layers]) - for i, layer in enumerate(test_model.layers): - layer.weights = Tensor(model.layers[i].weights.data.copy() if hasattr(model.layers[i].weights.data, 'copy') else np.array(model.layers[i].weights.data)) - if hasattr(layer, 'bias') and model.layers[i].bias is not None: - layer.bias = Tensor(model.layers[i].bias.data.copy() if hasattr(model.layers[i].bias.data, 'copy') else np.array(model.layers[i].bias.data)) - - # Apply quantization to all layers - total_error = 0 - for i, layer in enumerate(test_model.layers): - if isinstance(layer, Dense): - _, quant_info = quantize_layer_weights(layer, bits=bits) - total_error += quant_info['mse_error'] - - # Calculate quantized model size - dtype_map = {32: 'float32', 16: 'float16', 8: 'int8', 4: 'int8'} # Approximate for 4-bit - quantized_size = self.metrics.calculate_model_size(test_model, dtype=dtype_map.get(bits, 'int8')) - - # Memory and performance analysis - memory_reduction = baseline_size['size_mb'] / quantized_size['size_mb'] - - # Hardware-specific analysis - hardware_analysis = { - 'mobile_arm': { - 'memory_bandwidth_improvement': memory_reduction * 0.8, # ARM efficiency - 'inference_speedup': min(memory_reduction * 0.6, 4.0), # Conservative estimate - 'power_reduction': memory_reduction * 0.7, # Power scales with memory access - 'deployment_feasibility': 'excellent' if quantized_size['size_mb'] < 10 else 'good' if quantized_size['size_mb'] < 50 else 'limited' - }, - 'edge_tpu': { - 'quantization_compatibility': 'native' if bits == 8 else 'emulated', - 'inference_speedup': 8.0 if bits == 8 else 1.0, # TPUs optimized for INT8 - 'power_efficiency': 'optimal' if bits == 8 else 'suboptimal', - 'deployment_feasibility': 'excellent' if bits == 8 and quantized_size['size_mb'] < 20 else 'limited' - }, - 'gpu_cloud': { - 'tensor_core_acceleration': True if bits in [16, 8] else False, - 'batch_throughput_improvement': memory_reduction * 1.2, # GPU batch efficiency - 'memory_capacity_improvement': memory_reduction, - 'deployment_feasibility': 'excellent' # Cloud has fewer constraints - } - } - - results['quantization_analysis'][f'{bits}bit'] = { - 'bits': bits, - 'model_size_mb': quantized_size['size_mb'], - 'memory_reduction_factor': memory_reduction, - 'quantization_error': total_error / len(test_model.layers), - 'compression_ratio': baseline_size['size_mb'] / quantized_size['size_mb'], - 'hardware_analysis': hardware_analysis - } - - # Generate deployment recommendations - results['deployment_scenarios'] = { - 'mobile_deployment': { - 'recommended_bits': 8, - 'rationale': 'INT8 provides optimal balance of size reduction and ARM processor efficiency', - 'expected_benefits': 'Memory reduction, inference speedup, improved battery life', - 'considerations': 'Monitor accuracy degradation, test on target devices' - }, - 'edge_inference': { - 'recommended_bits': 8, - 'rationale': 'Edge TPUs and similar hardware optimized for INT8 quantization', - 'expected_benefits': 'Maximum hardware acceleration, minimal power consumption', - 'considerations': 'Ensure quantization-aware training for best accuracy' - }, - 'cloud_serving': { - 'recommended_bits': 16, - 'rationale': 'FP16 provides good compression with minimal accuracy loss and GPU acceleration', - 'expected_benefits': 'Increased batch throughput, reduced memory usage', - 'considerations': 'Consider mixed precision for optimal performance' - } - } - - return results - ### END SOLUTION - - def measure_inference_speedup(self, original_model: Sequential, compressed_model: Sequential, - batch_sizes: List[int] = [1, 8, 32, 128]) -> Dict[str, Any]: - """ - Measure theoretical inference speedup from compression techniques. - - Args: - original_model: Baseline model - compressed_model: Compressed model to compare - batch_sizes: Different batch sizes for analysis - - Returns: - Inference speedup analysis across different scenarios - """ - results = { - 'flops_analysis': {}, - 'memory_analysis': {}, - 'speedup_estimates': {} - } - - # Calculate FLOPs for both models - original_flops = self._calculate_model_flops(original_model) - compressed_flops = self._calculate_model_flops(compressed_model) - - # Memory analysis - original_size = self.metrics.calculate_model_size(original_model) - compressed_size = self.metrics.calculate_model_size(compressed_model) - - results['flops_analysis'] = { - 'original_flops': original_flops, - 'compressed_flops': compressed_flops, - 'flops_reduction': (original_flops - compressed_flops) / original_flops, - 'computational_speedup': original_flops / compressed_flops if compressed_flops > 0 else float('inf') - } - - results['memory_analysis'] = { - 'original_size_mb': original_size['size_mb'], - 'compressed_size_mb': compressed_size['size_mb'], - 'memory_reduction': (original_size['size_mb'] - compressed_size['size_mb']) / original_size['size_mb'], - 'memory_speedup': original_size['size_mb'] / compressed_size['size_mb'] - } - - # Estimate speedup for different scenarios - for batch_size in batch_sizes: - compute_time_original = original_flops * batch_size / 1e9 # Assume 1 GFLOPS baseline - compute_time_compressed = compressed_flops * batch_size / 1e9 - - memory_time_original = original_size['size_mb'] * batch_size / 100 # Assume 100 MB/s memory bandwidth - memory_time_compressed = compressed_size['size_mb'] * batch_size / 100 - - total_time_original = compute_time_original + memory_time_original - total_time_compressed = compute_time_compressed + memory_time_compressed - - results['speedup_estimates'][f'batch_{batch_size}'] = { - 'compute_speedup': compute_time_original / compute_time_compressed if compute_time_compressed > 0 else float('inf'), - 'memory_speedup': memory_time_original / memory_time_compressed if memory_time_compressed > 0 else float('inf'), - 'total_speedup': total_time_original / total_time_compressed if total_time_compressed > 0 else float('inf') - } - - return results - - def analyze_accuracy_tradeoffs(self, model: Sequential, compression_levels: List[float] = [0.1, 0.3, 0.5, 0.7, 0.9]) -> Dict[str, Any]: - """ - Analyze accuracy vs compression tradeoffs across different compression levels. - - Args: - model: Model to analyze - compression_levels: Different compression ratios to test - - Returns: - Analysis of accuracy degradation patterns - """ - results = { - 'compression_curves': {}, - 'optimal_operating_points': {}, - 'production_recommendations': {} - } - - baseline_size = self.metrics.calculate_model_size(model) - - for level in compression_levels: - # Test different compression techniques at this level - techniques = { - 'magnitude_pruning': self._apply_magnitude_pruning(model, level), - 'structured_pruning': self._apply_structured_pruning(model, 1 - level), - 'quantization': self._apply_quantization(model, max(4, int(32 * (1 - level)))) - } - - for technique_name, compressed_model in techniques.items(): - if compressed_model is not None: - compressed_size = self.metrics.calculate_model_size(compressed_model) - compression_ratio = baseline_size['size_mb'] / compressed_size['size_mb'] - - if technique_name not in results['compression_curves']: - results['compression_curves'][technique_name] = [] - - results['compression_curves'][technique_name].append({ - 'compression_level': level, - 'compression_ratio': compression_ratio, - 'size_mb': compressed_size['size_mb'], - 'estimated_accuracy_retention': 1.0 - (level * 0.5) # Simplified model - }) - - # Find optimal operating points - for technique in results['compression_curves']: - curves = results['compression_curves'][technique] - # Find point with best accuracy/compression balance - best_point = max(curves, key=lambda x: x['compression_ratio'] * x['estimated_accuracy_retention']) - results['optimal_operating_points'][technique] = best_point - - return results - - def _calculate_model_flops(self, model: Sequential) -> int: - """Calculate FLOPs for a Sequential model.""" - total_flops = 0 - for layer in model.layers: - if isinstance(layer, Dense): - total_flops += layer.input_size * layer.output_size * 2 # Multiply-add operations - return total_flops - - def _apply_magnitude_pruning(self, model: Sequential, pruning_ratio: float) -> Optional[Sequential]: - """Apply magnitude pruning to a model copy.""" - try: - test_model = Sequential([Dense(layer.input_size, layer.output_size) for layer in model.layers]) - for i, layer in enumerate(test_model.layers): - layer.weights = Tensor(model.layers[i].weights.data.copy() if hasattr(model.layers[i].weights.data, 'copy') else np.array(model.layers[i].weights.data)) - if hasattr(layer, 'bias') and model.layers[i].bias is not None: - layer.bias = Tensor(model.layers[i].bias.data.copy() if hasattr(model.layers[i].bias.data, 'copy') else np.array(model.layers[i].bias.data)) - prune_weights_by_magnitude(layer, pruning_ratio) - return test_model - except Exception: - return None - - def _apply_structured_pruning(self, model: Sequential, keep_ratio: float) -> Optional[Sequential]: - """Apply structured pruning to a model copy.""" - try: - test_model = Sequential([Dense(layer.input_size, layer.output_size) for layer in model.layers]) - for i, layer in enumerate(test_model.layers): - layer.weights = Tensor(model.layers[i].weights.data.copy() if hasattr(model.layers[i].weights.data, 'copy') else np.array(model.layers[i].weights.data)) - if hasattr(layer, 'bias') and model.layers[i].bias is not None: - layer.bias = Tensor(model.layers[i].bias.data.copy() if hasattr(model.layers[i].bias.data, 'copy') else np.array(model.layers[i].bias.data)) - pruned_layer, _ = prune_layer_neurons(layer, keep_ratio) - test_model.layers[i] = pruned_layer - return test_model - except Exception: - return None - - def _apply_quantization(self, model: Sequential, bits: int) -> Optional[Sequential]: - """Apply quantization to a model copy.""" - try: - test_model = Sequential([Dense(layer.input_size, layer.output_size) for layer in model.layers]) - for i, layer in enumerate(test_model.layers): - layer.weights = Tensor(model.layers[i].weights.data.copy() if hasattr(model.layers[i].weights.data, 'copy') else np.array(model.layers[i].weights.data)) - if hasattr(layer, 'bias') and model.layers[i].bias is not None: - layer.bias = Tensor(model.layers[i].bias.data.copy() if hasattr(model.layers[i].bias.data, 'copy') else np.array(model.layers[i].bias.data)) - quantize_layer_weights(layer, bits) - return test_model - except Exception: - return None - -# %% ../../modules/source/temp_holding/16_regularization/regularization_dev.ipynb 30 -def compare_compression_techniques(original_model: Sequential) -> Dict[str, Dict[str, Any]]: - """ - Compare all compression techniques on the same model. - - Args: - original_model: Base model to compress using different techniques - - Returns: - Dictionary comparing results from different compression approaches - - TODO: Implement comprehensive compression comparison. - - STEP-BY-STEP IMPLEMENTATION: - 1. Set up baseline metrics from original model - 2. Apply each compression technique individually - 3. Apply combined compression techniques - 4. Measure and compare all results - 5. Return comprehensive comparison data - - COMPARISON DIMENSIONS: - - Model size (MB) - - Parameter count - - Compression ratio - - Memory reduction - - Estimated speedup (for structured techniques) - - IMPLEMENTATION HINTS: - - Create separate model copies for each technique - - Use consistent parameters across techniques - - Track both individual and combined effects - - Include baseline for reference - - LEARNING CONNECTIONS: - - This is how research papers compare compression methods - - Production systems need this analysis for deployment decisions - - Understanding trade-offs guides technique selection - """ - ### BEGIN SOLUTION - results = {} - metrics = CompressionMetrics() - - # Baseline: Original model - baseline_params = metrics.count_parameters(original_model) - baseline_size = metrics.calculate_model_size(original_model) - - results['baseline'] = { - 'technique': 'Original Model', - 'parameters': baseline_params['total_parameters'], - 'size_mb': baseline_size['size_mb'], - 'compression_ratio': 1.0, - 'memory_reduction': 0.0 - } - - # Technique 1: Magnitude-based pruning only - model_pruning = Sequential([Dense(layer.input_size, layer.output_size) for layer in original_model.layers]) - for i, layer in enumerate(model_pruning.layers): - layer.weights = Tensor(original_model.layers[i].weights.data.copy() if hasattr(original_model.layers[i].weights.data, 'copy') else np.array(original_model.layers[i].weights.data)) - if hasattr(layer, 'bias') and original_model.layers[i].bias is not None: - layer.bias = Tensor(original_model.layers[i].bias.data.copy() if hasattr(original_model.layers[i].bias.data, 'copy') else np.array(original_model.layers[i].bias.data)) - - # Apply magnitude pruning to each layer - total_sparsity = 0 - for i, layer in enumerate(model_pruning.layers): - if isinstance(layer, Dense): - _, prune_info = prune_weights_by_magnitude(layer, pruning_ratio=0.3) - total_sparsity += prune_info['sparsity'] - - avg_sparsity = total_sparsity / len(model_pruning.layers) - pruning_params = metrics.count_parameters(model_pruning) - pruning_size = metrics.calculate_model_size(model_pruning) - - results['magnitude_pruning'] = { - 'technique': 'Magnitude Pruning (30%)', - 'parameters': pruning_params['total_parameters'], - 'size_mb': pruning_size['size_mb'], - 'compression_ratio': baseline_size['size_mb'] / pruning_size['size_mb'], - 'memory_reduction': (baseline_size['size_mb'] - pruning_size['size_mb']) / baseline_size['size_mb'], - 'sparsity': avg_sparsity - } - - # Technique 2: Quantization only - model_quantization = Sequential([Dense(layer.input_size, layer.output_size) for layer in original_model.layers]) - for i, layer in enumerate(model_quantization.layers): - layer.weights = Tensor(original_model.layers[i].weights.data.copy() if hasattr(original_model.layers[i].weights.data, 'copy') else np.array(original_model.layers[i].weights.data)) - if hasattr(layer, 'bias') and original_model.layers[i].bias is not None: - layer.bias = Tensor(original_model.layers[i].bias.data.copy() if hasattr(original_model.layers[i].bias.data, 'copy') else np.array(original_model.layers[i].bias.data)) - - # Apply quantization to each layer - total_memory_reduction = 0 - for i, layer in enumerate(model_quantization.layers): - if isinstance(layer, Dense): - _, quant_info = quantize_layer_weights(layer, bits=8) - total_memory_reduction += quant_info['memory_reduction'] - - avg_memory_reduction = total_memory_reduction / len(model_quantization.layers) - quantization_size = metrics.calculate_model_size(model_quantization, dtype='int8') - - results['quantization'] = { - 'technique': 'Quantization (INT8)', - 'parameters': baseline_params['total_parameters'], - 'size_mb': quantization_size['size_mb'], - 'compression_ratio': baseline_size['size_mb'] / quantization_size['size_mb'], - 'memory_reduction': (baseline_size['size_mb'] - quantization_size['size_mb']) / baseline_size['size_mb'], - 'avg_memory_reduction_factor': avg_memory_reduction - } - - # Technique 3: Structured pruning only - model_structured = Sequential([Dense(layer.input_size, layer.output_size) for layer in original_model.layers]) - for i, layer in enumerate(model_structured.layers): - layer.weights = Tensor(original_model.layers[i].weights.data.copy() if hasattr(original_model.layers[i].weights.data, 'copy') else np.array(original_model.layers[i].weights.data)) - if hasattr(layer, 'bias') and original_model.layers[i].bias is not None: - layer.bias = Tensor(original_model.layers[i].bias.data.copy() if hasattr(original_model.layers[i].bias.data, 'copy') else np.array(original_model.layers[i].bias.data)) - - # Apply structured pruning to each layer - total_param_reduction = 0 - for i, layer in enumerate(model_structured.layers): - if isinstance(layer, Dense): - pruned_layer, struct_info = prune_layer_neurons(layer, keep_ratio=0.75) - model_structured.layers[i] = pruned_layer - total_param_reduction += struct_info['param_reduction'] - - avg_param_reduction = total_param_reduction / len(model_structured.layers) - structured_params = metrics.count_parameters(model_structured) - structured_size = metrics.calculate_model_size(model_structured) - - results['structured_pruning'] = { - 'technique': 'Structured Pruning (75% neurons kept)', - 'parameters': structured_params['total_parameters'], - 'size_mb': structured_size['size_mb'], - 'compression_ratio': baseline_size['size_mb'] / structured_size['size_mb'], - 'memory_reduction': (baseline_size['size_mb'] - structured_size['size_mb']) / baseline_size['size_mb'], - 'param_reduction': avg_param_reduction - } - - # Technique 4: Combined approach - model_combined = Sequential([Dense(layer.input_size, layer.output_size) for layer in original_model.layers]) - for i, layer in enumerate(model_combined.layers): - layer.weights = Tensor(original_model.layers[i].weights.data.copy() if hasattr(original_model.layers[i].weights.data, 'copy') else np.array(original_model.layers[i].weights.data)) - if hasattr(layer, 'bias') and original_model.layers[i].bias is not None: - layer.bias = Tensor(original_model.layers[i].bias.data.copy() if hasattr(original_model.layers[i].bias.data, 'copy') else np.array(original_model.layers[i].bias.data)) - - # Apply magnitude pruning + quantization + structured pruning - for i, layer in enumerate(model_combined.layers): - if isinstance(layer, Dense): - # Step 1: Magnitude pruning - _, _ = prune_weights_by_magnitude(layer, pruning_ratio=0.2) - # Step 2: Quantization - _, _ = quantize_layer_weights(layer, bits=8) - # Step 3: Structured pruning - pruned_layer, _ = prune_layer_neurons(layer, keep_ratio=0.8) - model_combined.layers[i] = pruned_layer - - combined_params = metrics.count_parameters(model_combined) - combined_size = metrics.calculate_model_size(model_combined, dtype='int8') - - results['combined'] = { - 'technique': 'Combined (Pruning + Quantization + Structured)', - 'parameters': combined_params['total_parameters'], - 'size_mb': combined_size['size_mb'], - 'compression_ratio': baseline_size['size_mb'] / combined_size['size_mb'], - 'memory_reduction': (baseline_size['size_mb'] - combined_size['size_mb']) / baseline_size['size_mb'] - } - - return results - ### END SOLUTION diff --git a/tinytorch/core/dataloader.py b/tinytorch/core/dataloader.py deleted file mode 100644 index 0e70a2c9..00000000 --- a/tinytorch/core/dataloader.py +++ /dev/null @@ -1,473 +0,0 @@ -# ╔═══════════════════════════════════════════════════════════════════════════════╗ -# ║ 🚨 CRITICAL WARNING 🚨 ║ -# ║ AUTOGENERATED! DO NOT EDIT! ║ -# ║ ║ -# ║ This file is AUTOMATICALLY GENERATED from source modules. ║ -# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║ -# ║ ║ -# ║ ✅ TO EDIT: modules/source/08_dataloader/dataloader_dev.py ║ -# ║ ✅ TO EXPORT: Run 'tito module complete ' ║ -# ║ ║ -# ║ 🛡️ STUDENT PROTECTION: This file contains optimized implementations. ║ -# ║ Editing it directly may break module functionality and training. ║ -# ║ ║ -# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║ -# ║ happens! The tinytorch/ directory is just the compiled output. ║ -# ╚═══════════════════════════════════════════════════════════════════════════════╝ - -# %% auto 0 -__all__ = ['Dataset', 'DataLoader', 'SimpleDataset', 'download_cifar10', 'CIFAR10Dataset'] - -# %% ../../modules/source/07_dataloader/dataloader_dev.ipynb 1 -import numpy as np -import sys -import os -from typing import Tuple, Optional, Iterator -import urllib.request -import tarfile -import pickle -import time - -# Import our building blocks - try package first, then local modules -try: - from tinytorch.core.tensor import Tensor -except ImportError: - # For development, import from local modules - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '01_tensor')) - from tensor_dev import Tensor - -# %% ../../modules/source/07_dataloader/dataloader_dev.ipynb 7 -class Dataset: - """ - Base Dataset class: Abstract interface for all datasets. - - The fundamental abstraction for data loading in TinyTorch. - Students implement concrete datasets by inheriting from this class. - """ - - def __getitem__(self, index: int) -> Tuple[Tensor, Tensor]: - """ - Get a single sample and label by index. - - Args: - index: Index of the sample to retrieve - - Returns: - Tuple of (data, label) tensors - - TODO: Implement abstract method for getting samples. - - STEP-BY-STEP IMPLEMENTATION: - 1. This is an abstract method - subclasses will implement it - 2. Return a tuple of (data, label) tensors - 3. Data should be the input features, label should be the target - - EXAMPLE: - dataset[0] should return (Tensor(image_data), Tensor(label)) - - LEARNING CONNECTIONS: - - **PyTorch Integration**: This follows the exact same pattern as torch.utils.data.Dataset - - **Production Data**: Real datasets like ImageNet, CIFAR-10 use this interface - - **Memory Efficiency**: On-demand loading prevents loading entire dataset into memory - - **Batching Foundation**: DataLoader uses __getitem__ to create batches efficiently - - HINTS: - - This is an abstract method that subclasses must override - - Always return a tuple of (data, label) tensors - - Data contains the input features, label contains the target - """ - ### BEGIN SOLUTION - # This is an abstract method - subclasses must implement it - raise NotImplementedError("Subclasses must implement __getitem__") - ### END SOLUTION - - def __len__(self) -> int: - """ - Get the total number of samples in the dataset. - - TODO: Implement abstract method for getting dataset size. - - STEP-BY-STEP IMPLEMENTATION: - 1. This is an abstract method - subclasses will implement it - 2. Return the total number of samples in the dataset - - EXAMPLE: - len(dataset) should return 50000 for CIFAR-10 training set - - LEARNING CONNECTIONS: - - **Memory Planning**: DataLoader uses len() to calculate number of batches - - **Progress Tracking**: Training loops use len() for progress bars and epoch calculations - - **Distributed Training**: Multi-GPU systems need dataset size for work distribution - - **Statistical Sampling**: Some training strategies require knowing total dataset size - - HINTS: - - This is an abstract method that subclasses must override - - Return an integer representing the total number of samples - """ - ### BEGIN SOLUTION - # This is an abstract method - subclasses must implement it - raise NotImplementedError("Subclasses must implement __len__") - ### END SOLUTION - - def get_sample_shape(self) -> Tuple[int, ...]: - """ - Get the shape of a single data sample. - - TODO: Implement method to get sample shape. - - STEP-BY-STEP IMPLEMENTATION: - 1. Get the first sample using self[0] - 2. Extract the data part (first element of tuple) - 3. Return the shape of the data tensor - - EXAMPLE: - For CIFAR-10: returns (3, 32, 32) for RGB images - - LEARNING CONNECTIONS: - - **Model Architecture**: Neural networks need to know input shape for first layer - - **Batch Planning**: Systems use sample shape to calculate memory requirements - - **Preprocessing Validation**: Ensures all samples have consistent shape - - **Framework Integration**: Similar to PyTorch's dataset shape inspection - - HINTS: - - Use self[0] to get the first sample - - Extract data from the (data, label) tuple - - Return data.shape - """ - ### BEGIN SOLUTION - # Get the first sample to determine shape - data, _ = self[0] - return data.shape - ### END SOLUTION - - def get_num_classes(self) -> int: - """ - Get the number of classes in the dataset. - - TODO: Implement abstract method for getting number of classes. - - STEP-BY-STEP IMPLEMENTATION: - 1. This is an abstract method - subclasses will implement it - 2. Return the number of unique classes in the dataset - - EXAMPLE: - For CIFAR-10: returns 10 (classes 0-9) - - LEARNING CONNECTIONS: - - **Output Layer Design**: Neural networks need num_classes for final layer size - - **Loss Function Setup**: CrossEntropyLoss uses num_classes for proper computation - - **Evaluation Metrics**: Accuracy calculation depends on number of classes - - **Model Validation**: Ensures model predictions match expected class range - - HINTS: - - This is an abstract method that subclasses must override - - Return the number of unique classes/categories - """ - # This is an abstract method - subclasses must implement it - raise NotImplementedError("Subclasses must implement get_num_classes") - -# %% ../../modules/source/07_dataloader/dataloader_dev.ipynb 11 -class DataLoader: - """ - DataLoader: Efficiently batch and iterate through datasets. - - Provides batching, shuffling, and efficient iteration over datasets. - Essential for training neural networks efficiently. - """ - - def __init__(self, dataset: Dataset, batch_size: int = 32, shuffle: bool = True): - """ - Initialize DataLoader. - - Args: - dataset: Dataset to load from - batch_size: Number of samples per batch - shuffle: Whether to shuffle data each epoch - - TODO: Store configuration and dataset. - - APPROACH: - 1. Store dataset as self.dataset - 2. Store batch_size as self.batch_size - 3. Store shuffle as self.shuffle - - EXAMPLE: - DataLoader(dataset, batch_size=32, shuffle=True) - - HINTS: - - Store all parameters as instance variables - - These will be used in __iter__ for batching - """ - # Input validation - if dataset is None: - raise TypeError("Dataset cannot be None") - if not isinstance(batch_size, int) or batch_size <= 0: - raise ValueError(f"Batch size must be a positive integer, got {batch_size}") - - self.dataset = dataset - self.batch_size = batch_size - self.shuffle = shuffle - - def __iter__(self) -> Iterator[Tuple[Tensor, Tensor]]: - """ - Iterate through dataset in batches. - - Returns: - Iterator yielding (batch_data, batch_labels) tuples - - TODO: Implement batching and shuffling logic. - - STEP-BY-STEP IMPLEMENTATION: - 1. Create indices list: list(range(len(dataset))) - 2. Shuffle indices if self.shuffle is True - 3. Loop through indices in batch_size chunks - 4. For each batch: collect samples, stack them, yield batch - - EXAMPLE: - for batch_data, batch_labels in dataloader: - # batch_data.shape: (batch_size, ...) - # batch_labels.shape: (batch_size,) - - LEARNING CONNECTIONS: - - **GPU Efficiency**: Batching maximizes GPU utilization by processing multiple samples together - - **Training Stability**: Shuffling prevents overfitting to data order and improves generalization - - **Memory Management**: Batches fit in GPU memory while full dataset may not - - **Gradient Estimation**: Batch gradients provide better estimates than single-sample gradients - - HINTS: - - Use list(range(len(self.dataset))) for indices - - Use np.random.shuffle() if self.shuffle is True - - Loop in chunks of self.batch_size - - Collect samples and stack with np.stack() - """ - # Create indices for all samples - indices = list(range(len(self.dataset))) - - # Shuffle if requested - if self.shuffle: - np.random.shuffle(indices) - - # Iterate through indices in batches - for i in range(0, len(indices), self.batch_size): - batch_indices = indices[i:i + self.batch_size] - - # Collect samples for this batch - batch_data = [] - batch_labels = [] - - for idx in batch_indices: - data, label = self.dataset[idx] - batch_data.append(data.data) - batch_labels.append(label.data) - - # Stack into batch tensors - batch_data_array = np.stack(batch_data, axis=0) - batch_labels_array = np.stack(batch_labels, axis=0) - - yield Tensor(batch_data_array), Tensor(batch_labels_array) - - def __len__(self) -> int: - """ - Get the number of batches per epoch. - - TODO: Calculate number of batches. - - APPROACH: - 1. Get dataset size: len(self.dataset) - 2. Divide by batch_size and round up - 3. Use ceiling division: (n + batch_size - 1) // batch_size - - EXAMPLE: - Dataset size 100, batch size 32 → 4 batches - - HINTS: - - Use len(self.dataset) for dataset size - - Use ceiling division for exact batch count - - Formula: (dataset_size + batch_size - 1) // batch_size - """ - # Calculate number of batches using ceiling division - dataset_size = len(self.dataset) - return (dataset_size + self.batch_size - 1) // self.batch_size - -# %% ../../modules/source/07_dataloader/dataloader_dev.ipynb 15 -class SimpleDataset(Dataset): - """ - Simple dataset for testing and demonstration. - - Generates synthetic data with configurable size and properties. - Perfect for understanding the Dataset pattern. - """ - - def __init__(self, size: int = 100, num_features: int = 4, num_classes: int = 3): - """ - Initialize SimpleDataset. - - Args: - size: Number of samples in the dataset - num_features: Number of features per sample - num_classes: Number of classes - - TODO: Initialize the dataset with synthetic data. - - APPROACH: - 1. Store the configuration parameters - 2. Generate synthetic data and labels - 3. Make data deterministic for testing - - EXAMPLE: - SimpleDataset(size=100, num_features=4, num_classes=3) - creates 100 samples with 4 features each, 3 classes - - HINTS: - - Store size, num_features, num_classes as instance variables - - Use np.random.seed() for reproducible data - - Generate random data with np.random.randn() - - Generate random labels with np.random.randint() - """ - self.size = size - self.num_features = num_features - self.num_classes = num_classes - - # Generate synthetic data (deterministic for testing) - np.random.seed(42) # For reproducible data - self.data = np.random.randn(size, num_features).astype(np.float32) - self.labels = np.random.randint(0, num_classes, size=size) - - def __getitem__(self, index: int) -> Tuple[Tensor, Tensor]: - """ - Get a sample by index. - - Args: - index: Index of the sample - - Returns: - Tuple of (data, label) tensors - - TODO: Return the sample at the given index. - - APPROACH: - 1. Get data sample from self.data[index] - 2. Get label from self.labels[index] - 3. Convert both to Tensors and return as tuple - - EXAMPLE: - dataset[0] returns (Tensor(features), Tensor(label)) - - HINTS: - - Use self.data[index] for the data - - Use self.labels[index] for the label - - Convert to Tensors: Tensor(data), Tensor(label) - """ - data = self.data[index] - label = self.labels[index] - return Tensor(data), Tensor(label) - - def __len__(self) -> int: - """ - Get the dataset size. - - TODO: Return the dataset size. - - APPROACH: - 1. Return self.size - - EXAMPLE: - len(dataset) returns 100 for dataset with 100 samples - - HINTS: - - Simply return self.size - """ - return self.size - - def get_num_classes(self) -> int: - """ - Get the number of classes. - - TODO: Return the number of classes. - - APPROACH: - 1. Return self.num_classes - - EXAMPLE: - dataset.get_num_classes() returns 3 for 3-class dataset - - HINTS: - - Simply return self.num_classes - """ - return self.num_classes - -# %% ../../modules/source/07_dataloader/dataloader_dev.ipynb 17 -def download_cifar10(root: str = "./data") -> str: - """ - Download CIFAR-10 dataset. - - TODO: Download and extract CIFAR-10. - - HINTS: - - URL: https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz - - Use urllib.request.urlretrieve() - - Extract with tarfile - """ - ### BEGIN SOLUTION - os.makedirs(root, exist_ok=True) - dataset_dir = os.path.join(root, "cifar-10-batches-py") - - if os.path.exists(dataset_dir): - print(f"✅ CIFAR-10 found at {dataset_dir}") - return dataset_dir - - url = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz" - tar_path = os.path.join(root, "cifar-10.tar.gz") - - print(f"📥 Downloading CIFAR-10 (~170MB)...") - urllib.request.urlretrieve(url, tar_path) - print("✅ Downloaded!") - - print("📦 Extracting...") - with tarfile.open(tar_path, 'r:gz') as tar: - tar.extractall(root) - print("✅ Ready!") - - return dataset_dir - ### END SOLUTION - -class CIFAR10Dataset(Dataset): - """CIFAR-10 dataset for CNN training.""" - - def __init__(self, root="./data", train=True, download=False): - """Load CIFAR-10 data.""" - ### BEGIN SOLUTION - if download: - dataset_dir = download_cifar10(root) - else: - dataset_dir = os.path.join(root, "cifar-10-batches-py") - - if train: - data_list = [] - label_list = [] - for i in range(1, 6): - with open(os.path.join(dataset_dir, f"data_batch_{i}"), 'rb') as f: - batch = pickle.load(f, encoding='bytes') - data_list.append(batch[b'data']) - label_list.extend(batch[b'labels']) - self.data = np.concatenate(data_list) - self.labels = np.array(label_list) - else: - with open(os.path.join(dataset_dir, "test_batch"), 'rb') as f: - batch = pickle.load(f, encoding='bytes') - self.data = batch[b'data'] - self.labels = np.array(batch[b'labels']) - - # Reshape to (N, 3, 32, 32) and normalize - self.data = self.data.reshape(-1, 3, 32, 32).astype(np.float32) / 255.0 - print(f"✅ Loaded {len(self.data):,} images") - ### END SOLUTION - - def __getitem__(self, idx): - return Tensor(self.data[idx]), Tensor(self.labels[idx]) - - def __len__(self): - return len(self.data) - - def get_num_classes(self): - return 10 diff --git a/tinytorch/core/dense.py b/tinytorch/core/dense.py deleted file mode 100644 index 0dddea9e..00000000 --- a/tinytorch/core/dense.py +++ /dev/null @@ -1,239 +0,0 @@ -# ╔═══════════════════════════════════════════════════════════════════════════════╗ -# ║ 🚨 CRITICAL WARNING 🚨 ║ -# ║ AUTOGENERATED! DO NOT EDIT! ║ -# ║ ║ -# ║ This file is AUTOMATICALLY GENERATED from source modules. ║ -# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║ -# ║ ║ -# ║ ✅ TO EDIT: modules/source/05_dense/dense_dev.py ║ -# ║ ✅ TO EXPORT: Run 'tito module complete ' ║ -# ║ ║ -# ║ 🛡️ STUDENT PROTECTION: This file contains optimized implementations. ║ -# ║ Editing it directly may break module functionality and training. ║ -# ║ ║ -# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║ -# ║ happens! The tinytorch/ directory is just the compiled output. ║ -# ╚═══════════════════════════════════════════════════════════════════════════════╝ - -# %% auto 0 -__all__ = ['Sequential', 'create_mlp', 'MLP'] - -# %% ../../modules/source/05_networks/networks_dev.ipynb 1 -import numpy as np -import sys -import os -from typing import List, Optional -import matplotlib.pyplot as plt - -# Import all the building blocks we need - try package first, then local modules -try: - from tinytorch.core.tensor import Tensor - from tinytorch.core.layers import Dense - from tinytorch.core.activations import ReLU, Sigmoid, Tanh, Softmax -except ImportError: - # For development, import from local modules - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '02_tensor')) - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '03_activations')) - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '04_layers')) - from tensor_dev import Tensor - from activations_dev import ReLU, Sigmoid, Tanh, Softmax - from layers_dev import Dense - -# %% ../../modules/source/05_networks/networks_dev.ipynb 7 -class Sequential: - """ - Sequential Network: Composes layers in sequence - - The most fundamental network architecture. - Applies layers in order: f(x) = layer_n(...layer_2(layer_1(x))) - """ - - def __init__(self, layers: Optional[List] = None): - """ - Initialize Sequential network with layers. - - Args: - layers: List of layers to compose in order (optional, defaults to empty list) - - TODO: Store the layers and implement forward pass - - APPROACH: - 1. Store the layers list as an instance variable - 2. Initialize empty list if no layers provided - 3. Prepare for forward pass implementation - - EXAMPLE: - Sequential([Dense(3,4), ReLU(), Dense(4,2)]) - creates a 3-layer network: Dense → ReLU → Dense - - HINTS: - - Use self.layers to store the layers - - Handle empty initialization case - - LEARNING CONNECTIONS: - - This is equivalent to torch.nn.Sequential in PyTorch - - Used in every neural network to chain layers together - - Foundation for models like VGG, ResNet, and transformers - - Enables modular network design and experimentation - """ - ### BEGIN SOLUTION - self.layers = layers if layers is not None else [] - ### END SOLUTION - - def forward(self, x: Tensor) -> Tensor: - """ - Forward pass through all layers in sequence. - - Args: - x: Input tensor - - Returns: - Output tensor after passing through all layers - - TODO: Implement sequential forward pass through all layers - - APPROACH: - 1. Start with the input tensor - 2. Apply each layer in sequence - 3. Each layer's output becomes the next layer's input - 4. Return the final output - - EXAMPLE: - Input: Tensor([[1, 2, 3]]) - Layer1 (Dense): Tensor([[1.4, 2.8]]) - Layer2 (ReLU): Tensor([[1.4, 2.8]]) - Layer3 (Dense): Tensor([[0.7]]) - Output: Tensor([[0.7]]) - - HINTS: - - Use a for loop: for layer in self.layers: - - Apply each layer: x = layer(x) - - The output of one layer becomes input to the next - - Return the final result - - LEARNING CONNECTIONS: - - This is the core of feedforward neural networks - - Powers inference in every deployed model - - Critical for real-time predictions in production - - Foundation for gradient flow in backpropagation - """ - ### BEGIN SOLUTION - # Apply each layer in sequence - for layer in self.layers: - x = layer(x) - return x - ### END SOLUTION - - def __call__(self, x: Tensor) -> Tensor: - """Make the network callable: sequential(x) instead of sequential.forward(x)""" - return self.forward(x) - - def add(self, layer): - """Add a layer to the network.""" - self.layers.append(layer) - -# %% ../../modules/source/05_networks/networks_dev.ipynb 11 -def create_mlp(input_size: int, hidden_sizes: List[int], output_size: int, - activation=ReLU, output_activation=Sigmoid) -> Sequential: - """ - Create a Multi-Layer Perceptron (MLP) network. - - Args: - input_size: Number of input features - hidden_sizes: List of hidden layer sizes - output_size: Number of output features - activation: Activation function for hidden layers (default: ReLU) - output_activation: Activation function for output layer (default: Sigmoid) - - Returns: - Sequential network with MLP architecture - - TODO: Implement MLP creation with alternating Dense and activation layers. - - APPROACH: - 1. Start with an empty list of layers - 2. Add layers in this pattern: - - Dense(input_size → first_hidden_size) - - Activation() - - Dense(first_hidden_size → second_hidden_size) - - Activation() - - ... - - Dense(last_hidden_size → output_size) - - Output_activation() - 3. Return Sequential(layers) - - EXAMPLE: - create_mlp(3, [4, 2], 1) creates: - Dense(3→4) → ReLU → Dense(4→2) → ReLU → Dense(2→1) → Sigmoid - - HINTS: - - Start with layers = [] - - Track current_size starting with input_size - - For each hidden_size: add Dense(current_size, hidden_size), then activation - - Finally add Dense(last_hidden_size, output_size), then output_activation - - Return Sequential(layers) - - LEARNING CONNECTIONS: - - This pattern is used in every feedforward network implementation - - Foundation for architectures like autoencoders and GANs - - Enables rapid prototyping of neural architectures - - Similar to tf.keras.Sequential with Dense layers - """ - layers = [] - current_size = input_size - - # Add hidden layers with activations - for hidden_size in hidden_sizes: - layers.append(Dense(current_size, hidden_size)) - layers.append(activation()) - current_size = hidden_size - - # Add output layer with output activation - layers.append(Dense(current_size, output_size)) - layers.append(output_activation()) - - return Sequential(layers) - -# %% ../../modules/source/05_networks/networks_dev.ipynb 24 -class MLP: - """ - Multi-Layer Perceptron (MLP) class. - - A convenient wrapper around Sequential networks for standard MLP architectures. - Maintains parameter information and provides a clean interface. - - Args: - input_size: Number of input features - hidden_size: Size of the single hidden layer - output_size: Number of output features - activation: Activation function for hidden layer (default: ReLU) - output_activation: Activation function for output layer (default: Sigmoid) - """ - - def __init__(self, input_size: int, hidden_size: int, output_size: int, - activation=ReLU, output_activation=None): - self.input_size = input_size - self.hidden_size = hidden_size - self.output_size = output_size - - # Build the network layers - layers = [] - - # Input to hidden layer - layers.append(Dense(input_size, hidden_size)) - layers.append(activation()) - - # Hidden to output layer - layers.append(Dense(hidden_size, output_size)) - if output_activation is not None: - layers.append(output_activation()) - - self.network = Sequential(layers) - - def forward(self, x): - """Forward pass through the MLP network.""" - return self.network.forward(x) - - def __call__(self, x): - """Make the MLP callable.""" - return self.forward(x) diff --git a/tinytorch/core/embeddings.py b/tinytorch/core/embeddings.py deleted file mode 100644 index 1452d84b..00000000 --- a/tinytorch/core/embeddings.py +++ /dev/null @@ -1,715 +0,0 @@ -# ╔═══════════════════════════════════════════════════════════════════════════════╗ -# ║ 🚨 CRITICAL WARNING 🚨 ║ -# ║ AUTOGENERATED! DO NOT EDIT! ║ -# ║ ║ -# ║ This file is AUTOMATICALLY GENERATED from source modules. ║ -# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║ -# ║ ║ -# ║ ✅ TO EDIT: modules/source/XX_embeddings/embeddings_dev.py ║ -# ║ ✅ TO EXPORT: Run 'tito module complete ' ║ -# ║ ║ -# ║ 🛡️ STUDENT PROTECTION: This file contains optimized implementations. ║ -# ║ Editing it directly may break module functionality and training. ║ -# ║ ║ -# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║ -# ║ happens! The tinytorch/ directory is just the compiled output. ║ -# ╚═══════════════════════════════════════════════════════════════════════════════╝ - -# %% auto 0 -__all__ = ['Embedding', 'PositionalEncoding', 'LearnedPositionalEmbedding', 'EmbeddingProfiler', - 'analyze_embedding_system_design'] - -# %% ../../modules/12_embeddings/embeddings_dev.ipynb 1 -import math -import numpy as np -import os -import sys -from typing import Union, List, Optional, Tuple - -# Import our Tensor class - try from package first, then from local module -try: - from tinytorch.core.tensor import Tensor -except ImportError: - # For development, import from local tensor module - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '02_tensor')) - from tensor_dev import Tensor - -# Try to import tokenization classes -try: - from tinytorch.core.tokenization import CharTokenizer, BPETokenizer -except ImportError: - # For development, import from local module - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '11_tokenization')) - try: - from tokenization_dev import CharTokenizer, BPETokenizer - except ImportError: - # Create minimal mock classes if not available - class CharTokenizer: - def __init__(self): - self.vocab_size = 256 - class BPETokenizer: - def __init__(self, vocab_size=1000): - self.vocab_size = vocab_size - -# %% ../../modules/12_embeddings/embeddings_dev.ipynb 6 -class Embedding: - """ - Embedding layer that converts token indices to dense vector representations. - - This is the foundation of modern language models - a learnable lookup table - that maps discrete tokens to continuous vectors that capture semantic meaning. - """ - - def __init__(self, vocab_size: int, embedding_dim: int, - padding_idx: Optional[int] = None, - init_type: str = 'uniform'): - """ - Initialize embedding layer with learnable parameters. - - STEP-BY-STEP IMPLEMENTATION: - 1. Store configuration parameters - 2. Initialize embedding table with chosen initialization - 3. Handle special padding token if specified - 4. Set up for gradient tracking (will connect to autograd later) - - DESIGN DECISIONS: - - Embedding table shape: (vocab_size, embedding_dim) - - Initialization affects training dynamics - - Padding idx gets zero gradient to stay constant - - Args: - vocab_size: Number of tokens in vocabulary - embedding_dim: Size of dense vector for each token - padding_idx: Optional token index that should remain zero - init_type: Initialization strategy ('uniform', 'normal', 'xavier') - """ - ### BEGIN SOLUTION - self.vocab_size = vocab_size - self.embedding_dim = embedding_dim - self.padding_idx = padding_idx - self.init_type = init_type - - # Initialize embedding table based on strategy - if init_type == 'uniform': - # Uniform initialization in [-1/sqrt(dim), 1/sqrt(dim)] - bound = 1.0 / math.sqrt(embedding_dim) - self.weight = Tensor(np.random.uniform(-bound, bound, (vocab_size, embedding_dim))) - elif init_type == 'normal': - # Normal initialization with std=1/sqrt(dim) - std = 1.0 / math.sqrt(embedding_dim) - self.weight = Tensor(np.random.normal(0, std, (vocab_size, embedding_dim))) - elif init_type == 'xavier': - # Xavier/Glorot initialization - bound = math.sqrt(6.0 / (vocab_size + embedding_dim)) - self.weight = Tensor(np.random.uniform(-bound, bound, (vocab_size, embedding_dim))) - else: - raise ValueError(f"Unknown init_type: {init_type}") - - # Set padding token to zero if specified - if padding_idx is not None: - self.weight.data[padding_idx] = 0.0 - - # Track parameters for optimization - self.parameters = [self.weight] - ### END SOLUTION - - def forward(self, input_ids: Union[Tensor, List[int], np.ndarray]) -> Tensor: - """ - Look up embeddings for input token indices. - - TODO: Implement embedding lookup. - - STEP-BY-STEP IMPLEMENTATION: - 1. Convert input to numpy array if needed - 2. Validate token indices are within vocabulary - 3. Use advanced indexing to look up embeddings - 4. Return tensor with shape (batch_size, seq_len, embedding_dim) - - EXAMPLE: - embed = Embedding(vocab_size=100, embedding_dim=64) - tokens = Tensor([[1, 2, 3], [4, 5, 6]]) # Shape: (2, 3) - embeddings = embed.forward(tokens) # Shape: (2, 3, 64) - - IMPLEMENTATION HINTS: - - Handle both Tensor and list inputs - - Use numpy advanced indexing: weight[indices] - - Preserve batch and sequence dimensions - - Args: - input_ids: Token indices with shape (batch_size, seq_len) or (seq_len,) - - Returns: - Embeddings with shape (*input_shape, embedding_dim) - """ - ### BEGIN SOLUTION - # Convert input to numpy array - if isinstance(input_ids, Tensor): - indices = input_ids.data - elif isinstance(input_ids, list): - indices = np.array(input_ids) - else: - indices = input_ids - - # Validate indices - indices = indices.astype(int) - if np.any(indices < 0) or np.any(indices >= self.vocab_size): - raise ValueError(f"Token indices must be in range [0, {self.vocab_size})") - - # Look up embeddings using advanced indexing - # self.weight.data has shape (vocab_size, embedding_dim) - # indices has shape (...), result has shape (..., embedding_dim) - embeddings = self.weight.data[indices] - - return Tensor(embeddings) - ### END SOLUTION - - def __call__(self, input_ids: Union[Tensor, List[int], np.ndarray]) -> Tensor: - """Make the layer callable.""" - return self.forward(input_ids) - - def get_memory_usage(self): - """ - Calculate memory usage of embedding table. - - This function is PROVIDED to show memory analysis. - """ - # Embedding table memory - weight_memory_mb = self.weight.data.nbytes / (1024 * 1024) - - # Memory per token - memory_per_token_kb = (self.embedding_dim * 4) / 1024 # 4 bytes per float32 - - return { - 'total_memory_mb': weight_memory_mb, - 'memory_per_token_kb': memory_per_token_kb, - 'total_parameters': self.vocab_size * self.embedding_dim, - 'vocab_size': self.vocab_size, - 'embedding_dim': self.embedding_dim - } - -# %% ../../modules/12_embeddings/embeddings_dev.ipynb 10 -class PositionalEncoding: - """ - Sinusoidal positional encoding that adds position information to embeddings. - - Uses sine and cosine functions of different frequencies to create - unique position representations that the model can learn to use. - """ - - def __init__(self, embedding_dim: int, max_seq_length: int = 5000, - dropout: float = 0.0): - """ - Initialize positional encoding with sinusoidal patterns. - - TODO: Implement positional encoding initialization. - - STEP-BY-STEP IMPLEMENTATION: - 1. Create position matrix (max_seq_length, embedding_dim) - 2. For each position and dimension: - - Calculate frequency based on dimension - - Apply sine to even dimensions, cosine to odd dimensions - 3. Store the precomputed positional encodings - - MATHEMATICAL FOUNDATION: - PE(pos, 2i) = sin(pos / 10000^(2i/d_model)) - PE(pos, 2i+1) = cos(pos / 10000^(2i/d_model)) - - Where: - - pos = position in sequence - - i = dimension index - - d_model = embedding_dim - - Args: - embedding_dim: Dimension of embeddings (must be even) - max_seq_length: Maximum sequence length to precompute - dropout: Dropout rate (for future use) - """ - ### BEGIN SOLUTION - self.embedding_dim = embedding_dim - self.max_seq_length = max_seq_length - self.dropout = dropout - - # Create positional encoding matrix - pe = np.zeros((max_seq_length, embedding_dim)) - - # Create position vector (0, 1, 2, ..., max_seq_length-1) - position = np.arange(0, max_seq_length).reshape(-1, 1) # Shape: (max_seq_length, 1) - - # Create dimension indices for frequency calculation - # div_term calculates 10000^(2i/d_model) for i = 0, 1, 2, ... - div_term = np.exp(np.arange(0, embedding_dim, 2) * - -(math.log(10000.0) / embedding_dim)) - - # Apply sine to even dimensions (0, 2, 4, ...) - pe[:, 0::2] = np.sin(position * div_term) - - # Apply cosine to odd dimensions (1, 3, 5, ...) - if embedding_dim % 2 == 1: - # Handle odd embedding_dim - cosine gets one less dimension - pe[:, 1::2] = np.cos(position * div_term[:-1]) - else: - pe[:, 1::2] = np.cos(position * div_term) - - # Store as tensor - self.pe = Tensor(pe) - ### END SOLUTION - - def forward(self, embeddings: Tensor) -> Tensor: - """ - Add positional encoding to embeddings. - - TODO: Implement positional encoding addition. - - STEP-BY-STEP IMPLEMENTATION: - 1. Get sequence length from embeddings shape - 2. Extract relevant positional encodings - 3. Add positional encodings to embeddings - 4. Return position-aware embeddings - - EXAMPLE: - pos_enc = PositionalEncoding(embedding_dim=64) - embeddings = Tensor(np.random.randn(2, 10, 64)) # (batch, seq, dim) - pos_embeddings = pos_enc.forward(embeddings) - - Args: - embeddings: Input embeddings with shape (batch_size, seq_len, embedding_dim) - - Returns: - Position-aware embeddings with same shape as input - """ - ### BEGIN SOLUTION - # Get sequence length from embeddings - if len(embeddings.shape) == 3: - batch_size, seq_length, embed_dim = embeddings.shape - elif len(embeddings.shape) == 2: - seq_length, embed_dim = embeddings.shape - batch_size = None - else: - raise ValueError(f"Expected 2D or 3D embeddings, got shape {embeddings.shape}") - - if embed_dim != self.embedding_dim: - raise ValueError(f"Embedding dim mismatch: expected {self.embedding_dim}, got {embed_dim}") - - if seq_length > self.max_seq_length: - raise ValueError(f"Sequence length {seq_length} exceeds max {self.max_seq_length}") - - # Extract positional encodings for this sequence length - position_encodings = self.pe.data[:seq_length, :] - - # Add positional encodings to embeddings - if batch_size is not None: - # Broadcast positional encodings across batch dimension - # embeddings: (batch, seq, dim) + position_encodings: (seq, dim) - result = embeddings.data + position_encodings[np.newaxis, :, :] - else: - # embeddings: (seq, dim) + position_encodings: (seq, dim) - result = embeddings.data + position_encodings - - return Tensor(result) - ### END SOLUTION - - def __call__(self, embeddings: Tensor) -> Tensor: - """Make the class callable.""" - return self.forward(embeddings) - - def visualize_encoding(self, seq_length: int = 100, dims_to_show: int = 10) -> None: - """ - Visualize positional encoding patterns. - - This function is PROVIDED to show encoding patterns. - """ - print(f"📊 POSITIONAL ENCODING VISUALIZATION") - print(f"Sequence length: {seq_length}, Dimensions shown: {dims_to_show}") - print("=" * 60) - - # Get subset of positional encodings - pe_subset = self.pe.data[:seq_length, :dims_to_show] - - # Show patterns for first few positions - print("First 10 positions, first 10 dimensions:") - print("Pos", end="") - for d in range(min(dims_to_show, 10)): - print(f" Dim{d:2d}", end="") - print() - - for pos in range(min(seq_length, 10)): - print(f"{pos:3d}", end="") - for d in range(min(dims_to_show, 10)): - print(f"{pe_subset[pos, d]:8.3f}", end="") - print() - - # Show frequency analysis - print(f"\n📈 FREQUENCY ANALYSIS:") - print("Even dimensions (sine): Lower frequencies for early dimensions") - print("Odd dimensions (cosine): Same frequencies, phase-shifted") - - # Calculate frequency range - min_freq = 1.0 / 10000 - max_freq = 1.0 - print(f"Frequency range: {min_freq:.6f} to {max_freq:.6f}") - -# %% ../../modules/12_embeddings/embeddings_dev.ipynb 14 -class LearnedPositionalEmbedding: - """ - Learned positional embeddings - another embedding table for positions. - - Unlike sinusoidal encoding, these are learned parameters that - the model optimizes during training. Used in models like BERT. - """ - - def __init__(self, max_seq_length: int, embedding_dim: int): - """ - Initialize learned positional embeddings. - - TODO: Implement learned positional embedding initialization. - - STEP-BY-STEP IMPLEMENTATION: - 1. Create embedding layer for positions (0, 1, 2, ..., max_seq_length-1) - 2. Initialize with small random values - 3. Set up parameter tracking for optimization - - This is essentially an Embedding layer where the "vocabulary" - is the set of possible positions in a sequence. - - Args: - max_seq_length: Maximum sequence length supported - embedding_dim: Dimension of position embeddings - """ - ### BEGIN SOLUTION - self.max_seq_length = max_seq_length - self.embedding_dim = embedding_dim - - # Create learned positional embedding table - # This is like an embedding layer for positions - self.position_embedding = Embedding( - vocab_size=max_seq_length, - embedding_dim=embedding_dim, - init_type='normal' - ) - - # Track parameters for optimization - self.parameters = self.position_embedding.parameters - ### END SOLUTION - - def forward(self, embeddings: Tensor) -> Tensor: - """ - Add learned positional embeddings to input embeddings. - - TODO: Implement learned positional embedding addition. - - STEP-BY-STEP IMPLEMENTATION: - 1. Get sequence length from input shape - 2. Create position indices [0, 1, 2, ..., seq_length-1] - 3. Look up position embeddings using position indices - 4. Add position embeddings to input embeddings - - EXAMPLE: - learned_pos = LearnedPositionalEmbedding(max_seq_length=100, embedding_dim=64) - embeddings = Tensor(np.random.randn(2, 10, 64)) # (batch, seq, dim) - pos_embeddings = learned_pos.forward(embeddings) - - Args: - embeddings: Input embeddings with shape (batch_size, seq_len, embedding_dim) - - Returns: - Position-aware embeddings with same shape as input - """ - ### BEGIN SOLUTION - # Get sequence length from embeddings - if len(embeddings.shape) == 3: - batch_size, seq_length, embed_dim = embeddings.shape - elif len(embeddings.shape) == 2: - seq_length, embed_dim = embeddings.shape - batch_size = None - else: - raise ValueError(f"Expected 2D or 3D embeddings, got shape {embeddings.shape}") - - if embed_dim != self.embedding_dim: - raise ValueError(f"Embedding dim mismatch: expected {self.embedding_dim}, got {embed_dim}") - - if seq_length > self.max_seq_length: - raise ValueError(f"Sequence length {seq_length} exceeds max {self.max_seq_length}") - - # Create position indices [0, 1, 2, ..., seq_length-1] - position_ids = list(range(seq_length)) - - # Look up position embeddings - position_embeddings = self.position_embedding.forward(position_ids) - - # Add position embeddings to input embeddings - if batch_size is not None: - # Broadcast across batch dimension - result = embeddings.data + position_embeddings.data[np.newaxis, :, :] - else: - result = embeddings.data + position_embeddings.data - - return Tensor(result) - ### END SOLUTION - - def __call__(self, embeddings: Tensor) -> Tensor: - """Make the class callable.""" - return self.forward(embeddings) - -# %% ../../modules/12_embeddings/embeddings_dev.ipynb 18 -import time - -class EmbeddingProfiler: - """ - Performance profiling toolkit for embedding systems. - - Helps ML engineers understand memory usage, lookup performance, - and scaling characteristics of embedding layers. - """ - - def __init__(self): - self.results = {} - - def measure_lookup_performance(self, embedding_layer: Embedding, - batch_sizes: List[int], seq_lengths: List[int]): - """ - Measure embedding lookup performance across different batch sizes and sequence lengths. - - TODO: Implement embedding lookup performance measurement. - - STEP-BY-STEP IMPLEMENTATION: - 1. Create test token indices for each (batch_size, seq_length) combination - 2. Measure time to perform embedding lookup - 3. Calculate throughput metrics (tokens/second, memory bandwidth) - 4. Return comprehensive performance analysis - - METRICS TO CALCULATE: - - Lookup time (milliseconds) - - Tokens per second throughput - - Memory bandwidth utilization - - Scaling patterns with batch size and sequence length - - Args: - embedding_layer: Embedding layer to test - batch_sizes: List of batch sizes to test - seq_lengths: List of sequence lengths to test - - Returns: - Dictionary with performance metrics for each configuration - """ - ### BEGIN SOLUTION - results = {} - vocab_size = embedding_layer.vocab_size - - for batch_size in batch_sizes: - for seq_length in seq_lengths: - # Create random token indices - token_indices = np.random.randint(0, vocab_size, (batch_size, seq_length)) - - # Measure lookup performance - start_time = time.time() - embeddings = embedding_layer.forward(token_indices) - end_time = time.time() - - # Calculate metrics - lookup_time_ms = (end_time - start_time) * 1000 - total_tokens = batch_size * seq_length - tokens_per_second = total_tokens / (end_time - start_time) if end_time > start_time else 0 - - # Memory calculations - input_memory_mb = token_indices.nbytes / (1024 * 1024) - output_memory_mb = embeddings.data.nbytes / (1024 * 1024) - memory_bandwidth_mb_s = (input_memory_mb + output_memory_mb) / (end_time - start_time) if end_time > start_time else 0 - - config_key = f"batch_{batch_size}_seq_{seq_length}" - results[config_key] = { - 'batch_size': batch_size, - 'seq_length': seq_length, - 'total_tokens': total_tokens, - 'lookup_time_ms': lookup_time_ms, - 'tokens_per_second': tokens_per_second, - 'input_memory_mb': input_memory_mb, - 'output_memory_mb': output_memory_mb, - 'memory_bandwidth_mb_s': memory_bandwidth_mb_s, - 'time_per_token_us': lookup_time_ms * 1000 / total_tokens if total_tokens > 0 else 0 - } - - return results - ### END SOLUTION - - def analyze_memory_scaling(self, vocab_sizes: List[int], embedding_dims: List[int]): - """ - Analyze how embedding memory usage scales with vocabulary size and embedding dimension. - - This function is PROVIDED to show memory scaling analysis. - """ - print("📊 EMBEDDING MEMORY SCALING ANALYSIS") - print("=" * 60) - - scaling_results = {} - - print(f"{'Vocab Size':<12} {'Embed Dim':<10} {'Parameters':<12} {'Memory (MB)':<12} {'Lookup Time':<12}") - print("-" * 70) - - for vocab_size in vocab_sizes: - for embed_dim in embedding_dims: - # Create embedding layer - embed = Embedding(vocab_size=vocab_size, embedding_dim=embed_dim) - - # Calculate memory usage - memory_stats = embed.get_memory_usage() - total_memory_mb = memory_stats['total_memory_mb'] - total_params = memory_stats['total_parameters'] - - # Measure lookup time - test_tokens = np.random.randint(0, vocab_size, (32, 64)) # Standard batch - start_time = time.time() - _ = embed.forward(test_tokens) - lookup_time_ms = (time.time() - start_time) * 1000 - - # Store results - config_key = f"vocab_{vocab_size}_dim_{embed_dim}" - scaling_results[config_key] = { - 'vocab_size': vocab_size, - 'embedding_dim': embed_dim, - 'total_parameters': total_params, - 'memory_mb': total_memory_mb, - 'lookup_time_ms': lookup_time_ms - } - - print(f"{vocab_size:<12,} {embed_dim:<10} {total_params:<12,} {total_memory_mb:<12.2f} {lookup_time_ms:<12.2f}") - - # Analyze scaling patterns - print(f"\n📈 SCALING INSIGHTS:") - if len(vocab_sizes) > 1 and len(embedding_dims) > 1: - # Compare scaling with vocab size (fixed embedding dim) - fixed_dim = embedding_dims[0] - small_vocab = min(vocab_sizes) - large_vocab = max(vocab_sizes) - - small_key = f"vocab_{small_vocab}_dim_{fixed_dim}" - large_key = f"vocab_{large_vocab}_dim_{fixed_dim}" - - if small_key in scaling_results and large_key in scaling_results: - vocab_ratio = large_vocab / small_vocab - memory_ratio = scaling_results[large_key]['memory_mb'] / scaling_results[small_key]['memory_mb'] - print(f" Vocabulary scaling: {vocab_ratio:.1f}x vocab → {memory_ratio:.1f}x memory (Linear)") - - # Compare scaling with embedding dim (fixed vocab) - fixed_vocab = vocab_sizes[0] - small_dim = min(embedding_dims) - large_dim = max(embedding_dims) - - small_key = f"vocab_{fixed_vocab}_dim_{small_dim}" - large_key = f"vocab_{fixed_vocab}_dim_{large_dim}" - - if small_key in scaling_results and large_key in scaling_results: - dim_ratio = large_dim / small_dim - memory_ratio = scaling_results[large_key]['memory_mb'] / scaling_results[small_key]['memory_mb'] - print(f" Dimension scaling: {dim_ratio:.1f}x dim → {memory_ratio:.1f}x memory (Linear)") - - return scaling_results - - def compare_positional_encodings(self, seq_length: int = 100, embedding_dim: int = 256): - """ - Compare performance and characteristics of different positional encoding approaches. - - This function is PROVIDED to show positional encoding comparison. - """ - print(f"\n🔍 POSITIONAL ENCODING COMPARISON") - print("=" * 50) - - # Create test embeddings - batch_size = 16 - embeddings = Tensor(np.random.randn(batch_size, seq_length, embedding_dim)) - - # Test sinusoidal positional encoding - sinusoidal_pe = PositionalEncoding(embedding_dim=embedding_dim, max_seq_length=seq_length*2) - start_time = time.time() - sin_result = sinusoidal_pe.forward(embeddings) - sin_time = (time.time() - start_time) * 1000 - - # Test learned positional embedding - learned_pe = LearnedPositionalEmbedding(max_seq_length=seq_length*2, embedding_dim=embedding_dim) - start_time = time.time() - learned_result = learned_pe.forward(embeddings) - learned_time = (time.time() - start_time) * 1000 - - # Calculate memory usage - sin_memory = 0 # No learnable parameters - learned_memory = learned_pe.position_embedding.get_memory_usage()['total_memory_mb'] - - results = { - 'sinusoidal': { - 'computation_time_ms': sin_time, - 'memory_usage_mb': sin_memory, - 'parameters': 0, - 'deterministic': True, - 'extrapolation': 'Good (can handle longer sequences)' - }, - 'learned': { - 'computation_time_ms': learned_time, - 'memory_usage_mb': learned_memory, - 'parameters': seq_length * 2 * embedding_dim, - 'deterministic': False, - 'extrapolation': 'Limited (fixed max sequence length)' - } - } - - print(f"📊 COMPARISON RESULTS:") - print(f"{'Method':<12} {'Time (ms)':<10} {'Memory (MB)':<12} {'Parameters':<12} {'Extrapolation'}") - print("-" * 70) - print(f"{'Sinusoidal':<12} {sin_time:<10.2f} {sin_memory:<12.2f} {0:<12,} {'Good'}") - print(f"{'Learned':<12} {learned_time:<10.2f} {learned_memory:<12.2f} {results['learned']['parameters']:<12,} {'Limited'}") - - print(f"\n💡 INSIGHTS:") - print(f" - Sinusoidal: Zero parameters, deterministic, good extrapolation") - print(f" - Learned: Requires parameters, model-specific, limited extrapolation") - print(f" - Choice depends on: model capacity, sequence length requirements, extrapolation needs") - - return results - -def analyze_embedding_system_design(): - """ - Comprehensive analysis of embedding system design choices and their impact. - - This function is PROVIDED to show systems-level design thinking. - """ - print("🏗️ EMBEDDING SYSTEM DESIGN ANALYSIS") - print("=" * 60) - - # Example model configurations - model_configs = [ - {'name': 'Small GPT', 'vocab_size': 10000, 'embed_dim': 256, 'seq_length': 512}, - {'name': 'Medium GPT', 'vocab_size': 50000, 'embed_dim': 512, 'seq_length': 1024}, - {'name': 'Large GPT', 'vocab_size': 50000, 'embed_dim': 1024, 'seq_length': 2048} - ] - - print(f"📋 MODEL CONFIGURATION COMPARISON:") - print(f"{'Model':<12} {'Vocab Size':<10} {'Embed Dim':<10} {'Seq Len':<8} {'Embed Params':<12} {'Memory (MB)'}") - print("-" * 80) - - for config in model_configs: - # Calculate embedding parameters - embed_params = config['vocab_size'] * config['embed_dim'] - - # Calculate memory usage - embed_memory_mb = embed_params * 4 / (1024 * 1024) # 4 bytes per float32 - - print(f"{config['name']:<12} {config['vocab_size']:<10,} {config['embed_dim']:<10} " - f"{config['seq_length']:<8} {embed_params:<12,} {embed_memory_mb:<10.1f}") - - print(f"\n🎯 DESIGN TRADE-OFFS:") - print(f" 1. Vocabulary Size:") - print(f" - Larger vocab: Better text coverage, more parameters") - print(f" - Smaller vocab: Longer sequences, more compute") - print(f" 2. Embedding Dimension:") - print(f" - Higher dim: More model capacity, more memory") - print(f" - Lower dim: Faster computation, potential bottleneck") - print(f" 3. Position Encoding:") - print(f" - Sinusoidal: No parameters, good extrapolation") - print(f" - Learned: Model-specific, limited to training length") - print(f" 4. Memory Scaling:") - print(f" - Embedding table: O(vocab_size × embed_dim)") - print(f" - Sequence processing: O(batch_size × seq_length × embed_dim)") - print(f" - Total memory dominated by model size, not embedding table") - - print(f"\n🏭 PRODUCTION CONSIDERATIONS:") - print(f" - GPU memory limits affect maximum embedding table size") - print(f" - Embedding lookup is memory-bandwidth bound") - print(f" - Vocabulary size affects tokenization and model download size") - print(f" - Position encoding choice affects sequence length flexibility") diff --git a/tinytorch/core/kernels.py b/tinytorch/core/kernels.py deleted file mode 100644 index 8063ac63..00000000 --- a/tinytorch/core/kernels.py +++ /dev/null @@ -1,1268 +0,0 @@ -# ╔═══════════════════════════════════════════════════════════════════════════════╗ -# ║ 🚨 CRITICAL WARNING 🚨 ║ -# ║ AUTOGENERATED! DO NOT EDIT! ║ -# ║ ║ -# ║ This file is AUTOMATICALLY GENERATED from source modules. ║ -# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║ -# ║ ║ -# ║ ✅ TO EDIT: modules/source/13_kernels/kernels_dev.py ║ -# ║ ✅ TO EXPORT: Run 'tito module complete ' ║ -# ║ ║ -# ║ 🛡️ STUDENT PROTECTION: This file contains optimized implementations. ║ -# ║ Editing it directly may break module functionality and training. ║ -# ║ ║ -# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║ -# ║ happens! The tinytorch/ directory is just the compiled output. ║ -# ╚═══════════════════════════════════════════════════════════════════════════════╝ - -# %% auto 0 -__all__ = ['time_kernel', 'matmul_baseline', 'vectorized_relu', 'vectorized_operations', 'cache_friendly_matmul', 'parallel_relu', - 'parallel_batch_processing', 'quantized_matmul', 'quantized_relu', 'KernelOptimizationProfiler'] - -# %% ../../modules/source/temp_holding/13_kernels/kernels_dev.ipynb 1 -import numpy as np -import sys -import os -import time -import psutil -from typing import Callable, Dict, Any, Optional, Tuple, List - -# Import our existing components -try: - from tinytorch.core.tensor import Tensor - from tinytorch.core.layers import matmul_naive as matmul - from tinytorch.core.activations import ReLU, Sigmoid, Tanh - from tinytorch.core.cnn import Conv2D -except ImportError: - # For development, import from local modules - base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) - sys.path.extend([ - os.path.join(base_dir, '01_tensor'), - os.path.join(base_dir, '02_activations'), - os.path.join(base_dir, '03_layers'), - os.path.join(base_dir, '05_cnn'), - os.path.join(base_dir, 'utils') - ]) - - try: - from tensor_dev import Tensor - from layers_dev import matmul_naive as matmul - from activations_dev import ReLU, Sigmoid, Tanh - from cnn_dev import Conv2D - except ImportError: - # Create minimal mock for development - class Tensor: - def __init__(self, data): - self.data = np.array(data) - self.shape = self.data.shape - def __str__(self): - return f"Tensor({self.data})" - -# Simple timing utility for kernel performance measurement -def time_kernel(func, *args, **kwargs): - """ - Simple timing function for measuring kernel performance. - - Returns: - tuple: (result, time_in_microseconds) - """ - start = time.perf_counter() - result = func(*args, **kwargs) - end = time.perf_counter() - microseconds = (end - start) * 1_000_000 - return result, microseconds - -# %% ../../modules/source/temp_holding/13_kernels/kernels_dev.ipynb 7 -def matmul_baseline(A: Tensor, B: Tensor) -> Tensor: - """ - Baseline matrix multiplication using TinyTorch's proven implementation. - - This function demonstrates how to build on existing TinyTorch components - rather than reinventing the wheel. We use the standard matmul from Module 03 - as our baseline for comparison with optimized kernels. - - This is NOT a custom implementation - it's the standard TinyTorch matmul - wrapped for use in kernel comparisons and benchmarking. - - TODO: Use TinyTorch's standard matmul implementation as a baseline. - - STEP-BY-STEP IMPLEMENTATION: - 1. Import the standard matmul function from tinytorch.core.layers - 2. Extract numpy arrays from input Tensors - 3. Use the proven implementation from TinyTorch - 4. Wrap result back in Tensor format - 5. Return the result - - CODE REUSE PRINCIPLES: - 1. Always use the packaged version for reliability - 2. Don't duplicate working code - reference the source - 3. Use descriptive names that indicate what the function actually does - 4. Keep dependencies simple and reliable - - EXAMPLE USAGE: - ```python - A = Tensor([[1, 2], [3, 4]]) - B = Tensor([[5, 6], [7, 8]]) - C = matmul_baseline(A, B) - # Expected: [[19, 22], [43, 50]] - ``` - - LEARNING CONNECTIONS: - - This shows how to use TinyTorch as a library - - Demonstrates reliable dependency management - - Serves as baseline for kernel performance comparisons - - Shows proper software engineering practices - """ - ### BEGIN SOLUTION - # Extract numpy arrays from Tensors - A_data = A.data if hasattr(A, 'data') else A - B_data = B.data if hasattr(B, 'data') else B - - # Use NumPy's matrix multiplication as our baseline - # This is our baseline - reliable, tested, and consistent - result_data = np.dot(A_data, B_data) - - # Wrap the result back in a Tensor for consistency - result = Tensor(result_data) - - return result - ### END SOLUTION - -# %% ../../modules/source/temp_holding/13_kernels/kernels_dev.ipynb 11 -def vectorized_relu(x: Tensor) -> Tensor: - """ - Vectorized ReLU implementation demonstrating SIMD principles. - - This function shows how to write operations that take advantage of - CPU vectorization capabilities for better performance. - - TODO: Implement a vectorized ReLU that's optimized for performance. - - STEP-BY-STEP IMPLEMENTATION: - 1. Extract numpy array from Tensor - 2. Use NumPy's vectorized operations (these compile to SIMD instructions) - 3. Apply ReLU: f(x) = max(0, x) for all elements simultaneously - 4. Return result as Tensor - - VECTORIZATION TECHNIQUES: - 1. Use np.maximum instead of loops - this is vectorized - 2. Ensure input is contiguous in memory for better SIMD performance - 3. Consider using specific dtypes (float32 vs float64) for SIMD alignment - 4. Avoid conditional operations that break vectorization - - EXAMPLE USAGE: - ```python - x = Tensor([-2, -1, 0, 1, 2]) - y = vectorized_relu(x) - # Expected: [0, 0, 0, 1, 2] - ``` - - PERFORMANCE CONSIDERATIONS: - - np.maximum is vectorized and uses SIMD instructions - - Memory layout matters: contiguous arrays are faster - - Data type matters: float32 allows more SIMD parallelism than float64 - - Avoid Python loops - they can't be vectorized - - LEARNING CONNECTIONS: - - This is how PyTorch's ReLU is implemented under the hood - - GPU kernels use similar principles with thousands of parallel threads - - Modern CPUs can process 4-16 floats simultaneously with SIMD - """ - ### BEGIN SOLUTION - # Extract numpy array - x_data = x.data if hasattr(x, 'data') else x - - # Ensure contiguous memory layout for better SIMD performance - if not x_data.flags.c_contiguous: - x_data = np.ascontiguousarray(x_data) - - # Vectorized ReLU using NumPy's maximum function - # This compiles to SIMD instructions on modern CPUs - result = np.maximum(0, x_data) - - return Tensor(result) - ### END SOLUTION - -# %% ../../modules/source/temp_holding/13_kernels/kernels_dev.ipynb 12 -def vectorized_operations(x: Tensor, y: Tensor) -> Dict[str, Tensor]: - """ - Demonstration of various vectorized operations. - - Shows how multiple operations can be vectorized for better performance. - - TODO: Implement a collection of vectorized operations. - - STEP-BY-STEP IMPLEMENTATION: - 1. Extract numpy arrays from input Tensors - 2. Implement vectorized versions of common operations - 3. Use NumPy's built-in vectorized functions - 4. Return dictionary of results - - OPERATIONS TO IMPLEMENT: - - element_wise_multiply: x * y (element-wise) - - element_wise_add: x + y (element-wise) - - squared_difference: (x - y)^2 - - euclidean_distance: sqrt(sum((x - y)^2)) - - dot_product: sum(x * y) - - VECTORIZATION PRINCIPLES: - - Use NumPy operations instead of Python loops - - Combine operations when possible: (x - y)**2 instead of subtract then square - - Consider memory layout and data types - - Measure performance improvements - - EXAMPLE USAGE: - ```python - x = Tensor([1, 2, 3, 4]) - y = Tensor([2, 3, 4, 5]) - results = vectorized_operations(x, y) - # Returns dict with all vectorized operation results - ``` - """ - ### BEGIN SOLUTION - # Extract numpy arrays - x_data = x.data if hasattr(x, 'data') else x - y_data = y.data if hasattr(y, 'data') else y - - # Ensure arrays are the same shape for element-wise operations - assert x_data.shape == y_data.shape, f"Shape mismatch: {x_data.shape} vs {y_data.shape}" - - # Vectorized operations - results = { - 'element_wise_multiply': Tensor(x_data * y_data), - 'element_wise_add': Tensor(x_data + y_data), - 'squared_difference': Tensor((x_data - y_data) ** 2), - 'euclidean_distance': Tensor(np.sqrt(np.sum((x_data - y_data) ** 2))), - 'dot_product': Tensor(np.dot(x_data.flatten(), y_data.flatten())) - } - - return results - ### END SOLUTION - -# %% ../../modules/source/temp_holding/13_kernels/kernels_dev.ipynb 15 -def cache_friendly_matmul(A: Tensor, B: Tensor, block_size: int = 32) -> Tensor: - """ - Cache-friendly matrix multiplication using blocking technique. - - This implementation uses cache blocking to improve memory access patterns - and achieve better performance on modern CPUs. - - TODO: Implement cache-friendly matrix multiplication using blocking. - - STEP-BY-STEP IMPLEMENTATION: - 1. Extract numpy arrays and get dimensions - 2. Pre-allocate output matrix - 3. Use three nested loops for blocks: block_i, block_j, block_k - 4. Within each block, use three nested loops for elements: i, j, k - 5. Process data in cache-sized blocks for better locality - - BLOCKING ALGORITHM: - 1. Divide matrices into blocks of size block_size x block_size - 2. For each block of C, compute contribution from corresponding A and B blocks - 3. This keeps data in cache longer, reducing memory access time - - CACHE OPTIMIZATION PRINCIPLES: - - Process data in small blocks that fit in cache - - Reuse data as much as possible while it's in cache - - Access memory in predictable patterns - - Minimize cache misses - - EXAMPLE USAGE: - ```python - A = Tensor([[1, 2], [3, 4]]) - B = Tensor([[5, 6], [7, 8]]) - C = cache_friendly_matmul(A, B, block_size=2) - # Expected: [[19, 22], [43, 50]] - ``` - - PERFORMANCE HINTS: - - block_size should be chosen based on cache size - - Typical L1 cache: 32KB, so block_size=32 for float32 matrices - - Experiment with different block sizes for your hardware - - This algorithm is O(n^3) but with much better constants - - LEARNING CONNECTIONS: - - This is how BLAS libraries achieve high performance - - GPUs use similar tiling strategies for shared memory - - Modern compilers can sometimes do this automatically - """ - ### BEGIN SOLUTION - # Extract numpy arrays - A_data = A.data if hasattr(A, 'data') else A - B_data = B.data if hasattr(B, 'data') else B - - # Get dimensions - m, k = A_data.shape - k2, n = B_data.shape - assert k == k2, f"Cannot multiply {A_data.shape} and {B_data.shape}" - - # Pre-allocate output matrix - C = np.zeros((m, n), dtype=A_data.dtype) - - # Cache-friendly blocked matrix multiplication - for block_i in range(0, m, block_size): - for block_j in range(0, n, block_size): - for block_k in range(0, k, block_size): - # Define block boundaries - end_i = min(block_i + block_size, m) - end_j = min(block_j + block_size, n) - end_k = min(block_k + block_size, k) - - # Process block - good cache locality - for i in range(block_i, end_i): - for j in range(block_j, end_j): - for k_idx in range(block_k, end_k): - C[i, j] += A_data[i, k_idx] * B_data[k_idx, j] - - return Tensor(C) - ### END SOLUTION - -# %% ../../modules/source/temp_holding/13_kernels/kernels_dev.ipynb 18 -def parallel_relu(x: Tensor, num_workers: int = 4) -> Tensor: - """ - Parallel ReLU implementation using multiple CPU cores. - - This function demonstrates data parallelism by splitting the input - across multiple worker processes. - - TODO: Implement parallel ReLU using multiprocessing or threading. - - STEP-BY-STEP IMPLEMENTATION: - 1. Extract numpy array from Tensor - 2. Split array into chunks for parallel processing - 3. Define worker function that applies ReLU to a chunk - 4. Use ThreadPoolExecutor to process chunks in parallel - 5. Combine results from all workers - 6. Return result as Tensor - - PARALLELIZATION STRATEGY: - 1. Split input into num_workers chunks - 2. Each worker processes its chunk independently - 3. Apply ReLU: max(0, x) to each chunk - 4. Combine results preserving original order - - EXAMPLE USAGE: - ```python - x = Tensor(np.random.randn(1000)) - y = parallel_relu(x, num_workers=4) - # Processes data using 4 parallel workers - ``` - - PERFORMANCE CONSIDERATIONS: - - Overhead of parallel processing may not be worth it for small arrays - - Threading vs multiprocessing trade-offs - - Chunk size should be large enough to amortize overhead - - Consider memory bandwidth limitations - - LEARNING CONNECTIONS: - - This is how PyTorch processes batches in parallel - - GPUs naturally do this with thousands of parallel threads - - Modern deep learning frameworks heavily use parallelism - """ - ### BEGIN SOLUTION - from concurrent.futures import ThreadPoolExecutor - - # Extract numpy array - x_data = x.data if hasattr(x, 'data') else x - - # For small arrays, parallel processing isn't worth the overhead - if x_data.size < 1000: - return Tensor(np.maximum(0, x_data)) - - # Split array into chunks - chunk_size = max(1, x_data.size // num_workers) - chunks = [] - flat_data = x_data.flatten() - - for i in range(0, len(flat_data), chunk_size): - chunks.append(flat_data[i:i + chunk_size]) - - # Worker function - def relu_chunk(chunk): - return np.maximum(0, chunk) - - # Process chunks in parallel - with ThreadPoolExecutor(max_workers=num_workers) as executor: - future_to_chunk = {executor.submit(relu_chunk, chunk): i for i, chunk in enumerate(chunks)} - results = [None] * len(chunks) - - for future in future_to_chunk: - chunk_idx = future_to_chunk[future] - results[chunk_idx] = future.result() - - # Combine results - combined_result = np.concatenate(results) - - # Reshape back to original shape - result = combined_result.reshape(x_data.shape) - - return Tensor(result) - ### END SOLUTION - -# %% ../../modules/source/temp_holding/13_kernels/kernels_dev.ipynb 19 -def parallel_batch_processing(batch_data: List[Tensor], operation: Callable, num_workers: int = 4) -> List[Tensor]: - """ - Process a batch of tensors in parallel using multiple workers. - - This function demonstrates how to parallelize operations across - multiple data samples, similar to how modern ML frameworks work. - - TODO: Implement parallel batch processing. - - STEP-BY-STEP IMPLEMENTATION: - 1. Take a list of Tensors and an operation function - 2. Use ThreadPoolExecutor to process multiple tensors simultaneously - 3. Apply the operation to each tensor in parallel - 4. Return list of results in original order - - PARALLELIZATION STRATEGY: - 1. Each worker processes one tensor at a time - 2. Multiple workers can process different tensors simultaneously - 3. Preserve order of results to match input order - - EXAMPLE USAGE: - ```python - batch = [Tensor(np.random.randn(100, 100)) for _ in range(8)] - relu_op = lambda x: vectorized_relu(x) - results = parallel_batch_processing(batch, relu_op, num_workers=4) - # Processes 8 tensors using 4 parallel workers - ``` - - PERFORMANCE CONSIDERATIONS: - - Each tensor should be large enough to justify parallel overhead - - Balance number of workers with available CPU cores - - Consider memory usage with multiple workers - - Thread vs process pool trade-offs - - LEARNING CONNECTIONS: - - This is how PyTorch's DataLoader processes batches - - Similar to how GPUs process multiple samples simultaneously - - Foundation for distributed training across multiple nodes - """ - ### BEGIN SOLUTION - from concurrent.futures import ThreadPoolExecutor - - # For small batches, parallel processing might not be worth it - if len(batch_data) < num_workers: - return [operation(tensor) for tensor in batch_data] - - # Process batch in parallel - with ThreadPoolExecutor(max_workers=num_workers) as executor: - # Submit all tasks - future_to_index = {executor.submit(operation, tensor): i for i, tensor in enumerate(batch_data)} - - # Collect results in original order - results = [None] * len(batch_data) - for future in future_to_index: - index = future_to_index[future] - results[index] = future.result() - - return results - ### END SOLUTION - -# %% ../../modules/source/temp_holding/13_kernels/kernels_dev.ipynb 24 -def quantized_matmul(A: Tensor, B: Tensor, scale_A: float = 1.0, scale_B: float = 1.0) -> Tensor: - """ - Quantized matrix multiplication kernel for compressed models. - - This function demonstrates how to perform matrix multiplication - with quantized (int8) weights while maintaining numerical accuracy. - - TODO: Implement quantized matrix multiplication. - - STEP-BY-STEP IMPLEMENTATION: - 1. Extract numpy arrays from Tensors - 2. Quantize inputs to int8 using provided scales - 3. Perform integer matrix multiplication - 4. Rescale result back to appropriate range - 5. Return result as Tensor - - QUANTIZATION PROCESS: - 1. Quantize: int8_value = round(float_value / scale) - 2. Compute: int8_result = int8_A @ int8_B - 3. Rescale: float_result = int8_result * scale_A * scale_B - - EXAMPLE USAGE: - ```python - A = Tensor([[1.0, 2.0], [3.0, 4.0]]) - B = Tensor([[0.5, 1.5], [2.5, 3.5]]) - C = quantized_matmul(A, B, scale_A=1.0/127, scale_B=1.0/127) - # Should approximate regular matrix multiplication - ``` - - PERFORMANCE CONSIDERATIONS: - - int8 operations are often faster than float32 - - Memory usage is 4x lower - - Accumulation in int32 to prevent overflow - - Careful handling of scales to maintain precision - - LEARNING CONNECTIONS: - - This is how TensorFlow Lite performs quantized inference - - Similar to how mobile ML accelerators work - - Foundation for edge deployment of neural networks - """ - ### BEGIN SOLUTION - # Extract numpy arrays - A_data = A.data if hasattr(A, 'data') else A - B_data = B.data if hasattr(B, 'data') else B - - # Quantize inputs to int8 - A_int8 = np.round(A_data / scale_A).astype(np.int8) - B_int8 = np.round(B_data / scale_B).astype(np.int8) - - # Perform integer matrix multiplication - # Use int32 for accumulation to prevent overflow - C_int32 = np.dot(A_int8.astype(np.int32), B_int8.astype(np.int32)) - - # Rescale result back to float - C_float = C_int32 * scale_A * scale_B - - return Tensor(C_float) - ### END SOLUTION - -# %% ../../modules/source/temp_holding/13_kernels/kernels_dev.ipynb 25 -def quantized_relu(x: Tensor, scale: float = 1.0) -> Tensor: - """ - Quantized ReLU implementation for compressed models. - - This function shows how to apply ReLU activation to quantized values - while maintaining the quantization format. - - TODO: Implement quantized ReLU activation. - - STEP-BY-STEP IMPLEMENTATION: - 1. Extract numpy array from Tensor - 2. Quantize input to int8 using provided scale - 3. Apply ReLU in integer domain: max(0, x) - 4. Keep result in int8 format (no rescaling needed for ReLU) - 5. Convert back to float using scale - 6. Return result as Tensor - - QUANTIZED RELU PROCESS: - 1. Quantize: int8_value = round(float_value / scale) - 2. Apply ReLU: int8_result = max(0, int8_value) - 3. Dequantize: float_result = int8_result * scale - - EXAMPLE USAGE: - ```python - x = Tensor([-1.0, 0.0, 1.0, 2.0]) - y = quantized_relu(x, scale=1.0/127) - # Should produce [0.0, 0.0, 1.0, 2.0] (approximately) - ``` - - OPTIMIZATION NOTES: - - ReLU in int8 is just max(0, x) - very fast - - No floating-point operations needed during activation - - Maintains quantization format throughout - - Can be vectorized efficiently - - LEARNING CONNECTIONS: - - This is how quantized neural networks maintain speed - - Similar to how mobile processors optimize ML inference - - Foundation for real-time edge computing applications - """ - ### BEGIN SOLUTION - # Extract numpy array - x_data = x.data if hasattr(x, 'data') else x - - # Quantize input to int8 - x_int8 = np.round(x_data / scale).astype(np.int8) - - # Apply ReLU in integer domain - x_relu_int8 = np.maximum(0, x_int8) - - # Convert back to float - x_relu_float = x_relu_int8 * scale - - return Tensor(x_relu_float) - ### END SOLUTION - -# %% ../../modules/source/temp_holding/13_kernels/kernels_dev.ipynb 29 -class KernelOptimizationProfiler: - """ - Production-grade kernel optimization profiler for ML systems. - - This class provides comprehensive analysis tools for optimizing ML kernels - across different hardware architectures, focusing on GPU optimization patterns - and production deployment scenarios. - - Key Features: - - CUDA kernel performance analysis - - Memory coalescing pattern detection - - Warp divergence analysis - - Shared memory optimization - - Tensor core utilization metrics - - Kernel fusion opportunities - - Multi-GPU scaling analysis - """ - - def __init__(self, hardware_config: Optional[Dict[str, Any]] = None): - """ - Initialize the kernel optimization profiler. - - Args: - hardware_config: Dictionary containing hardware specifications - """ - self.hardware_config = hardware_config or self._detect_hardware() - self.profile_results = {} - self.optimization_recommendations = [] - - def _detect_hardware(self) -> Dict[str, Any]: - """Detect current hardware configuration.""" - return { - 'cpu_cores': psutil.cpu_count(), - 'memory_gb': psutil.virtual_memory().total // (1024**3), - 'cache_sizes': { - 'l1': 32768, # Typical L1 cache size in bytes - 'l2': 262144, # Typical L2 cache size in bytes - 'l3': 8388608 # Typical L3 cache size in bytes - }, - 'gpu_available': False, # Would check for CUDA/OpenCL in real implementation - 'gpu_memory_gb': 0, - 'tensor_cores': False, - 'warp_size': 32 # NVIDIA GPU warp size - } - - def analyze_cuda_kernel_performance(self, kernel_func: Callable, input_data: Tensor, - iterations: int = 100) -> Dict[str, Any]: - """ - Analyze CUDA kernel performance characteristics. - - In a real implementation, this would interface with CUDA profiling tools - to measure actual GPU kernel performance metrics. - """ - # Simulate CUDA kernel analysis - total_time = 0 - memory_bandwidth = 0 - compute_utilization = 0 - - for _ in range(iterations): - result, execution_time = time_kernel(kernel_func, input_data) - total_time += execution_time - - # Simulate GPU metrics calculation - data_size = input_data.data.nbytes - memory_bandwidth += (data_size * 2) / (execution_time / 1_000_000) # Read + Write - compute_utilization += np.random.uniform(0.3, 0.9) # Simulated utilization - - avg_time = total_time / iterations - avg_bandwidth = memory_bandwidth / iterations - avg_utilization = compute_utilization / iterations - - analysis = { - 'avg_execution_time_us': avg_time, - 'memory_bandwidth_gb_s': avg_bandwidth / (1024**3), - 'compute_utilization': avg_utilization, - 'theoretical_peak_bandwidth': 900, # GB/s for high-end GPU - 'bandwidth_efficiency': min(100, (avg_bandwidth / (1024**3)) / 900 * 100), - 'bottleneck_analysis': self._identify_bottlenecks(avg_bandwidth / (1024**3), avg_utilization) - } - - self.profile_results['cuda_analysis'] = analysis - return analysis - - def analyze_memory_coalescing(self, access_pattern: str, data_shape: Tuple[int, ...]) -> Dict[str, Any]: - """ - Analyze memory access patterns for GPU coalescing efficiency. - - Memory coalescing is critical for GPU performance - threads in a warp - should access contiguous memory locations. - """ - coalescing_efficiency = 1.0 - - if access_pattern == 'row_major': - # Good coalescing for row-major access - coalescing_efficiency = 0.95 - elif access_pattern == 'column_major': - # Poor coalescing for column-major access - coalescing_efficiency = 0.3 - elif access_pattern == 'strided': - # Moderate coalescing for strided access - stride = data_shape[1] if len(data_shape) > 1 else 1 - coalescing_efficiency = max(0.1, 1.0 / stride) - elif access_pattern == 'random': - # Very poor coalescing for random access - coalescing_efficiency = 0.1 - - analysis = { - 'access_pattern': access_pattern, - 'data_shape': data_shape, - 'coalescing_efficiency': coalescing_efficiency, - 'memory_transactions': self._calculate_memory_transactions(data_shape, coalescing_efficiency), - 'optimization_potential': 1.0 - coalescing_efficiency - } - - self.profile_results['memory_coalescing'] = analysis - return analysis - - def analyze_warp_divergence(self, conditional_operations: int, total_operations: int) -> Dict[str, Any]: - """ - Analyze warp divergence patterns in kernel execution. - - Warp divergence occurs when threads in a warp take different execution paths, - reducing parallelism efficiency. - """ - divergence_ratio = conditional_operations / total_operations - efficiency_loss = divergence_ratio * 0.5 # Simplified model - - analysis = { - 'conditional_operations': conditional_operations, - 'total_operations': total_operations, - 'divergence_ratio': divergence_ratio, - 'efficiency_loss': efficiency_loss, - 'warp_efficiency': 1.0 - efficiency_loss, - 'optimization_suggestions': self._generate_divergence_optimizations(divergence_ratio) - } - - self.profile_results['warp_divergence'] = analysis - return analysis - - def analyze_shared_memory_usage(self, kernel_data_size: int, reuse_factor: float) -> Dict[str, Any]: - """ - Analyze shared memory optimization opportunities. - - Shared memory is fast on-chip memory that can dramatically improve - performance when used effectively for data reuse. - """ - shared_memory_size = 48 * 1024 # 48KB typical shared memory per SM - bank_conflicts = self._estimate_bank_conflicts(kernel_data_size) - - analysis = { - 'data_size_bytes': kernel_data_size, - 'shared_memory_available': shared_memory_size, - 'utilization_ratio': min(1.0, kernel_data_size / shared_memory_size), - 'reuse_factor': reuse_factor, - 'bank_conflicts': bank_conflicts, - 'performance_gain': min(10.0, reuse_factor * (1.0 - bank_conflicts)), - 'optimization_opportunities': self._identify_shared_memory_optimizations(kernel_data_size, reuse_factor) - } - - self.profile_results['shared_memory'] = analysis - return analysis - - def analyze_tensor_core_utilization(self, operation_type: str, data_types: List[str]) -> Dict[str, Any]: - """ - Analyze tensor core utilization for mixed-precision operations. - - Tensor cores provide massive acceleration for mixed-precision matrix operations - when data shapes and types are optimized correctly. - """ - tensor_core_compatible = ( - operation_type in ['matmul', 'conv2d'] and - any(dtype in ['float16', 'bfloat16', 'int8'] for dtype in data_types) - ) - - if tensor_core_compatible: - theoretical_speedup = 4.0 # Typical tensor core speedup - actual_utilization = 0.7 # Realistic utilization - else: - theoretical_speedup = 1.0 - actual_utilization = 0.0 - - analysis = { - 'operation_type': operation_type, - 'data_types': data_types, - 'tensor_core_compatible': tensor_core_compatible, - 'theoretical_speedup': theoretical_speedup, - 'actual_utilization': actual_utilization, - 'performance_gain': theoretical_speedup * actual_utilization, - 'optimization_requirements': self._get_tensor_core_requirements() - } - - self.profile_results['tensor_core'] = analysis - return analysis - - def analyze_kernel_fusion_opportunities(self, operation_sequence: List[str]) -> Dict[str, Any]: - """ - Analyze opportunities for kernel fusion to reduce memory overhead. - - Kernel fusion combines multiple operations into a single kernel, - reducing memory bandwidth requirements and improving performance. - """ - fusable_patterns = [ - ['matmul', 'relu'], - ['conv2d', 'batchnorm', 'relu'], - ['add', 'relu'], - ['mul', 'add'] - ] - - fusion_opportunities = [] - memory_savings = 0 - - for pattern in fusable_patterns: - if self._sequence_contains_pattern(operation_sequence, pattern): - fusion_opportunities.append(pattern) - memory_savings += len(pattern) - 1 # Save intermediate results - - analysis = { - 'operation_sequence': operation_sequence, - 'fusion_opportunities': fusion_opportunities, - 'memory_savings_factor': memory_savings, - 'performance_improvement': min(2.0, 1 + memory_savings * 0.3), - 'implementation_complexity': len(fusion_opportunities) * 2 - } - - self.profile_results['kernel_fusion'] = analysis - return analysis - - def analyze_multi_gpu_scaling(self, data_size: int, num_gpus: int) -> Dict[str, Any]: - """ - Analyze multi-GPU scaling patterns and communication overhead. - - Multi-GPU deployments require careful optimization of data distribution - and communication patterns to achieve good scaling efficiency. - """ - communication_overhead = self._calculate_communication_overhead(data_size, num_gpus) - compute_scaling = min(num_gpus, data_size / 1000) # Simplified scaling model - - analysis = { - 'data_size': data_size, - 'num_gpus': num_gpus, - 'communication_overhead': communication_overhead, - 'compute_scaling': compute_scaling, - 'scaling_efficiency': compute_scaling / num_gpus, - 'bottleneck_type': 'communication' if communication_overhead > 0.3 else 'compute', - 'optimization_strategies': self._get_multi_gpu_optimizations(communication_overhead) - } - - self.profile_results['multi_gpu'] = analysis - return analysis - - def generate_optimization_report(self) -> str: - """Generate comprehensive optimization report with recommendations.""" - report = ["🚀 Kernel Optimization Analysis Report", "=" * 50, ""] - - for analysis_type, results in self.profile_results.items(): - report.append(f"📊 {analysis_type.replace('_', ' ').title()} Analysis:") - report.append("-" * 30) - - for key, value in results.items(): - if isinstance(value, float): - report.append(f" {key}: {value:.3f}") - elif isinstance(value, list): - report.append(f" {key}: {', '.join(map(str, value))}") - else: - report.append(f" {key}: {value}") - report.append("") - - # Add optimization recommendations - report.append("🎯 Optimization Recommendations:") - report.append("-" * 30) - for rec in self.optimization_recommendations: - report.append(f" • {rec}") - - return "\n".join(report) - - # Helper methods - def _identify_bottlenecks(self, bandwidth_gb_s: float, utilization: float) -> str: - """Identify performance bottlenecks.""" - if bandwidth_gb_s < 100: - return "Memory bandwidth limited" - elif utilization < 0.5: - return "Compute utilization limited" - else: - return "Well balanced" - - def _calculate_memory_transactions(self, shape: Tuple[int, ...], efficiency: float) -> int: - """Calculate memory transaction count.""" - total_elements = np.prod(shape) - return int(total_elements / (32 * efficiency)) # 32 threads per warp - - def _generate_divergence_optimizations(self, divergence_ratio: float) -> List[str]: - """Generate warp divergence optimization suggestions.""" - suggestions = [] - if divergence_ratio > 0.3: - suggestions.append("Reduce conditional operations in inner loops") - suggestions.append("Use predicated execution instead of branching") - if divergence_ratio > 0.5: - suggestions.append("Restructure algorithm to minimize thread divergence") - return suggestions - - def _estimate_bank_conflicts(self, data_size: int) -> float: - """Estimate shared memory bank conflicts.""" - # Simplified model - assumes some degree of bank conflicts - return min(0.5, data_size / (32 * 4)) # 32 banks, 4 bytes per bank - - def _identify_shared_memory_optimizations(self, size: int, reuse: float) -> List[str]: - """Identify shared memory optimization opportunities.""" - optimizations = [] - if reuse > 2.0: - optimizations.append("High reuse factor - shared memory beneficial") - if size < 16384: # 16KB - optimizations.append("Data fits in shared memory - implement tiling") - return optimizations - - def _get_tensor_core_requirements(self) -> List[str]: - """Get tensor core optimization requirements.""" - return [ - "Use mixed precision (float16/bfloat16)", - "Ensure matrix dimensions are multiples of 8", - "Use proper memory layout (NHWC for convolutions)" - ] - - def _sequence_contains_pattern(self, sequence: List[str], pattern: List[str]) -> bool: - """Check if operation sequence contains fusable pattern.""" - for i in range(len(sequence) - len(pattern) + 1): - if sequence[i:i+len(pattern)] == pattern: - return True - return False - - def _calculate_communication_overhead(self, data_size: int, num_gpus: int) -> float: - """Calculate multi-GPU communication overhead.""" - # Simplified model based on data size and GPU count - return min(0.8, (data_size / 1000) / num_gpus + 0.1) - - def _get_multi_gpu_optimizations(self, overhead: float) -> List[str]: - """Get multi-GPU optimization strategies.""" - strategies = [] - if overhead > 0.3: - strategies.append("Implement gradient compression") - strategies.append("Use asynchronous communication") - if overhead > 0.5: - strategies.append("Increase batch size to amortize communication") - return strategies - -# %% ../../modules/source/temp_holding/13_kernels/kernels_dev.ipynb 32 -class KernelOptimizationProfiler: - """ - Production-grade kernel optimization profiler for ML systems. - - This class provides comprehensive analysis tools for optimizing ML kernels - across different hardware architectures, focusing on GPU optimization patterns - and production deployment scenarios. - - Key Features: - - CUDA kernel performance analysis - - Memory coalescing pattern detection - - Warp divergence analysis - - Shared memory optimization - - Tensor core utilization metrics - - Kernel fusion opportunities - - Multi-GPU scaling analysis - """ - - def __init__(self, hardware_config: Optional[Dict[str, Any]] = None): - """ - Initialize the kernel optimization profiler. - - Args: - hardware_config: Dictionary containing hardware specifications - """ - self.hardware_config = hardware_config or self._detect_hardware() - self.profile_results = {} - self.optimization_recommendations = [] - - def _detect_hardware(self) -> Dict[str, Any]: - """Detect current hardware configuration.""" - return { - 'cpu_cores': psutil.cpu_count(), - 'memory_gb': psutil.virtual_memory().total // (1024**3), - 'cache_sizes': { - 'l1': 32768, # Typical L1 cache size in bytes - 'l2': 262144, # Typical L2 cache size in bytes - 'l3': 8388608 # Typical L3 cache size in bytes - }, - 'gpu_available': False, # Would check for CUDA/OpenCL in real implementation - 'gpu_memory_gb': 0, - 'tensor_cores': False, - 'warp_size': 32 # NVIDIA GPU warp size - } - - def analyze_cuda_kernel_performance(self, kernel_func: Callable, input_data: Tensor, - iterations: int = 100) -> Dict[str, Any]: - """ - Analyze CUDA kernel performance characteristics. - - In a real implementation, this would interface with CUDA profiling tools - to measure actual GPU kernel performance metrics. - """ - # Simulate CUDA kernel analysis - total_time = 0 - memory_bandwidth = 0 - compute_utilization = 0 - - for _ in range(iterations): - result, execution_time = time_kernel(kernel_func, input_data) - total_time += execution_time - - # Simulate GPU metrics calculation - data_size = input_data.data.nbytes - memory_bandwidth += (data_size * 2) / (execution_time / 1_000_000) # Read + Write - compute_utilization += np.random.uniform(0.3, 0.9) # Simulated utilization - - avg_time = total_time / iterations - avg_bandwidth = memory_bandwidth / iterations - avg_utilization = compute_utilization / iterations - - analysis = { - 'avg_execution_time_us': avg_time, - 'memory_bandwidth_gb_s': avg_bandwidth / (1024**3), - 'compute_utilization': avg_utilization, - 'theoretical_peak_bandwidth': 900, # GB/s for high-end GPU - 'bandwidth_efficiency': min(100, (avg_bandwidth / (1024**3)) / 900 * 100), - 'bottleneck_analysis': self._identify_bottlenecks(avg_bandwidth / (1024**3), avg_utilization) - } - - self.profile_results['cuda_analysis'] = analysis - return analysis - - def analyze_memory_coalescing(self, access_pattern: str, data_shape: Tuple[int, ...]) -> Dict[str, Any]: - """ - Analyze memory access patterns for GPU coalescing efficiency. - - Memory coalescing is critical for GPU performance - threads in a warp - should access contiguous memory locations. - """ - coalescing_efficiency = 1.0 - - if access_pattern == 'row_major': - # Good coalescing for row-major access - coalescing_efficiency = 0.95 - elif access_pattern == 'column_major': - # Poor coalescing for column-major access - coalescing_efficiency = 0.3 - elif access_pattern == 'strided': - # Moderate coalescing for strided access - stride = data_shape[1] if len(data_shape) > 1 else 1 - coalescing_efficiency = max(0.1, 1.0 / stride) - elif access_pattern == 'random': - # Very poor coalescing for random access - coalescing_efficiency = 0.1 - - analysis = { - 'access_pattern': access_pattern, - 'data_shape': data_shape, - 'coalescing_efficiency': coalescing_efficiency, - 'memory_transactions': self._calculate_memory_transactions(data_shape, coalescing_efficiency), - 'optimization_potential': 1.0 - coalescing_efficiency - } - - self.profile_results['memory_coalescing'] = analysis - return analysis - - def analyze_warp_divergence(self, conditional_operations: int, total_operations: int) -> Dict[str, Any]: - """ - Analyze warp divergence patterns in kernel execution. - - Warp divergence occurs when threads in a warp take different execution paths, - reducing parallelism efficiency. - """ - divergence_ratio = conditional_operations / total_operations - efficiency_loss = divergence_ratio * 0.5 # Simplified model - - analysis = { - 'conditional_operations': conditional_operations, - 'total_operations': total_operations, - 'divergence_ratio': divergence_ratio, - 'efficiency_loss': efficiency_loss, - 'warp_efficiency': 1.0 - efficiency_loss, - 'optimization_suggestions': self._generate_divergence_optimizations(divergence_ratio) - } - - self.profile_results['warp_divergence'] = analysis - return analysis - - def analyze_shared_memory_usage(self, kernel_data_size: int, reuse_factor: float) -> Dict[str, Any]: - """ - Analyze shared memory optimization opportunities. - - Shared memory is fast on-chip memory that can dramatically improve - performance when used effectively for data reuse. - """ - shared_memory_size = 48 * 1024 # 48KB typical shared memory per SM - bank_conflicts = self._estimate_bank_conflicts(kernel_data_size) - - analysis = { - 'data_size_bytes': kernel_data_size, - 'shared_memory_available': shared_memory_size, - 'utilization_ratio': min(1.0, kernel_data_size / shared_memory_size), - 'reuse_factor': reuse_factor, - 'bank_conflicts': bank_conflicts, - 'performance_gain': min(10.0, reuse_factor * (1.0 - bank_conflicts)), - 'optimization_opportunities': self._identify_shared_memory_optimizations(kernel_data_size, reuse_factor) - } - - self.profile_results['shared_memory'] = analysis - return analysis - - def analyze_tensor_core_utilization(self, operation_type: str, data_types: List[str]) -> Dict[str, Any]: - """ - Analyze tensor core utilization for mixed-precision operations. - - Tensor cores provide massive acceleration for mixed-precision matrix operations - when data shapes and types are optimized correctly. - """ - tensor_core_compatible = ( - operation_type in ['matmul', 'conv2d'] and - any(dtype in ['float16', 'bfloat16', 'int8'] for dtype in data_types) - ) - - if tensor_core_compatible: - theoretical_speedup = 4.0 # Typical tensor core speedup - actual_utilization = 0.7 # Realistic utilization - else: - theoretical_speedup = 1.0 - actual_utilization = 0.0 - - analysis = { - 'operation_type': operation_type, - 'data_types': data_types, - 'tensor_core_compatible': tensor_core_compatible, - 'theoretical_speedup': theoretical_speedup, - 'actual_utilization': actual_utilization, - 'performance_gain': theoretical_speedup * actual_utilization, - 'optimization_requirements': self._get_tensor_core_requirements() - } - - self.profile_results['tensor_core'] = analysis - return analysis - - def analyze_kernel_fusion_opportunities(self, operation_sequence: List[str]) -> Dict[str, Any]: - """ - Analyze opportunities for kernel fusion to reduce memory overhead. - - Kernel fusion combines multiple operations into a single kernel, - reducing memory bandwidth requirements and improving performance. - """ - fusable_patterns = [ - ['matmul', 'relu'], - ['conv2d', 'batchnorm', 'relu'], - ['add', 'relu'], - ['mul', 'add'] - ] - - fusion_opportunities = [] - memory_savings = 0 - - for pattern in fusable_patterns: - if self._sequence_contains_pattern(operation_sequence, pattern): - fusion_opportunities.append(pattern) - memory_savings += len(pattern) - 1 # Save intermediate results - - analysis = { - 'operation_sequence': operation_sequence, - 'fusion_opportunities': fusion_opportunities, - 'memory_savings_factor': memory_savings, - 'performance_improvement': min(2.0, 1 + memory_savings * 0.3), - 'implementation_complexity': len(fusion_opportunities) * 2 - } - - self.profile_results['kernel_fusion'] = analysis - return analysis - - def analyze_multi_gpu_scaling(self, data_size: int, num_gpus: int) -> Dict[str, Any]: - """ - Analyze multi-GPU scaling patterns and communication overhead. - - Multi-GPU deployments require careful optimization of data distribution - and communication patterns to achieve good scaling efficiency. - """ - communication_overhead = self._calculate_communication_overhead(data_size, num_gpus) - compute_scaling = min(num_gpus, data_size / 1000) # Simplified scaling model - - analysis = { - 'data_size': data_size, - 'num_gpus': num_gpus, - 'communication_overhead': communication_overhead, - 'compute_scaling': compute_scaling, - 'scaling_efficiency': compute_scaling / num_gpus, - 'bottleneck_type': 'communication' if communication_overhead > 0.3 else 'compute', - 'optimization_strategies': self._get_multi_gpu_optimizations(communication_overhead) - } - - self.profile_results['multi_gpu'] = analysis - return analysis - - def generate_optimization_report(self) -> str: - """Generate comprehensive optimization report with recommendations.""" - report = ["🚀 Kernel Optimization Analysis Report", "=" * 50, ""] - - for analysis_type, results in self.profile_results.items(): - report.append(f"📊 {analysis_type.replace('_', ' ').title()} Analysis:") - report.append("-" * 30) - - for key, value in results.items(): - if isinstance(value, float): - report.append(f" {key}: {value:.3f}") - elif isinstance(value, list): - report.append(f" {key}: {', '.join(map(str, value))}") - else: - report.append(f" {key}: {value}") - report.append("") - - # Add optimization recommendations - report.append("🎯 Optimization Recommendations:") - report.append("-" * 30) - for rec in self.optimization_recommendations: - report.append(f" • {rec}") - - return "\n".join(report) - - # Helper methods - def _identify_bottlenecks(self, bandwidth_gb_s: float, utilization: float) -> str: - """Identify performance bottlenecks.""" - if bandwidth_gb_s < 100: - return "Memory bandwidth limited" - elif utilization < 0.5: - return "Compute utilization limited" - else: - return "Well balanced" - - def _calculate_memory_transactions(self, shape: Tuple[int, ...], efficiency: float) -> int: - """Calculate memory transaction count.""" - total_elements = np.prod(shape) - return int(total_elements / (32 * efficiency)) # 32 threads per warp - - def _generate_divergence_optimizations(self, divergence_ratio: float) -> List[str]: - """Generate warp divergence optimization suggestions.""" - suggestions = [] - if divergence_ratio > 0.3: - suggestions.append("Reduce conditional operations in inner loops") - suggestions.append("Use predicated execution instead of branching") - if divergence_ratio > 0.5: - suggestions.append("Restructure algorithm to minimize thread divergence") - return suggestions - - def _estimate_bank_conflicts(self, data_size: int) -> float: - """Estimate shared memory bank conflicts.""" - # Simplified model - assumes some degree of bank conflicts - return min(0.5, data_size / (32 * 4)) # 32 banks, 4 bytes per bank - - def _identify_shared_memory_optimizations(self, size: int, reuse: float) -> List[str]: - """Identify shared memory optimization opportunities.""" - optimizations = [] - if reuse > 2.0: - optimizations.append("High reuse factor - shared memory beneficial") - if size < 16384: # 16KB - optimizations.append("Data fits in shared memory - implement tiling") - return optimizations - - def _get_tensor_core_requirements(self) -> List[str]: - """Get tensor core optimization requirements.""" - return [ - "Use mixed precision (float16/bfloat16)", - "Ensure matrix dimensions are multiples of 8", - "Use proper memory layout (NHWC for convolutions)" - ] - - def _sequence_contains_pattern(self, sequence: List[str], pattern: List[str]) -> bool: - """Check if operation sequence contains fusable pattern.""" - for i in range(len(sequence) - len(pattern) + 1): - if sequence[i:i+len(pattern)] == pattern: - return True - return False - - def _calculate_communication_overhead(self, data_size: int, num_gpus: int) -> float: - """Calculate multi-GPU communication overhead.""" - # Simplified model based on data size and GPU count - return min(0.8, (data_size / 1000) / num_gpus + 0.1) - - def _get_multi_gpu_optimizations(self, overhead: float) -> List[str]: - """Get multi-GPU optimization strategies.""" - strategies = [] - if overhead > 0.3: - strategies.append("Implement gradient compression") - strategies.append("Use asynchronous communication") - if overhead > 0.5: - strategies.append("Increase batch size to amortize communication") - return strategies diff --git a/tinytorch/core/layers.py b/tinytorch/core/layers.py index 2c2bfd3c..2f117f88 100644 --- a/tinytorch/core/layers.py +++ b/tinytorch/core/layers.py @@ -14,342 +14,203 @@ # ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║ # ║ happens! The tinytorch/ directory is just the compiled output. ║ # ╚═══════════════════════════════════════════════════════════════════════════════╝ - # %% auto 0 -__all__ = ['Dense', 'Module', 'matmul', 'Linear'] +__all__ = ['Linear', 'Dropout'] -# %% ../../modules/source/04_layers/layers_dev.ipynb 1 +# %% ../../modules/source/03_layers/layers_dev.ipynb 1 import numpy as np import sys import os -from typing import Union, Tuple, Optional, Any -# Import our building blocks - try package first, then local modules -try: - from tinytorch.core.tensor import Tensor, Parameter -except ImportError: - # For development, import from local modules - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '02_tensor')) - from tensor_dev import Tensor, Parameter +# Import dependencies from tinytorch package +from .tensor import Tensor +from .activations import ReLU, Sigmoid -# %% ../../modules/source/04_layers/layers_dev.ipynb 4 -class Module: +# %% ../../modules/source/03_layers/layers_dev.ipynb 6 +class Linear: """ - Base class for all neural network modules. - - Provides automatic parameter collection, forward pass management, - and clean composition patterns. All layers (Dense, Conv2d, etc.) - inherit from this class. - - Key Features: - - Automatic parameter registration when you assign Tensors with requires_grad=True - - Recursive parameter collection from sub-modules - - Clean __call__ interface: model(x) instead of model.forward(x) - - Extensible for custom layers - - Example Usage: - class MLP(Module): - def __init__(self): - super().__init__() - self.layer1 = Dense(784, 128) # Auto-registered! - self.layer2 = Dense(128, 10) # Auto-registered! - - def forward(self, x): - x = self.layer1(x) - return self.layer2(x) - - model = MLP() - params = model.parameters() # Gets all parameters automatically! - output = model(input) # Clean interface! - """ - - def __init__(self): - """Initialize module with empty parameter and sub-module storage.""" - self._parameters = [] - self._modules = [] - - def __setattr__(self, name, value): - """ - Intercept attribute assignment to auto-register parameters and modules. - - When you do self.weight = Parameter(...), this automatically adds - the parameter to our collection for easy optimization. - """ - # Check if it's a tensor that needs gradients (a parameter) - if hasattr(value, 'requires_grad') and value.requires_grad: - self._parameters.append(value) - # Check if it's another Module (sub-module) - elif isinstance(value, Module): - self._modules.append(value) - - # Always call parent to actually set the attribute - super().__setattr__(name, value) - - def parameters(self): - """ - Recursively collect all parameters from this module and sub-modules. - - Returns: - List of all parameters (Tensors with requires_grad=True) - - This enables: optimizer = Adam(model.parameters()) - """ - # Start with our own parameters - params = list(self._parameters) - - # Add parameters from sub-modules recursively - for module in self._modules: - params.extend(module.parameters()) - - return params - - def __call__(self, *args, **kwargs): - """ - Makes modules callable: model(x) instead of model.forward(x). - - This is the magic that enables clean syntax like: - output = model(input) - instead of: - output = model.forward(input) - """ - return self.forward(*args, **kwargs) - - def forward(self, *args, **kwargs): - """ - Forward pass - must be implemented by subclasses. - - This is where the actual computation happens. Every layer - defines its own forward() method. - """ - raise NotImplementedError("Subclasses must implement forward()") + Linear (fully connected) layer: y = xW + b -# %% ../../modules/source/04_layers/layers_dev.ipynb 7 -def matmul(a: Tensor, b: Tensor) -> Tensor: + This is the fundamental building block of neural networks. + Applies a linear transformation to incoming data. """ - Matrix multiplication for tensors. - - Args: - a: Left tensor (shape: ..., m, k) - b: Right tensor (shape: ..., k, n) - - Returns: - Result tensor (shape: ..., m, n) - - TODO: Implement matrix multiplication using numpy's @ operator. - - STEP-BY-STEP IMPLEMENTATION: - 1. Extract numpy arrays from both tensors using .data - 2. Perform matrix multiplication: result_data = a_data @ b_data - 3. Wrap result in a new Tensor and return - - LEARNING CONNECTIONS: - - This is the core operation in Dense layers: output = input @ weights - - PyTorch uses optimized BLAS libraries for this operation - - GPU implementations parallelize this across thousands of cores - - Understanding this operation is key to neural network performance - - EXAMPLE: - ```python - a = Tensor([[1, 2], [3, 4]]) # shape (2, 2) - b = Tensor([[5, 6], [7, 8]]) # shape (2, 2) - result = matmul(a, b) - # result.data = [[19, 22], [43, 50]] - ``` - - IMPLEMENTATION HINTS: - - Use the @ operator for clean matrix multiplication - - Ensure you return a Tensor, not a numpy array - - The operation should work for any compatible matrix shapes - """ - ### BEGIN SOLUTION - # Check if we're dealing with Variables (autograd) or plain Tensors - a_is_variable = hasattr(a, 'requires_grad') and hasattr(a, 'grad_fn') - b_is_variable = hasattr(b, 'requires_grad') and hasattr(b, 'grad_fn') - - # Extract numpy data appropriately - if a_is_variable: - a_data = a.data.data # Variable.data is a Tensor, so .data.data gets numpy array - else: - a_data = a.data # Tensor.data is numpy array directly - - if b_is_variable: - b_data = b.data.data - else: - b_data = b.data - - # Perform matrix multiplication - result_data = a_data @ b_data - - # If any input is a Variable, return Variable with gradient tracking - if a_is_variable or b_is_variable: - # Import Variable locally to avoid circular imports - if 'Variable' not in globals(): - try: - from tinytorch.core.autograd import Variable - except ImportError: - from autograd_dev import Variable - - # Create gradient function for matrix multiplication - def grad_fn(grad_output): - # Matrix multiplication backward pass: - # If C = A @ B, then: - # dA = grad_output @ B^T - # dB = A^T @ grad_output - - if a_is_variable and a.requires_grad: - # Gradient w.r.t. A: grad_output @ B^T - grad_a_data = grad_output.data.data @ b_data.T - a.backward(Variable(grad_a_data)) - - if b_is_variable and b.requires_grad: - # Gradient w.r.t. B: A^T @ grad_output - grad_b_data = a_data.T @ grad_output.data.data - b.backward(Variable(grad_b_data)) - - # Determine if result should require gradients - requires_grad = (a_is_variable and a.requires_grad) or (b_is_variable and b.requires_grad) - - return Variable(result_data, requires_grad=requires_grad, grad_fn=grad_fn) - else: - # Both inputs are Tensors, return Tensor (backward compatible) - return Tensor(result_data) - ### END SOLUTION -# %% ../../modules/source/04_layers/layers_dev.ipynb 11 -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): + def __init__(self, in_features, out_features, bias=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 + Initialize linear layer with proper weight initialization. + + TODO: Initialize weights and bias with Xavier initialization + + APPROACH: + 1. Create weight matrix (in_features, out_features) with Xavier scaling + 2. Create bias vector (out_features,) initialized to zeros if bias=True + 3. Set requires_grad=True for parameters (ready for Module 05) + + EXAMPLE: + >>> layer = Linear(784, 10) # MNIST classifier final layer + >>> print(layer.weight.shape) + (784, 10) + >>> print(layer.bias.shape) + (10,) + + HINTS: + - Xavier init: scale = sqrt(1/in_features) + - Use np.random.randn() for normal distribution + - bias=None when bias=False """ ### 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_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_data = np.random.randn(output_size) * 0.1 - self.bias = Parameter(bias_data) # Auto-registers for optimization! + self.in_features = in_features + self.out_features = out_features + + # Xavier/Glorot initialization for stable gradients + scale = np.sqrt(1.0 / in_features) + weight_data = np.random.randn(in_features, out_features) * scale + self.weight = Tensor(weight_data, requires_grad=True) + + # Initialize bias to zeros or None + if bias: + bias_data = np.zeros(out_features) + self.bias = Tensor(bias_data, requires_grad=True) else: self.bias = None ### END SOLUTION - - def forward(self, x: Union[Tensor, 'Variable']) -> Union[Tensor, 'Variable']: + + def forward(self, x): """ - Forward pass through the Linear layer. - - Args: - x: Input tensor or Variable (shape: ..., input_size) - - Returns: - Output tensor or Variable (shape: ..., output_size) - Preserves Variable type for gradient tracking in training - - TODO: Implement autograd-aware forward pass: output = input @ weights + bias - - STEP-BY-STEP IMPLEMENTATION: - 1. Perform matrix multiplication: output = matmul(x, self.weights) - 2. If bias exists, add it appropriately based on input type - 3. Preserve Variable type for gradient tracking if input is Variable - 4. Return result maintaining autograd capabilities - - AUTOGRAD CONSIDERATIONS: - - If x is Variable: weights and bias should also be Variables for training - - Preserve gradient tracking through the entire computation - - Enable backpropagation through this layer's parameters - - Handle mixed Tensor/Variable scenarios gracefully - - LEARNING CONNECTIONS: - - This is the core neural network transformation - - Matrix multiplication scales input features to output features - - Bias provides offset (like y-intercept in linear equations) - - Broadcasting handles different batch sizes automatically - - Autograd support enables automatic parameter optimization - - IMPLEMENTATION HINTS: - - Use the matmul function you implemented above (now autograd-aware) - - Handle bias addition based on input/output types - - Variables support + operator for gradient-tracked addition - - Check if self.bias is not None before adding + Forward pass through linear layer. + + TODO: Implement y = xW + b + + APPROACH: + 1. Matrix multiply input with weights: xW + 2. Add bias if it exists + 3. Return result as new Tensor + + EXAMPLE: + >>> layer = Linear(3, 2) + >>> x = Tensor([[1, 2, 3], [4, 5, 6]]) # 2 samples, 3 features + >>> y = layer.forward(x) + >>> print(y.shape) + (2, 2) # 2 samples, 2 outputs + + HINTS: + - Use tensor.matmul() for matrix multiplication + - Handle bias=None case + - Broadcasting automatically handles bias addition """ ### BEGIN SOLUTION - # Matrix multiplication: input @ weights (now autograd-aware) - output = matmul(x, self.weights) - - # Add bias if it exists - # The addition will preserve Variable type if output is Variable + # Linear transformation: y = xW + output = x.matmul(self.weight) + + # Add bias if present if self.bias is not None: - # Check if we need Variable-aware addition - if hasattr(output, 'requires_grad'): - # output is a Variable, use Variable addition - if hasattr(self.bias, 'requires_grad'): - # bias is also Variable, direct addition works - output = output + self.bias - else: - # bias is Tensor, convert to Variable for addition - # Import Variable if not already available - if 'Variable' not in globals(): - try: - from tinytorch.core.autograd import Variable - except ImportError: - from autograd_dev import Variable - - bias_var = Variable(self.bias.data, requires_grad=False) - output = output + bias_var - else: - # output is Tensor, use regular addition - output = output + self.bias - + output = output + self.bias + return output ### END SOLUTION -# Backward compatibility alias -#| export -Dense = Linear + def __call__(self, x): + """Allows the layer to be called like a function.""" + return self.forward(x) + + def parameters(self): + """ + Return list of trainable parameters. + + TODO: Return all tensors that need gradients + + APPROACH: + 1. Start with weight (always present) + 2. Add bias if it exists + 3. Return as list for optimizer + """ + ### BEGIN SOLUTION + params = [self.weight] + if self.bias is not None: + params.append(self.bias) + return params + ### END SOLUTION + + def __repr__(self): + """String representation for debugging.""" + bias_str = f", bias={self.bias is not None}" + return f"Linear(in_features={self.in_features}, out_features={self.out_features}{bias_str})" + +# %% ../../modules/source/03_layers/layers_dev.ipynb 10 +class Dropout: + """ + Dropout layer for regularization. + + During training: randomly zeros elements with probability p + During inference: scales outputs by (1-p) to maintain expected value + + This prevents overfitting by forcing the network to not rely on specific neurons. + """ + + def __init__(self, p=0.5): + """ + Initialize dropout layer. + + TODO: Store dropout probability + + Args: + p: Probability of zeroing each element (0.0 = no dropout, 1.0 = zero everything) + + EXAMPLE: + >>> dropout = Dropout(0.5) # Zero 50% of elements during training + """ + ### BEGIN SOLUTION + if not 0.0 <= p <= 1.0: + raise ValueError(f"Dropout probability must be between 0 and 1, got {p}") + self.p = p + ### END SOLUTION + + def forward(self, x, training=True): + """ + Forward pass through dropout layer. + + TODO: Apply dropout during training, pass through during inference + + APPROACH: + 1. If not training, return input unchanged + 2. If training, create random mask with probability (1-p) + 3. Multiply input by mask and scale by 1/(1-p) + 4. Return result as new Tensor + + EXAMPLE: + >>> dropout = Dropout(0.5) + >>> x = Tensor([1, 2, 3, 4]) + >>> y_train = dropout.forward(x, training=True) # Some elements zeroed + >>> y_eval = dropout.forward(x, training=False) # All elements preserved + + HINTS: + - Use np.random.random() < keep_prob for mask + - Scale by 1/(1-p) to maintain expected value + - training=False should return input unchanged + """ + ### BEGIN SOLUTION + if not training or self.p == 0.0: + # During inference or no dropout, pass through unchanged + return x + + if self.p == 1.0: + # Drop everything + return Tensor(np.zeros_like(x.data)) + + # During training, apply dropout + keep_prob = 1.0 - self.p + + # Create random mask: True where we keep elements + mask = np.random.random(x.data.shape) < keep_prob + + # Apply mask and scale to maintain expected value + output_data = (x.data * mask) / keep_prob + return Tensor(output_data) + ### END SOLUTION + + def parameters(self): + """Dropout has no parameters.""" + return [] + + def __repr__(self): + return f"Dropout(p={self.p})" diff --git a/tinytorch/core/mlops.py b/tinytorch/core/mlops.py deleted file mode 100644 index b7233760..00000000 --- a/tinytorch/core/mlops.py +++ /dev/null @@ -1,2839 +0,0 @@ -# ╔═══════════════════════════════════════════════════════════════════════════════╗ -# ║ 🚨 CRITICAL WARNING 🚨 ║ -# ║ AUTOGENERATED! DO NOT EDIT! ║ -# ║ ║ -# ║ This file is AUTOMATICALLY GENERATED from source modules. ║ -# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║ -# ║ ║ -# ║ ✅ TO EDIT: modules/source/XX_mlops/mlops_dev.py ║ -# ║ ✅ TO EXPORT: Run 'tito module complete ' ║ -# ║ ║ -# ║ 🛡️ STUDENT PROTECTION: This file contains optimized implementations. ║ -# ║ Editing it directly may break module functionality and training. ║ -# ║ ║ -# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║ -# ║ happens! The tinytorch/ directory is just the compiled output. ║ -# ╚═══════════════════════════════════════════════════════════════════════════════╝ - -# %% auto 0 -__all__ = ['ModelMonitor', 'DriftDetector', 'RetrainingTrigger', 'MLOpsPipeline', 'ModelVersion', 'DeploymentStrategy', - 'ProductionMLOpsProfiler'] - -# %% ../../modules/source/temp_holding/15_mlops/mlops_dev.ipynb 1 -import numpy as np -import os -import sys -import time -import json -from typing import Dict, List, Tuple, Optional, Any, Callable -from dataclasses import dataclass, field -from datetime import datetime, timedelta -from collections import defaultdict - -# Import our dependencies - try from package first, then local modules -try: - from tinytorch.core.tensor import Tensor - from tinytorch.core.training import Trainer, MeanSquaredError, CrossEntropyLoss, Accuracy - from tinytorch.core.benchmarking import TinyTorchPerf, StatisticalValidator - from tinytorch.core.compression import quantize_layer_weights, prune_weights_by_magnitude - from tinytorch.core.networks import Sequential - from tinytorch.core.layers import Dense - from tinytorch.core.activations import ReLU, Sigmoid, Softmax -except ImportError: - # For development, import from local modules - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '01_tensor')) - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '09_training')) - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '12_benchmarking')) - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '10_compression')) - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '04_networks')) - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '03_layers')) - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '02_activations')) - try: - from tensor_dev import Tensor - from training_dev import Trainer, MeanSquaredError, CrossEntropyLoss, Accuracy - from benchmarking_dev import TinyTorchPerf, StatisticalValidator - from compression_dev import quantize_layer_weights, prune_weights_by_magnitude - from networks_dev import Sequential - from layers_dev import Dense - from activations_dev import ReLU, Sigmoid, Softmax - except ImportError: - print("⚠️ Development imports failed - some functionality may be limited") - -# %% ../../modules/source/temp_holding/15_mlops/mlops_dev.ipynb 7 -@dataclass -class ModelMonitor: - """ - Monitors ML model performance over time and detects degradation. - - Tracks key metrics, stores history, and alerts when performance drops. - """ - - def __init__(self, model_name: str, baseline_accuracy: float = 0.95): - """ - TODO: Initialize the ModelMonitor for tracking model performance. - - STEP-BY-STEP IMPLEMENTATION: - 1. Store the model_name and baseline_accuracy - 2. Create empty lists to store metric history: - - accuracy_history: List[float] - - latency_history: List[float] - - timestamp_history: List[datetime] - 3. Set performance thresholds: - - accuracy_threshold: baseline_accuracy * 0.9 (10% drop triggers alert) - - latency_threshold: 200.0 (milliseconds) - 4. Initialize alert flags: - - accuracy_alert: False - - latency_alert: False - - EXAMPLE USAGE: - ```python - monitor = ModelMonitor("image_classifier", baseline_accuracy=0.93) - monitor.record_performance(accuracy=0.92, latency=150.0) - alerts = monitor.check_alerts() - ``` - - IMPLEMENTATION HINTS: - - Use self.model_name = model_name - - Initialize lists with self.accuracy_history = [] - - Use datetime.now() for timestamps - - Set thresholds relative to baseline (e.g., 90% of baseline) - - LEARNING CONNECTIONS: - - This builds on benchmarking concepts from Module 12 - - Performance tracking is essential for production systems - - Thresholds prevent false alarms while catching real issues - """ - ### BEGIN SOLUTION - self.model_name = model_name - self.baseline_accuracy = baseline_accuracy - - # Metric history storage - self.accuracy_history = [] - self.latency_history = [] - self.timestamp_history = [] - - # Performance thresholds - self.accuracy_threshold = baseline_accuracy * 0.9 # 10% drop triggers alert - self.latency_threshold = 200.0 # milliseconds - - # Alert flags - self.accuracy_alert = False - self.latency_alert = False - ### END SOLUTION - - def record_performance(self, accuracy: float, latency: float): - """ - TODO: Record a new performance measurement. - - STEP-BY-STEP IMPLEMENTATION: - 1. Get current timestamp with datetime.now() - 2. Append accuracy to self.accuracy_history - 3. Append latency to self.latency_history - 4. Append timestamp to self.timestamp_history - 5. Check if accuracy is below threshold: - - If accuracy < self.accuracy_threshold: set self.accuracy_alert = True - - Else: set self.accuracy_alert = False - 6. Check if latency is above threshold: - - If latency > self.latency_threshold: set self.latency_alert = True - - Else: set self.latency_alert = False - - EXAMPLE BEHAVIOR: - ```python - monitor.record_performance(0.94, 120.0) # Good performance - monitor.record_performance(0.84, 250.0) # Triggers both alerts - ``` - - IMPLEMENTATION HINTS: - - Use datetime.now() for timestamps - - Update alert flags based on current measurement - - Don't forget to store all three values (accuracy, latency, timestamp) - """ - ### BEGIN SOLUTION - current_time = datetime.now() - - # Record the measurements - self.accuracy_history.append(accuracy) - self.latency_history.append(latency) - self.timestamp_history.append(current_time) - - # Check thresholds and update alerts - self.accuracy_alert = accuracy < self.accuracy_threshold - self.latency_alert = latency > self.latency_threshold - ### END SOLUTION - - def check_alerts(self) -> Dict[str, Any]: - """ - TODO: Check current alert status and return alert information. - - STEP-BY-STEP IMPLEMENTATION: - 1. Create result dictionary with basic info: - - "model_name": self.model_name - - "accuracy_alert": self.accuracy_alert - - "latency_alert": self.latency_alert - 2. If accuracy_alert is True, add: - - "accuracy_message": f"Accuracy below threshold: {current_accuracy:.3f} < {self.accuracy_threshold:.3f}" - - "current_accuracy": most recent accuracy from history - 3. If latency_alert is True, add: - - "latency_message": f"Latency above threshold: {current_latency:.1f}ms > {self.latency_threshold:.1f}ms" - - "current_latency": most recent latency from history - 4. Add overall alert status: - - "any_alerts": True if any alert is active - - EXAMPLE RETURN: - ```python - { - "model_name": "image_classifier", - "accuracy_alert": True, - "latency_alert": False, - "accuracy_message": "Accuracy below threshold: 0.840 < 0.855", - "current_accuracy": 0.840, - "any_alerts": True - } - ``` - - IMPLEMENTATION HINTS: - - Use self.accuracy_history[-1] for most recent values - - Format numbers with f-strings for readability - - Include both alert flags and descriptive messages - """ - ### BEGIN SOLUTION - result = { - "model_name": self.model_name, - "accuracy_alert": self.accuracy_alert, - "latency_alert": self.latency_alert - } - - if self.accuracy_alert and self.accuracy_history: - current_accuracy = self.accuracy_history[-1] - result["accuracy_message"] = f"Accuracy below threshold: {current_accuracy:.3f} < {self.accuracy_threshold:.3f}" - result["current_accuracy"] = current_accuracy - - if self.latency_alert and self.latency_history: - current_latency = self.latency_history[-1] - result["latency_message"] = f"Latency above threshold: {current_latency:.1f}ms > {self.latency_threshold:.1f}ms" - result["current_latency"] = current_latency - - result["any_alerts"] = self.accuracy_alert or self.latency_alert - return result - ### END SOLUTION - - def get_performance_trend(self) -> Dict[str, Any]: - """ - TODO: Analyze performance trends over time. - - STEP-BY-STEP IMPLEMENTATION: - 1. Check if we have enough data (at least 2 measurements) - 2. Calculate accuracy trend: - - If accuracy_history has < 2 points: trend = "insufficient_data" - - Else: compare recent avg (last 3) vs older avg (first 3) - - If recent > older: trend = "improving" - - If recent < older: trend = "degrading" - - Else: trend = "stable" - 3. Calculate similar trend for latency - 4. Return dictionary with: - - "measurements_count": len(self.accuracy_history) - - "accuracy_trend": trend analysis - - "latency_trend": trend analysis - - "baseline_accuracy": self.baseline_accuracy - - "current_accuracy": most recent accuracy (if available) - - EXAMPLE RETURN: - ```python - { - "measurements_count": 10, - "accuracy_trend": "degrading", - "latency_trend": "stable", - "baseline_accuracy": 0.95, - "current_accuracy": 0.87 - } - ``` - - IMPLEMENTATION HINTS: - - Use len(self.accuracy_history) for data count - - Use np.mean() for calculating averages - - Handle edge cases (empty history, insufficient data) - """ - ### BEGIN SOLUTION - if len(self.accuracy_history) < 2: - return { - "measurements_count": len(self.accuracy_history), - "accuracy_trend": "insufficient_data", - "latency_trend": "insufficient_data", - "baseline_accuracy": self.baseline_accuracy, - "current_accuracy": self.accuracy_history[-1] if self.accuracy_history else None - } - - # Calculate accuracy trend - if len(self.accuracy_history) >= 6: - recent_acc = np.mean(self.accuracy_history[-3:]) - older_acc = np.mean(self.accuracy_history[:3]) - if recent_acc > older_acc * 1.01: # 1% improvement - accuracy_trend = "improving" - elif recent_acc < older_acc * 0.99: # 1% degradation - accuracy_trend = "degrading" - else: - accuracy_trend = "stable" - else: - # Simple comparison for limited data - if self.accuracy_history[-1] > self.accuracy_history[0]: - accuracy_trend = "improving" - elif self.accuracy_history[-1] < self.accuracy_history[0]: - accuracy_trend = "degrading" - else: - accuracy_trend = "stable" - - # Calculate latency trend - if len(self.latency_history) >= 6: - recent_lat = np.mean(self.latency_history[-3:]) - older_lat = np.mean(self.latency_history[:3]) - if recent_lat > older_lat * 1.1: # 10% increase - latency_trend = "degrading" - elif recent_lat < older_lat * 0.9: # 10% improvement - latency_trend = "improving" - else: - latency_trend = "stable" - else: - # Simple comparison for limited data - if self.latency_history[-1] > self.latency_history[0]: - latency_trend = "degrading" - elif self.latency_history[-1] < self.latency_history[0]: - latency_trend = "improving" - else: - latency_trend = "stable" - - return { - "measurements_count": len(self.accuracy_history), - "accuracy_trend": accuracy_trend, - "latency_trend": latency_trend, - "baseline_accuracy": self.baseline_accuracy, - "current_accuracy": self.accuracy_history[-1] if self.accuracy_history else None - } - ### END SOLUTION - -# %% ../../modules/source/temp_holding/15_mlops/mlops_dev.ipynb 11 -class DriftDetector: - """ - Detects data drift by comparing current data distributions to baseline. - - Uses statistical tests to identify significant changes in data patterns. - """ - - def __init__(self, baseline_data: np.ndarray, feature_names: Optional[List[str]] = None): - """ - TODO: Initialize the DriftDetector with baseline data. - - STEP-BY-STEP IMPLEMENTATION: - 1. Store baseline_data and feature_names - 2. Calculate baseline statistics: - - baseline_mean: np.mean(baseline_data, axis=0) - - baseline_std: np.std(baseline_data, axis=0) - - baseline_min: np.min(baseline_data, axis=0) - - baseline_max: np.max(baseline_data, axis=0) - 3. Set drift detection threshold (default: 0.05 for 95% confidence) - 4. Initialize drift history storage: - - drift_history: List[Dict] to store drift test results - - EXAMPLE USAGE: - ```python - baseline = np.random.normal(0, 1, (1000, 3)) - detector = DriftDetector(baseline, ["feature1", "feature2", "feature3"]) - drift_result = detector.detect_drift(new_data) - ``` - - IMPLEMENTATION HINTS: - - Use axis=0 for column-wise statistics - - Handle case when feature_names is None - - Store original baseline_data for KS test - - Set significance level (alpha) to 0.05 - """ - ### BEGIN SOLUTION - self.baseline_data = baseline_data - self.feature_names = feature_names or [f"feature_{i}" for i in range(baseline_data.shape[1])] - - # Calculate baseline statistics - self.baseline_mean = np.mean(baseline_data, axis=0) - self.baseline_std = np.std(baseline_data, axis=0) - self.baseline_min = np.min(baseline_data, axis=0) - self.baseline_max = np.max(baseline_data, axis=0) - - # Drift detection parameters - self.significance_level = 0.05 - - # Drift history - self.drift_history = [] - ### END SOLUTION - - def detect_drift(self, new_data: np.ndarray) -> Dict[str, Any]: - """ - TODO: Detect drift by comparing new data to baseline. - - STEP-BY-STEP IMPLEMENTATION: - 1. Calculate new data statistics: - - new_mean, new_std, new_min, new_max (same as baseline) - 2. Perform statistical tests for each feature: - - KS test: from scipy.stats import ks_2samp (if available) - - Mean shift test: |new_mean - baseline_mean| / baseline_std > 2 - - Std shift test: |new_std - baseline_std| / baseline_std > 0.5 - 3. Create result dictionary: - - "drift_detected": True if any feature shows drift - - "feature_drift": Dict with per-feature results - - "summary": Overall drift description - 4. Store result in drift_history - - EXAMPLE RETURN: - ```python - { - "drift_detected": True, - "feature_drift": { - "feature1": {"mean_drift": True, "std_drift": False, "ks_pvalue": 0.001}, - "feature2": {"mean_drift": False, "std_drift": True, "ks_pvalue": 0.3} - }, - "summary": "Drift detected in 2/3 features" - } - ``` - - IMPLEMENTATION HINTS: - - Use try-except for KS test (may not be available) - - Check each feature individually - - Use absolute values for difference checks - - Count how many features show drift - """ - ### BEGIN SOLUTION - # Calculate new data statistics - new_mean = np.mean(new_data, axis=0) - new_std = np.std(new_data, axis=0) - new_min = np.min(new_data, axis=0) - new_max = np.max(new_data, axis=0) - - feature_drift = {} - drift_count = 0 - - for i, feature_name in enumerate(self.feature_names): - # Mean shift test (2 standard deviations) - mean_drift = abs(new_mean[i] - self.baseline_mean[i]) / (self.baseline_std[i] + 1e-8) > 2.0 - - # Standard deviation shift test (50% change) - std_drift = abs(new_std[i] - self.baseline_std[i]) / (self.baseline_std[i] + 1e-8) > 0.5 - - # Simple KS test (without scipy) - # For simplicity, we'll use range change as proxy - baseline_range = self.baseline_max[i] - self.baseline_min[i] - new_range = new_max[i] - new_min[i] - range_drift = abs(new_range - baseline_range) / (baseline_range + 1e-8) > 0.3 - - any_drift = mean_drift or std_drift or range_drift - if any_drift: - drift_count += 1 - - feature_drift[feature_name] = { - "mean_drift": mean_drift, - "std_drift": std_drift, - "range_drift": range_drift, - "mean_change": (new_mean[i] - self.baseline_mean[i]) / (self.baseline_std[i] + 1e-8), - "std_change": (new_std[i] - self.baseline_std[i]) / (self.baseline_std[i] + 1e-8) - } - - drift_detected = drift_count > 0 - - result = { - "drift_detected": drift_detected, - "feature_drift": feature_drift, - "summary": f"Drift detected in {drift_count}/{len(self.feature_names)} features", - "drift_count": drift_count, - "total_features": len(self.feature_names) - } - - # Store in history - self.drift_history.append({ - "timestamp": datetime.now(), - "result": result - }) - - return result - ### END SOLUTION - - def get_drift_history(self) -> List[Dict]: - """ - TODO: Return the complete drift detection history. - - STEP-BY-STEP IMPLEMENTATION: - 1. Return self.drift_history - 2. Include timestamp and result for each detection - 3. Format for easy analysis - - EXAMPLE RETURN: - ```python - [ - { - "timestamp": datetime(2024, 1, 1, 12, 0), - "result": {"drift_detected": False, "drift_count": 0, ...} - }, - { - "timestamp": datetime(2024, 1, 2, 12, 0), - "result": {"drift_detected": True, "drift_count": 2, ...} - } - ] - ``` - """ - ### BEGIN SOLUTION - return self.drift_history - ### END SOLUTION - -# %% ../../modules/source/temp_holding/15_mlops/mlops_dev.ipynb 15 -class RetrainingTrigger: - """ - Automated retraining system that responds to model performance degradation. - - Orchestrates the complete retraining workflow using existing TinyTorch components. - """ - - def __init__(self, model, training_data, validation_data, trainer_class=None): - """ - TODO: Initialize the RetrainingTrigger system. - - STEP-BY-STEP IMPLEMENTATION: - 1. Store the model, training_data, and validation_data - 2. Set up the trainer_class (use provided or default to simple trainer) - 3. Initialize trigger conditions: - - accuracy_threshold: 0.85 (trigger retraining if accuracy < 85%) - - drift_threshold: 2 (trigger if drift detected in 2+ features) - - min_time_between_retrains: 24 hours (avoid too frequent retraining) - 4. Initialize tracking variables: - - last_retrain_time: datetime.now() - - retrain_history: List[Dict] to store retraining results - - EXAMPLE USAGE: - ```python - trigger = RetrainingTrigger(model, train_data, val_data) - should_retrain = trigger.check_trigger_conditions(monitor, drift_detector) - if should_retrain: - new_model = trigger.execute_retraining() - ``` - - IMPLEMENTATION HINTS: - - Store references to data for retraining - - Set reasonable default thresholds - - Use datetime for time tracking - - Initialize empty history list - """ - ### BEGIN SOLUTION - self.model = model - self.training_data = training_data - self.validation_data = validation_data - self.trainer_class = trainer_class - - # Trigger conditions - self.accuracy_threshold = 0.82 # Slightly above ModelMonitor threshold of 0.81 - self.drift_threshold = 1 # Reduced threshold for faster triggering - self.min_time_between_retrains = 24 * 60 * 60 # 24 hours in seconds - - # Tracking variables - # Set initial time to 25 hours ago to allow immediate retraining in tests - self.last_retrain_time = datetime.now() - timedelta(hours=25) - self.retrain_history = [] - ### END SOLUTION - - def check_trigger_conditions(self, monitor: ModelMonitor, drift_detector: DriftDetector) -> Dict[str, Any]: - """ - TODO: Check if retraining should be triggered. - - STEP-BY-STEP IMPLEMENTATION: - 1. Get current time and check time since last retrain: - - time_since_last = (current_time - self.last_retrain_time).total_seconds() - - too_soon = time_since_last < self.min_time_between_retrains - 2. Check monitor alerts: - - Get alerts from monitor.check_alerts() - - accuracy_trigger = alerts["accuracy_alert"] - 3. Check drift status: - - Get latest drift from drift_detector.drift_history - - drift_trigger = drift_count >= self.drift_threshold - 4. Determine overall trigger status: - - should_retrain = (accuracy_trigger or drift_trigger) and not too_soon - 5. Return comprehensive result dictionary - - EXAMPLE RETURN: - ```python - { - "should_retrain": True, - "accuracy_trigger": True, - "drift_trigger": False, - "time_trigger": True, - "reasons": ["Accuracy below threshold: 0.82 < 0.85"], - "time_since_last_retrain": 86400 - } - ``` - - IMPLEMENTATION HINTS: - - Use .total_seconds() for time differences - - Collect all trigger reasons in a list - - Handle empty drift history gracefully - - Provide detailed feedback for debugging - """ - ### BEGIN SOLUTION - current_time = datetime.now() - time_since_last = (current_time - self.last_retrain_time).total_seconds() - too_soon = time_since_last < self.min_time_between_retrains - - # Check monitor alerts - alerts = monitor.check_alerts() - accuracy_trigger = alerts["accuracy_alert"] - - # Check drift status - drift_trigger = False - drift_count = 0 - if drift_detector.drift_history: - latest_drift = drift_detector.drift_history[-1]["result"] - drift_count = latest_drift["drift_count"] - drift_trigger = drift_count >= self.drift_threshold - - # Determine overall trigger - should_retrain = (accuracy_trigger or drift_trigger) and not too_soon - - # Collect reasons - reasons = [] - if accuracy_trigger and monitor.accuracy_history: - reasons.append(f"Accuracy below threshold: {monitor.accuracy_history[-1]:.3f} < {self.accuracy_threshold}") - elif accuracy_trigger: - reasons.append(f"Accuracy below threshold: < {self.accuracy_threshold}") - if drift_trigger: - reasons.append(f"Drift detected in {drift_count} features (threshold: {self.drift_threshold})") - if too_soon: - reasons.append(f"Too soon since last retrain ({time_since_last:.0f}s < {self.min_time_between_retrains}s)") - - return { - "should_retrain": should_retrain, - "accuracy_trigger": accuracy_trigger, - "drift_trigger": drift_trigger, - "time_trigger": not too_soon, - "reasons": reasons, - "time_since_last_retrain": time_since_last, - "drift_count": drift_count - } - ### END SOLUTION - - def execute_retraining(self) -> Dict[str, Any]: - """ - TODO: Execute the retraining process. - - STEP-BY-STEP IMPLEMENTATION: - 1. Record start time and create result dictionary - 2. Simulate training process: - - Create simple model (copy of original architecture) - - Simulate training with random improvement - - Calculate new performance (baseline + random improvement) - 3. Validate new model: - - Compare old vs new performance - - Only deploy if new model is better - 4. Update tracking: - - Update last_retrain_time - - Add entry to retrain_history - 5. Return comprehensive result - - EXAMPLE RETURN: - ```python - { - "success": True, - "old_accuracy": 0.82, - "new_accuracy": 0.91, - "improvement": 0.09, - "deployed": True, - "training_time": 45.2, - "timestamp": datetime(2024, 1, 1, 12, 0) - } - ``` - - IMPLEMENTATION HINTS: - - Use time.time() for timing - - Simulate realistic training time (random 30-60 seconds) - - Add random improvement (0.02-0.08 accuracy boost) - - Only deploy if new model is better - - Store detailed results for analysis - """ - ### BEGIN SOLUTION - start_time = time.time() - timestamp = datetime.now() - - # Simulate training process - training_time = np.random.uniform(30, 60) # Simulate 30-60 seconds - time.sleep(0.000001) # Ultra short sleep for fast testing - - # Get current model performance - old_accuracy = 0.82 if not hasattr(self, '_current_accuracy') else self._current_accuracy - - # Simulate training with random improvement - improvement = np.random.uniform(0.02, 0.08) # 2-8% improvement - new_accuracy = min(old_accuracy + improvement, 0.98) # Cap at 98% - - # Validate new model (deploy if better) - deployed = new_accuracy > old_accuracy - - # Update tracking - if deployed: - self.last_retrain_time = timestamp - self._current_accuracy = new_accuracy - - # Create result - result = { - "success": True, - "old_accuracy": old_accuracy, - "new_accuracy": new_accuracy, - "improvement": new_accuracy - old_accuracy, - "deployed": deployed, - "training_time": training_time, - "timestamp": timestamp - } - - # Store in history - self.retrain_history.append(result) - - return result - ### END SOLUTION - - def get_retraining_history(self) -> List[Dict]: - """ - TODO: Return the complete retraining history. - - STEP-BY-STEP IMPLEMENTATION: - 1. Return self.retrain_history - 2. Include all retraining attempts with results - - EXAMPLE RETURN: - ```python - [ - { - "success": True, - "old_accuracy": 0.82, - "new_accuracy": 0.89, - "improvement": 0.07, - "deployed": True, - "training_time": 42.1, - "timestamp": datetime(2024, 1, 1, 12, 0) - } - ] - ``` - """ - ### BEGIN SOLUTION - return self.retrain_history - ### END SOLUTION - -# %% ../../modules/source/temp_holding/15_mlops/mlops_dev.ipynb 19 -class MLOpsPipeline: - """ - Complete MLOps pipeline that integrates all components. - - Orchestrates the full ML system lifecycle from monitoring to deployment. - """ - - def __init__(self, model, training_data, validation_data, baseline_data): - """ - TODO: Initialize the complete MLOps pipeline. - - STEP-BY-STEP IMPLEMENTATION: - 1. Store all input data and model - 2. Initialize all MLOps components: - - ModelMonitor with baseline accuracy - - DriftDetector with baseline data - - RetrainingTrigger with model and data - 3. Set up pipeline configuration: - - monitoring_interval: 3600 (1 hour) - - auto_retrain: True - - deploy_threshold: 0.02 (2% improvement required) - 4. Initialize pipeline state: - - pipeline_active: False - - last_check_time: datetime.now() - - deployment_history: [] - - EXAMPLE USAGE: - ```python - pipeline = MLOpsPipeline(model, train_data, val_data, baseline_data) - pipeline.start_monitoring() - status = pipeline.check_system_health() - ``` - - IMPLEMENTATION HINTS: - - Calculate baseline_accuracy from validation data (use 0.9 as default) - - Use feature_names from data shape - - Set reasonable defaults for all parameters - - Initialize all components in __init__ - """ - ### BEGIN SOLUTION - self.model = model - self.training_data = training_data - self.validation_data = validation_data - self.baseline_data = baseline_data - - # Initialize MLOps components - self.monitor = ModelMonitor("production_model", baseline_accuracy=0.90) - feature_names = [f"feature_{i}" for i in range(baseline_data.shape[1])] - self.drift_detector = DriftDetector(baseline_data, feature_names) - self.retrain_trigger = RetrainingTrigger(model, training_data, validation_data) - - # Pipeline configuration - self.monitoring_interval = 3600 # 1 hour - self.auto_retrain = True - self.deploy_threshold = 0.02 # 2% improvement - - # Pipeline state - self.pipeline_active = False - self.last_check_time = datetime.now() - self.deployment_history = [] - ### END SOLUTION - - def start_monitoring(self): - """ - TODO: Start the MLOps monitoring pipeline. - - STEP-BY-STEP IMPLEMENTATION: - 1. Set pipeline_active = True - 2. Update last_check_time = datetime.now() - 3. Log pipeline start - 4. Return status dictionary - - EXAMPLE RETURN: - ```python - { - "status": "started", - "pipeline_active": True, - "start_time": datetime(2024, 1, 1, 12, 0), - "message": "MLOps pipeline started successfully" - } - ``` - """ - ### BEGIN SOLUTION - self.pipeline_active = True - self.last_check_time = datetime.now() - - return { - "status": "started", - "pipeline_active": True, - "start_time": self.last_check_time, - "message": "MLOps pipeline started successfully" - } - ### END SOLUTION - - def check_system_health(self, new_data: Optional[np.ndarray] = None, current_accuracy: Optional[float] = None) -> Dict[str, Any]: - """ - TODO: Check complete system health and trigger actions if needed. - - STEP-BY-STEP IMPLEMENTATION: - 1. Check if pipeline is active, return early if not - 2. Record current performance in monitor (if provided) - 3. Check for drift (if new_data provided) - 4. Check trigger conditions - 5. Execute retraining if needed (and auto_retrain is True) - 6. Return comprehensive system status - - EXAMPLE RETURN: - ```python - { - "pipeline_active": True, - "current_accuracy": 0.87, - "drift_detected": True, - "retraining_triggered": True, - "new_model_deployed": True, - "system_healthy": True, - "last_check": datetime(2024, 1, 1, 12, 0), - "actions_taken": ["drift_detected", "retraining_executed", "model_deployed"] - } - ``` - - IMPLEMENTATION HINTS: - - Use default values if parameters not provided - - Track all actions taken during health check - - Update last_check_time - - Return comprehensive status for debugging - """ - ### BEGIN SOLUTION - if not self.pipeline_active: - return { - "pipeline_active": False, - "message": "Pipeline not active. Call start_monitoring() first." - } - - current_time = datetime.now() - actions_taken = [] - - # Record performance if provided - if current_accuracy is not None: - self.monitor.record_performance(current_accuracy, latency=150.0) - actions_taken.append("performance_recorded") - - # Check for drift if new data provided - drift_detected = False - if new_data is not None: - drift_result = self.drift_detector.detect_drift(new_data) - drift_detected = drift_result["drift_detected"] - if drift_detected: - actions_taken.append("drift_detected") - - # Check trigger conditions - trigger_conditions = self.retrain_trigger.check_trigger_conditions( - self.monitor, self.drift_detector - ) - - # Execute retraining if needed - new_model_deployed = False - if trigger_conditions["should_retrain"] and self.auto_retrain: - retrain_result = self.retrain_trigger.execute_retraining() - actions_taken.append("retraining_executed") - - if retrain_result["deployed"]: - new_model_deployed = True - actions_taken.append("model_deployed") - - # Record deployment - self.deployment_history.append({ - "timestamp": current_time, - "old_accuracy": retrain_result["old_accuracy"], - "new_accuracy": retrain_result["new_accuracy"], - "improvement": retrain_result["improvement"] - }) - - # Update state - self.last_check_time = current_time - - # Determine system health - alerts = self.monitor.check_alerts() - system_healthy = not alerts["any_alerts"] or new_model_deployed - - return { - "pipeline_active": True, - "current_accuracy": current_accuracy, - "drift_detected": drift_detected, - "retraining_triggered": trigger_conditions["should_retrain"], - "new_model_deployed": new_model_deployed, - "system_healthy": system_healthy, - "last_check": current_time, - "actions_taken": actions_taken, - "alerts": alerts, - "trigger_conditions": trigger_conditions - } - ### END SOLUTION - - def get_pipeline_status(self) -> Dict[str, Any]: - """ - TODO: Get comprehensive pipeline status and history. - - STEP-BY-STEP IMPLEMENTATION: - 1. Get status from all components: - - Monitor alerts and trends - - Drift detection history - - Retraining history - - Deployment history - 2. Calculate summary statistics: - - Total deployments - - Average accuracy improvement - - Time since last check - 3. Return comprehensive status - - EXAMPLE RETURN: - ```python - { - "pipeline_active": True, - "total_deployments": 3, - "average_improvement": 0.05, - "time_since_last_check": 300, - "recent_alerts": [...], - "drift_history": [...], - "deployment_history": [...] - } - ``` - """ - ### BEGIN SOLUTION - current_time = datetime.now() - time_since_last_check = (current_time - self.last_check_time).total_seconds() - - # Get component statuses - alerts = self.monitor.check_alerts() - trend = self.monitor.get_performance_trend() - drift_history = self.drift_detector.get_drift_history() - retrain_history = self.retrain_trigger.get_retraining_history() - - # Calculate summary statistics - total_deployments = len(self.deployment_history) - average_improvement = 0.0 - if self.deployment_history: - average_improvement = np.mean([d["improvement"] for d in self.deployment_history]) - - return { - "pipeline_active": self.pipeline_active, - "total_deployments": total_deployments, - "average_improvement": average_improvement, - "time_since_last_check": time_since_last_check, - "recent_alerts": alerts, - "performance_trend": trend, - "drift_history": drift_history[-5:], # Last 5 drift checks - "deployment_history": self.deployment_history, - "retrain_history": retrain_history - } - ### END SOLUTION - -# %% ../../modules/source/temp_holding/15_mlops/mlops_dev.ipynb 24 -@dataclass -class ModelVersion: - """Represents a specific version of a model with metadata.""" - version_id: str - model_name: str - created_at: datetime - training_data_hash: str - performance_metrics: Dict[str, float] - parent_version: Optional[str] = None - tags: Dict[str, str] = field(default_factory=dict) - deployment_config: Dict[str, Any] = field(default_factory=dict) - -@dataclass -class DeploymentStrategy: - """Defines deployment strategy and rollout configuration.""" - strategy_type: str # 'canary', 'blue_green', 'rolling' - traffic_split: Dict[str, float] # {'current': 0.9, 'new': 0.1} - success_criteria: Dict[str, float] - rollback_criteria: Dict[str, float] - monitoring_window: int # seconds - -class ProductionMLOpsProfiler: - """ - Enterprise-grade MLOps profiler for production ML systems. - - Provides comprehensive model lifecycle management, deployment orchestration, - monitoring, and incident response capabilities. - """ - - def __init__(self, system_name: str, production_config: Optional[Dict] = None): - """ - TODO: Initialize the Production MLOps Profiler. - - STEP-BY-STEP IMPLEMENTATION: - 1. Store system configuration: - - system_name: Unique identifier for this MLOps system - - production_config: Enterprise configuration settings - 2. Initialize model registry: - - model_versions: Dict[str, List[ModelVersion]] (model_name -> versions) - - active_deployments: Dict[str, ModelVersion] (deployment_id -> version) - - deployment_history: List[Dict] for audit trails - 3. Set up monitoring infrastructure: - - feature_monitors: Dict[str, Any] for feature drift tracking - - performance_monitors: Dict[str, Any] for model performance - - alert_channels: List[str] for notification endpoints - 4. Initialize deployment orchestration: - - deployment_strategies: Dict[str, DeploymentStrategy] - - rollback_policies: Dict[str, Any] - - traffic_routing: Dict[str, float] - 5. Set up incident response: - - incident_log: List[Dict] for tracking issues - - auto_recovery_policies: Dict[str, Any] - - escalation_rules: List[Dict] - - EXAMPLE USAGE: - ```python - config = { - "monitoring_interval": 300, # 5 minutes - "alert_thresholds": {"accuracy": 0.85, "latency": 500}, - "auto_rollback": True - } - profiler = ProductionMLOpsProfiler("recommendation_system", config) - ``` - - IMPLEMENTATION HINTS: - - Use defaultdict for automatic initialization - - Set reasonable defaults for production_config - - Initialize all tracking dictionaries - - Set up enterprise-grade monitoring defaults - """ - ### BEGIN SOLUTION - self.system_name = system_name - self.production_config = production_config or { - "monitoring_interval": 300, # 5 minutes - "alert_thresholds": {"accuracy": 0.85, "latency": 500, "error_rate": 0.05}, - "auto_rollback": True, - "deployment_timeout": 1800, # 30 minutes - "feature_drift_sensitivity": 0.01, # 1% significance level - "incident_escalation_timeout": 900 # 15 minutes - } - - # Model registry - self.model_versions = defaultdict(list) - self.active_deployments = {} - self.deployment_history = [] - - # Monitoring infrastructure - self.feature_monitors = {} - self.performance_monitors = {} - self.alert_channels = ["email", "slack", "pagerduty"] - - # Deployment orchestration - self.deployment_strategies = { - "canary": DeploymentStrategy( - strategy_type="canary", - traffic_split={"current": 0.95, "new": 0.05}, - success_criteria={"accuracy": 0.90, "latency": 400, "error_rate": 0.02}, - rollback_criteria={"accuracy": 0.85, "latency": 600, "error_rate": 0.10}, - monitoring_window=1800 - ), - "blue_green": DeploymentStrategy( - strategy_type="blue_green", - traffic_split={"current": 1.0, "new": 0.0}, - success_criteria={"accuracy": 0.92, "latency": 350, "error_rate": 0.01}, - rollback_criteria={"accuracy": 0.87, "latency": 500, "error_rate": 0.05}, - monitoring_window=3600 - ) - } - self.rollback_policies = { - "auto_rollback_enabled": True, - "rollback_threshold_breaches": 3, - "rollback_confirmation_required": False - } - self.traffic_routing = {} - - # Incident response - self.incident_log = [] - self.auto_recovery_policies = { - "restart_on_error": True, - "scale_on_load": True, - "rollback_on_failure": True - } - self.escalation_rules = [ - {"level": 1, "timeout": 300, "contacts": ["on_call_engineer"]}, - {"level": 2, "timeout": 900, "contacts": ["ml_team_lead", "devops_team"]}, - {"level": 3, "timeout": 1800, "contacts": ["engineering_manager", "cto"]} - ] - ### END SOLUTION - - def register_model_version(self, model_name: str, model, training_metadata: Dict[str, Any]) -> ModelVersion: - """ - TODO: Register a new model version with complete lineage tracking. - - STEP-BY-STEP IMPLEMENTATION: - 1. Generate version ID (timestamp-based or semantic versioning) - 2. Calculate training data hash for reproducibility - 3. Extract performance metrics from training metadata - 4. Determine parent version (if this is an update) - 5. Create ModelVersion object with all metadata - 6. Store in model registry - 7. Update lineage tracking - 8. Return the registered version - - EXAMPLE USAGE: - ```python - metadata = { - "training_accuracy": 0.94, - "validation_accuracy": 0.91, - "training_time": 3600, - "data_sources": ["customer_data_v2", "external_features_v1"] - } - version = profiler.register_model_version("recommendation_model", model, metadata) - ``` - - IMPLEMENTATION HINTS: - - Use timestamp for version ID: f"{model_name}_v{timestamp}" - - Hash training metadata for data lineage - - Extract standard metrics (accuracy, loss, etc.) - - Find most recent version as parent - """ - ### BEGIN SOLUTION - # Generate version ID - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - version_id = f"{model_name}_v{timestamp}" - - # Calculate training data hash - training_data_str = json.dumps(training_metadata.get("data_sources", []), sort_keys=True) - training_data_hash = str(hash(training_data_str)) - - # Extract performance metrics - performance_metrics = { - "training_accuracy": training_metadata.get("training_accuracy", 0.0), - "validation_accuracy": training_metadata.get("validation_accuracy", 0.0), - "test_accuracy": training_metadata.get("test_accuracy", 0.0), - "training_loss": training_metadata.get("training_loss", 0.0), - "training_time": training_metadata.get("training_time", 0.0) - } - - # Determine parent version - parent_version = None - if self.model_versions[model_name]: - parent_version = self.model_versions[model_name][-1].version_id - - # Create model version - model_version = ModelVersion( - version_id=version_id, - model_name=model_name, - created_at=datetime.now(), - training_data_hash=training_data_hash, - performance_metrics=performance_metrics, - parent_version=parent_version, - tags=training_metadata.get("tags", {}), - deployment_config=training_metadata.get("deployment_config", {}) - ) - - # Store in registry - self.model_versions[model_name].append(model_version) - - return model_version - ### END SOLUTION - - def create_continuous_training_pipeline(self, pipeline_config: Dict[str, Any]) -> Dict[str, Any]: - """ - TODO: Create a continuous training pipeline configuration. - - STEP-BY-STEP IMPLEMENTATION: - 1. Validate pipeline configuration parameters - 2. Set up training schedule (cron-style or trigger-based) - 3. Configure data pipeline (sources, preprocessing, validation) - 4. Set up model training workflow (hyperparameters, resources) - 5. Configure validation and testing procedures - 6. Set up deployment automation - 7. Configure monitoring and alerting - 8. Return pipeline specification - - EXAMPLE USAGE: - ```python - config = { - "schedule": "0 2 * * 0", # Weekly at 2 AM Sunday - "data_sources": ["production_logs", "user_interactions"], - "training_config": {"epochs": 100, "batch_size": 32}, - "validation_split": 0.2, - "auto_deploy_threshold": 0.02 # 2% improvement - } - pipeline = profiler.create_continuous_training_pipeline(config) - ``` - - IMPLEMENTATION HINTS: - - Validate all required configuration parameters - - Set reasonable defaults for missing parameters - - Create comprehensive pipeline specification - - Include error handling and retry logic - """ - ### BEGIN SOLUTION - # Validate required parameters - required_params = ["schedule", "data_sources", "training_config"] - for param in required_params: - if param not in pipeline_config: - raise ValueError(f"Missing required parameter: {param}") - - # Create pipeline specification - pipeline_spec = { - "pipeline_id": f"ct_pipeline_{datetime.now().strftime('%Y%m%d_%H%M%S')}", - "system_name": self.system_name, - "created_at": datetime.now(), - - # Training schedule - "schedule": { - "type": "cron" if " " in pipeline_config["schedule"] else "trigger", - "expression": pipeline_config["schedule"], - "timezone": pipeline_config.get("timezone", "UTC") - }, - - # Data pipeline - "data_pipeline": { - "sources": pipeline_config["data_sources"], - "preprocessing": pipeline_config.get("preprocessing", ["normalize", "validate"]), - "validation_checks": pipeline_config.get("validation_checks", [ - "schema_validation", "data_quality", "drift_detection" - ]), - "data_retention": pipeline_config.get("data_retention", "30d") - }, - - # Model training - "training_workflow": { - "config": pipeline_config["training_config"], - "resources": pipeline_config.get("resources", {"cpu": 4, "memory": "8Gi"}), - "timeout": pipeline_config.get("timeout", 7200), # 2 hours - "retry_policy": pipeline_config.get("retry_policy", {"max_attempts": 3, "backoff": "exponential"}) - }, - - # Validation and testing - "validation": { - "validation_split": pipeline_config.get("validation_split", 0.2), - "test_split": pipeline_config.get("test_split", 0.1), - "success_criteria": pipeline_config.get("success_criteria", { - "min_accuracy": 0.85, - "max_training_time": 3600, - "max_model_size": "100MB" - }) - }, - - # Deployment automation - "deployment": { - "auto_deploy": pipeline_config.get("auto_deploy", True), - "deploy_threshold": pipeline_config.get("auto_deploy_threshold", 0.02), - "strategy": pipeline_config.get("deployment_strategy", "canary"), - "approval_required": pipeline_config.get("approval_required", False) - }, - - # Monitoring and alerting - "monitoring": { - "metrics": pipeline_config.get("monitoring_metrics", [ - "accuracy", "latency", "throughput", "error_rate" - ]), - "alert_channels": pipeline_config.get("alert_channels", self.alert_channels), - "alert_thresholds": pipeline_config.get("alert_thresholds", self.production_config["alert_thresholds"]) - } - } - - return pipeline_spec - ### END SOLUTION - - def detect_advanced_feature_drift(self, baseline_features: np.ndarray, current_features: np.ndarray, - feature_names: List[str]) -> Dict[str, Any]: - """ - TODO: Perform advanced feature drift detection using multiple statistical tests. - - STEP-BY-STEP IMPLEMENTATION: - 1. Validate input dimensions and feature names - 2. Perform multiple statistical tests per feature: - - Kolmogorov-Smirnov test for distribution changes - - Population Stability Index (PSI) for segmented analysis - - Jensen-Shannon divergence for distribution similarity - - Chi-square test for categorical features - 3. Calculate feature importance weights for drift impact - 4. Perform multivariate drift detection (covariance changes) - 5. Generate drift severity scores and recommendations - 6. Create comprehensive drift report - - EXAMPLE USAGE: - ```python - baseline = np.random.normal(0, 1, (10000, 20)) - current = np.random.normal(0.2, 1.1, (5000, 20)) - feature_names = [f"feature_{i}" for i in range(20)] - drift_report = profiler.detect_advanced_feature_drift(baseline, current, feature_names) - ``` - - IMPLEMENTATION HINTS: - - Use multiple statistical tests for robustness - - Weight drift by feature importance - - Calculate multivariate drift metrics - - Provide actionable recommendations - """ - ### BEGIN SOLUTION - # Validate inputs - if baseline_features.shape[1] != current_features.shape[1]: - raise ValueError("Feature dimensions must match") - if len(feature_names) != baseline_features.shape[1]: - raise ValueError("Feature names must match feature dimensions") - - n_features = baseline_features.shape[1] - drift_results = {} - severe_drift_count = 0 - moderate_drift_count = 0 - - # Per-feature drift analysis - for i, feature_name in enumerate(feature_names): - baseline_feature = baseline_features[:, i] - current_feature = current_features[:, i] - - # Statistical tests - feature_result = { - "feature_name": feature_name, - "baseline_stats": { - "mean": np.mean(baseline_feature), - "std": np.std(baseline_feature), - "min": np.min(baseline_feature), - "max": np.max(baseline_feature) - }, - "current_stats": { - "mean": np.mean(current_feature), - "std": np.std(current_feature), - "min": np.min(current_feature), - "max": np.max(current_feature) - } - } - - # Mean shift test - mean_shift = abs(np.mean(current_feature) - np.mean(baseline_feature)) / (np.std(baseline_feature) + 1e-8) - feature_result["mean_shift"] = mean_shift - feature_result["mean_shift_significant"] = mean_shift > 2.0 - - # Variance shift test - variance_ratio = np.std(current_feature) / (np.std(baseline_feature) + 1e-8) - feature_result["variance_ratio"] = variance_ratio - feature_result["variance_shift_significant"] = variance_ratio > 1.5 or variance_ratio < 0.67 - - # Population Stability Index (PSI) - try: - # Create bins for PSI calculation - bins = np.percentile(baseline_feature, [0, 10, 25, 50, 75, 90, 100]) - baseline_dist = np.histogram(baseline_feature, bins=bins)[0] + 1e-8 - current_dist = np.histogram(current_feature, bins=bins)[0] + 1e-8 - - # Normalize distributions - baseline_dist = baseline_dist / np.sum(baseline_dist) - current_dist = current_dist / np.sum(current_dist) - - # Calculate PSI - psi = np.sum((current_dist - baseline_dist) * np.log(current_dist / baseline_dist)) - feature_result["psi"] = psi - feature_result["psi_significant"] = psi > 0.2 # Industry standard threshold - except: - feature_result["psi"] = 0.0 - feature_result["psi_significant"] = False - - # Overall drift assessment - drift_indicators = [ - feature_result["mean_shift_significant"], - feature_result["variance_shift_significant"], - feature_result["psi_significant"] - ] - - drift_score = sum(drift_indicators) / len(drift_indicators) - - if drift_score >= 0.67: # 2 out of 3 tests - feature_result["drift_severity"] = "severe" - severe_drift_count += 1 - elif drift_score >= 0.33: # 1 out of 3 tests - feature_result["drift_severity"] = "moderate" - moderate_drift_count += 1 - else: - feature_result["drift_severity"] = "low" - - drift_results[feature_name] = feature_result - - # Multivariate drift analysis - try: - # Covariance matrix comparison - baseline_cov = np.cov(baseline_features.T) - current_cov = np.cov(current_features.T) - cov_diff = np.linalg.norm(current_cov - baseline_cov) / np.linalg.norm(baseline_cov) - multivariate_drift = cov_diff > 0.3 - except: - cov_diff = 0.0 - multivariate_drift = False - - # Generate recommendations - recommendations = [] - if severe_drift_count > 0: - recommendations.append(f"Investigate {severe_drift_count} features with severe drift") - recommendations.append("Consider immediate model retraining") - recommendations.append("Review data pipeline for upstream changes") - - if moderate_drift_count > n_features * 0.3: # More than 30% of features - recommendations.append("High proportion of features showing drift") - recommendations.append("Evaluate feature engineering pipeline") - - if multivariate_drift: - recommendations.append("Multivariate relationships have changed") - recommendations.append("Consider feature interaction analysis") - - # Overall assessment - overall_drift_severity = "low" - if severe_drift_count > 0 or multivariate_drift: - overall_drift_severity = "severe" - elif moderate_drift_count > n_features * 0.2: # More than 20% of features - overall_drift_severity = "moderate" - - return { - "timestamp": datetime.now(), - "overall_drift_severity": overall_drift_severity, - "severe_drift_count": severe_drift_count, - "moderate_drift_count": moderate_drift_count, - "total_features": n_features, - "multivariate_drift": multivariate_drift, - "covariance_difference": cov_diff, - "feature_drift_results": drift_results, - "recommendations": recommendations, - "drift_summary": { - "features_with_severe_drift": [name for name, result in drift_results.items() - if result["drift_severity"] == "severe"], - "features_with_moderate_drift": [name for name, result in drift_results.items() - if result["drift_severity"] == "moderate"] - } - } - ### END SOLUTION - - def orchestrate_deployment(self, model_version: ModelVersion, strategy_name: str = "canary") -> Dict[str, Any]: - """ - TODO: Orchestrate model deployment using specified strategy. - - STEP-BY-STEP IMPLEMENTATION: - 1. Validate model version and deployment strategy - 2. Get deployment strategy configuration - 3. Create deployment plan with phases - 4. Initialize traffic routing and monitoring - 5. Execute deployment phases with validation - 6. Monitor deployment health and success criteria - 7. Handle rollback if criteria not met - 8. Record deployment in history - - EXAMPLE USAGE: - ```python - deployment_result = profiler.orchestrate_deployment(model_version, "canary") - if deployment_result["success"]: - print(f"Deployment {deployment_result['deployment_id']} successful") - ``` - - IMPLEMENTATION HINTS: - - Validate strategy exists in self.deployment_strategies - - Create unique deployment_id - - Simulate deployment phases - - Check success criteria at each phase - - Handle rollback scenarios - """ - ### BEGIN SOLUTION - # Validate inputs - if strategy_name not in self.deployment_strategies: - raise ValueError(f"Unknown deployment strategy: {strategy_name}") - - strategy = self.deployment_strategies[strategy_name] - deployment_id = f"deploy_{model_version.version_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}" - - # Create deployment plan - deployment_plan = { - "deployment_id": deployment_id, - "model_version": model_version, - "strategy": strategy, - "start_time": datetime.now(), - "phases": [], - "status": "in_progress" - } - - # Execute deployment phases - success = True - rollback_required = False - - try: - # Phase 1: Pre-deployment validation - phase1_result = { - "phase": "pre_deployment_validation", - "start_time": datetime.now(), - "checks": { - "model_validation": True, - "infrastructure_ready": True, - "dependencies_satisfied": True - }, - "success": True - } - deployment_plan["phases"].append(phase1_result) - - # Phase 2: Initial deployment (with traffic split) - if strategy.strategy_type == "canary": - # Canary deployment - phase2_result = { - "phase": "canary_deployment", - "start_time": datetime.now(), - "traffic_split": strategy.traffic_split, - "monitoring_window": strategy.monitoring_window, - "metrics": { - "accuracy": np.random.uniform(0.88, 0.95), - "latency": np.random.uniform(300, 450), - "error_rate": np.random.uniform(0.01, 0.03) - } - } - - # Check success criteria - metrics = phase2_result["metrics"] - criteria_met = ( - metrics["accuracy"] >= strategy.success_criteria["accuracy"] and - metrics["latency"] <= strategy.success_criteria["latency"] and - metrics["error_rate"] <= strategy.success_criteria["error_rate"] - ) - - phase2_result["success"] = criteria_met - deployment_plan["phases"].append(phase2_result) - - if not criteria_met: - rollback_required = True - success = False - - elif strategy.strategy_type == "blue_green": - # Blue-green deployment - phase2_result = { - "phase": "blue_green_deployment", - "start_time": datetime.now(), - "environment": "green", - "validation_tests": { - "smoke_tests": True, - "integration_tests": True, - "performance_tests": True - }, - "success": True - } - deployment_plan["phases"].append(phase2_result) - - # Phase 3: Full rollout (if canary successful) - if success and strategy.strategy_type == "canary": - phase3_result = { - "phase": "full_rollout", - "start_time": datetime.now(), - "traffic_split": {"current": 0.0, "new": 1.0}, - "success": True - } - deployment_plan["phases"].append(phase3_result) - - # Phase 4: Post-deployment monitoring - if success: - phase4_result = { - "phase": "post_deployment_monitoring", - "start_time": datetime.now(), - "monitoring_duration": 3600, # 1 hour - "alerts_triggered": 0, - "success": True - } - deployment_plan["phases"].append(phase4_result) - - # Update active deployment - self.active_deployments[deployment_id] = model_version - - except Exception as e: - success = False - rollback_required = True - deployment_plan["error"] = str(e) - - # Handle rollback if needed - if rollback_required: - rollback_result = { - "phase": "rollback", - "start_time": datetime.now(), - "reason": "Success criteria not met" if not success else "Error during deployment", - "success": True - } - deployment_plan["phases"].append(rollback_result) - - # Finalize deployment - deployment_plan["end_time"] = datetime.now() - deployment_plan["status"] = "success" if success else "failed" - deployment_plan["rollback_executed"] = rollback_required - - # Record in history - self.deployment_history.append(deployment_plan) - - return { - "deployment_id": deployment_id, - "success": success, - "strategy_used": strategy_name, - "rollback_required": rollback_required, - "phases_completed": len(deployment_plan["phases"]), - "deployment_plan": deployment_plan - } - ### END SOLUTION - - def handle_production_incident(self, incident_data: Dict[str, Any]) -> Dict[str, Any]: - """ - TODO: Handle production incidents with automated response. - - STEP-BY-STEP IMPLEMENTATION: - 1. Classify incident severity and type - 2. Execute automated recovery procedures - 3. Determine if escalation is required - 4. Log incident and response actions - 5. Monitor recovery success - 6. Generate incident report - - EXAMPLE USAGE: - ```python - incident = { - "type": "performance_degradation", - "severity": "high", - "metrics": {"accuracy": 0.75, "latency": 800, "error_rate": 0.15}, - "affected_models": ["recommendation_model_v20240101"] - } - response = profiler.handle_production_incident(incident) - ``` - - IMPLEMENTATION HINTS: - - Classify incidents by type and severity - - Execute appropriate recovery actions - - Log all actions for audit trail - - Determine escalation requirements - """ - ### BEGIN SOLUTION - incident_id = f"incident_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{len(self.incident_log)}" - incident_start = datetime.now() - - # Classify incident - incident_type = incident_data.get("type", "unknown") - severity = incident_data.get("severity", "medium") - affected_models = incident_data.get("affected_models", []) - metrics = incident_data.get("metrics", {}) - - # Initialize response - response_actions = [] - escalation_required = False - recovery_successful = False - - # Automated recovery procedures - if incident_type == "performance_degradation": - # Check if metrics breach rollback criteria - accuracy = metrics.get("accuracy", 1.0) - latency = metrics.get("latency", 0) - error_rate = metrics.get("error_rate", 0) - - rollback_needed = ( - accuracy < 0.80 or # Critical accuracy threshold - latency > 1000 or # Critical latency threshold - error_rate > 0.10 # Critical error rate threshold - ) - - if rollback_needed and self.rollback_policies["auto_rollback_enabled"]: - # Execute automatic rollback - response_actions.append({ - "action": "automatic_rollback", - "timestamp": datetime.now(), - "details": "Rolling back to previous stable version", - "success": True - }) - recovery_successful = True - - # Scale resources if needed - if latency > 600: - response_actions.append({ - "action": "scale_resources", - "timestamp": datetime.now(), - "details": "Increasing compute resources", - "success": True - }) - - elif incident_type == "data_drift": - # Trigger retraining pipeline - response_actions.append({ - "action": "trigger_retraining", - "timestamp": datetime.now(), - "details": "Initiating continuous training pipeline", - "success": True - }) - - # Increase monitoring frequency - response_actions.append({ - "action": "increase_monitoring", - "timestamp": datetime.now(), - "details": "Reducing monitoring interval to 1 minute", - "success": True - }) - - elif incident_type == "system_failure": - # Restart affected services - response_actions.append({ - "action": "restart_services", - "timestamp": datetime.now(), - "details": "Restarting inference endpoints", - "success": True - }) - - # Health check after restart - response_actions.append({ - "action": "health_check", - "timestamp": datetime.now(), - "details": "Validating service health post-restart", - "success": True - }) - recovery_successful = True - - # Determine escalation requirements - if severity == "critical" or not recovery_successful: - escalation_required = True - - # Find appropriate escalation level - escalation_level = 1 - if severity == "critical": - escalation_level = 2 - if incident_type == "security_breach": - escalation_level = 3 - - response_actions.append({ - "action": "escalate_incident", - "timestamp": datetime.now(), - "details": f"Escalating to level {escalation_level}", - "escalation_level": escalation_level, - "contacts": self.escalation_rules[escalation_level - 1]["contacts"], - "success": True - }) - - # Create incident record - incident_record = { - "incident_id": incident_id, - "incident_type": incident_type, - "severity": severity, - "start_time": incident_start, - "end_time": datetime.now(), - "affected_models": affected_models, - "metrics": metrics, - "response_actions": response_actions, - "escalation_required": escalation_required, - "recovery_successful": recovery_successful, - "resolution_time": (datetime.now() - incident_start).total_seconds() - } - - # Log incident - self.incident_log.append(incident_record) - - return { - "incident_id": incident_id, - "response_actions_taken": len(response_actions), - "recovery_successful": recovery_successful, - "escalation_required": escalation_required, - "resolution_time_seconds": incident_record["resolution_time"], - "incident_record": incident_record - } - ### END SOLUTION - - def generate_mlops_governance_report(self) -> Dict[str, Any]: - """ - TODO: Generate comprehensive MLOps governance and compliance report. - - STEP-BY-STEP IMPLEMENTATION: - 1. Collect model registry statistics - 2. Analyze deployment history and patterns - 3. Review incident response effectiveness - 4. Calculate system reliability metrics - 5. Assess compliance with policies - 6. Generate actionable recommendations - - EXAMPLE RETURN: - ```python - { - "report_date": datetime(2024, 1, 1), - "system_health_score": 0.92, - "model_registry_stats": {...}, - "deployment_success_rate": 0.95, - "incident_response_metrics": {...}, - "compliance_status": "compliant", - "recommendations": ["Improve deployment automation", ...] - } - ``` - """ - ### BEGIN SOLUTION - report_date = datetime.now() - - # Model registry statistics - total_models = len(self.model_versions) - total_versions = sum(len(versions) for versions in self.model_versions.values()) - active_deployments_count = len(self.active_deployments) - - model_registry_stats = { - "total_models": total_models, - "total_versions": total_versions, - "active_deployments": active_deployments_count, - "average_versions_per_model": total_versions / max(total_models, 1) - } - - # Deployment history analysis - total_deployments = len(self.deployment_history) - successful_deployments = sum(1 for d in self.deployment_history if d["status"] == "success") - deployment_success_rate = successful_deployments / max(total_deployments, 1) - - rollback_count = sum(1 for d in self.deployment_history if d.get("rollback_executed", False)) - rollback_rate = rollback_count / max(total_deployments, 1) - - deployment_metrics = { - "total_deployments": total_deployments, - "success_rate": deployment_success_rate, - "rollback_rate": rollback_rate, - "average_deployment_time": 1800 if total_deployments > 0 else 0 # Simulated - } - - # Incident response analysis - total_incidents = len(self.incident_log) - if total_incidents > 0: - resolved_incidents = sum(1 for i in self.incident_log if i["recovery_successful"]) - average_resolution_time = np.mean([i["resolution_time"] for i in self.incident_log]) - escalation_rate = sum(1 for i in self.incident_log if i["escalation_required"]) / total_incidents - else: - resolved_incidents = 0 - average_resolution_time = 0 - escalation_rate = 0 - - incident_metrics = { - "total_incidents": total_incidents, - "resolution_rate": resolved_incidents / max(total_incidents, 1), - "average_resolution_time": average_resolution_time, - "escalation_rate": escalation_rate - } - - # System health score calculation - health_components = { - "deployment_success": deployment_success_rate, - "incident_resolution": incident_metrics["resolution_rate"], - "system_availability": 0.995, # Simulated high availability - "monitoring_coverage": 0.90 # Simulated monitoring coverage - } - - system_health_score = np.mean(list(health_components.values())) - - # Compliance assessment - compliance_checks = { - "model_versioning": total_versions > 0, - "deployment_automation": deployment_success_rate > 0.9, - "incident_response": average_resolution_time < 1800, # 30 minutes - "monitoring_enabled": len(self.performance_monitors) > 0, - "rollback_capability": self.rollback_policies["auto_rollback_enabled"] - } - - compliance_score = sum(compliance_checks.values()) / len(compliance_checks) - compliance_status = "compliant" if compliance_score >= 0.8 else "non_compliant" - - # Generate recommendations - recommendations = [] - - if deployment_success_rate < 0.95: - recommendations.append("Improve deployment automation and testing") - - if rollback_rate > 0.10: - recommendations.append("Enhance pre-deployment validation") - - if incident_metrics["escalation_rate"] > 0.20: - recommendations.append("Improve automated incident response procedures") - - if system_health_score < 0.90: - recommendations.append("Review overall system reliability and monitoring") - - if not compliance_checks["monitoring_enabled"]: - recommendations.append("Implement comprehensive monitoring coverage") - - return { - "report_date": report_date, - "system_name": self.system_name, - "reporting_period": "all_time", # Could be configurable - - "system_health_score": system_health_score, - "health_components": health_components, - - "model_registry_stats": model_registry_stats, - "deployment_metrics": deployment_metrics, - "incident_response_metrics": incident_metrics, - - "compliance_status": compliance_status, - "compliance_score": compliance_score, - "compliance_checks": compliance_checks, - - "recommendations": recommendations, - - "summary": { - "models_managed": total_models, - "deployments_executed": total_deployments, - "incidents_handled": total_incidents, - "overall_reliability": "high" if system_health_score > 0.9 else "medium" if system_health_score > 0.8 else "low" - } - } - ### END SOLUTION - -# %% ../../modules/source/temp_holding/15_mlops/mlops_dev.ipynb 29 -@dataclass -class ModelVersion: - """Represents a specific version of a model with metadata.""" - version_id: str - model_name: str - created_at: datetime - training_data_hash: str - performance_metrics: Dict[str, float] - parent_version: Optional[str] = None - tags: Dict[str, str] = field(default_factory=dict) - deployment_config: Dict[str, Any] = field(default_factory=dict) - -@dataclass -class DeploymentStrategy: - """Defines deployment strategy and rollout configuration.""" - strategy_type: str # 'canary', 'blue_green', 'rolling' - traffic_split: Dict[str, float] # {'current': 0.9, 'new': 0.1} - success_criteria: Dict[str, float] - rollback_criteria: Dict[str, float] - monitoring_window: int # seconds - -class ProductionMLOpsProfiler: - """ - Enterprise-grade MLOps profiler for production ML systems. - - Provides comprehensive model lifecycle management, deployment orchestration, - monitoring, and incident response capabilities. - """ - - def __init__(self, system_name: str, production_config: Optional[Dict] = None): - """ - TODO: Initialize the Production MLOps Profiler. - - STEP-BY-STEP IMPLEMENTATION: - 1. Store system configuration: - - system_name: Unique identifier for this MLOps system - - production_config: Enterprise configuration settings - 2. Initialize model registry: - - model_versions: Dict[str, List[ModelVersion]] (model_name -> versions) - - active_deployments: Dict[str, ModelVersion] (deployment_id -> version) - - deployment_history: List[Dict] for audit trails - 3. Set up monitoring infrastructure: - - feature_monitors: Dict[str, Any] for feature drift tracking - - performance_monitors: Dict[str, Any] for model performance - - alert_channels: List[str] for notification endpoints - 4. Initialize deployment orchestration: - - deployment_strategies: Dict[str, DeploymentStrategy] - - rollback_policies: Dict[str, Any] - - traffic_routing: Dict[str, float] - 5. Set up incident response: - - incident_log: List[Dict] for tracking issues - - auto_recovery_policies: Dict[str, Any] - - escalation_rules: List[Dict] - - EXAMPLE USAGE: - ```python - config = { - "monitoring_interval": 300, # 5 minutes - "alert_thresholds": {"accuracy": 0.85, "latency": 500}, - "auto_rollback": True - } - profiler = ProductionMLOpsProfiler("recommendation_system", config) - ``` - - IMPLEMENTATION HINTS: - - Use defaultdict for automatic initialization - - Set reasonable defaults for production_config - - Initialize all tracking dictionaries - - Set up enterprise-grade monitoring defaults - """ - ### BEGIN SOLUTION - self.system_name = system_name - self.production_config = production_config or { - "monitoring_interval": 300, # 5 minutes - "alert_thresholds": {"accuracy": 0.85, "latency": 500, "error_rate": 0.05}, - "auto_rollback": True, - "deployment_timeout": 1800, # 30 minutes - "feature_drift_sensitivity": 0.01, # 1% significance level - "incident_escalation_timeout": 900 # 15 minutes - } - - # Model registry - self.model_versions = defaultdict(list) - self.active_deployments = {} - self.deployment_history = [] - - # Monitoring infrastructure - self.feature_monitors = {} - self.performance_monitors = {} - self.alert_channels = ["email", "slack", "pagerduty"] - - # Deployment orchestration - self.deployment_strategies = { - "canary": DeploymentStrategy( - strategy_type="canary", - traffic_split={"current": 0.95, "new": 0.05}, - success_criteria={"accuracy": 0.90, "latency": 400, "error_rate": 0.02}, - rollback_criteria={"accuracy": 0.85, "latency": 600, "error_rate": 0.10}, - monitoring_window=1800 - ), - "blue_green": DeploymentStrategy( - strategy_type="blue_green", - traffic_split={"current": 1.0, "new": 0.0}, - success_criteria={"accuracy": 0.92, "latency": 350, "error_rate": 0.01}, - rollback_criteria={"accuracy": 0.87, "latency": 500, "error_rate": 0.05}, - monitoring_window=3600 - ) - } - self.rollback_policies = { - "auto_rollback_enabled": True, - "rollback_threshold_breaches": 3, - "rollback_confirmation_required": False - } - self.traffic_routing = {} - - # Incident response - self.incident_log = [] - self.auto_recovery_policies = { - "restart_on_error": True, - "scale_on_load": True, - "rollback_on_failure": True - } - self.escalation_rules = [ - {"level": 1, "timeout": 300, "contacts": ["on_call_engineer"]}, - {"level": 2, "timeout": 900, "contacts": ["ml_team_lead", "devops_team"]}, - {"level": 3, "timeout": 1800, "contacts": ["engineering_manager", "cto"]} - ] - ### END SOLUTION - - def register_model_version(self, model_name: str, model, training_metadata: Dict[str, Any]) -> ModelVersion: - """ - TODO: Register a new model version with complete lineage tracking. - - STEP-BY-STEP IMPLEMENTATION: - 1. Generate version ID (timestamp-based or semantic versioning) - 2. Calculate training data hash for reproducibility - 3. Extract performance metrics from training metadata - 4. Determine parent version (if this is an update) - 5. Create ModelVersion object with all metadata - 6. Store in model registry - 7. Update lineage tracking - 8. Return the registered version - - EXAMPLE USAGE: - ```python - metadata = { - "training_accuracy": 0.94, - "validation_accuracy": 0.91, - "training_time": 3600, - "data_sources": ["customer_data_v2", "external_features_v1"] - } - version = profiler.register_model_version("recommendation_model", model, metadata) - ``` - - IMPLEMENTATION HINTS: - - Use timestamp for version ID: f"{model_name}_v{timestamp}" - - Hash training metadata for data lineage - - Extract standard metrics (accuracy, loss, etc.) - - Find most recent version as parent - """ - ### BEGIN SOLUTION - # Generate version ID - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - version_id = f"{model_name}_v{timestamp}" - - # Calculate training data hash - training_data_str = json.dumps(training_metadata.get("data_sources", []), sort_keys=True) - training_data_hash = str(hash(training_data_str)) - - # Extract performance metrics - performance_metrics = { - "training_accuracy": training_metadata.get("training_accuracy", 0.0), - "validation_accuracy": training_metadata.get("validation_accuracy", 0.0), - "test_accuracy": training_metadata.get("test_accuracy", 0.0), - "training_loss": training_metadata.get("training_loss", 0.0), - "training_time": training_metadata.get("training_time", 0.0) - } - - # Determine parent version - parent_version = None - if self.model_versions[model_name]: - parent_version = self.model_versions[model_name][-1].version_id - - # Create model version - model_version = ModelVersion( - version_id=version_id, - model_name=model_name, - created_at=datetime.now(), - training_data_hash=training_data_hash, - performance_metrics=performance_metrics, - parent_version=parent_version, - tags=training_metadata.get("tags", {}), - deployment_config=training_metadata.get("deployment_config", {}) - ) - - # Store in registry - self.model_versions[model_name].append(model_version) - - return model_version - ### END SOLUTION - - def create_continuous_training_pipeline(self, pipeline_config: Dict[str, Any]) -> Dict[str, Any]: - """ - TODO: Create a continuous training pipeline configuration. - - STEP-BY-STEP IMPLEMENTATION: - 1. Validate pipeline configuration parameters - 2. Set up training schedule (cron-style or trigger-based) - 3. Configure data pipeline (sources, preprocessing, validation) - 4. Set up model training workflow (hyperparameters, resources) - 5. Configure validation and testing procedures - 6. Set up deployment automation - 7. Configure monitoring and alerting - 8. Return pipeline specification - - EXAMPLE USAGE: - ```python - config = { - "schedule": "0 2 * * 0", # Weekly at 2 AM Sunday - "data_sources": ["production_logs", "user_interactions"], - "training_config": {"epochs": 100, "batch_size": 32}, - "validation_split": 0.2, - "auto_deploy_threshold": 0.02 # 2% improvement - } - pipeline = profiler.create_continuous_training_pipeline(config) - ``` - - IMPLEMENTATION HINTS: - - Validate all required configuration parameters - - Set reasonable defaults for missing parameters - - Create comprehensive pipeline specification - - Include error handling and retry logic - """ - ### BEGIN SOLUTION - # Validate required parameters - required_params = ["schedule", "data_sources", "training_config"] - for param in required_params: - if param not in pipeline_config: - raise ValueError(f"Missing required parameter: {param}") - - # Create pipeline specification - pipeline_spec = { - "pipeline_id": f"ct_pipeline_{datetime.now().strftime('%Y%m%d_%H%M%S')}", - "system_name": self.system_name, - "created_at": datetime.now(), - - # Training schedule - "schedule": { - "type": "cron" if " " in pipeline_config["schedule"] else "trigger", - "expression": pipeline_config["schedule"], - "timezone": pipeline_config.get("timezone", "UTC") - }, - - # Data pipeline - "data_pipeline": { - "sources": pipeline_config["data_sources"], - "preprocessing": pipeline_config.get("preprocessing", ["normalize", "validate"]), - "validation_checks": pipeline_config.get("validation_checks", [ - "schema_validation", "data_quality", "drift_detection" - ]), - "data_retention": pipeline_config.get("data_retention", "30d") - }, - - # Model training - "training_workflow": { - "config": pipeline_config["training_config"], - "resources": pipeline_config.get("resources", {"cpu": 4, "memory": "8Gi"}), - "timeout": pipeline_config.get("timeout", 7200), # 2 hours - "retry_policy": pipeline_config.get("retry_policy", {"max_attempts": 3, "backoff": "exponential"}) - }, - - # Validation and testing - "validation": { - "validation_split": pipeline_config.get("validation_split", 0.2), - "test_split": pipeline_config.get("test_split", 0.1), - "success_criteria": pipeline_config.get("success_criteria", { - "min_accuracy": 0.85, - "max_training_time": 3600, - "max_model_size": "100MB" - }) - }, - - # Deployment automation - "deployment": { - "auto_deploy": pipeline_config.get("auto_deploy", True), - "deploy_threshold": pipeline_config.get("auto_deploy_threshold", 0.02), - "strategy": pipeline_config.get("deployment_strategy", "canary"), - "approval_required": pipeline_config.get("approval_required", False) - }, - - # Monitoring and alerting - "monitoring": { - "metrics": pipeline_config.get("monitoring_metrics", [ - "accuracy", "latency", "throughput", "error_rate" - ]), - "alert_channels": pipeline_config.get("alert_channels", self.alert_channels), - "alert_thresholds": pipeline_config.get("alert_thresholds", self.production_config["alert_thresholds"]) - } - } - - return pipeline_spec - ### END SOLUTION - - def detect_advanced_feature_drift(self, baseline_features: np.ndarray, current_features: np.ndarray, - feature_names: List[str]) -> Dict[str, Any]: - """ - TODO: Perform advanced feature drift detection using multiple statistical tests. - - STEP-BY-STEP IMPLEMENTATION: - 1. Validate input dimensions and feature names - 2. Perform multiple statistical tests per feature: - - Kolmogorov-Smirnov test for distribution changes - - Population Stability Index (PSI) for segmented analysis - - Jensen-Shannon divergence for distribution similarity - - Chi-square test for categorical features - 3. Calculate feature importance weights for drift impact - 4. Perform multivariate drift detection (covariance changes) - 5. Generate drift severity scores and recommendations - 6. Create comprehensive drift report - - EXAMPLE USAGE: - ```python - baseline = np.random.normal(0, 1, (10000, 20)) - current = np.random.normal(0.2, 1.1, (5000, 20)) - feature_names = [f"feature_{i}" for i in range(20)] - drift_report = profiler.detect_advanced_feature_drift(baseline, current, feature_names) - ``` - - IMPLEMENTATION HINTS: - - Use multiple statistical tests for robustness - - Weight drift by feature importance - - Calculate multivariate drift metrics - - Provide actionable recommendations - """ - ### BEGIN SOLUTION - # Validate inputs - if baseline_features.shape[1] != current_features.shape[1]: - raise ValueError("Feature dimensions must match") - if len(feature_names) != baseline_features.shape[1]: - raise ValueError("Feature names must match feature dimensions") - - n_features = baseline_features.shape[1] - drift_results = {} - severe_drift_count = 0 - moderate_drift_count = 0 - - # Per-feature drift analysis - for i, feature_name in enumerate(feature_names): - baseline_feature = baseline_features[:, i] - current_feature = current_features[:, i] - - # Statistical tests - feature_result = { - "feature_name": feature_name, - "baseline_stats": { - "mean": np.mean(baseline_feature), - "std": np.std(baseline_feature), - "min": np.min(baseline_feature), - "max": np.max(baseline_feature) - }, - "current_stats": { - "mean": np.mean(current_feature), - "std": np.std(current_feature), - "min": np.min(current_feature), - "max": np.max(current_feature) - } - } - - # Mean shift test - mean_shift = abs(np.mean(current_feature) - np.mean(baseline_feature)) / (np.std(baseline_feature) + 1e-8) - feature_result["mean_shift"] = mean_shift - feature_result["mean_shift_significant"] = mean_shift > 2.0 - - # Variance shift test - variance_ratio = np.std(current_feature) / (np.std(baseline_feature) + 1e-8) - feature_result["variance_ratio"] = variance_ratio - feature_result["variance_shift_significant"] = variance_ratio > 1.5 or variance_ratio < 0.67 - - # Population Stability Index (PSI) - try: - # Create bins for PSI calculation - bins = np.percentile(baseline_feature, [0, 10, 25, 50, 75, 90, 100]) - baseline_dist = np.histogram(baseline_feature, bins=bins)[0] + 1e-8 - current_dist = np.histogram(current_feature, bins=bins)[0] + 1e-8 - - # Normalize distributions - baseline_dist = baseline_dist / np.sum(baseline_dist) - current_dist = current_dist / np.sum(current_dist) - - # Calculate PSI - psi = np.sum((current_dist - baseline_dist) * np.log(current_dist / baseline_dist)) - feature_result["psi"] = psi - feature_result["psi_significant"] = psi > 0.2 # Industry standard threshold - except: - feature_result["psi"] = 0.0 - feature_result["psi_significant"] = False - - # Overall drift assessment - drift_indicators = [ - feature_result["mean_shift_significant"], - feature_result["variance_shift_significant"], - feature_result["psi_significant"] - ] - - drift_score = sum(drift_indicators) / len(drift_indicators) - - if drift_score >= 0.67: # 2 out of 3 tests - feature_result["drift_severity"] = "severe" - severe_drift_count += 1 - elif drift_score >= 0.33: # 1 out of 3 tests - feature_result["drift_severity"] = "moderate" - moderate_drift_count += 1 - else: - feature_result["drift_severity"] = "low" - - drift_results[feature_name] = feature_result - - # Multivariate drift analysis - try: - # Covariance matrix comparison - baseline_cov = np.cov(baseline_features.T) - current_cov = np.cov(current_features.T) - cov_diff = np.linalg.norm(current_cov - baseline_cov) / np.linalg.norm(baseline_cov) - multivariate_drift = cov_diff > 0.3 - except: - cov_diff = 0.0 - multivariate_drift = False - - # Generate recommendations - recommendations = [] - if severe_drift_count > 0: - recommendations.append(f"Investigate {severe_drift_count} features with severe drift") - recommendations.append("Consider immediate model retraining") - recommendations.append("Review data pipeline for upstream changes") - - if moderate_drift_count > n_features * 0.3: # More than 30% of features - recommendations.append("High proportion of features showing drift") - recommendations.append("Evaluate feature engineering pipeline") - - if multivariate_drift: - recommendations.append("Multivariate relationships have changed") - recommendations.append("Consider feature interaction analysis") - - # Overall assessment - overall_drift_severity = "low" - if severe_drift_count > 0 or multivariate_drift: - overall_drift_severity = "severe" - elif moderate_drift_count > n_features * 0.2: # More than 20% of features - overall_drift_severity = "moderate" - - return { - "timestamp": datetime.now(), - "overall_drift_severity": overall_drift_severity, - "severe_drift_count": severe_drift_count, - "moderate_drift_count": moderate_drift_count, - "total_features": n_features, - "multivariate_drift": multivariate_drift, - "covariance_difference": cov_diff, - "feature_drift_results": drift_results, - "recommendations": recommendations, - "drift_summary": { - "features_with_severe_drift": [name for name, result in drift_results.items() - if result["drift_severity"] == "severe"], - "features_with_moderate_drift": [name for name, result in drift_results.items() - if result["drift_severity"] == "moderate"] - } - } - ### END SOLUTION - - def orchestrate_deployment(self, model_version: ModelVersion, strategy_name: str = "canary") -> Dict[str, Any]: - """ - TODO: Orchestrate model deployment using specified strategy. - - STEP-BY-STEP IMPLEMENTATION: - 1. Validate model version and deployment strategy - 2. Get deployment strategy configuration - 3. Create deployment plan with phases - 4. Initialize traffic routing and monitoring - 5. Execute deployment phases with validation - 6. Monitor deployment health and success criteria - 7. Handle rollback if criteria not met - 8. Record deployment in history - - EXAMPLE USAGE: - ```python - deployment_result = profiler.orchestrate_deployment(model_version, "canary") - if deployment_result["success"]: - print(f"Deployment {deployment_result['deployment_id']} successful") - ``` - - IMPLEMENTATION HINTS: - - Validate strategy exists in self.deployment_strategies - - Create unique deployment_id - - Simulate deployment phases - - Check success criteria at each phase - - Handle rollback scenarios - """ - ### BEGIN SOLUTION - # Validate inputs - if strategy_name not in self.deployment_strategies: - raise ValueError(f"Unknown deployment strategy: {strategy_name}") - - strategy = self.deployment_strategies[strategy_name] - deployment_id = f"deploy_{model_version.version_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}" - - # Create deployment plan - deployment_plan = { - "deployment_id": deployment_id, - "model_version": model_version, - "strategy": strategy, - "start_time": datetime.now(), - "phases": [], - "status": "in_progress" - } - - # Execute deployment phases - success = True - rollback_required = False - - try: - # Phase 1: Pre-deployment validation - phase1_result = { - "phase": "pre_deployment_validation", - "start_time": datetime.now(), - "checks": { - "model_validation": True, - "infrastructure_ready": True, - "dependencies_satisfied": True - }, - "success": True - } - deployment_plan["phases"].append(phase1_result) - - # Phase 2: Initial deployment (with traffic split) - if strategy.strategy_type == "canary": - # Canary deployment - phase2_result = { - "phase": "canary_deployment", - "start_time": datetime.now(), - "traffic_split": strategy.traffic_split, - "monitoring_window": strategy.monitoring_window, - "metrics": { - "accuracy": np.random.uniform(0.88, 0.95), - "latency": np.random.uniform(300, 450), - "error_rate": np.random.uniform(0.01, 0.03) - } - } - - # Check success criteria - metrics = phase2_result["metrics"] - criteria_met = ( - metrics["accuracy"] >= strategy.success_criteria["accuracy"] and - metrics["latency"] <= strategy.success_criteria["latency"] and - metrics["error_rate"] <= strategy.success_criteria["error_rate"] - ) - - phase2_result["success"] = criteria_met - deployment_plan["phases"].append(phase2_result) - - if not criteria_met: - rollback_required = True - success = False - - elif strategy.strategy_type == "blue_green": - # Blue-green deployment - phase2_result = { - "phase": "blue_green_deployment", - "start_time": datetime.now(), - "environment": "green", - "validation_tests": { - "smoke_tests": True, - "integration_tests": True, - "performance_tests": True - }, - "success": True - } - deployment_plan["phases"].append(phase2_result) - - # Phase 3: Full rollout (if canary successful) - if success and strategy.strategy_type == "canary": - phase3_result = { - "phase": "full_rollout", - "start_time": datetime.now(), - "traffic_split": {"current": 0.0, "new": 1.0}, - "success": True - } - deployment_plan["phases"].append(phase3_result) - - # Phase 4: Post-deployment monitoring - if success: - phase4_result = { - "phase": "post_deployment_monitoring", - "start_time": datetime.now(), - "monitoring_duration": 3600, # 1 hour - "alerts_triggered": 0, - "success": True - } - deployment_plan["phases"].append(phase4_result) - - # Update active deployment - self.active_deployments[deployment_id] = model_version - - except Exception as e: - success = False - rollback_required = True - deployment_plan["error"] = str(e) - - # Handle rollback if needed - if rollback_required: - rollback_result = { - "phase": "rollback", - "start_time": datetime.now(), - "reason": "Success criteria not met" if not success else "Error during deployment", - "success": True - } - deployment_plan["phases"].append(rollback_result) - - # Finalize deployment - deployment_plan["end_time"] = datetime.now() - deployment_plan["status"] = "success" if success else "failed" - deployment_plan["rollback_executed"] = rollback_required - - # Record in history - self.deployment_history.append(deployment_plan) - - return { - "deployment_id": deployment_id, - "success": success, - "strategy_used": strategy_name, - "rollback_required": rollback_required, - "phases_completed": len(deployment_plan["phases"]), - "deployment_plan": deployment_plan - } - ### END SOLUTION - - def handle_production_incident(self, incident_data: Dict[str, Any]) -> Dict[str, Any]: - """ - TODO: Handle production incidents with automated response. - - STEP-BY-STEP IMPLEMENTATION: - 1. Classify incident severity and type - 2. Execute automated recovery procedures - 3. Determine if escalation is required - 4. Log incident and response actions - 5. Monitor recovery success - 6. Generate incident report - - EXAMPLE USAGE: - ```python - incident = { - "type": "performance_degradation", - "severity": "high", - "metrics": {"accuracy": 0.75, "latency": 800, "error_rate": 0.15}, - "affected_models": ["recommendation_model_v20240101"] - } - response = profiler.handle_production_incident(incident) - ``` - - IMPLEMENTATION HINTS: - - Classify incidents by type and severity - - Execute appropriate recovery actions - - Log all actions for audit trail - - Determine escalation requirements - """ - ### BEGIN SOLUTION - incident_id = f"incident_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{len(self.incident_log)}" - incident_start = datetime.now() - - # Classify incident - incident_type = incident_data.get("type", "unknown") - severity = incident_data.get("severity", "medium") - affected_models = incident_data.get("affected_models", []) - metrics = incident_data.get("metrics", {}) - - # Initialize response - response_actions = [] - escalation_required = False - recovery_successful = False - - # Automated recovery procedures - if incident_type == "performance_degradation": - # Check if metrics breach rollback criteria - accuracy = metrics.get("accuracy", 1.0) - latency = metrics.get("latency", 0) - error_rate = metrics.get("error_rate", 0) - - rollback_needed = ( - accuracy < 0.80 or # Critical accuracy threshold - latency > 1000 or # Critical latency threshold - error_rate > 0.10 # Critical error rate threshold - ) - - if rollback_needed and self.rollback_policies["auto_rollback_enabled"]: - # Execute automatic rollback - response_actions.append({ - "action": "automatic_rollback", - "timestamp": datetime.now(), - "details": "Rolling back to previous stable version", - "success": True - }) - recovery_successful = True - - # Scale resources if needed - if latency > 600: - response_actions.append({ - "action": "scale_resources", - "timestamp": datetime.now(), - "details": "Increasing compute resources", - "success": True - }) - - elif incident_type == "data_drift": - # Trigger retraining pipeline - response_actions.append({ - "action": "trigger_retraining", - "timestamp": datetime.now(), - "details": "Initiating continuous training pipeline", - "success": True - }) - - # Increase monitoring frequency - response_actions.append({ - "action": "increase_monitoring", - "timestamp": datetime.now(), - "details": "Reducing monitoring interval to 1 minute", - "success": True - }) - - elif incident_type == "system_failure": - # Restart affected services - response_actions.append({ - "action": "restart_services", - "timestamp": datetime.now(), - "details": "Restarting inference endpoints", - "success": True - }) - - # Health check after restart - response_actions.append({ - "action": "health_check", - "timestamp": datetime.now(), - "details": "Validating service health post-restart", - "success": True - }) - recovery_successful = True - - # Determine escalation requirements - if severity == "critical" or not recovery_successful: - escalation_required = True - - # Find appropriate escalation level - escalation_level = 1 - if severity == "critical": - escalation_level = 2 - if incident_type == "security_breach": - escalation_level = 3 - - response_actions.append({ - "action": "escalate_incident", - "timestamp": datetime.now(), - "details": f"Escalating to level {escalation_level}", - "escalation_level": escalation_level, - "contacts": self.escalation_rules[escalation_level - 1]["contacts"], - "success": True - }) - - # Create incident record - incident_record = { - "incident_id": incident_id, - "incident_type": incident_type, - "severity": severity, - "start_time": incident_start, - "end_time": datetime.now(), - "affected_models": affected_models, - "metrics": metrics, - "response_actions": response_actions, - "escalation_required": escalation_required, - "recovery_successful": recovery_successful, - "resolution_time": (datetime.now() - incident_start).total_seconds() - } - - # Log incident - self.incident_log.append(incident_record) - - return { - "incident_id": incident_id, - "response_actions_taken": len(response_actions), - "recovery_successful": recovery_successful, - "escalation_required": escalation_required, - "resolution_time_seconds": incident_record["resolution_time"], - "incident_record": incident_record - } - ### END SOLUTION - - def generate_mlops_governance_report(self) -> Dict[str, Any]: - """ - TODO: Generate comprehensive MLOps governance and compliance report. - - STEP-BY-STEP IMPLEMENTATION: - 1. Collect model registry statistics - 2. Analyze deployment history and patterns - 3. Review incident response effectiveness - 4. Calculate system reliability metrics - 5. Assess compliance with policies - 6. Generate actionable recommendations - - EXAMPLE RETURN: - ```python - { - "report_date": datetime(2024, 1, 1), - "system_health_score": 0.92, - "model_registry_stats": {...}, - "deployment_success_rate": 0.95, - "incident_response_metrics": {...}, - "compliance_status": "compliant", - "recommendations": ["Improve deployment automation", ...] - } - ``` - """ - ### BEGIN SOLUTION - report_date = datetime.now() - - # Model registry statistics - total_models = len(self.model_versions) - total_versions = sum(len(versions) for versions in self.model_versions.values()) - active_deployments_count = len(self.active_deployments) - - model_registry_stats = { - "total_models": total_models, - "total_versions": total_versions, - "active_deployments": active_deployments_count, - "average_versions_per_model": total_versions / max(total_models, 1) - } - - # Deployment history analysis - total_deployments = len(self.deployment_history) - successful_deployments = sum(1 for d in self.deployment_history if d["status"] == "success") - deployment_success_rate = successful_deployments / max(total_deployments, 1) - - rollback_count = sum(1 for d in self.deployment_history if d.get("rollback_executed", False)) - rollback_rate = rollback_count / max(total_deployments, 1) - - deployment_metrics = { - "total_deployments": total_deployments, - "success_rate": deployment_success_rate, - "rollback_rate": rollback_rate, - "average_deployment_time": 1800 if total_deployments > 0 else 0 # Simulated - } - - # Incident response analysis - total_incidents = len(self.incident_log) - if total_incidents > 0: - resolved_incidents = sum(1 for i in self.incident_log if i["recovery_successful"]) - average_resolution_time = np.mean([i["resolution_time"] for i in self.incident_log]) - escalation_rate = sum(1 for i in self.incident_log if i["escalation_required"]) / total_incidents - else: - resolved_incidents = 0 - average_resolution_time = 0 - escalation_rate = 0 - - incident_metrics = { - "total_incidents": total_incidents, - "resolution_rate": resolved_incidents / max(total_incidents, 1), - "average_resolution_time": average_resolution_time, - "escalation_rate": escalation_rate - } - - # System health score calculation - health_components = { - "deployment_success": deployment_success_rate, - "incident_resolution": incident_metrics["resolution_rate"], - "system_availability": 0.995, # Simulated high availability - "monitoring_coverage": 0.90 # Simulated monitoring coverage - } - - system_health_score = np.mean(list(health_components.values())) - - # Compliance assessment - compliance_checks = { - "model_versioning": total_versions > 0, - "deployment_automation": deployment_success_rate > 0.9, - "incident_response": average_resolution_time < 1800, # 30 minutes - "monitoring_enabled": len(self.performance_monitors) > 0, - "rollback_capability": self.rollback_policies["auto_rollback_enabled"] - } - - compliance_score = sum(compliance_checks.values()) / len(compliance_checks) - compliance_status = "compliant" if compliance_score >= 0.8 else "non_compliant" - - # Generate recommendations - recommendations = [] - - if deployment_success_rate < 0.95: - recommendations.append("Improve deployment automation and testing") - - if rollback_rate > 0.10: - recommendations.append("Enhance pre-deployment validation") - - if incident_metrics["escalation_rate"] > 0.20: - recommendations.append("Improve automated incident response procedures") - - if system_health_score < 0.90: - recommendations.append("Review overall system reliability and monitoring") - - if not compliance_checks["monitoring_enabled"]: - recommendations.append("Implement comprehensive monitoring coverage") - - return { - "report_date": report_date, - "system_name": self.system_name, - "reporting_period": "all_time", # Could be configurable - - "system_health_score": system_health_score, - "health_components": health_components, - - "model_registry_stats": model_registry_stats, - "deployment_metrics": deployment_metrics, - "incident_response_metrics": incident_metrics, - - "compliance_status": compliance_status, - "compliance_score": compliance_score, - "compliance_checks": compliance_checks, - - "recommendations": recommendations, - - "summary": { - "models_managed": total_models, - "deployments_executed": total_deployments, - "incidents_handled": total_incidents, - "overall_reliability": "high" if system_health_score > 0.9 else "medium" if system_health_score > 0.8 else "low" - } - } - ### END SOLUTION diff --git a/tinytorch/core/networks.py b/tinytorch/core/networks.py deleted file mode 100644 index 9c9ed228..00000000 --- a/tinytorch/core/networks.py +++ /dev/null @@ -1,236 +0,0 @@ -# ╔═══════════════════════════════════════════════════════════════════════════════╗ -# ║ 🚨 CRITICAL WARNING 🚨 ║ -# ║ AUTOGENERATED! DO NOT EDIT! ║ -# ║ ║ -# ║ This file is AUTOMATICALLY GENERATED from source modules. ║ -# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║ -# ║ ║ -# ║ ✅ TO EDIT: modules/source/16_tinygpt/tinygpt_dev.ipynb ║ -# ║ ✅ TO EXPORT: Run 'tito module complete ' ║ -# ║ ║ -# ║ 🛡️ STUDENT PROTECTION: This file contains optimized implementations. ║ -# ║ Editing it directly may break module functionality and training. ║ -# ║ ║ -# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║ -# ║ happens! The tinytorch/ directory is just the compiled output. ║ -# ╚═══════════════════════════════════════════════════════════════════════════════╝ - -# %% auto 0 -__all__ = ['Sequential', 'create_mlp', 'MLP'] - -# %% ../../modules/source/05_dense/dense_dev.ipynb 1 -import numpy as np -import sys -import os -from typing import List, Union, Optional, Callable -import matplotlib.pyplot as plt - -# Import all the building blocks we need - try package first, then local modules -try: - from tinytorch.core.tensor import Tensor - from tinytorch.core.layers import Dense - from tinytorch.core.activations import ReLU, Sigmoid, Tanh, Softmax -except ImportError: - # For development, import from local modules - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '01_tensor')) - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '02_activations')) - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '03_layers')) - from tensor_dev import Tensor - from activations_dev import ReLU, Sigmoid, Tanh, Softmax - from layers_dev import Dense - -# %% ../../modules/source/05_dense/dense_dev.ipynb 2 -def _should_show_plots(): - """Check if we should show plots (disable during testing)""" - # Check multiple conditions that indicate we're in test mode - is_pytest = ( - 'pytest' in sys.modules or - 'test' in sys.argv or - os.environ.get('PYTEST_CURRENT_TEST') is not None or - any('test' in arg for arg in sys.argv) or - any('pytest' in arg for arg in sys.argv) - ) - - # Show plots in development mode (when not in test mode) - return not is_pytest - -# %% ../../modules/source/05_dense/dense_dev.ipynb 7 -class Sequential: - """ - Sequential Network: Composes layers in sequence - - The most fundamental network architecture. - Applies layers in order: f(x) = layer_n(...layer_2(layer_1(x))) - """ - - def __init__(self, layers: Optional[List] = None): - """ - Initialize Sequential network with layers. - - Args: - layers: List of layers to compose in order (optional, defaults to empty list) - - TODO: Store the layers and implement forward pass - - APPROACH: - 1. Store the layers list as an instance variable - 2. Initialize empty list if no layers provided - 3. Prepare for forward pass implementation - - EXAMPLE: - Sequential([Dense(3,4), ReLU(), Dense(4,2)]) - creates a 3-layer network: Dense → ReLU → Dense - - HINTS: - - Use self.layers to store the layers - - Handle empty initialization case - """ - ### BEGIN SOLUTION - self.layers = layers if layers is not None else [] - ### END SOLUTION - - def forward(self, x: Tensor) -> Tensor: - """ - Forward pass through all layers in sequence. - - Args: - x: Input tensor - - Returns: - Output tensor after passing through all layers - - TODO: Implement sequential forward pass through all layers - - APPROACH: - 1. Start with the input tensor - 2. Apply each layer in sequence - 3. Each layer's output becomes the next layer's input - 4. Return the final output - - EXAMPLE: - Input: Tensor([[1, 2, 3]]) - Layer1 (Dense): Tensor([[1.4, 2.8]]) - Layer2 (ReLU): Tensor([[1.4, 2.8]]) - Layer3 (Dense): Tensor([[0.7]]) - Output: Tensor([[0.7]]) - - HINTS: - - Use a for loop: for layer in self.layers: - - Apply each layer: x = layer(x) - - The output of one layer becomes input to the next - - Return the final result - """ - ### BEGIN SOLUTION - # Apply each layer in sequence - for layer in self.layers: - x = layer(x) - return x - ### END SOLUTION - - def __call__(self, x: Tensor) -> Tensor: - """Make the network callable: sequential(x) instead of sequential.forward(x)""" - return self.forward(x) - - def add(self, layer): - """Add a layer to the network.""" - self.layers.append(layer) - -# %% ../../modules/source/05_dense/dense_dev.ipynb 11 -def create_mlp(input_size: int, hidden_sizes: List[int], output_size: int, - activation=ReLU, output_activation=Sigmoid) -> Sequential: - """ - Create a Multi-Layer Perceptron (MLP) network. - - Args: - input_size: Number of input features - hidden_sizes: List of hidden layer sizes - output_size: Number of output features - activation: Activation function for hidden layers (default: ReLU) - output_activation: Activation function for output layer (default: Sigmoid) - - Returns: - Sequential network with MLP architecture - - TODO: Implement MLP creation with alternating Dense and activation layers. - - APPROACH: - 1. Start with an empty list of layers - 2. Add layers in this pattern: - - Dense(input_size → first_hidden_size) - - Activation() - - Dense(first_hidden_size → second_hidden_size) - - Activation() - - ... - - Dense(last_hidden_size → output_size) - - Output_activation() - 3. Return Sequential(layers) - - EXAMPLE: - create_mlp(3, [4, 2], 1) creates: - Dense(3→4) → ReLU → Dense(4→2) → ReLU → Dense(2→1) → Sigmoid - - HINTS: - - Start with layers = [] - - Track current_size starting with input_size - - For each hidden_size: add Dense(current_size, hidden_size), then activation - - Finally add Dense(last_hidden_size, output_size), then output_activation - - Return Sequential(layers) - """ - layers = [] - current_size = input_size - - # Add hidden layers with activations - for hidden_size in hidden_sizes: - layers.append(Dense(current_size, hidden_size)) - layers.append(activation()) - current_size = hidden_size - - # Add output layer with output activation - layers.append(Dense(current_size, output_size)) - layers.append(output_activation()) - - return Sequential(layers) - -# %% ../../modules/source/05_dense/dense_dev.ipynb 19 -class MLP: - """ - Multi-Layer Perceptron (MLP) class. - - A convenient wrapper around Sequential networks for standard MLP architectures. - Maintains parameter information and provides a clean interface. - - Args: - input_size: Number of input features - hidden_size: Size of the single hidden layer - output_size: Number of output features - activation: Activation function for hidden layer (default: ReLU) - output_activation: Activation function for output layer (default: Sigmoid) - """ - - def __init__(self, input_size: int, hidden_size: int, output_size: int, - activation=ReLU, output_activation=None): - self.input_size = input_size - self.hidden_size = hidden_size - self.output_size = output_size - - # Build the network layers - layers = [] - - # Input to hidden layer - layers.append(Dense(input_size, hidden_size)) - layers.append(activation()) - - # Hidden to output layer - layers.append(Dense(hidden_size, output_size)) - if output_activation is not None: - layers.append(output_activation()) - - self.network = Sequential(layers) - - def forward(self, x): - """Forward pass through the MLP network.""" - return self.network.forward(x) - - def __call__(self, x): - """Make the MLP callable.""" - return self.forward(x) diff --git a/tinytorch/core/optimizers.py b/tinytorch/core/optimizers.py deleted file mode 100644 index 9e269116..00000000 --- a/tinytorch/core/optimizers.py +++ /dev/null @@ -1,1580 +0,0 @@ -# ╔═══════════════════════════════════════════════════════════════════════════════╗ -# ║ 🚨 CRITICAL WARNING 🚨 ║ -# ║ AUTOGENERATED! DO NOT EDIT! ║ -# ║ ║ -# ║ This file is AUTOMATICALLY GENERATED from source modules. ║ -# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║ -# ║ ║ -# ║ ✅ TO EDIT: modules/source/10_optimizers/optimizers_dev.py ║ -# ║ ✅ TO EXPORT: Run 'tito module complete ' ║ -# ║ ║ -# ║ 🛡️ STUDENT PROTECTION: This file contains optimized implementations. ║ -# ║ Editing it directly may break module functionality and training. ║ -# ║ ║ -# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║ -# ║ happens! The tinytorch/ directory is just the compiled output. ║ -# ╚═══════════════════════════════════════════════════════════════════════════════╝ - -# %% auto 0 -__all__ = ['setup_import_paths', 'gradient_descent_step', 'SGD', 'Adam', 'StepLR', 'OptimizerConvergenceProfiler', - 'AdvancedOptimizerFeatures'] - -# %% ../../modules/08_optimizers/optimizers_dev.ipynb 1 -import numpy as np -import sys -import os -from typing import List, Dict, Any, Optional, Union -from collections import defaultdict - -# Helper function to set up import paths -def setup_import_paths(): - """Set up import paths for development modules.""" - import sys - import os - - # Add module directories to path - base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) - tensor_dir = os.path.join(base_dir, '01_tensor') - autograd_dir = os.path.join(base_dir, '06_autograd') # Fixed: Module 6, not 7 - - if tensor_dir not in sys.path: - sys.path.append(tensor_dir) - if autograd_dir not in sys.path: - sys.path.append(autograd_dir) - -# Import our existing components -try: - from tinytorch.core.tensor import Tensor - from tinytorch.core.autograd import Variable -except ImportError: - # For development, try local imports - try: - setup_import_paths() - from tensor_dev import Tensor - from autograd_dev import Variable - except ImportError: - # Create simplified fallback classes for basic gradient operations - print("Warning: Using simplified classes for basic gradient operations") - - class Tensor: - def __init__(self, data): - self.data = np.array(data) - self.shape = self.data.shape - - def __str__(self): - return f"Tensor({self.data})" - - class Variable: - def __init__(self, data, requires_grad=True): - if isinstance(data, (int, float)): - self.data = Tensor([data]) - else: - self.data = Tensor(data) - self.requires_grad = requires_grad - self.grad = None # Simple gradient storage - - def zero_grad(self): - """Reset gradients to None (basic operation from Module 6)""" - self.grad = None - - def __str__(self): - return f"Variable({self.data.data})" - -# %% ../../modules/08_optimizers/optimizers_dev.ipynb 7 -def gradient_descent_step(parameter: Variable, learning_rate: float) -> None: - """ - Perform one step of gradient descent on a parameter. - - Args: - parameter: Variable with gradient information - learning_rate: How much to update parameter - - TODO: Implement basic gradient descent parameter update. - - STEP-BY-STEP IMPLEMENTATION: - 1. Check if parameter has a gradient - 2. Get current parameter value and gradient - 3. Update parameter: new_value = old_value - learning_rate * gradient - 4. Update parameter data with new value - 5. Handle edge cases (no gradient, invalid values) - - EXAMPLE USAGE: - ```python - # Parameter with gradient - w = Variable(2.0, requires_grad=True) - w.grad = Variable(0.5) # Gradient from loss - - # Update parameter - gradient_descent_step(w, learning_rate=0.1) - # w.data now contains: 2.0 - 0.1 * 0.5 = 1.95 - ``` - - IMPLEMENTATION HINTS: - - Check if parameter.grad is not None - - Use parameter.grad.data.data to get gradient value - - Update parameter.data with new Tensor - - Don't modify gradient (it's used for logging) - - LEARNING CONNECTIONS: - - This is the foundation of all neural network training - - PyTorch's optimizer.step() does exactly this - - The learning rate determines convergence speed - """ - ### BEGIN SOLUTION - if parameter.grad is not None: - # Get current parameter value and gradient - current_value = parameter.data.data - gradient_value = parameter.grad.data.data - - # Update parameter: new_value = old_value - learning_rate * gradient - new_value = current_value - learning_rate * gradient_value - - # Update parameter data - parameter.data = Tensor(new_value) - ### END SOLUTION - -# %% ../../modules/08_optimizers/optimizers_dev.ipynb 11 -class SGD: - """ - Simplified SGD Optimizer - - Implements basic stochastic gradient descent with optional momentum. - Uses simple gradient operations from Module 6. - - Mathematical Update Rule: - parameter = parameter - learning_rate * gradient - - With momentum: - velocity = momentum * velocity + gradient - parameter = parameter - learning_rate * velocity - """ - - def __init__(self, parameters: List[Variable], learning_rate: float = 0.01, - momentum: float = 0.0): - """ - Initialize SGD optimizer with basic parameters. - - Args: - parameters: List of Variables to optimize (from Module 6) - learning_rate: Learning rate (default: 0.01) - momentum: Momentum coefficient (default: 0.0) - - TODO: Implement basic SGD optimizer initialization. - - APPROACH: - 1. Store parameters and learning rate - 2. Store momentum coefficient - 3. Initialize simple momentum buffers - - EXAMPLE: - ```python - # Basic optimizer setup - w = Variable(1.0, requires_grad=True) - b = Variable(0.0, requires_grad=True) - optimizer = SGD([w, b], learning_rate=0.01) - - # In training: - optimizer.zero_grad() - # ... compute gradients ... - optimizer.step() - ``` - """ - ### BEGIN SOLUTION - self.parameters = parameters - self.learning_rate = learning_rate - self.momentum = momentum - - # Simple momentum storage (using basic dict) - self.velocity = {} - for i, param in enumerate(parameters): - if self.momentum > 0: - self.velocity[i] = 0.0 # Initialize velocity to zero - ### END SOLUTION - - def step(self) -> None: - """ - Perform one optimization step using basic gradient operations. - - TODO: Implement simplified SGD parameter update. - - APPROACH: - 1. Iterate through all parameters - 2. For each parameter with gradient (from Module 6): - a. Get gradient using simple param.grad access - b. Apply momentum if specified - c. Update parameter with learning rate - - SIMPLIFIED MATHEMATICAL FORMULATION: - - Without momentum: parameter = parameter - learning_rate * gradient - - With momentum: velocity = momentum * velocity + gradient - parameter = parameter - learning_rate * velocity - - IMPLEMENTATION HINTS: - - Use basic param.grad access (from Module 6) - - Simple momentum using self.velocity dict - - Basic parameter update using scalar operations - """ - ### BEGIN SOLUTION - for i, param in enumerate(self.parameters): - if param.grad is not None: - # Get gradient data (works for both Tensor and Variable) - # In modern PyTorch style, grad.data gives us the numpy array - gradient = param.grad.data - - if self.momentum > 0: - # Apply momentum (simplified) - if i in self.velocity: - self.velocity[i] = self.momentum * self.velocity[i] + gradient - else: - self.velocity[i] = gradient - update = self.velocity[i] - else: - # Simple gradient descent (no momentum) - update = gradient - - # Clean parameter update - PyTorch style - # NOTE: In production PyTorch, this is an in-place operation (param.data.sub_()) - # for memory efficiency. We create a new Tensor here for clarity, but real - # systems modify the existing memory to avoid allocation overhead. - from tinytorch.core.tensor import Tensor - new_value = param.data - self.learning_rate * update - param.data = Tensor(new_value) - ### END SOLUTION - - def zero_grad(self) -> None: - """ - Zero out gradients for all parameters. - - TODO: Implement gradient zeroing. - - APPROACH: - 1. Iterate through all parameters - 2. Set gradient to None for each parameter - 3. This prepares for next backward pass - - IMPLEMENTATION HINTS: - - Simply set param.grad = None - - This is called before loss.backward() - - Essential for proper gradient accumulation - """ - ### BEGIN SOLUTION - for param in self.parameters: - param.grad = None - ### END SOLUTION - -# %% ../../modules/08_optimizers/optimizers_dev.ipynb 15 -class Adam: - """ - Simplified Adam Optimizer - - Implements a simplified version of Adam algorithm with adaptive learning rates. - Educational focus on understanding optimization concepts rather than complex implementation. - - Key concepts: - - Momentum: Running average of gradients (first moment) - - Adaptive learning: Running average of squared gradients (second moment) - - Bias correction: Adjust for initialization bias - """ - - def __init__(self, parameters: List[Variable], learning_rate: float = 0.001, - beta1: float = 0.9, beta2: float = 0.999, epsilon: float = 1e-8): - """ - Initialize simplified Adam optimizer. - - Args: - parameters: List of Variables to optimize (from Module 6) - learning_rate: Learning rate (default: 0.001) - beta1: Decay rate for momentum (default: 0.9) - beta2: Decay rate for squared gradients (default: 0.999) - epsilon: Small constant for numerical stability (default: 1e-8) - - TODO: Implement simplified Adam optimizer initialization. - - APPROACH: - 1. Store parameters and learning rate - 2. Store Adam hyperparameters (beta1, beta2, epsilon) - 3. Initialize simple moment storage - - EDUCATIONAL FOCUS: - - Understand Adam concepts: momentum + adaptive learning - - Learn why Adam uses running averages - - See how bias correction helps early training - - EXAMPLE: - ```python - # Simple Adam setup - w = Variable(1.0, requires_grad=True) - b = Variable(0.0, requires_grad=True) - optimizer = Adam([w, b], learning_rate=0.001) - ``` - """ - ### BEGIN SOLUTION - self.parameters = parameters - self.learning_rate = learning_rate - self.beta1 = beta1 - self.beta2 = beta2 - self.epsilon = epsilon - - # Simple moment storage (using basic dict with indices) - # MEMORY INSIGHT: Adam uses 3x memory of SGD because it stores: - # 1. Parameters (1x memory) - # 2. First moment estimates m[i] (1x memory) - # 3. Second moment estimates v[i] (1x memory) - # This is why Adam can be problematic for very large models! - self.m = {} # First moment (momentum) - self.v = {} # Second moment (squared gradients) - - # Initialize moments for each parameter - for i, param in enumerate(parameters): - self.m[i] = 0.0 - self.v[i] = 0.0 - - # Step counter for bias correction - self.t = 0 - ### END SOLUTION - - def step(self) -> None: - """ - Perform one optimization step using simplified Adam algorithm. - - TODO: Implement simplified Adam parameter update. - - APPROACH: - 1. Increment step counter - 2. For each parameter with gradient: - a. Get gradient (basic operation from Module 6) - b. Update momentum (first moment) - c. Update squared gradient average (second moment) - d. Apply bias correction - e. Update parameter with adaptive learning rate - - SIMPLIFIED MATHEMATICAL FORMULATION: - - m = beta1 * m + (1 - beta1) * gradient (momentum) - - v = beta2 * v + (1 - beta2) * gradient² (squared gradients) - - m_corrected = m / (1 - beta1^t) (bias correction) - - v_corrected = v / (1 - beta2^t) (bias correction) - - parameter = parameter - lr * m_corrected / (√v_corrected + ε) - - EDUCATIONAL INSIGHTS: - - Momentum helps accelerate learning - - Squared gradients adapt learning rate per parameter - - Bias correction prevents slow start - """ - ### BEGIN SOLUTION - self.t += 1 # Increment step counter - - for i, param in enumerate(self.parameters): - if param.grad is not None: - # Get gradient data - clean PyTorch style - gradient = param.grad.data - - # Update first moment (momentum) - self.m[i] = self.beta1 * self.m[i] + (1 - self.beta1) * gradient - - # Update second moment (squared gradients) - self.v[i] = self.beta2 * self.v[i] + (1 - self.beta2) * gradient * gradient - - # Bias correction - m_corrected = self.m[i] / (1 - self.beta1 ** self.t) - v_corrected = self.v[i] / (1 - self.beta2 ** self.t) - - # Clean adaptive parameter update - PyTorch style - # NOTE: In production PyTorch, parameters are updated in-place for efficiency. - # We create a new Tensor for educational clarity, but real systems use - # param.data.add_(-update) to modify memory directly without allocation. - update = self.learning_rate * m_corrected / (np.sqrt(v_corrected) + self.epsilon) - from tinytorch.core.tensor import Tensor - new_value = param.data - update - param.data = Tensor(new_value) - ### END SOLUTION - - def zero_grad(self) -> None: - """ - Zero out gradients for all parameters. - - TODO: Implement gradient zeroing (same as SGD). - - IMPLEMENTATION HINTS: - - Set param.grad = None for all parameters - - This is identical to SGD implementation - """ - ### BEGIN SOLUTION - for param in self.parameters: - param.grad = None - ### END SOLUTION - -# %% ../../modules/08_optimizers/optimizers_dev.ipynb 20 -class StepLR: - """ - Step Learning Rate Scheduler - - Decays learning rate by gamma every step_size epochs: - learning_rate = initial_lr * (gamma ^ (epoch // step_size)) - """ - - def __init__(self, optimizer: Union[SGD, Adam], step_size: int, gamma: float = 0.1): - """ - Initialize step learning rate scheduler. - - Args: - optimizer: Optimizer to schedule - step_size: Number of epochs between decreases - gamma: Multiplicative factor for learning rate decay - - TODO: Implement learning rate scheduler initialization. - - APPROACH: - 1. Store optimizer reference - 2. Store scheduling parameters - 3. Save initial learning rate - 4. Initialize step counter - - EXAMPLE: - ```python - optimizer = SGD([w1, w2], learning_rate=0.1) - scheduler = StepLR(optimizer, step_size=10, gamma=0.1) - - # In training loop: - for epoch in range(100): - train_one_epoch() - scheduler.step() # Update learning rate - ``` - - HINTS: - - Store optimizer reference - - Save initial learning rate from optimizer - - Initialize step counter to 0 - - gamma is the decay factor (0.1 = 10x reduction) - """ - ### BEGIN SOLUTION - self.optimizer = optimizer - self.step_size = step_size - self.gamma = gamma - self.initial_lr = optimizer.learning_rate - self.step_count = 0 - ### END SOLUTION - - def step(self) -> None: - """ - Update learning rate based on current step. - - TODO: Implement learning rate update. - - APPROACH: - 1. Increment step counter - 2. Calculate new learning rate using step decay formula - 3. Update optimizer's learning rate - - MATHEMATICAL FORMULATION: - new_lr = initial_lr * (gamma ^ ((step_count - 1) // step_size)) - - IMPLEMENTATION HINTS: - - Use // for integer division - - Use ** for exponentiation - - Update optimizer.learning_rate directly - """ - ### BEGIN SOLUTION - self.step_count += 1 - - # Calculate new learning rate - decay_factor = self.gamma ** ((self.step_count - 1) // self.step_size) - new_lr = self.initial_lr * decay_factor - - # Update optimizer's learning rate - self.optimizer.learning_rate = new_lr - ### END SOLUTION - - def get_lr(self) -> float: - """ - Get current learning rate. - - TODO: Return current learning rate. - - IMPLEMENTATION HINTS: - - Return optimizer.learning_rate - """ - ### BEGIN SOLUTION - return self.optimizer.learning_rate - ### END SOLUTION - -# %% ../../modules/08_optimizers/optimizers_dev.ipynb 28 -class OptimizerConvergenceProfiler: - """ - ML Systems Tool: Optimizer Performance and Convergence Analysis - - Profiles convergence patterns, learning rate sensitivity, and computational costs - across different optimizers to guide production optimizer selection. - - This is 60% implementation focusing on core analysis capabilities: - - Convergence rate comparison across optimizers - - Learning rate sensitivity analysis - - Gradient statistics tracking - - Memory usage estimation - - Performance recommendations - """ - - def __init__(self): - """ - Initialize optimizer convergence profiler. - - TODO: Implement profiler initialization. - - APPROACH: - 1. Initialize tracking dictionaries for different metrics - 2. Set up convergence analysis parameters - 3. Prepare memory and performance tracking - 4. Initialize recommendation engine components - - PRODUCTION CONTEXT: - In production, this profiler would run on representative tasks to: - - Select optimal optimizers for new models - - Tune hyperparameters before expensive training runs - - Predict training time and resource requirements - - Monitor training stability and convergence - - IMPLEMENTATION HINTS: - - Track convergence history per optimizer - - Store gradient statistics over time - - Monitor memory usage patterns - - Prepare for comparative analysis - """ - ### BEGIN SOLUTION - # Convergence tracking - self.convergence_history = defaultdict(list) # {optimizer_name: [losses]} - self.gradient_norms = defaultdict(list) # {optimizer_name: [grad_norms]} - self.learning_rates = defaultdict(list) # {optimizer_name: [lr_values]} - self.step_times = defaultdict(list) # {optimizer_name: [step_durations]} - - # Performance metrics - self.memory_usage = defaultdict(list) # {optimizer_name: [memory_estimates]} - self.convergence_rates = {} # {optimizer_name: convergence_rate} - self.stability_scores = {} # {optimizer_name: stability_score} - - # Analysis parameters - self.convergence_threshold = 1e-6 - self.stability_window = 10 - self.gradient_explosion_threshold = 1e6 - - # Recommendations - self.optimizer_rankings = {} - self.hyperparameter_suggestions = {} - ### END SOLUTION - - def profile_optimizer_convergence(self, optimizer_name: str, optimizer: Union[SGD, Adam], - training_function, initial_loss: float, - max_steps: int = 100) -> Dict[str, Any]: - """ - Profile convergence behavior of an optimizer on a specific task. - - Args: - optimizer_name: Name identifier for the optimizer - optimizer: Optimizer instance to profile - training_function: Function that performs one training step and returns loss - initial_loss: Starting loss value - max_steps: Maximum training steps to profile - - Returns: - Dictionary containing convergence analysis results - - TODO: Implement optimizer convergence profiling. - - APPROACH: - 1. Run training loop with the optimizer - 2. Track loss, gradients, learning rates at each step - 3. Measure step execution time - 4. Estimate memory usage - 5. Analyze convergence patterns and stability - 6. Generate performance metrics - - CONVERGENCE ANALYSIS: - - Track loss reduction over time - - Measure convergence rate (loss reduction per step) - - Detect convergence plateaus - - Identify gradient explosion or vanishing - - Assess training stability - - PRODUCTION INSIGHTS: - This analysis helps determine: - - Which optimizers converge fastest for specific model types - - Optimal learning rates for different optimizers - - Memory vs performance trade-offs - - Training stability and robustness - - IMPLEMENTATION HINTS: - - Use time.time() to measure step duration - - Calculate gradient norms across all parameters - - Track learning rate changes (for schedulers) - - Estimate memory from optimizer state size - """ - ### BEGIN SOLUTION - import time - - print(f"🔍 Profiling {optimizer_name} convergence...") - - # Initialize tracking - losses = [] - grad_norms = [] - step_durations = [] - lr_values = [] - - previous_loss = initial_loss - convergence_step = None - - for step in range(max_steps): - step_start = time.time() - - # Perform training step - try: - current_loss = training_function() - losses.append(current_loss) - - # Calculate gradient norm - total_grad_norm = 0.0 - param_count = 0 - for param in optimizer.parameters: - if param.grad is not None: - grad_data = param.grad.data.data - if hasattr(grad_data, 'flatten'): - grad_norm = np.linalg.norm(grad_data.flatten()) - else: - grad_norm = abs(float(grad_data)) - total_grad_norm += grad_norm ** 2 - param_count += 1 - - if param_count > 0: - total_grad_norm = (total_grad_norm / param_count) ** 0.5 - grad_norms.append(total_grad_norm) - - # Track learning rate - lr_values.append(optimizer.learning_rate) - - # Check convergence - if convergence_step is None and abs(current_loss - previous_loss) < self.convergence_threshold: - convergence_step = step - - previous_loss = current_loss - - except Exception as e: - print(f"⚠️ Training step {step} failed: {e}") - break - - step_end = time.time() - step_durations.append(step_end - step_start) - - # Early stopping for exploded gradients - if total_grad_norm > self.gradient_explosion_threshold: - print(f"⚠️ Gradient explosion detected at step {step}") - break - - # Store results - self.convergence_history[optimizer_name] = losses - self.gradient_norms[optimizer_name] = grad_norms - self.learning_rates[optimizer_name] = lr_values - self.step_times[optimizer_name] = step_durations - - # Analyze results - analysis = self._analyze_convergence_profile(optimizer_name, losses, grad_norms, - step_durations, convergence_step) - - return analysis - ### END SOLUTION - - def compare_optimizers(self, profiles: Dict[str, Dict]) -> Dict[str, Any]: - """ - Compare multiple optimizer profiles and generate recommendations. - - Args: - profiles: Dictionary mapping optimizer names to their profile results - - Returns: - Comprehensive comparison analysis with recommendations - - TODO: Implement optimizer comparison and ranking. - - APPROACH: - 1. Analyze convergence speed across optimizers - 2. Compare final performance and stability - 3. Assess computational efficiency - 4. Generate rankings and recommendations - 5. Identify optimal hyperparameters - - COMPARISON METRICS: - - Steps to convergence - - Final loss achieved - - Training stability (loss variance) - - Computational cost per step - - Memory efficiency - - Gradient explosion resistance - - PRODUCTION VALUE: - This comparison guides: - - Optimizer selection for new projects - - Hyperparameter optimization strategies - - Resource allocation decisions - - Training pipeline design - - IMPLEMENTATION HINTS: - - Normalize metrics for fair comparison - - Weight different factors based on importance - - Generate actionable recommendations - - Consider trade-offs between speed and stability - """ - ### BEGIN SOLUTION - comparison = { - 'convergence_speed': {}, - 'final_performance': {}, - 'stability': {}, - 'efficiency': {}, - 'rankings': {}, - 'recommendations': {} - } - - print("📊 Comparing optimizer performance...") - - # Analyze each optimizer - for opt_name, profile in profiles.items(): - # Convergence speed - convergence_step = profile.get('convergence_step', len(self.convergence_history[opt_name])) - comparison['convergence_speed'][opt_name] = convergence_step - - # Final performance - losses = self.convergence_history[opt_name] - if losses: - final_loss = losses[-1] - comparison['final_performance'][opt_name] = final_loss - - # Stability (coefficient of variation in last 10 steps) - if len(losses) >= self.stability_window: - recent_losses = losses[-self.stability_window:] - stability = 1.0 / (1.0 + np.std(recent_losses) / (np.mean(recent_losses) + 1e-8)) - comparison['stability'][opt_name] = stability - - # Efficiency (loss reduction per unit time) - step_times = self.step_times[opt_name] - if losses and step_times: - initial_loss = losses[0] - final_loss = losses[-1] - total_time = sum(step_times) - efficiency = (initial_loss - final_loss) / (total_time + 1e-8) - comparison['efficiency'][opt_name] = efficiency - - # Generate rankings - metrics = ['convergence_speed', 'final_performance', 'stability', 'efficiency'] - for metric in metrics: - if comparison[metric]: - if metric == 'convergence_speed': - # Lower is better for convergence speed - sorted_opts = sorted(comparison[metric].items(), key=lambda x: x[1]) - elif metric == 'final_performance': - # Lower is better for final loss - sorted_opts = sorted(comparison[metric].items(), key=lambda x: x[1]) - else: - # Higher is better for stability and efficiency - sorted_opts = sorted(comparison[metric].items(), key=lambda x: x[1], reverse=True) - - comparison['rankings'][metric] = [opt for opt, _ in sorted_opts] - - # Generate recommendations - recommendations = [] - - # Best overall optimizer - if comparison['rankings']: - # Simple scoring: rank position across metrics - scores = defaultdict(float) - for metric, ranking in comparison['rankings'].items(): - for i, opt_name in enumerate(ranking): - scores[opt_name] += len(ranking) - i - - best_optimizer = max(scores.items(), key=lambda x: x[1])[0] - recommendations.append(f"🏆 Best overall optimizer: {best_optimizer}") - - # Specific recommendations - if 'convergence_speed' in comparison['rankings']: - fastest = comparison['rankings']['convergence_speed'][0] - recommendations.append(f"⚡ Fastest convergence: {fastest}") - - if 'stability' in comparison['rankings']: - most_stable = comparison['rankings']['stability'][0] - recommendations.append(f"🎯 Most stable training: {most_stable}") - - if 'efficiency' in comparison['rankings']: - most_efficient = comparison['rankings']['efficiency'][0] - recommendations.append(f"💰 Most compute-efficient: {most_efficient}") - - comparison['recommendations']['summary'] = recommendations - - return comparison - ### END SOLUTION - - def analyze_learning_rate_sensitivity(self, optimizer_class, learning_rates: List[float], - training_function, steps: int = 50) -> Dict[str, Any]: - """ - Analyze optimizer sensitivity to different learning rates. - - Args: - optimizer_class: Optimizer class (SGD or Adam) - learning_rates: List of learning rates to test - training_function: Function that creates and runs training - steps: Number of training steps per learning rate - - Returns: - Learning rate sensitivity analysis - - TODO: Implement learning rate sensitivity analysis. - - APPROACH: - 1. Test optimizer with different learning rates - 2. Measure convergence performance for each rate - 3. Identify optimal learning rate range - 4. Detect learning rate instability regions - 5. Generate learning rate recommendations - - SENSITIVITY ANALYSIS: - - Plot loss curves for different learning rates - - Identify optimal learning rate range - - Detect gradient explosion thresholds - - Measure convergence robustness - - Generate adaptive scheduling suggestions - - PRODUCTION INSIGHTS: - This analysis enables: - - Automatic learning rate tuning - - Learning rate scheduling optimization - - Gradient explosion prevention - - Training stability improvement - - IMPLEMENTATION HINTS: - - Reset model state for each learning rate test - - Track convergence metrics consistently - - Identify learning rate sweet spots - - Flag unstable learning rate regions - """ - ### BEGIN SOLUTION - print("🔍 Analyzing learning rate sensitivity...") - - lr_analysis = { - 'learning_rates': learning_rates, - 'final_losses': [], - 'convergence_steps': [], - 'stability_scores': [], - 'gradient_explosions': [], - 'optimal_range': None, - 'recommendations': [] - } - - # Test each learning rate - for lr in learning_rates: - print(f" Testing learning rate: {lr}") - - try: - # Create optimizer with current learning rate - # This is a simplified test - in production, would reset model state - losses, grad_norms = training_function(lr, steps) - - if losses: - final_loss = losses[-1] - lr_analysis['final_losses'].append(final_loss) - - # Find convergence step - convergence_step = steps - for i in range(1, len(losses)): - if abs(losses[i] - losses[i-1]) < self.convergence_threshold: - convergence_step = i - break - lr_analysis['convergence_steps'].append(convergence_step) - - # Calculate stability - if len(losses) >= 10: - recent_losses = losses[-10:] - stability = 1.0 / (1.0 + np.std(recent_losses) / (np.mean(recent_losses) + 1e-8)) - lr_analysis['stability_scores'].append(stability) - else: - lr_analysis['stability_scores'].append(0.0) - - # Check for gradient explosion - max_grad_norm = max(grad_norms) if grad_norms else 0.0 - explosion = max_grad_norm > self.gradient_explosion_threshold - lr_analysis['gradient_explosions'].append(explosion) - - else: - # Failed to get losses - lr_analysis['final_losses'].append(float('inf')) - lr_analysis['convergence_steps'].append(steps) - lr_analysis['stability_scores'].append(0.0) - lr_analysis['gradient_explosions'].append(True) - - except Exception as e: - print(f" ⚠️ Failed with lr={lr}: {e}") - lr_analysis['final_losses'].append(float('inf')) - lr_analysis['convergence_steps'].append(steps) - lr_analysis['stability_scores'].append(0.0) - lr_analysis['gradient_explosions'].append(True) - - # Find optimal learning rate range - valid_indices = [i for i, (loss, explosion) in - enumerate(zip(lr_analysis['final_losses'], lr_analysis['gradient_explosions'])) - if not explosion and loss != float('inf')] - - if valid_indices: - # Find learning rate with best final loss among stable ones - stable_losses = [(i, lr_analysis['final_losses'][i]) for i in valid_indices] - best_idx = min(stable_losses, key=lambda x: x[1])[0] - - # Define optimal range around best learning rate - best_lr = learning_rates[best_idx] - lr_analysis['optimal_range'] = (best_lr * 0.1, best_lr * 10.0) - - # Generate recommendations - recommendations = [] - recommendations.append(f"🎯 Optimal learning rate: {best_lr:.2e}") - recommendations.append(f"📈 Safe range: {lr_analysis['optimal_range'][0]:.2e} - {lr_analysis['optimal_range'][1]:.2e}") - - # Learning rate scheduling suggestions - if best_idx > 0: - recommendations.append("💡 Consider starting with higher LR and decaying") - if any(lr_analysis['gradient_explosions']): - max_safe_lr = max([learning_rates[i] for i in valid_indices]) - recommendations.append(f"⚠️ Avoid learning rates above {max_safe_lr:.2e}") - - lr_analysis['recommendations'] = recommendations - else: - lr_analysis['recommendations'] = ["⚠️ No stable learning rates found - try lower values"] - - return lr_analysis - ### END SOLUTION - - def estimate_memory_usage(self, optimizer: Union[SGD, Adam], num_parameters: int) -> Dict[str, float]: - """ - Estimate memory usage for different optimizers. - - Args: - optimizer: Optimizer instance - num_parameters: Number of model parameters - - Returns: - Memory usage estimates in MB - - TODO: Implement memory usage estimation. - - APPROACH: - 1. Calculate parameter memory requirements - 2. Estimate optimizer state memory - 3. Account for gradient storage - 4. Include temporary computation memory - 5. Provide memory scaling predictions - - MEMORY ANALYSIS: - - Parameter storage: num_params * 4 bytes (float32) - - Gradient storage: num_params * 4 bytes - - Optimizer state: varies by optimizer type - - SGD momentum: num_params * 4 bytes - - Adam: num_params * 8 bytes (first + second moments) - - PRODUCTION VALUE: - Memory estimation helps: - - Select optimizers for memory-constrained environments - - Plan GPU memory allocation - - Scale to larger models - - Optimize batch sizes - - IMPLEMENTATION HINTS: - - Use typical float32 size (4 bytes) - - Account for optimizer-specific state - - Include gradient accumulation overhead - - Provide scaling estimates - """ - ### BEGIN SOLUTION - # Base memory requirements - bytes_per_param = 4 # float32 - - memory_breakdown = { - 'parameters_mb': num_parameters * bytes_per_param / (1024 * 1024), - 'gradients_mb': num_parameters * bytes_per_param / (1024 * 1024), - 'optimizer_state_mb': 0.0, - 'total_mb': 0.0 - } - - # Optimizer-specific state memory - if isinstance(optimizer, SGD): - if optimizer.momentum > 0: - # Momentum buffers - memory_breakdown['optimizer_state_mb'] = num_parameters * bytes_per_param / (1024 * 1024) - else: - memory_breakdown['optimizer_state_mb'] = 0.0 - elif isinstance(optimizer, Adam): - # First and second moment estimates - memory_breakdown['optimizer_state_mb'] = num_parameters * 2 * bytes_per_param / (1024 * 1024) - - # Calculate total - memory_breakdown['total_mb'] = ( - memory_breakdown['parameters_mb'] + - memory_breakdown['gradients_mb'] + - memory_breakdown['optimizer_state_mb'] - ) - - # Add efficiency estimates - memory_breakdown['memory_efficiency'] = memory_breakdown['parameters_mb'] / memory_breakdown['total_mb'] - memory_breakdown['overhead_ratio'] = memory_breakdown['optimizer_state_mb'] / memory_breakdown['parameters_mb'] - - return memory_breakdown - ### END SOLUTION - - def generate_production_recommendations(self, analysis_results: Dict[str, Any]) -> List[str]: - """ - Generate actionable recommendations for production optimizer usage. - - Args: - analysis_results: Combined results from convergence and sensitivity analysis - - Returns: - List of production recommendations - - TODO: Implement production recommendation generation. - - APPROACH: - 1. Analyze convergence patterns and stability - 2. Consider computational efficiency requirements - 3. Account for memory constraints - 4. Generate optimizer selection guidance - 5. Provide hyperparameter tuning suggestions - - RECOMMENDATION CATEGORIES: - - Optimizer selection for different scenarios - - Learning rate and scheduling strategies - - Memory optimization techniques - - Training stability improvements - - Production deployment considerations - - PRODUCTION CONTEXT: - These recommendations guide: - - ML engineer optimizer selection - - DevOps resource allocation - - Training pipeline optimization - - Cost reduction strategies - - IMPLEMENTATION HINTS: - - Provide specific, actionable advice - - Consider different deployment scenarios - - Include quantitative guidelines - - Address common production challenges - """ - ### BEGIN SOLUTION - recommendations = [] - - # Optimizer selection recommendations - recommendations.append("🔧 OPTIMIZER SELECTION GUIDE:") - recommendations.append(" • SGD + Momentum: Best for large batch training, proven stability") - recommendations.append(" • Adam: Best for rapid prototyping, adaptive learning rates") - recommendations.append(" • Consider memory constraints: SGD uses ~50% less memory than Adam") - - # Learning rate recommendations - if 'learning_rate_analysis' in analysis_results: - lr_analysis = analysis_results['learning_rate_analysis'] - if lr_analysis.get('optimal_range'): - opt_range = lr_analysis['optimal_range'] - recommendations.append(f"📈 LEARNING RATE GUIDANCE:") - recommendations.append(f" • Start with: {opt_range[0]:.2e}") - recommendations.append(f" • Safe upper bound: {opt_range[1]:.2e}") - recommendations.append(" • Use learning rate scheduling for best results") - - # Convergence recommendations - if 'convergence_comparison' in analysis_results: - comparison = analysis_results['convergence_comparison'] - if 'recommendations' in comparison and 'summary' in comparison['recommendations']: - recommendations.append("🎯 CONVERGENCE OPTIMIZATION:") - for rec in comparison['recommendations']['summary']: - recommendations.append(f" • {rec}") - - # Production deployment recommendations - recommendations.append("🚀 PRODUCTION DEPLOYMENT:") - recommendations.append(" • Monitor gradient norms to detect training instability") - recommendations.append(" • Implement gradient clipping for large models") - recommendations.append(" • Use learning rate warmup for transformer architectures") - recommendations.append(" • Consider mixed precision training to reduce memory usage") - - # Scaling recommendations - recommendations.append("📊 SCALING CONSIDERATIONS:") - recommendations.append(" • Large batch training: Prefer SGD with linear learning rate scaling") - recommendations.append(" • Distributed training: Use synchronized optimizers") - recommendations.append(" • Memory-constrained: Choose SGD or use gradient accumulation") - recommendations.append(" • Fine-tuning: Use lower learning rates (10x-100x smaller)") - - # Monitoring recommendations - recommendations.append("📈 MONITORING & DEBUGGING:") - recommendations.append(" • Track loss smoothness to detect learning rate issues") - recommendations.append(" • Monitor gradient norms for explosion/vanishing detection") - recommendations.append(" • Log learning rate schedules for reproducibility") - recommendations.append(" • Profile memory usage to optimize batch sizes") - - return recommendations - ### END SOLUTION - - def _analyze_convergence_profile(self, optimizer_name: str, losses: List[float], - grad_norms: List[float], step_durations: List[float], - convergence_step: Optional[int]) -> Dict[str, Any]: - """ - Internal helper to analyze convergence profile data. - - Args: - optimizer_name: Name of the optimizer - losses: List of loss values over training - grad_norms: List of gradient norms over training - step_durations: List of step execution times - convergence_step: Step where convergence was detected (if any) - - Returns: - Analysis results dictionary - """ - ### BEGIN SOLUTION - analysis = { - 'optimizer_name': optimizer_name, - 'total_steps': len(losses), - 'convergence_step': convergence_step, - 'final_loss': losses[-1] if losses else float('inf'), - 'initial_loss': losses[0] if losses else float('inf'), - 'loss_reduction': 0.0, - 'convergence_rate': 0.0, - 'stability_score': 0.0, - 'average_step_time': 0.0, - 'gradient_health': 'unknown' - } - - if losses: - # Calculate loss reduction - initial_loss = losses[0] - final_loss = losses[-1] - analysis['loss_reduction'] = initial_loss - final_loss - - # Calculate convergence rate (loss reduction per step) - if len(losses) > 1: - analysis['convergence_rate'] = analysis['loss_reduction'] / len(losses) - - # Calculate stability (inverse of coefficient of variation) - if len(losses) >= self.stability_window: - recent_losses = losses[-self.stability_window:] - mean_loss = np.mean(recent_losses) - std_loss = np.std(recent_losses) - analysis['stability_score'] = 1.0 / (1.0 + std_loss / (mean_loss + 1e-8)) - - # Average step time - if step_durations: - analysis['average_step_time'] = np.mean(step_durations) - - # Gradient health assessment - if grad_norms: - max_grad_norm = max(grad_norms) - avg_grad_norm = np.mean(grad_norms) - - if max_grad_norm > self.gradient_explosion_threshold: - analysis['gradient_health'] = 'exploding' - elif avg_grad_norm < 1e-8: - analysis['gradient_health'] = 'vanishing' - elif np.std(grad_norms) / (avg_grad_norm + 1e-8) > 2.0: - analysis['gradient_health'] = 'unstable' - else: - analysis['gradient_health'] = 'healthy' - - return analysis - ### END SOLUTION - -# %% ../../modules/08_optimizers/optimizers_dev.ipynb 32 -class AdvancedOptimizerFeatures: - """ - Advanced optimizer features for production ML systems. - - Implements production-ready optimizer enhancements: - - Gradient clipping for stability - - Learning rate warmup strategies - - Gradient accumulation for large batches - - Mixed precision optimization patterns - - Distributed optimizer synchronization - """ - - def __init__(self): - """ - Initialize advanced optimizer features. - - TODO: Implement advanced features initialization. - - PRODUCTION CONTEXT: - These features are essential for: - - Training large language models (GPT, BERT) - - Computer vision at scale (ImageNet, COCO) - - Distributed training across multiple GPUs - - Memory-efficient training with limited resources - - IMPLEMENTATION HINTS: - - Initialize gradient clipping parameters - - Set up warmup scheduling state - - Prepare accumulation buffers - - Configure synchronization patterns - """ - ### BEGIN SOLUTION - # Gradient clipping - self.max_grad_norm = 1.0 - self.clip_enabled = False - - # Learning rate warmup - self.warmup_steps = 0 - self.warmup_factor = 0.1 - self.base_lr = 0.001 - - # Gradient accumulation - self.accumulation_steps = 1 - self.accumulated_gradients = {} - self.accumulation_count = 0 - - # Mixed precision simulation - self.use_fp16 = False - self.loss_scale = 1.0 - self.dynamic_loss_scaling = False - - # Distributed training simulation - self.world_size = 1 - self.rank = 0 - ### END SOLUTION - - def apply_gradient_clipping(self, optimizer: Union[SGD, Adam], max_norm: float = 1.0) -> float: - """ - Apply gradient clipping to prevent gradient explosion. - - Args: - optimizer: Optimizer with parameters to clip - max_norm: Maximum allowed gradient norm - - Returns: - Actual gradient norm before clipping - - TODO: Implement gradient clipping. - - APPROACH: - 1. Calculate total gradient norm across all parameters - 2. If norm exceeds max_norm, scale all gradients down - 3. Apply scaling factor to maintain gradient direction - 4. Return original norm for monitoring - - MATHEMATICAL FORMULATION: - total_norm = sqrt(sum(param_grad_norm^2 for all params)) - if total_norm > max_norm: - clip_factor = max_norm / total_norm - for each param: param.grad *= clip_factor - - PRODUCTION VALUE: - Gradient clipping is essential for: - - Training RNNs and Transformers - - Preventing training instability - - Enabling higher learning rates - - Improving convergence reliability - - IMPLEMENTATION HINTS: - - Calculate global gradient norm - - Apply uniform scaling to all gradients - - Preserve gradient directions - - Return unclipped norm for logging - """ - ### BEGIN SOLUTION - # Calculate total gradient norm - total_norm = 0.0 - param_count = 0 - - for param in optimizer.parameters: - if param.grad is not None: - grad_data = param.grad.data.data - if hasattr(grad_data, 'flatten'): - param_norm = np.linalg.norm(grad_data.flatten()) - else: - param_norm = abs(float(grad_data)) - total_norm += param_norm ** 2 - param_count += 1 - - if param_count > 0: - total_norm = total_norm ** 0.5 - else: - return 0.0 - - # Apply clipping if necessary - if total_norm > max_norm: - clip_factor = max_norm / total_norm - - for param in optimizer.parameters: - if param.grad is not None: - grad_data = param.grad.data.data - clipped_grad = grad_data * clip_factor - param.grad.data = Tensor(clipped_grad) - - return total_norm - ### END SOLUTION - - def apply_warmup_schedule(self, optimizer: Union[SGD, Adam], step: int, - warmup_steps: int, base_lr: float) -> float: - """ - Apply learning rate warmup schedule. - - Args: - optimizer: Optimizer to apply warmup to - step: Current training step - warmup_steps: Number of warmup steps - base_lr: Target learning rate after warmup - - Returns: - Current learning rate - - TODO: Implement learning rate warmup. - - APPROACH: - 1. If step < warmup_steps: gradually increase learning rate - 2. Use linear or polynomial warmup schedule - 3. Update optimizer's learning rate - 4. Return current learning rate for logging - - WARMUP STRATEGIES: - - Linear: lr = base_lr * (step / warmup_steps) - - Polynomial: lr = base_lr * ((step / warmup_steps) ^ power) - - Constant: lr = base_lr * warmup_factor for warmup_steps - - PRODUCTION VALUE: - Warmup prevents: - - Early training instability - - Poor initialization effects - - Gradient explosion at start - - Suboptimal convergence paths - - IMPLEMENTATION HINTS: - - Handle step=0 case (avoid division by zero) - - Use linear warmup for simplicity - - Update optimizer.learning_rate directly - - Smoothly transition to base learning rate - """ - ### BEGIN SOLUTION - if step < warmup_steps and warmup_steps > 0: - # Linear warmup - warmup_factor = step / warmup_steps - current_lr = base_lr * warmup_factor - else: - # After warmup, use base learning rate - current_lr = base_lr - - # Update optimizer learning rate - optimizer.learning_rate = current_lr - - return current_lr - ### END SOLUTION - - def accumulate_gradients(self, optimizer: Union[SGD, Adam], accumulation_steps: int) -> bool: - """ - Accumulate gradients to simulate larger batch sizes. - - Args: - optimizer: Optimizer with parameters to accumulate - accumulation_steps: Number of steps to accumulate before update - - Returns: - True if ready to perform optimizer step, False otherwise - - TODO: Implement gradient accumulation. - - APPROACH: - 1. Add current gradients to accumulated gradient buffers - 2. Increment accumulation counter - 3. If counter reaches accumulation_steps: - a. Average accumulated gradients - b. Set as current gradients - c. Return True (ready for optimizer step) - d. Reset accumulation - 4. Otherwise return False (continue accumulating) - - MATHEMATICAL FORMULATION: - accumulated_grad += current_grad - if accumulation_count == accumulation_steps: - final_grad = accumulated_grad / accumulation_steps - reset accumulation - return True - - PRODUCTION VALUE: - Gradient accumulation enables: - - Large effective batch sizes on limited memory - - Training large models on small GPUs - - Consistent training across different hardware - - Memory-efficient distributed training - - IMPLEMENTATION HINTS: - - Store accumulated gradients per parameter - - Use parameter id() as key for tracking - - Average gradients before optimizer step - - Reset accumulation after each update - """ - ### BEGIN SOLUTION - # Initialize accumulation if first time - if not hasattr(self, 'accumulation_count'): - self.accumulation_count = 0 - self.accumulated_gradients = {} - - # Accumulate gradients - for param in optimizer.parameters: - if param.grad is not None: - param_id = id(param) - grad_data = param.grad.data.data - - if param_id not in self.accumulated_gradients: - self.accumulated_gradients[param_id] = np.zeros_like(grad_data) - - self.accumulated_gradients[param_id] += grad_data - - self.accumulation_count += 1 - - # Check if ready to update - if self.accumulation_count >= accumulation_steps: - # Average accumulated gradients and set as current gradients - for param in optimizer.parameters: - if param.grad is not None: - param_id = id(param) - if param_id in self.accumulated_gradients: - averaged_grad = self.accumulated_gradients[param_id] / accumulation_steps - param.grad.data = Tensor(averaged_grad) - - # Reset accumulation - self.accumulation_count = 0 - self.accumulated_gradients = {} - - return True # Ready for optimizer step - - return False # Continue accumulating - ### END SOLUTION - - def simulate_mixed_precision(self, optimizer: Union[SGD, Adam], loss_scale: float = 1.0) -> bool: - """ - Simulate mixed precision training effects. - - Args: - optimizer: Optimizer to apply mixed precision to - loss_scale: Loss scaling factor for gradient preservation - - Returns: - True if gradients are valid (no overflow), False if overflow detected - - TODO: Implement mixed precision simulation. - - APPROACH: - 1. Scale gradients by loss_scale factor - 2. Check for gradient overflow (inf or nan values) - 3. If overflow detected, skip optimizer step - 4. If valid, descale gradients before optimizer step - 5. Return overflow status - - MIXED PRECISION CONCEPTS: - - Use FP16 for forward pass (memory savings) - - Use FP32 for backward pass (numerical stability) - - Scale loss to prevent gradient underflow - - Check for overflow before optimization - - PRODUCTION VALUE: - Mixed precision provides: - - 50% memory reduction - - Faster training on modern GPUs - - Maintained numerical stability - - Automatic overflow detection - - IMPLEMENTATION HINTS: - - Scale gradients by loss_scale - - Check for inf/nan in gradients - - Descale before optimizer step - - Return overflow status for dynamic scaling - """ - ### BEGIN SOLUTION - # Check for gradient overflow before scaling - has_overflow = False - - for param in optimizer.parameters: - if param.grad is not None: - grad_data = param.grad.data.data - if hasattr(grad_data, 'flatten'): - grad_flat = grad_data.flatten() - if np.any(np.isinf(grad_flat)) or np.any(np.isnan(grad_flat)): - has_overflow = True - break - else: - if np.isinf(grad_data) or np.isnan(grad_data): - has_overflow = True - break - - if has_overflow: - # Zero gradients to prevent corruption - for param in optimizer.parameters: - if param.grad is not None: - param.grad = None - return False # Overflow detected - - # Descale gradients (simulate unscaling from FP16) - if loss_scale > 1.0: - for param in optimizer.parameters: - if param.grad is not None: - grad_data = param.grad.data.data - descaled_grad = grad_data / loss_scale - param.grad.data = Tensor(descaled_grad) - - return True # No overflow, safe to proceed - ### END SOLUTION - - def simulate_distributed_sync(self, optimizer: Union[SGD, Adam], world_size: int = 1) -> None: - """ - Simulate distributed training gradient synchronization. - - Args: - optimizer: Optimizer with gradients to synchronize - world_size: Number of distributed processes - - TODO: Implement distributed gradient synchronization simulation. - - APPROACH: - 1. Simulate all-reduce operation on gradients - 2. Average gradients across all processes - 3. Update local gradients with synchronized values - 4. Handle communication overhead simulation - - DISTRIBUTED CONCEPTS: - - All-reduce: Combine gradients from all GPUs - - Averaging: Divide by world_size for consistency - - Synchronization: Ensure all GPUs have same gradients - - Communication: Network overhead for gradient sharing - - PRODUCTION VALUE: - Distributed training enables: - - Scaling to multiple GPUs/nodes - - Training large models efficiently - - Reduced training time - - Consistent convergence across devices - - IMPLEMENTATION HINTS: - - Simulate averaging by keeping gradients unchanged - - Add small noise to simulate communication variance - - Scale learning rate by world_size if needed - - Log synchronization overhead - """ - ### BEGIN SOLUTION - if world_size <= 1: - return # No synchronization needed for single process - - # Simulate all-reduce operation (averaging gradients) - for param in optimizer.parameters: - if param.grad is not None: - grad_data = param.grad.data.data - - # In real distributed training, gradients would be averaged across all processes - # Here we simulate this by keeping gradients unchanged (already "averaged") - # In practice, this would involve MPI/NCCL communication - - # Simulate communication noise (very small) - if hasattr(grad_data, 'shape'): - noise = np.random.normal(0, 1e-10, grad_data.shape) - synchronized_grad = grad_data + noise - else: - noise = np.random.normal(0, 1e-10) - synchronized_grad = grad_data + noise - - param.grad.data = Tensor(synchronized_grad) - - # In distributed training, learning rate is often scaled by world_size - # to maintain effective learning rate with larger batch sizes - if hasattr(optimizer, 'base_learning_rate'): - optimizer.learning_rate = optimizer.base_learning_rate * world_size - ### END SOLUTION diff --git a/tinytorch/core/setup.py b/tinytorch/core/setup.py deleted file mode 100644 index 52f94deb..00000000 --- a/tinytorch/core/setup.py +++ /dev/null @@ -1,166 +0,0 @@ -# ╔═══════════════════════════════════════════════════════════════════════════════╗ -# ║ 🚨 CRITICAL WARNING 🚨 ║ -# ║ AUTOGENERATED! DO NOT EDIT! ║ -# ║ ║ -# ║ This file is AUTOMATICALLY GENERATED from source modules. ║ -# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║ -# ║ ║ -# ║ ✅ TO EDIT: modules/source/XX_setup/setup_dev.py ║ -# ║ ✅ TO EXPORT: Run 'tito module complete ' ║ -# ║ ║ -# ║ 🛡️ STUDENT PROTECTION: This file contains optimized implementations. ║ -# ║ Editing it directly may break module functionality and training. ║ -# ║ ║ -# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║ -# ║ happens! The tinytorch/ directory is just the compiled output. ║ -# ╚═══════════════════════════════════════════════════════════════════════════════╝ - -# %% auto 0 -__all__ = ['personal_info', 'system_info'] - -# %% ../../modules/source/01_setup/setup_dev.ipynb 1 -import sys -import platform -import psutil -from typing import Dict, Any - -# %% ../../modules/source/01_setup/setup_dev.ipynb 7 -def personal_info() -> Dict[str, str]: - """ - Return personal information for this TinyTorch installation. - - This function configures your personal TinyTorch installation with your identity. - It's the foundation of proper ML engineering practices - every system needs - to know who built it and how to contact them. - - TODO: Implement personal information configuration. - - STEP-BY-STEP IMPLEMENTATION: - 1. Create a dictionary with your personal details - 2. Include all required keys: developer, email, institution, system_name, version - 3. Use your actual information (not placeholder text) - 4. Make system_name unique and descriptive - 5. Keep version as '1.0.0' for now - - EXAMPLE USAGE: - ```python - # Get your personal configuration - info = personal_info() - print(info['developer']) # Expected: "Your Name" (not placeholder) - print(info['email']) # Expected: "you@domain.com" (valid email) - print(info['system_name']) # Expected: "YourName-Dev" (unique identifier) - print(info) # Expected: Complete dict with 5 fields - # Output: { - # 'developer': 'Your Name', - # 'email': 'you@domain.com', - # 'institution': 'Your Institution', - # 'system_name': 'YourName-TinyTorch-Dev', - # 'version': '1.0.0' - # } - ``` - - IMPLEMENTATION HINTS: - - Replace the example with your real information - - Use a descriptive system_name (e.g., 'YourName-TinyTorch-Dev') - - Keep email format valid (contains @ and domain) - - Make sure all values are strings - - Consider how this info will be used in debugging and collaboration - - LEARNING CONNECTIONS: - - This is like the 'author' field in Git commits - - Similar to maintainer info in Docker images - - Parallels author info in Python packages - - Foundation for professional ML development - """ - ### BEGIN SOLUTION - return { - 'developer': 'Student Name', - 'email': 'student@university.edu', - 'institution': 'University Name', - 'system_name': 'StudentName-TinyTorch-Dev', - 'version': '1.0.0' - } - ### END SOLUTION - -# %% ../../modules/source/01_setup/setup_dev.ipynb 12 -def system_info() -> Dict[str, Any]: - """ - Query and return system information for this TinyTorch installation. - - This function gathers crucial hardware and software information that affects - ML performance, compatibility, and debugging. It's the foundation of - hardware-aware ML systems. - - TODO: Implement system information queries. - - STEP-BY-STEP IMPLEMENTATION: - 1. Get Python version using sys.version_info - 2. Get platform using platform.system() - 3. Get architecture using platform.machine() - 4. Get CPU count using psutil.cpu_count() - 5. Get memory using psutil.virtual_memory().total - 6. Convert memory from bytes to GB (divide by 1024^3) - 7. Return all information in a dictionary - - EXAMPLE USAGE: - ```python - # Query system information - sys_info = system_info() - print(f"Python: {sys_info['python_version']}") # Expected: "3.x.x" - print(f"Platform: {sys_info['platform']}") # Expected: "Darwin"/"Linux"/"Windows" - print(f"CPUs: {sys_info['cpu_count']}") # Expected: 4, 8, 16, etc. - print(f"Memory: {sys_info['memory_gb']} GB") # Expected: 8.0, 16.0, 32.0, etc. - - # Full output example: - print(sys_info) - # Expected: { - # 'python_version': '3.9.7', - # 'platform': 'Darwin', - # 'architecture': 'arm64', - # 'cpu_count': 8, - # 'memory_gb': 16.0 - # } - ``` - - IMPLEMENTATION HINTS: - - Use f-string formatting for Python version: f"{major}.{minor}.{micro}" - - Memory conversion: bytes / (1024^3) = GB - - Round memory to 1 decimal place for readability - - Make sure data types are correct (strings for text, int for cpu_count, float for memory_gb) - - LEARNING CONNECTIONS: - - This is like `torch.cuda.is_available()` in PyTorch - - Similar to system info in MLflow experiment tracking - - Parallels hardware detection in TensorFlow - - Foundation for performance optimization in ML systems - - PERFORMANCE IMPLICATIONS: - - cpu_count affects parallel processing capabilities - - memory_gb determines maximum model and batch sizes - - platform affects file system and process management - - architecture influences numerical precision and optimization - """ - ### BEGIN SOLUTION - # Get Python version - version_info = sys.version_info - python_version = f"{version_info.major}.{version_info.minor}.{version_info.micro}" - - # Get platform information - platform_name = platform.system() - architecture = platform.machine() - - # Get CPU information - cpu_count = psutil.cpu_count() - - # Get memory information (convert bytes to GB) - memory_bytes = psutil.virtual_memory().total - memory_gb = round(memory_bytes / (1024**3), 1) - - return { - 'python_version': python_version, - 'platform': platform_name, - 'architecture': architecture, - 'cpu_count': cpu_count, - 'memory_gb': memory_gb - } - ### END SOLUTION diff --git a/tinytorch/core/spatial.py b/tinytorch/core/spatial.py deleted file mode 100644 index ae91db3f..00000000 --- a/tinytorch/core/spatial.py +++ /dev/null @@ -1,611 +0,0 @@ -# ╔═══════════════════════════════════════════════════════════════════════════════╗ -# ║ 🚨 CRITICAL WARNING 🚨 ║ -# ║ AUTOGENERATED! DO NOT EDIT! ║ -# ║ ║ -# ║ This file is AUTOMATICALLY GENERATED from source modules. ║ -# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║ -# ║ ║ -# ║ ✅ TO EDIT: modules/source/06_spatial/spatial_dev.py ║ -# ║ ✅ TO EXPORT: Run 'tito module complete ' ║ -# ║ ║ -# ║ 🛡️ STUDENT PROTECTION: This file contains optimized implementations. ║ -# ║ Editing it directly may break module functionality and training. ║ -# ║ ║ -# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║ -# ║ happens! The tinytorch/ directory is just the compiled output. ║ -# ╚═══════════════════════════════════════════════════════════════════════════════╝ -# %% auto 0 -__all__ = ['Conv2d', 'MaxPool2d', 'AvgPool2d', 'SimpleCNN'] - -# %% ../../modules/source/09_spatial/spatial_dev.ipynb 1 -import numpy as np -import sys -import os -import time - -# Smart import system for development and production compatibility -if 'tinytorch' in sys.modules: - # Production: Import from installed package - from tinytorch.core.tensor import Tensor - from tinytorch.core.layers import Module -else: - # Development: Use simplified local implementations to avoid import loops - - # Simplified Tensor class for development - class Tensor: - """Simplified tensor for spatial operations development.""" - - def __init__(self, data, requires_grad=False): - self.data = np.array(data, dtype=np.float32) - self.shape = self.data.shape - self.requires_grad = requires_grad - self.grad = None - - def __repr__(self): - return f"Tensor(shape={self.shape}, data=\n{self.data})" - - def __add__(self, other): - if isinstance(other, Tensor): - return Tensor(self.data + other.data) - return Tensor(self.data + other) - - def __mul__(self, other): - if isinstance(other, Tensor): - return Tensor(self.data * other.data) - return Tensor(self.data * other) - - def sum(self): - return Tensor(np.sum(self.data)) - - def mean(self): - return Tensor(np.mean(self.data)) - - # Create a simple Module base class for inheritance - class Module: - """Simple base class for neural network modules.""" - def __init__(self): - pass - - def forward(self, x): - raise NotImplementedError("Subclasses must implement forward()") - - def parameters(self): - """Return list of parameters for this module.""" - params = [] - for attr_name in dir(self): - attr = getattr(self, attr_name) - if hasattr(attr, 'data') and hasattr(attr, 'requires_grad'): - params.append(attr) - return params - -# %% ../../modules/source/09_spatial/spatial_dev.ipynb 6 -class Conv2d(Module): - """ - 2D Convolution layer for spatial feature extraction. - - Implements convolution with explicit loops to demonstrate - computational complexity and memory access patterns. - - Args: - in_channels: Number of input channels - out_channels: Number of output feature maps - kernel_size: Size of convolution kernel (int or tuple) - stride: Stride of convolution (default: 1) - padding: Zero-padding added to input (default: 0) - bias: Whether to add learnable bias (default: True) - """ - - def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=True): - """ - Initialize Conv2d layer with proper weight initialization. - - TODO: Complete Conv2d initialization - - APPROACH: - 1. Store hyperparameters (channels, kernel_size, stride, padding) - 2. Initialize weights using He initialization for ReLU compatibility - 3. Initialize bias (if enabled) to zeros - 4. Use proper shapes: weight (out_channels, in_channels, kernel_h, kernel_w) - - WEIGHT INITIALIZATION: - - He init: std = sqrt(2 / (in_channels * kernel_h * kernel_w)) - - This prevents vanishing/exploding gradients with ReLU - - HINT: Convert kernel_size to tuple if it's an integer - """ - super().__init__() - - ### BEGIN SOLUTION - self.in_channels = in_channels - self.out_channels = out_channels - - # Handle kernel_size as int or tuple - if isinstance(kernel_size, int): - self.kernel_size = (kernel_size, kernel_size) - else: - self.kernel_size = kernel_size - - self.stride = stride - self.padding = padding - - # He initialization for ReLU networks - kernel_h, kernel_w = self.kernel_size - fan_in = in_channels * kernel_h * kernel_w - std = np.sqrt(2.0 / fan_in) - - # Weight shape: (out_channels, in_channels, kernel_h, kernel_w) - self.weight = Tensor(np.random.normal(0, std, - (out_channels, in_channels, kernel_h, kernel_w))) - - # Bias initialization - if bias: - self.bias = Tensor(np.zeros(out_channels)) - else: - self.bias = None - ### END SOLUTION - - def forward(self, x): - """ - Forward pass through Conv2d layer. - - TODO: Implement convolution with explicit loops - - APPROACH: - 1. Extract input dimensions and validate - 2. Calculate output dimensions - 3. Apply padding if needed - 4. Implement 6 nested loops for full convolution - 5. Add bias if present - - LOOP STRUCTURE: - for batch in range(batch_size): - for out_ch in range(out_channels): - for out_h in range(out_height): - for out_w in range(out_width): - for k_h in range(kernel_height): - for k_w in range(kernel_width): - for in_ch in range(in_channels): - # Accumulate: out += input * weight - - EXAMPLE: - >>> conv = Conv2d(3, 16, kernel_size=3, padding=1) - >>> x = Tensor(np.random.randn(2, 3, 32, 32)) # batch=2, RGB, 32x32 - >>> out = conv(x) - >>> print(out.shape) # Should be (2, 16, 32, 32) - - HINTS: - - Handle padding by creating padded input array - - Watch array bounds in inner loops - - Accumulate products for each output position - """ - ### BEGIN SOLUTION - # Input validation and shape extraction - if len(x.shape) != 4: - raise ValueError(f"Expected 4D input (batch, channels, height, width), got {x.shape}") - - batch_size, in_channels, in_height, in_width = x.shape - out_channels = self.out_channels - kernel_h, kernel_w = self.kernel_size - - # Calculate output dimensions - out_height = (in_height + 2 * self.padding - kernel_h) // self.stride + 1 - out_width = (in_width + 2 * self.padding - kernel_w) // self.stride + 1 - - # Apply padding if needed - if self.padding > 0: - padded_input = np.pad(x.data, - ((0, 0), (0, 0), (self.padding, self.padding), (self.padding, self.padding)), - mode='constant', constant_values=0) - else: - padded_input = x.data - - # Initialize output - output = np.zeros((batch_size, out_channels, out_height, out_width)) - - # Explicit 6-nested loop convolution to show complexity - for b in range(batch_size): - for out_ch in range(out_channels): - for out_h in range(out_height): - for out_w in range(out_width): - # Calculate input region for this output position - in_h_start = out_h * self.stride - in_w_start = out_w * self.stride - - # Accumulate convolution result - conv_sum = 0.0 - for k_h in range(kernel_h): - for k_w in range(kernel_w): - for in_ch in range(in_channels): - # Get input and weight values - input_val = padded_input[b, in_ch, - in_h_start + k_h, - in_w_start + k_w] - weight_val = self.weight.data[out_ch, in_ch, k_h, k_w] - - # Accumulate - conv_sum += input_val * weight_val - - # Store result - output[b, out_ch, out_h, out_w] = conv_sum - - # Add bias if present - if self.bias is not None: - # Broadcast bias across spatial dimensions - for out_ch in range(out_channels): - output[:, out_ch, :, :] += self.bias.data[out_ch] - - return Tensor(output) - ### END SOLUTION - - def parameters(self): - """Return trainable parameters.""" - params = [self.weight] - if self.bias is not None: - params.append(self.bias) - return params - - def __call__(self, x): - """Enable model(x) syntax.""" - return self.forward(x) - -# %% ../../modules/source/09_spatial/spatial_dev.ipynb 11 -class MaxPool2d(Module): - """ - 2D Max Pooling layer for spatial dimension reduction. - - Applies maximum operation over spatial windows, preserving - the strongest activations while reducing computational load. - - Args: - kernel_size: Size of pooling window (int or tuple) - stride: Stride of pooling operation (default: same as kernel_size) - padding: Zero-padding added to input (default: 0) - """ - - def __init__(self, kernel_size, stride=None, padding=0): - """ - Initialize MaxPool2d layer. - - TODO: Store pooling parameters - - APPROACH: - 1. Convert kernel_size to tuple if needed - 2. Set stride to kernel_size if not provided (non-overlapping) - 3. Store padding parameter - - HINT: Default stride equals kernel_size for non-overlapping windows - """ - super().__init__() - - ### BEGIN SOLUTION - # Handle kernel_size as int or tuple - if isinstance(kernel_size, int): - self.kernel_size = (kernel_size, kernel_size) - else: - self.kernel_size = kernel_size - - # Default stride equals kernel_size (non-overlapping) - if stride is None: - self.stride = self.kernel_size[0] - else: - self.stride = stride - - self.padding = padding - ### END SOLUTION - - def forward(self, x): - """ - Forward pass through MaxPool2d layer. - - TODO: Implement max pooling with explicit loops - - APPROACH: - 1. Extract input dimensions - 2. Calculate output dimensions - 3. Apply padding if needed - 4. Implement nested loops for pooling windows - 5. Find maximum value in each window - - LOOP STRUCTURE: - for batch in range(batch_size): - for channel in range(channels): - for out_h in range(out_height): - for out_w in range(out_width): - # Find max in window [in_h:in_h+k_h, in_w:in_w+k_w] - max_val = -infinity - for k_h in range(kernel_height): - for k_w in range(kernel_width): - max_val = max(max_val, input[...]) - - EXAMPLE: - >>> pool = MaxPool2d(kernel_size=2, stride=2) - >>> x = Tensor(np.random.randn(1, 3, 8, 8)) - >>> out = pool(x) - >>> print(out.shape) # Should be (1, 3, 4, 4) - - HINTS: - - Initialize max_val to negative infinity - - Handle stride correctly when accessing input - - No parameters to update (pooling has no weights) - """ - ### BEGIN SOLUTION - # Input validation and shape extraction - if len(x.shape) != 4: - raise ValueError(f"Expected 4D input (batch, channels, height, width), got {x.shape}") - - batch_size, channels, in_height, in_width = x.shape - kernel_h, kernel_w = self.kernel_size - - # Calculate output dimensions - out_height = (in_height + 2 * self.padding - kernel_h) // self.stride + 1 - out_width = (in_width + 2 * self.padding - kernel_w) // self.stride + 1 - - # Apply padding if needed - if self.padding > 0: - padded_input = np.pad(x.data, - ((0, 0), (0, 0), (self.padding, self.padding), (self.padding, self.padding)), - mode='constant', constant_values=-np.inf) - else: - padded_input = x.data - - # Initialize output - output = np.zeros((batch_size, channels, out_height, out_width)) - - # Explicit nested loop max pooling - for b in range(batch_size): - for c in range(channels): - for out_h in range(out_height): - for out_w in range(out_width): - # Calculate input region for this output position - in_h_start = out_h * self.stride - in_w_start = out_w * self.stride - - # Find maximum in window - max_val = -np.inf - for k_h in range(kernel_h): - for k_w in range(kernel_w): - input_val = padded_input[b, c, - in_h_start + k_h, - in_w_start + k_w] - max_val = max(max_val, input_val) - - # Store result - output[b, c, out_h, out_w] = max_val - - return Tensor(output) - ### END SOLUTION - - def parameters(self): - """Return empty list (pooling has no parameters).""" - return [] - - def __call__(self, x): - """Enable model(x) syntax.""" - return self.forward(x) - -# %% ../../modules/source/09_spatial/spatial_dev.ipynb 13 -class AvgPool2d(Module): - """ - 2D Average Pooling layer for spatial dimension reduction. - - Applies average operation over spatial windows, smoothing - features while reducing computational load. - - Args: - kernel_size: Size of pooling window (int or tuple) - stride: Stride of pooling operation (default: same as kernel_size) - padding: Zero-padding added to input (default: 0) - """ - - def __init__(self, kernel_size, stride=None, padding=0): - """ - Initialize AvgPool2d layer. - - TODO: Store pooling parameters (same as MaxPool2d) - - APPROACH: - 1. Convert kernel_size to tuple if needed - 2. Set stride to kernel_size if not provided - 3. Store padding parameter - """ - super().__init__() - - ### BEGIN SOLUTION - # Handle kernel_size as int or tuple - if isinstance(kernel_size, int): - self.kernel_size = (kernel_size, kernel_size) - else: - self.kernel_size = kernel_size - - # Default stride equals kernel_size (non-overlapping) - if stride is None: - self.stride = self.kernel_size[0] - else: - self.stride = stride - - self.padding = padding - ### END SOLUTION - - def forward(self, x): - """ - Forward pass through AvgPool2d layer. - - TODO: Implement average pooling with explicit loops - - APPROACH: - 1. Similar structure to MaxPool2d - 2. Instead of max, compute average of window - 3. Divide sum by window area for true average - - LOOP STRUCTURE: - for batch in range(batch_size): - for channel in range(channels): - for out_h in range(out_height): - for out_w in range(out_width): - # Compute average in window - window_sum = 0 - for k_h in range(kernel_height): - for k_w in range(kernel_width): - window_sum += input[...] - avg_val = window_sum / (kernel_height * kernel_width) - - HINT: Remember to divide by window area to get true average - """ - ### BEGIN SOLUTION - # Input validation and shape extraction - if len(x.shape) != 4: - raise ValueError(f"Expected 4D input (batch, channels, height, width), got {x.shape}") - - batch_size, channels, in_height, in_width = x.shape - kernel_h, kernel_w = self.kernel_size - - # Calculate output dimensions - out_height = (in_height + 2 * self.padding - kernel_h) // self.stride + 1 - out_width = (in_width + 2 * self.padding - kernel_w) // self.stride + 1 - - # Apply padding if needed - if self.padding > 0: - padded_input = np.pad(x.data, - ((0, 0), (0, 0), (self.padding, self.padding), (self.padding, self.padding)), - mode='constant', constant_values=0) - else: - padded_input = x.data - - # Initialize output - output = np.zeros((batch_size, channels, out_height, out_width)) - - # Explicit nested loop average pooling - for b in range(batch_size): - for c in range(channels): - for out_h in range(out_height): - for out_w in range(out_width): - # Calculate input region for this output position - in_h_start = out_h * self.stride - in_w_start = out_w * self.stride - - # Compute sum in window - window_sum = 0.0 - for k_h in range(kernel_h): - for k_w in range(kernel_w): - input_val = padded_input[b, c, - in_h_start + k_h, - in_w_start + k_w] - window_sum += input_val - - # Compute average - avg_val = window_sum / (kernel_h * kernel_w) - - # Store result - output[b, c, out_h, out_w] = avg_val - - return Tensor(output) - ### END SOLUTION - - def parameters(self): - """Return empty list (pooling has no parameters).""" - return [] - - def __call__(self, x): - """Enable model(x) syntax.""" - return self.forward(x) - -# %% ../../modules/source/09_spatial/spatial_dev.ipynb 21 -class SimpleCNN(Module): - """ - Simple CNN demonstrating spatial operations integration. - - Architecture: - - Conv2d(3→16, 3×3) + ReLU + MaxPool(2×2) - - Conv2d(16→32, 3×3) + ReLU + MaxPool(2×2) - - Flatten + Linear(features→num_classes) - """ - - def __init__(self, num_classes=10): - """ - Initialize SimpleCNN. - - TODO: Build CNN architecture with spatial and dense layers - - APPROACH: - 1. Conv layer 1: 3 → 16 channels, 3×3 kernel, padding=1 - 2. Pool layer 1: 2×2 max pooling - 3. Conv layer 2: 16 → 32 channels, 3×3 kernel, padding=1 - 4. Pool layer 2: 2×2 max pooling - 5. Calculate flattened size and add final linear layer - - HINT: For 32×32 input → 32→16→8→4 spatial reduction - Final feature size: 32 channels × 4×4 = 512 features - """ - super().__init__() - - ### BEGIN SOLUTION - # Convolutional layers - self.conv1 = Conv2d(in_channels=3, out_channels=16, kernel_size=3, padding=1) - self.pool1 = MaxPool2d(kernel_size=2, stride=2) - - self.conv2 = Conv2d(in_channels=16, out_channels=32, kernel_size=3, padding=1) - self.pool2 = MaxPool2d(kernel_size=2, stride=2) - - # Calculate flattened size - # Input: 32×32 → Conv1+Pool1: 16×16 → Conv2+Pool2: 8×8 - # Wait, let's recalculate: 32×32 → Pool1: 16×16 → Pool2: 8×8 - # Final: 32 channels × 8×8 = 2048 features - self.flattened_size = 32 * 8 * 8 - - # Import Linear layer (we'll implement a simple version) - # For now, we'll use a placeholder that we can replace - # This represents the final classification layer - self.num_classes = num_classes - self.flattened_size = 32 * 8 * 8 # Will be used when we add Linear layer - ### END SOLUTION - - def forward(self, x): - """ - Forward pass through SimpleCNN. - - TODO: Implement CNN forward pass - - APPROACH: - 1. Apply conv1 → ReLU → pool1 - 2. Apply conv2 → ReLU → pool2 - 3. Flatten spatial dimensions - 4. Apply final linear layer (when available) - - For now, return features before final linear layer - since we haven't imported Linear from layers module yet. - """ - ### BEGIN SOLUTION - # First conv block - x = self.conv1(x) - x = self.relu(x) # ReLU activation - x = self.pool1(x) - - # Second conv block - x = self.conv2(x) - x = self.relu(x) # ReLU activation - x = self.pool2(x) - - # Flatten for classification (reshape to 2D) - batch_size = x.shape[0] - x_flat = x.data.reshape(batch_size, -1) - - # Return flattened features - # In a complete implementation, this would go through a Linear layer - return Tensor(x_flat) - ### END SOLUTION - - def relu(self, x): - """Simple ReLU implementation for CNN.""" - return Tensor(np.maximum(0, x.data)) - - def parameters(self): - """Return all trainable parameters.""" - params = [] - params.extend(self.conv1.parameters()) - params.extend(self.conv2.parameters()) - # Linear layer parameters would be added here - return params - - def __call__(self, x): - """Enable model(x) syntax.""" - return self.forward(x) diff --git a/tinytorch/core/tensor.py b/tinytorch/core/tensor.py index 871ef8f3..fb786066 100644 --- a/tinytorch/core/tensor.py +++ b/tinytorch/core/tensor.py @@ -14,660 +14,466 @@ # ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║ # ║ happens! The tinytorch/ directory is just the compiled output. ║ # ╚═══════════════════════════════════════════════════════════════════════════════╝ - # %% auto 0 -__all__ = ['Tensor', 'Parameter'] +__all__ = ['Tensor'] -# %% ../../modules/02_tensor/tensor_dev.ipynb 1 +# %% ../../modules/source/01_tensor/tensor_dev.ipynb 1 import numpy as np -import sys -from typing import Union, Tuple, Optional, Any -# %% ../../modules/02_tensor/tensor_dev.ipynb 3 +# %% ../../modules/source/01_tensor/tensor_dev.ipynb 6 class Tensor: - """ - TinyTorch Tensor: N-dimensional array with ML operations. + """Educational tensor that grows with student knowledge. - The fundamental data structure for all TinyTorch operations. - Wraps NumPy arrays with ML-specific functionality. + This class starts simple but includes dormant features for future modules: + - requires_grad: Will be used for automatic differentiation (Module 05) + - grad: Will store computed gradients (Module 05) + - backward(): Will compute gradients (Module 05) + + For now, focus on: data, shape, and basic operations. """ - def __init__(self, data: Any, dtype: Optional[str] = None, requires_grad: bool = False): + def __init__(self, data, requires_grad=False): """ Create a new tensor from data. - Args: - data: Input data (scalar, list, or numpy array) - dtype: Data type ('float32', 'int32', etc.). Defaults to auto-detect. - requires_grad: Whether this tensor needs gradients for training. Defaults to False. + TODO: Initialize tensor attributes - TODO: Implement tensor creation with proper type handling. - - STEP-BY-STEP: - 1. Check if data is a scalar (int/float) - convert to numpy array - 2. Check if data is a list - convert to numpy array - 3. Check if data is already a numpy array - use as-is - 4. Apply dtype conversion if specified - 5. Store the result in self._data + APPROACH: + 1. Convert data to NumPy array - handles lists, scalars, etc. + 2. Store shape and size for quick access + 3. Set up gradient tracking (dormant until Module 05) EXAMPLE: - Tensor(5) → stores np.array(5) - Tensor([1, 2, 3]) → stores np.array([1, 2, 3]) - Tensor(np.array([1, 2, 3])) → stores the array directly + >>> tensor = Tensor([1, 2, 3]) + >>> print(tensor.data) + [1 2 3] + >>> print(tensor.shape) + (3,) - HINTS: - - Use isinstance() to check data types - - Use np.array() for conversion - - Handle dtype parameter for type conversion - - Store the array in self._data + HINT: np.array() handles type conversion automatically """ ### BEGIN SOLUTION - # Convert input to numpy array - if isinstance(data, (int, float, np.number)): - # Handle Python and NumPy scalars - if dtype is None: - # Auto-detect type: int for integers, float32 for floats - if isinstance(data, int) or (isinstance(data, np.number) and np.issubdtype(type(data), np.integer)): - dtype = 'int32' - else: - dtype = 'float32' - self._data = np.array(data, dtype=dtype) - elif isinstance(data, list): - # Let NumPy auto-detect type, then convert if needed - temp_array = np.array(data) - if dtype is None: - # Use NumPy's auto-detected type, but prefer float32 for floats - if temp_array.dtype == np.float64: - dtype = 'float32' - else: - dtype = str(temp_array.dtype) - self._data = np.array(data, dtype=dtype) - elif isinstance(data, np.ndarray): - # Already a numpy array - if dtype is None: - # Keep existing dtype, but prefer float32 for float64 - if data.dtype == np.float64: - dtype = 'float32' - else: - dtype = str(data.dtype) - self._data = data.astype(dtype) if dtype != data.dtype else data.copy() - elif isinstance(data, Tensor): - # Input is another Tensor - extract its data - if dtype is None: - # Keep existing dtype, but prefer float32 for float64 - if data.data.dtype == np.float64: - dtype = 'float32' - else: - dtype = str(data.data.dtype) - self._data = data.data.astype(dtype) if dtype != str(data.data.dtype) else data.data.copy() - else: - # Try to convert unknown types - self._data = np.array(data, dtype=dtype) + # Core tensor data - always present + self.data = np.array(data, dtype=np.float32) # Consistent float32 for ML + self.shape = self.data.shape + self.size = self.data.size + self.dtype = self.data.dtype - # Initialize gradient tracking attributes + # Gradient features (dormant until Module 05) self.requires_grad = requires_grad - self.grad = None if requires_grad else None - self._grad_fn = None - ### END SOLUTION - - @property - def data(self) -> np.ndarray: - """ - Access underlying numpy array. - - TODO: Return the stored numpy array. - - STEP-BY-STEP IMPLEMENTATION: - 1. Access the internal _data attribute - 2. Return the numpy array directly - 3. This provides access to underlying data for NumPy operations - - LEARNING CONNECTIONS: - Real-world relevance: - - PyTorch: tensor.numpy() converts to NumPy for visualization/analysis - - TensorFlow: tensor.numpy() enables integration with scientific Python - - Production: Data scientists need to access raw arrays for debugging - - Performance: Direct access avoids copying for read-only operations - - HINT: Return self._data (the array you stored in __init__) - """ - ### BEGIN SOLUTION - return self._data - ### END SOLUTION - - @data.setter - def data(self, value: Union[np.ndarray, 'Tensor']) -> None: - """ - Set the underlying data of the tensor. - - Args: - value: New data (numpy array or Tensor) - """ - if isinstance(value, Tensor): - self._data = value._data.copy() - else: - self._data = np.array(value) - - @property - def shape(self) -> Tuple[int, ...]: - """ - Get tensor shape. - - TODO: Return the shape of the stored numpy array. - - STEP-BY-STEP IMPLEMENTATION: - 1. Access the _data attribute (the NumPy array) - 2. Get the shape property from the NumPy array - 3. Return the shape tuple directly - - LEARNING CONNECTIONS: - Real-world relevance: - - Neural networks: Layer compatibility requires matching shapes - - Computer vision: Image shape (height, width, channels) determines architecture - - NLP: Sequence length and vocabulary size affect model design - - Debugging: Shape mismatches are the #1 cause of ML errors - - HINT: Use .shape attribute of the numpy array - EXAMPLE: Tensor([1, 2, 3]).shape should return (3,) - """ - ### BEGIN SOLUTION - return self._data.shape - ### END SOLUTION - - @property - def size(self) -> int: - """ - Get total number of elements. - - TODO: Return the total number of elements in the tensor. - - STEP-BY-STEP IMPLEMENTATION: - 1. Access the _data attribute (the NumPy array) - 2. Get the size property from the NumPy array - 3. Return the total element count as an integer - - LEARNING CONNECTIONS: - Real-world relevance: - - Memory planning: Calculate RAM requirements for large tensors - - Model architecture: Determine parameter counts for layers - - Performance optimization: Size affects computation time - - Batch processing: Total elements determines vectorization efficiency - - HINT: Use .size attribute of the numpy array - EXAMPLE: Tensor([1, 2, 3]).size should return 3 - """ - ### BEGIN SOLUTION - return self._data.size - ### END SOLUTION - - @property - def dtype(self) -> np.dtype: - """ - Get data type as numpy dtype. - - TODO: Return the data type of the stored numpy array. - - STEP-BY-STEP IMPLEMENTATION: - 1. Access the _data attribute (the NumPy array) - 2. Get the dtype property from the NumPy array - 3. Return the NumPy dtype object directly - - LEARNING CONNECTIONS: - Real-world relevance: - - Precision vs speed: float32 is faster, float64 more accurate - - Memory optimization: int8 uses 1/4 memory of int32 - - GPU compatibility: Some operations only work with specific types - - Model deployment: Mobile/edge devices prefer smaller data types - - HINT: Use .dtype attribute of the numpy array - EXAMPLE: Tensor([1, 2, 3]).dtype should return dtype('int32') - """ - ### BEGIN SOLUTION - return self._data.dtype - ### END SOLUTION - - def __repr__(self) -> str: - """ - String representation. - - TODO: Create a clear string representation of the tensor. - - STEP-BY-STEP IMPLEMENTATION: - 1. Convert the numpy array to a list using .tolist() - 2. Get shape and dtype information from properties - 3. Format as "Tensor([data], shape=shape, dtype=dtype)" - 4. Return the formatted string - - LEARNING CONNECTIONS: - Real-world relevance: - - Debugging: Clear tensor representation speeds debugging - - Jupyter notebooks: Good __repr__ improves data exploration - - Logging: Production systems log tensor info for monitoring - - Education: Students understand tensors better with clear output - - APPROACH: - 1. Convert the numpy array to a list for readable output - 2. Include the shape and dtype information - 3. Format: "Tensor([data], shape=shape, dtype=dtype)" - - EXAMPLE: - Tensor([1, 2, 3]) → "Tensor([1, 2, 3], shape=(3,), dtype=int32)" - - HINTS: - - Use .tolist() to convert numpy array to list - - Include shape and dtype information - - Keep format consistent and readable - """ - ### BEGIN SOLUTION - return f"Tensor({self._data.tolist()}, shape={self.shape}, dtype={self.dtype})" - ### END SOLUTION - - def add(self, other: 'Tensor') -> 'Tensor': - """ - Add two tensors element-wise. - - TODO: Implement tensor addition. - - STEP-BY-STEP IMPLEMENTATION: - 1. Extract numpy arrays from both tensors - 2. Use NumPy's + operator for element-wise addition - 3. Create a new Tensor object with the result - 4. Return the new tensor - - LEARNING CONNECTIONS: - Real-world relevance: - - Neural networks: Adding bias terms to linear layer outputs - - Residual connections: skip connections in ResNet architectures - - Gradient updates: Adding computed gradients to parameters - - Ensemble methods: Combining predictions from multiple models - - APPROACH: - 1. Add the numpy arrays using + - 2. Return a new Tensor with the result - 3. Handle broadcasting automatically - - EXAMPLE: - Tensor([1, 2]) + Tensor([3, 4]) → Tensor([4, 6]) - - HINTS: - - Use self._data + other._data - - Return Tensor(result) - - NumPy handles broadcasting automatically - """ - ### BEGIN SOLUTION - result = self._data + other._data - return Tensor(result) - ### END SOLUTION - - def multiply(self, other: 'Tensor') -> 'Tensor': - """ - Multiply two tensors element-wise. - - TODO: Implement tensor multiplication. - - STEP-BY-STEP IMPLEMENTATION: - 1. Extract numpy arrays from both tensors - 2. Use NumPy's * operator for element-wise multiplication - 3. Create a new Tensor object with the result - 4. Return the new tensor - - LEARNING CONNECTIONS: - Real-world relevance: - - Activation functions: Element-wise operations like ReLU masking - - Attention mechanisms: Element-wise scaling in transformer models - - Feature scaling: Multiplying features by learned scaling factors - - Gating: Element-wise gating in LSTM and GRU cells - - APPROACH: - 1. Multiply the numpy arrays using * - 2. Return a new Tensor with the result - 3. Handle broadcasting automatically - - EXAMPLE: - Tensor([1, 2]) * Tensor([3, 4]) → Tensor([3, 8]) - - HINTS: - - Use self._data * other._data - - Return Tensor(result) - - This is element-wise, not matrix multiplication - """ - ### BEGIN SOLUTION - result = self._data * other._data - return Tensor(result) - ### END SOLUTION - - def __add__(self, other: Union['Tensor', int, float]) -> 'Tensor': - """ - Addition operator: tensor + other - - TODO: Implement + operator for tensors. - - STEP-BY-STEP IMPLEMENTATION: - 1. Check if other is a Tensor object - 2. If Tensor, call the add() method directly - 3. If scalar, convert to Tensor then call add() - 4. Return the result from add() method - - LEARNING CONNECTIONS: - Real-world relevance: - - Natural syntax: tensor + scalar enables intuitive code - - Broadcasting: Adding scalars to tensors is common in ML - - Operator overloading: Python's magic methods enable math-like syntax - - API design: Clean interfaces reduce cognitive load for researchers - - APPROACH: - 1. If other is a Tensor, use tensor addition - 2. If other is a scalar, convert to Tensor first - 3. Return the result - - EXAMPLE: - Tensor([1, 2]) + Tensor([3, 4]) → Tensor([4, 6]) - Tensor([1, 2]) + 5 → Tensor([6, 7]) - """ - ### BEGIN SOLUTION - if isinstance(other, Tensor): - return self.add(other) - else: - return self.add(Tensor(other)) - ### END SOLUTION - - def __mul__(self, other: Union['Tensor', int, float]) -> 'Tensor': - """ - Multiplication operator: tensor * other - - TODO: Implement * operator for tensors. - - STEP-BY-STEP IMPLEMENTATION: - 1. Check if other is a Tensor object - 2. If Tensor, call the multiply() method directly - 3. If scalar, convert to Tensor then call multiply() - 4. Return the result from multiply() method - - LEARNING CONNECTIONS: - Real-world relevance: - - Scaling features: tensor * learning_rate for gradient updates - - Masking: tensor * mask for attention mechanisms - - Regularization: tensor * dropout_mask during training - - Normalization: tensor * scale_factor in batch normalization - - APPROACH: - 1. If other is a Tensor, use tensor multiplication - 2. If other is a scalar, convert to Tensor first - 3. Return the result - - EXAMPLE: - Tensor([1, 2]) * Tensor([3, 4]) → Tensor([3, 8]) - Tensor([1, 2]) * 3 → Tensor([3, 6]) - """ - ### BEGIN SOLUTION - if isinstance(other, Tensor): - return self.multiply(other) - else: - return self.multiply(Tensor(other)) - ### END SOLUTION - - def __sub__(self, other: Union['Tensor', int, float]) -> 'Tensor': - """ - Subtraction operator: tensor - other - - TODO: Implement - operator for tensors. - - STEP-BY-STEP IMPLEMENTATION: - 1. Check if other is a Tensor object - 2. If Tensor, subtract other._data from self._data - 3. If scalar, subtract scalar directly from self._data - 4. Create new Tensor with result and return - - LEARNING CONNECTIONS: - Real-world relevance: - - Gradient computation: parameter - learning_rate * gradient - - Residual connections: output - skip_connection in some architectures - - Error calculation: predicted - actual for loss computation - - Centering data: tensor - mean for zero-centered inputs - - APPROACH: - 1. Convert other to Tensor if needed - 2. Subtract using numpy arrays - 3. Return new Tensor with result - - EXAMPLE: - Tensor([5, 6]) - Tensor([1, 2]) → Tensor([4, 4]) - Tensor([5, 6]) - 1 → Tensor([4, 5]) - """ - ### BEGIN SOLUTION - if isinstance(other, Tensor): - result = self._data - other._data - else: - result = self._data - other - return Tensor(result) - ### END SOLUTION - - def __truediv__(self, other: Union['Tensor', int, float]) -> 'Tensor': - """ - Division operator: tensor / other - - TODO: Implement / operator for tensors. - - STEP-BY-STEP IMPLEMENTATION: - 1. Check if other is a Tensor object - 2. If Tensor, divide self._data by other._data - 3. If scalar, divide self._data by scalar directly - 4. Create new Tensor with result and return - - LEARNING CONNECTIONS: - Real-world relevance: - - Normalization: tensor / std_deviation for standard scaling - - Learning rate decay: parameter / decay_factor over time - - Probability computation: counts / total_counts for frequencies - - Temperature scaling: logits / temperature in softmax functions - - APPROACH: - 1. Convert other to Tensor if needed - 2. Divide using numpy arrays - 3. Return new Tensor with result - - EXAMPLE: - Tensor([6, 8]) / Tensor([2, 4]) → Tensor([3, 2]) - Tensor([6, 8]) / 2 → Tensor([3, 4]) - """ - ### BEGIN SOLUTION - if isinstance(other, Tensor): - result = self._data / other._data - else: - result = self._data / other - return Tensor(result) - ### END SOLUTION - - def mean(self) -> 'Tensor': - """Computes the mean of the tensor's elements.""" - return Tensor(np.mean(self.data)) - - def matmul(self, other: 'Tensor') -> 'Tensor': - """ - Perform matrix multiplication between two tensors. - - TODO: Implement matrix multiplication. - - STEP-BY-STEP IMPLEMENTATION: - 1. Extract numpy arrays from both tensors - 2. Use np.matmul() for proper matrix multiplication - 3. Create new Tensor object with the result - 4. Return the new tensor - - LEARNING CONNECTIONS: - Real-world relevance: - - Linear layers: input @ weight matrices in neural networks - - Transformer attention: Q @ K^T for attention scores - - CNN convolutions: Implemented as matrix multiplications - - Batch processing: Matrix ops enable parallel computation - - APPROACH: - 1. Use np.matmul() to perform matrix multiplication - 2. Return a new Tensor with the result - 3. Handle broadcasting automatically - - EXAMPLE: - Tensor([[1, 2], [3, 4]]) @ Tensor([[5, 6], [7, 8]]) → Tensor([[19, 22], [43, 50]]) - - HINTS: - - Use np.matmul(self._data, other._data) - - Return Tensor(result) - - This is matrix multiplication, not element-wise multiplication - """ - ### BEGIN SOLUTION - result = np.matmul(self._data, other._data) - return Tensor(result) - ### END SOLUTION - - def __matmul__(self, other: 'Tensor') -> 'Tensor': - """ - Matrix multiplication operator: tensor @ other - - Enables the @ operator for matrix multiplication, providing - clean syntax for neural network operations. - """ - return self.matmul(other) - - def backward(self, gradient=None): - """ - Compute gradients for this tensor and propagate backward. - - This is a stub for now - full implementation in Module 09 (Autograd). - For now, just accumulates gradients if requires_grad=True. - - Args: - gradient: Gradient from upstream. If None, assumes scalar with grad=1 - """ - if not self.requires_grad: - return - - if gradient is None: - # Scalar case - gradient is 1 - gradient = Tensor(np.ones_like(self._data)) - - # Accumulate gradients - if self.grad is None: - self.grad = gradient - else: - self.grad = self.grad + gradient - - def zero_grad(self): - """ - Reset gradients to None. Used by optimizers before backward pass. - - This method is called by optimizers to clear gradients before - computing new ones, preventing gradient accumulation across batches. - """ self.grad = None + ### END SOLUTION - def reshape(self, *shape: int) -> 'Tensor': + def __repr__(self): + """String representation of tensor for debugging.""" + grad_info = f", requires_grad={self.requires_grad}" if self.requires_grad else "" + return f"Tensor(data={self.data}, shape={self.shape}{grad_info})" + + def __str__(self): + """Human-readable string representation.""" + return f"Tensor({self.data})" + + def numpy(self): + """Return the underlying NumPy array.""" + return self.data + + # nbgrader={\"grade\": false, \"grade_id\": \"addition-impl\", \"solution\": true} + def __add__(self, other): """ - Return a new tensor with the same data but different shape. + Add two tensors element-wise with broadcasting support. - Args: - *shape: New shape dimensions. Use -1 for automatic sizing. + TODO: Implement tensor addition with automatic broadcasting - Returns: - New Tensor with reshaped data + APPROACH: + 1. Handle both Tensor and scalar inputs + 2. Use NumPy's broadcasting for automatic shape alignment + 3. Return new Tensor with result (don't modify self) - Example: - tensor.reshape(2, -1) # Reshape to 2 rows, auto columns - tensor.reshape(4, 3) # Reshape to 4x3 matrix + EXAMPLE: + >>> a = Tensor([1, 2, 3]) + >>> b = Tensor([4, 5, 6]) + >>> result = a + b + >>> print(result.data) + [5. 7. 9.] + + BROADCASTING EXAMPLE: + >>> matrix = Tensor([[1, 2], [3, 4]]) # Shape: (2, 2) + >>> vector = Tensor([10, 20]) # Shape: (2,) + >>> result = matrix + vector # Broadcasting: (2,2) + (2,) → (2,2) + >>> print(result.data) + [[11. 22.] + [13. 24.]] + + HINTS: + - Use isinstance() to check if other is a Tensor + - NumPy handles broadcasting automatically with + + - Always return a new Tensor, don't modify self + - Preserve gradient tracking for future modules """ - reshaped_data = self._data.reshape(*shape) + ### BEGIN SOLUTION + if isinstance(other, Tensor): + # Tensor + Tensor: let NumPy handle broadcasting + result_data = self.data + other.data + else: + # Tensor + scalar: NumPy broadcasts automatically + result_data = self.data + other + + # Create new tensor with result + result = Tensor(result_data) + + # Preserve gradient tracking if either operand requires gradients + if hasattr(self, 'requires_grad') and hasattr(other, 'requires_grad'): + result.requires_grad = self.requires_grad or (isinstance(other, Tensor) and other.requires_grad) + elif hasattr(self, 'requires_grad'): + result.requires_grad = self.requires_grad + + return result + ### END SOLUTION + + # nbgrader={"grade": false, "grade_id": "more-arithmetic", "solution": true} + def __sub__(self, other): + """ + Subtract two tensors element-wise. + + Common use: Centering data (x - mean), computing differences for loss functions. + """ + if isinstance(other, Tensor): + return Tensor(self.data - other.data) + else: + return Tensor(self.data - other) + + def __mul__(self, other): + """ + Multiply two tensors element-wise (NOT matrix multiplication). + + Common use: Scaling features, applying masks, gating mechanisms in neural networks. + Note: This is * operator, not @ (which will be matrix multiplication). + """ + if isinstance(other, Tensor): + return Tensor(self.data * other.data) + else: + return Tensor(self.data * other) + + def __truediv__(self, other): + """ + Divide two tensors element-wise. + + Common use: Normalization (x / std), converting counts to probabilities. + """ + if isinstance(other, Tensor): + return Tensor(self.data / other.data) + else: + return Tensor(self.data / other) + + # nbgrader={"grade": false, "grade_id": "matmul-impl", "solution": true} + def matmul(self, other): + """ + Matrix multiplication of two tensors. + + TODO: Implement matrix multiplication using np.dot with proper validation + + APPROACH: + 1. Validate inputs are Tensors + 2. Check dimension compatibility (inner dimensions must match) + 3. Use np.dot for optimized computation + 4. Return new Tensor with result + + EXAMPLE: + >>> a = Tensor([[1, 2], [3, 4]]) # 2×2 + >>> b = Tensor([[5, 6], [7, 8]]) # 2×2 + >>> result = a.matmul(b) # 2×2 result + >>> # Result: [[1×5+2×7, 1×6+2×8], [3×5+4×7, 3×6+4×8]] = [[19, 22], [43, 50]] + + SHAPE RULES: + - (M, K) @ (K, N) → (M, N) ✓ Valid + - (M, K) @ (J, N) → Error ✗ K ≠ J + + COMPLEXITY: O(M×N×K) for (M×K) @ (K×N) matrices + + HINTS: + - np.dot handles the optimization for us + - Check self.shape[-1] == other.shape[-2] for compatibility + - Provide clear error messages for debugging + """ + ### BEGIN SOLUTION + if not isinstance(other, Tensor): + raise TypeError(f"Expected Tensor for matrix multiplication, got {type(other)}") + + # Handle edge cases + if self.shape == () or other.shape == (): + # Scalar multiplication + return Tensor(self.data * other.data) + + # For matrix multiplication, we need at least 1D tensors + if len(self.shape) == 0 or len(other.shape) == 0: + return Tensor(self.data * other.data) + + # Check dimension compatibility for matrix multiplication + if len(self.shape) >= 2 and len(other.shape) >= 2: + if self.shape[-1] != other.shape[-2]: + raise ValueError( + f"Cannot perform matrix multiplication: {self.shape} @ {other.shape}. " + f"Inner dimensions must match: {self.shape[-1]} ≠ {other.shape[-2]}. " + f"💡 HINT: For (M,K) @ (K,N) → (M,N), the K dimensions must be equal." + ) + elif len(self.shape) == 1 and len(other.shape) == 2: + # Vector @ Matrix + if self.shape[0] != other.shape[0]: + raise ValueError( + f"Cannot multiply vector {self.shape} with matrix {other.shape}. " + f"Vector length {self.shape[0]} must match matrix rows {other.shape[0]}." + ) + elif len(self.shape) == 2 and len(other.shape) == 1: + # Matrix @ Vector + if self.shape[1] != other.shape[0]: + raise ValueError( + f"Cannot multiply matrix {self.shape} with vector {other.shape}. " + f"Matrix columns {self.shape[1]} must match vector length {other.shape[0]}." + ) + + # Perform optimized matrix multiplication + result_data = np.dot(self.data, other.data) + return Tensor(result_data) + ### END SOLUTION + + # nbgrader={"grade": false, "grade_id": "shape-ops", "solution": true} + def reshape(self, *shape): + """ + Reshape tensor to new dimensions. + + TODO: Implement tensor reshaping with validation + + APPROACH: + 1. Handle different calling conventions: reshape(2, 3) vs reshape((2, 3)) + 2. Validate total elements remain the same + 3. Use NumPy's reshape for the actual operation + 4. Return new Tensor (keep immutability) + + EXAMPLE: + >>> tensor = Tensor([1, 2, 3, 4, 5, 6]) # Shape: (6,) + >>> reshaped = tensor.reshape(2, 3) # Shape: (2, 3) + >>> print(reshaped.data) + [[1. 2. 3.] + [4. 5. 6.]] + + COMMON USAGE: + >>> # Flatten for MLP input + >>> image = Tensor(np.random.rand(3, 32, 32)) # (channels, height, width) + >>> flattened = image.reshape(-1) # (3072,) - all pixels in vector + >>> + >>> # Prepare batch for convolution + >>> batch = Tensor(np.random.rand(32, 784)) # (batch, features) + >>> images = batch.reshape(32, 1, 28, 28) # (batch, channels, height, width) + + HINTS: + - Handle both reshape(2, 3) and reshape((2, 3)) calling styles + - Check np.prod(new_shape) == self.size for validation + - Use descriptive error messages for debugging + """ + ### BEGIN SOLUTION + # Handle both reshape(2, 3) and reshape((2, 3)) calling conventions + if len(shape) == 1 and isinstance(shape[0], (tuple, list)): + new_shape = tuple(shape[0]) + else: + new_shape = shape + + # Handle -1 for automatic dimension inference (like NumPy) + if -1 in new_shape: + if new_shape.count(-1) > 1: + raise ValueError("Can only specify one unknown dimension with -1") + + # Calculate the unknown dimension + known_size = 1 + unknown_idx = new_shape.index(-1) + for i, dim in enumerate(new_shape): + if i != unknown_idx: + known_size *= dim + + unknown_dim = self.size // known_size + new_shape = list(new_shape) + new_shape[unknown_idx] = unknown_dim + new_shape = tuple(new_shape) + + # Validate total elements remain the same + if np.prod(new_shape) != self.size: + raise ValueError( + f"Cannot reshape tensor of size {self.size} to shape {new_shape}. " + f"Total elements must match: {self.size} ≠ {np.prod(new_shape)}. " + f"💡 HINT: Make sure new_shape dimensions multiply to {self.size}" + ) + + # Reshape the data (NumPy handles the memory layout efficiently) + reshaped_data = np.reshape(self.data, new_shape) return Tensor(reshaped_data) + ### END SOLUTION + def transpose(self, dim0=None, dim1=None): + """ + Transpose tensor dimensions. -# # Testing Your Implementation -# -# Now let's test our tensor implementation with comprehensive tests that validate all functionality. + TODO: Implement tensor transposition -# ### 🧪 Unit Test: Tensor Creation -# -# Let's test your tensor creation implementation right away! This gives you immediate feedback on whether your `__init__` method works correctly. -# -# **This is a unit test** - it tests one specific function (tensor creation) in isolation. + APPROACH: + 1. Handle default case (transpose last two dimensions) + 2. Handle specific dimension swapping + 3. Use NumPy's transpose with proper axis specification + 4. Return new Tensor -# %% ../../modules/02_tensor/tensor_dev.ipynb 14 -def Parameter(data, dtype=None): - """ - Convenience function for creating trainable tensors. + EXAMPLE: + >>> matrix = Tensor([[1, 2, 3], [4, 5, 6]]) # (2, 3) + >>> transposed = matrix.transpose() # (3, 2) + >>> print(transposed.data) + [[1. 4.] + [2. 5.] + [3. 6.]] - This is equivalent to Tensor(data, requires_grad=True) but provides - cleaner syntax for neural network parameters. + NEURAL NETWORK USAGE: + >>> # Weight matrix transpose for backward pass + >>> W = Tensor([[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]]) # (3, 2) + >>> W_T = W.transpose() # (2, 3) - for gradient computation + >>> + >>> # Attention mechanism + >>> Q = Tensor([[1, 2], [3, 4]]) # queries (2, 2) + >>> K = Tensor([[5, 6], [7, 8]]) # keys (2, 2) + >>> attention_scores = Q.matmul(K.transpose()) # Q @ K^T - Args: - data: Input data (scalar, list, or numpy array) - dtype: Data type ('float32', 'int32', etc.). Defaults to auto-detect. + HINTS: + - Default: transpose last two dimensions (most common case) + - Use np.transpose() with axes parameter + - Handle 1D tensors gracefully (transpose is identity) + """ + ### BEGIN SOLUTION + if dim0 is None and dim1 is None: + # Default: transpose last two dimensions + if len(self.shape) < 2: + # For 1D tensors, transpose is identity operation + return Tensor(self.data.copy()) + else: + # Transpose last two dimensions (most common in ML) + axes = list(range(len(self.shape))) + axes[-2], axes[-1] = axes[-1], axes[-2] + transposed_data = np.transpose(self.data, axes) + else: + # Specific dimensions to transpose + if dim0 is None or dim1 is None: + raise ValueError("Both dim0 and dim1 must be specified for specific dimension transpose") - Returns: - Tensor with requires_grad=True + # Validate dimensions exist + if dim0 >= len(self.shape) or dim1 >= len(self.shape) or dim0 < 0 or dim1 < 0: + raise ValueError( + f"Dimension out of range for tensor with shape {self.shape}. " + f"Got dim0={dim0}, dim1={dim1}, but tensor has {len(self.shape)} dimensions." + ) - Examples: - weight = Parameter(np.random.randn(784, 128)) # Neural network weight - bias = Parameter(np.zeros(128)) # Neural network bias - """ - return Tensor(data, dtype=dtype, requires_grad=True) + # Create axes list and swap the specified dimensions + axes = list(range(len(self.shape))) + axes[dim0], axes[dim1] = axes[dim1], axes[dim0] + transposed_data = np.transpose(self.data, axes) + return Tensor(transposed_data) + ### END SOLUTION -# # MODULE SUMMARY: Tensor Foundation -# -# Congratulations! You've successfully implemented the fundamental data structure that powers all machine learning: -# -# ## What You've Built -# - **Tensor Class**: N-dimensional array wrapper with professional interfaces -# - **Core Operations**: Creation, property access, and arithmetic operations -# - **Shape Management**: Automatic shape tracking and validation -# - **Data Types**: Proper NumPy integration and type handling -# - **Foundation**: The building block for all subsequent TinyTorch modules -# -# ## Key Learning Outcomes -# - **Understanding**: How tensors work as the foundation of machine learning -# - **Implementation**: Built tensor operations from scratch -# - **Professional patterns**: Clean APIs, proper error handling, comprehensive testing -# - **Real-world connection**: Understanding PyTorch/TensorFlow tensor foundations -# - **Systems thinking**: Building reliable, reusable components -# -# ## Mathematical Foundations Mastered -# - **N-dimensional arrays**: Shape, size, and dimensionality concepts -# - **Element-wise operations**: Addition, subtraction, multiplication, division -# - **Broadcasting**: Understanding how operations work with different shapes -# - **Memory management**: Efficient data storage and access patterns -# -# ## Professional Skills Developed -# - **API design**: Clean, intuitive interfaces for tensor operations -# - **Error handling**: Graceful handling of invalid operations and edge cases -# - **Testing methodology**: Comprehensive validation of tensor functionality -# - **Documentation**: Clear, educational documentation with examples -# -# ## Ready for Advanced Applications -# Your tensor implementation now enables: -# - **Neural Networks**: Foundation for all layer implementations -# - **Automatic Differentiation**: Gradient computation through computational graphs -# - **Complex Models**: CNNs, RNNs, Transformers - all built on tensors -# - **Real Applications**: Training models on real datasets -# -# ## Connection to Real ML Systems -# Your implementation mirrors production systems: -# - **PyTorch**: `torch.Tensor` provides identical functionality -# - **TensorFlow**: `tf.Tensor` implements similar concepts -# - **NumPy**: `numpy.ndarray` serves as the foundation -# - **Industry Standard**: Every major ML framework uses these exact principles -# -# ## The Power of Tensors -# You've built the fundamental data structure of modern AI: -# - **Universality**: Tensors represent all data: images, text, audio, video -# - **Efficiency**: Vectorized operations enable fast computation -# - **Scalability**: Handles everything from single numbers to massive matrices -# - **Flexibility**: Foundation for any mathematical operation -# -# ## What's Next -# Your tensor implementation is the foundation for: -# - **Activations**: Nonlinear functions that enable complex learning -# - **Layers**: Linear transformations and neural network building blocks -# - **Networks**: Composing layers into powerful architectures -# - **Training**: Optimizing networks to solve real problems -# -# **Next Module**: Activation functions - adding the nonlinearity that makes neural networks powerful! -# -# You've built the foundation of modern AI. Now let's add the mathematical functions that enable machines to learn complex patterns! + # nbgrader={"grade": false, "grade_id": "reduction-ops", "solution": true} + def sum(self, axis=None, keepdims=False): + """ + Sum tensor along specified axis. + + TODO: Implement tensor sum with axis control + + APPROACH: + 1. Use NumPy's sum with axis parameter + 2. Handle axis=None (sum all elements) vs specific axis + 3. Support keepdims to maintain shape for broadcasting + 4. Return new Tensor with result + + EXAMPLE: + >>> tensor = Tensor([[1, 2], [3, 4]]) + >>> total = tensor.sum() # Sum all elements: 10 + >>> col_sum = tensor.sum(axis=0) # Sum columns: [4, 6] + >>> row_sum = tensor.sum(axis=1) # Sum rows: [3, 7] + + NEURAL NETWORK USAGE: + >>> # Batch loss computation + >>> batch_losses = Tensor([0.1, 0.3, 0.2, 0.4]) # Individual losses + >>> total_loss = batch_losses.sum() # Total: 1.0 + >>> avg_loss = batch_losses.mean() # Average: 0.25 + >>> + >>> # Global average pooling + >>> feature_maps = Tensor(np.random.rand(32, 256, 7, 7)) # (batch, channels, h, w) + >>> global_features = feature_maps.sum(axis=(2, 3)) # (batch, channels) + + HINTS: + - np.sum handles all the complexity for us + - axis=None sums all elements (returns scalar) + - axis=0 sums along first dimension, axis=1 along second, etc. + - keepdims=True preserves dimensions for broadcasting + """ + ### BEGIN SOLUTION + result = np.sum(self.data, axis=axis, keepdims=keepdims) + return Tensor(result) + ### END SOLUTION + + def mean(self, axis=None, keepdims=False): + """ + Compute mean of tensor along specified axis. + + Common usage: Batch normalization, loss averaging, global pooling. + """ + ### BEGIN SOLUTION + result = np.mean(self.data, axis=axis, keepdims=keepdims) + return Tensor(result) + ### END SOLUTION + + def max(self, axis=None, keepdims=False): + """ + Find maximum values along specified axis. + + Common usage: Max pooling, finding best predictions, activation clipping. + """ + ### BEGIN SOLUTION + result = np.max(self.data, axis=axis, keepdims=keepdims) + return Tensor(result) + ### END SOLUTION + + # nbgrader={"grade": false, "grade_id": "gradient-placeholder", "solution": true} + def backward(self): + """ + Compute gradients (implemented in Module 05: Autograd). + + TODO: Placeholder implementation for gradient computation + + STUDENT NOTE: + This method exists but does nothing until Module 05: Autograd. + Don't worry about it for now - focus on the basic tensor operations. + + In Module 05, we'll implement: + - Gradient computation via chain rule + - Automatic differentiation + - Backpropagation through operations + - Computation graph construction + + FUTURE IMPLEMENTATION PREVIEW: + ```python + def backward(self, gradient=None): + # Module 05 will implement: + # 1. Set gradient for this tensor + # 2. Propagate to parent operations + # 3. Apply chain rule recursively + # 4. Accumulate gradients properly + pass + ``` + + CURRENT BEHAVIOR: + >>> x = Tensor([1, 2, 3], requires_grad=True) + >>> y = x * 2 + >>> y.sum().backward() # Calls this method - does nothing + >>> print(x.grad) # Still None + None + """ + ### BEGIN SOLUTION + # Placeholder - will be implemented in Module 05 + # For now, just ensure it doesn't crash when called + # This allows students to experiment with gradient syntax + # without getting confusing errors about missing methods + pass + ### END SOLUTION diff --git a/tinytorch/core/training.py b/tinytorch/core/training.py deleted file mode 100644 index 1223cbf0..00000000 --- a/tinytorch/core/training.py +++ /dev/null @@ -1,1183 +0,0 @@ -# ╔═══════════════════════════════════════════════════════════════════════════════╗ -# ║ 🚨 CRITICAL WARNING 🚨 ║ -# ║ AUTOGENERATED! DO NOT EDIT! ║ -# ║ ║ -# ║ This file is AUTOMATICALLY GENERATED from source modules. ║ -# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║ -# ║ ║ -# ║ ✅ TO EDIT: modules/source/11_training/training_dev.py ║ -# ║ ✅ TO EXPORT: Run 'tito module complete ' ║ -# ║ ║ -# ║ 🛡️ STUDENT PROTECTION: This file contains optimized implementations. ║ -# ║ Editing it directly may break module functionality and training. ║ -# ║ ║ -# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║ -# ║ happens! The tinytorch/ directory is just the compiled output. ║ -# ╚═══════════════════════════════════════════════════════════════════════════════╝ - -# %% auto 0 -__all__ = ['MeanSquaredError', 'CrossEntropyLoss', 'BinaryCrossEntropyLoss', 'Accuracy', 'Trainer', 'TrainingPipelineProfiler', - 'ProductionTrainingOptimizer'] - -# %% ../../modules/source/10_training/training_dev.ipynb 1 -import numpy as np -import sys -import os -from collections import defaultdict -import time -import pickle - -# Note: Module imports corrected to match actual learning progression: -# Module 6: autograd, Module 7: spatial, Module 8: optimizers, Module 9: dataloader - -# Helper function to set up import paths -# No longer needed, will use direct relative imports - -# Set up paths -# No longer needed - -# Import all the building blocks we need -from .tensor import Tensor -from .activations import ReLU, Sigmoid, Tanh, Softmax -from .layers import Dense -from .networks import Sequential, create_mlp -from .spatial import Conv2D, flatten -from .dataloader import Dataset, DataLoader -from .autograd import Variable # FOR AUTOGRAD INTEGRATION -from .optimizers import SGD, Adam - -# 🔥 AUTOGRAD INTEGRATION: Loss functions now return Variables that support .backward() -# This enables automatic gradient computation for neural network training! - -# %% ../../modules/source/10_training/training_dev.ipynb 4 -class MeanSquaredError: - """ - Mean Squared Error Loss for Regression - - Measures the average squared difference between predictions and targets. - MSE = (1/n) * Σ(y_pred - y_true)² - """ - - def __init__(self): - """Initialize MSE loss function.""" - pass - - def __call__(self, y_pred, y_true): - """ - Compute MSE loss between predictions and targets. - - Args: - y_pred: Model predictions (Tensor or Variable, shape: [batch_size, ...]) - y_true: True targets (Tensor or Variable, shape: [batch_size, ...]) - - Returns: - Variable with scalar loss value that supports .backward() - - TODO: Implement Mean SquaredError loss computation with autograd support. - - STEP-BY-STEP IMPLEMENTATION: - 1. Convert inputs to Variables if needed for autograd support - 2. Compute difference using Variable arithmetic: diff = y_pred - y_true - 3. Square the differences: squared_diff = diff * diff - 4. Take mean over all elements using Variable operations - 5. Return as Variable that supports .backward() for gradient computation - - EXAMPLE: - y_pred = Variable([[1.0, 2.0], [3.0, 4.0]], requires_grad=True) - y_true = Variable([[1.5, 2.5], [2.5, 3.5]], requires_grad=False) - loss = mse_loss(y_pred, y_true) - loss.backward() # Computes gradients for y_pred - - LEARNING CONNECTIONS: - - **Autograd Integration**: Loss functions must participate in computational graph for backpropagation - - **Gradient Flow**: MSE provides smooth gradients that flow backward through the network - - **Variable Operations**: Using Variables keeps computation in the autograd system - - **Training Pipeline**: Loss.backward() triggers gradient computation for entire network - - HINTS: - - Convert inputs to Variables if needed: Variable(tensor_data, requires_grad=True) - - Use Variable arithmetic to maintain autograd graph - - Use operations that preserve gradient computation - - Return Variable that supports .backward() method - """ - ### BEGIN SOLUTION - # Convert to Variables if needed to support autograd - if not isinstance(y_pred, Variable): - if hasattr(y_pred, 'data'): - y_pred = Variable(y_pred.data, requires_grad=True) - else: - y_pred = Variable(y_pred, requires_grad=True) - - if not isinstance(y_true, Variable): - if hasattr(y_true, 'data'): - y_true = Variable(y_true.data, requires_grad=False) # Targets don't need gradients - else: - y_true = Variable(y_true, requires_grad=False) - - # Compute MSE using Variable operations to maintain autograd graph - diff = y_pred - y_true # Variable subtraction - squared_diff = diff * diff # Variable multiplication - - # Mean operation that preserves gradients - # Create a simple mean operation for Variables - if hasattr(squared_diff.data, 'data'): - mean_data = np.mean(squared_diff.data.data) - else: - mean_data = np.mean(squared_diff.data) - - # Create loss Variable with gradient function for MSE - def mse_grad_fn(grad_output): - # MSE gradient: 2 * (y_pred - y_true) / n - if y_pred.requires_grad: - if hasattr(y_pred.data, 'data'): - batch_size = np.prod(y_pred.data.data.shape) - grad_data = 2.0 * (y_pred.data.data - y_true.data.data) / batch_size - else: - batch_size = np.prod(y_pred.data.shape) - grad_data = 2.0 * (y_pred.data - y_true.data) / batch_size - - if hasattr(grad_output.data, 'data'): - final_grad = grad_data * grad_output.data.data - else: - final_grad = grad_data * grad_output.data - - y_pred.backward(Variable(final_grad)) - - loss = Variable(mean_data, requires_grad=y_pred.requires_grad, grad_fn=mse_grad_fn) - return loss - ### END SOLUTION - - def forward(self, y_pred, y_true): - """Alternative interface for forward pass.""" - return self.__call__(y_pred, y_true) - -# %% ../../modules/source/10_training/training_dev.ipynb 7 -class CrossEntropyLoss: - """ - Cross-Entropy Loss for Multi-Class Classification - - Measures the difference between predicted probability distribution and true labels. - CrossEntropy = -Σ y_true * log(y_pred) - """ - - def __init__(self): - """Initialize CrossEntropy loss function.""" - pass - - def __call__(self, y_pred, y_true): - """ - Compute CrossEntropy loss between predictions and targets. - - Args: - y_pred: Model predictions (Tensor or Variable, shape: [batch_size, num_classes]) - y_true: True class indices (Tensor or Variable, shape: [batch_size]) or one-hot - - Returns: - Variable with scalar loss value that supports .backward() - - TODO: Implement Cross-Entropy loss computation with autograd support. - - STEP-BY-STEP IMPLEMENTATION: - 1. Convert inputs to Variables if needed for autograd support - 2. Handle both class indices and one-hot encoded labels - 3. Apply softmax to predictions for probability distribution - 4. Compute log probabilities while maintaining gradient flow - 5. Calculate cross-entropy and return Variable with gradient function - - EXAMPLE: - y_pred = Variable([[2.0, 1.0, 0.1], [0.5, 2.1, 0.9]], requires_grad=True) - y_true = Variable([0, 1], requires_grad=False) # Class indices - loss = crossentropy_loss(y_pred, y_true) - loss.backward() # Computes gradients for y_pred - - LEARNING CONNECTIONS: - - **Autograd Integration**: CrossEntropy must support gradient computation for classification training - - **Softmax Gradients**: Combined softmax + cross-entropy has well-defined gradients - - **Classification Training**: Standard loss for multi-class problems in neural networks - - **Gradient Flow**: Enables backpropagation through classification layers - - HINTS: - - Convert inputs to Variables to support autograd - - Apply softmax for probability distribution - - Use numerically stable computations - - Implement gradient function for cross-entropy + softmax - """ - ### BEGIN SOLUTION - # Convert to Variables if needed to support autograd - if not isinstance(y_pred, Variable): - if hasattr(y_pred, 'data'): - y_pred = Variable(y_pred.data, requires_grad=True) - else: - y_pred = Variable(y_pred, requires_grad=True) - - if not isinstance(y_true, Variable): - if hasattr(y_true, 'data'): - y_true = Variable(y_true.data, requires_grad=False) - else: - y_true = Variable(y_true, requires_grad=False) - - # Get data for computation - if hasattr(y_pred.data, 'data'): - pred_data = y_pred.data.data - else: - pred_data = y_pred.data - - if hasattr(y_true.data, 'data'): - true_data = y_true.data.data - else: - true_data = y_true.data - - # Handle both 1D and 2D prediction arrays - if pred_data.ndim == 1: - pred_data = pred_data.reshape(1, -1) - - # Apply softmax to get probability distribution (numerically stable) - exp_pred = np.exp(pred_data - np.max(pred_data, axis=1, keepdims=True)) - softmax_pred = exp_pred / np.sum(exp_pred, axis=1, keepdims=True) - - # Add small epsilon to avoid log(0) - epsilon = 1e-15 - softmax_pred = np.clip(softmax_pred, epsilon, 1.0 - epsilon) - - # Handle class indices vs one-hot encoding - if len(true_data.shape) == 1: - # y_true contains class indices - batch_size = true_data.shape[0] - log_probs = np.log(softmax_pred[np.arange(batch_size), true_data.astype(int)]) - loss_value = -np.mean(log_probs) - - # Create one-hot for gradient computation - one_hot = np.zeros_like(softmax_pred) - one_hot[np.arange(batch_size), true_data.astype(int)] = 1.0 - else: - # y_true is one-hot encoded - one_hot = true_data - log_probs = np.log(softmax_pred) - loss_value = -np.mean(np.sum(true_data * log_probs, axis=1)) - - # Create gradient function for CrossEntropy + Softmax - def crossentropy_grad_fn(grad_output): - if y_pred.requires_grad: - # Gradient of CrossEntropy + Softmax: (softmax_pred - one_hot) / batch_size - batch_size = softmax_pred.shape[0] - grad_data = (softmax_pred - one_hot) / batch_size - - if hasattr(grad_output.data, 'data'): - final_grad = grad_data * grad_output.data.data - else: - final_grad = grad_data * grad_output.data - - y_pred.backward(Variable(final_grad)) - - loss = Variable(loss_value, requires_grad=y_pred.requires_grad, grad_fn=crossentropy_grad_fn) - return loss - ### END SOLUTION - - def forward(self, y_pred, y_true): - """Alternative interface for forward pass.""" - return self.__call__(y_pred, y_true) - -# Test function defined (called in main block) - -# %% ../../modules/source/10_training/training_dev.ipynb 10 -class BinaryCrossEntropyLoss: - """ - Binary Cross-Entropy Loss for Binary Classification - - Measures the difference between predicted probabilities and binary labels. - BCE = -y_true * log(y_pred) - (1-y_true) * log(1-y_pred) - """ - - def __init__(self): - """Initialize Binary CrossEntropy loss function.""" - pass - - def __call__(self, y_pred, y_true): - """ - Compute Binary CrossEntropy loss between predictions and targets. - - Args: - y_pred: Model predictions (Tensor or Variable, shape: [batch_size, 1] or [batch_size]) - y_true: True binary labels (Tensor or Variable, shape: [batch_size, 1] or [batch_size]) - - Returns: - Variable with scalar loss value that supports .backward() - - TODO: Implement Binary Cross-Entropy loss computation with autograd support. - - STEP-BY-STEP IMPLEMENTATION: - 1. Convert inputs to Variables if needed for autograd support - 2. Apply sigmoid to predictions for probability values (numerically stable) - 3. Compute binary cross-entropy loss while maintaining gradient flow - 4. Create gradient function for sigmoid + BCE combination - 5. Return Variable that supports .backward() for gradient computation - - EXAMPLE: - y_pred = Variable([[2.0], [0.0], [-1.0]], requires_grad=True) # Raw logits - y_true = Variable([[1.0], [1.0], [0.0]], requires_grad=False) # Binary labels - loss = bce_loss(y_pred, y_true) - loss.backward() # Computes gradients for y_pred - - LEARNING CONNECTIONS: - - **Autograd Integration**: Binary CrossEntropy must support gradient computation for binary classification training - - **Sigmoid + BCE Gradients**: Combined sigmoid + BCE has well-defined gradients - - **Binary Classification**: Standard loss for binary problems in neural networks - - **Numerical Stability**: Use log-sum-exp tricks to avoid overflow/underflow - - HINTS: - - Convert inputs to Variables to support autograd - - Use numerically stable sigmoid computation - - Implement gradient function for sigmoid + BCE - - Handle both logits and probability inputs - """ - ### BEGIN SOLUTION - # Convert to Variables if needed to support autograd - if not isinstance(y_pred, Variable): - if hasattr(y_pred, 'data'): - y_pred = Variable(y_pred.data, requires_grad=True) - else: - y_pred = Variable(y_pred, requires_grad=True) - - if not isinstance(y_true, Variable): - if hasattr(y_true, 'data'): - y_true = Variable(y_true.data, requires_grad=False) - else: - y_true = Variable(y_true, requires_grad=False) - - # Get data for computation - if hasattr(y_pred.data, 'data'): - logits = y_pred.data.data.flatten() - else: - logits = y_pred.data.flatten() - - if hasattr(y_true.data, 'data'): - labels = y_true.data.data.flatten() - else: - labels = y_true.data.flatten() - - # Numerically stable binary cross-entropy from logits - def stable_bce_with_logits(logits, labels): - # Use the stable formulation: max(x, 0) - x * y + log(1 + exp(-abs(x))) - stable_loss = np.maximum(logits, 0) - logits * labels + np.log(1 + np.exp(-np.abs(logits))) - return stable_loss - - # Compute loss for each sample - losses = stable_bce_with_logits(logits, labels) - mean_loss = np.mean(losses) - - # Compute sigmoid for gradient computation - sigmoid_pred = 1.0 / (1.0 + np.exp(-np.clip(logits, -250, 250))) # Clipped for stability - - # Create gradient function for Binary CrossEntropy + Sigmoid - def bce_grad_fn(grad_output): - if y_pred.requires_grad: - # Gradient of BCE + Sigmoid: (sigmoid_pred - labels) / batch_size - batch_size = len(labels) - grad_data = (sigmoid_pred - labels) / batch_size - - # Reshape to match original y_pred shape - if hasattr(y_pred.data, 'data'): - original_shape = y_pred.data.data.shape - else: - original_shape = y_pred.data.shape - - if len(original_shape) > 1: - grad_data = grad_data.reshape(original_shape) - - if hasattr(grad_output.data, 'data'): - final_grad = grad_data * grad_output.data.data - else: - final_grad = grad_data * grad_output.data - - y_pred.backward(Variable(final_grad)) - - loss = Variable(mean_loss, requires_grad=y_pred.requires_grad, grad_fn=bce_grad_fn) - return loss - ### END SOLUTION - - def forward(self, y_pred, y_true): - """Alternative interface for forward pass.""" - return self.__call__(y_pred, y_true) - -# Test function defined (called in main block) - -# %% ../../modules/source/10_training/training_dev.ipynb 14 -class Accuracy: - """ - Accuracy Metric for Classification - - Computes the fraction of correct predictions. - Accuracy = (Correct Predictions) / (Total Predictions) - """ - - def __init__(self): - """Initialize Accuracy metric.""" - pass - - def __call__(self, y_pred: Tensor, y_true: Tensor) -> float: - """ - Compute accuracy between predictions and targets. - - Args: - y_pred: Model predictions (shape: [batch_size, num_classes] or [batch_size]) - y_true: True class labels (shape: [batch_size] or [batch_size]) - - Returns: - Accuracy as a float value between 0 and 1 - - TODO: Implement accuracy computation. - - STEP-BY-STEP IMPLEMENTATION: - 1. Convert predictions to class indices (argmax for multi-class) - 2. Convert true labels to class indices if needed - 3. Count correct predictions - 4. Divide by total predictions - 5. Return as float - - EXAMPLE: - y_pred = Tensor([[0.9, 0.1], [0.2, 0.8], [0.6, 0.4]]) # Probabilities - y_true = Tensor([0, 1, 0]) # True classes - accuracy = accuracy_metric(y_pred, y_true) - # Should return: 2/3 = 0.667 (first and second predictions correct) - - LEARNING CONNECTIONS: - - **Model Evaluation**: Primary metric for classification model performance - - **Business KPIs**: Often directly tied to business objectives and success metrics - - **Baseline Comparison**: Standard metric for comparing different models - - **Production Monitoring**: Real-time accuracy monitoring for model health - - HINTS: - - Use np.argmax(axis=1) for multi-class predictions - - Handle both probability and class index inputs - - Use np.mean() for averaging - - Return Python float, not Tensor - """ - ### BEGIN SOLUTION - # Convert predictions to class indices - if len(y_pred.data.shape) > 1 and y_pred.data.shape[1] > 1: - # Multi-class: use argmax - pred_classes = np.argmax(y_pred.data, axis=1) - else: - # Binary classification: threshold at 0.5 - pred_classes = (y_pred.data.flatten() > 0.5).astype(int) - - # Convert true labels to class indices if needed - if len(y_true.data.shape) > 1 and y_true.data.shape[1] > 1: - # One-hot encoded - true_classes = np.argmax(y_true.data, axis=1) - else: - # Already class indices - true_classes = y_true.data.flatten().astype(int) - - # Compute accuracy - correct = np.sum(pred_classes == true_classes) - total = len(true_classes) - accuracy = correct / total - - return float(accuracy) - ### END SOLUTION - - def forward(self, y_pred: Tensor, y_true: Tensor) -> float: - """Alternative interface for forward pass.""" - return self.__call__(y_pred, y_true) - -# %% ../../modules/source/10_training/training_dev.ipynb 18 -class Trainer: - """ - Training Loop Orchestrator - - Coordinates model training with loss functions, optimizers, and metrics. - """ - - def __init__(self, model, optimizer, loss_function, metrics=None): - """ - Initialize trainer with model and training components. - - Args: - model: Neural network model to train - optimizer: Optimizer for parameter updates - loss_function: Loss function for training - metrics: List of metrics to track (optional) - - TODO: Initialize the trainer with all necessary components. - - APPROACH: - 1. Store model, optimizer, loss function, and metrics - 2. Initialize history tracking for losses and metrics - 3. Set up training state (epoch, step counters) - 4. Prepare for training and validation loops - - EXAMPLE: - model = Sequential([Dense(10, 5), ReLU(), Dense(5, 2)]) - optimizer = Adam(model.parameters, learning_rate=0.001) - loss_fn = CrossEntropyLoss() - metrics = [Accuracy()] - trainer = Trainer(model, optimizer, loss_fn, metrics) - - HINTS: - - Store all components as instance variables - - Initialize empty history dictionaries - - Set metrics to empty list if None provided - - Initialize epoch and step counters to 0 - """ - ### BEGIN SOLUTION - self.model = model - self.optimizer = optimizer - self.loss_function = loss_function - self.metrics = metrics or [] - - # Training history - self.history = { - 'train_loss': [], - 'val_loss': [], - 'epoch': [] - } - - # Add metric history tracking - for metric in self.metrics: - metric_name = metric.__class__.__name__.lower() - self.history[f'train_{metric_name}'] = [] - self.history[f'val_{metric_name}'] = [] - - # Training state - self.current_epoch = 0 - self.current_step = 0 - ### END SOLUTION - - def train_epoch(self, dataloader): - """ - Train for one epoch on the given dataloader. - - Args: - dataloader: DataLoader containing training data - - Returns: - Dictionary with epoch training metrics - - TODO: Implement single epoch training logic. - - STEP-BY-STEP IMPLEMENTATION: - 1. Initialize epoch metrics tracking - 2. Iterate through batches in dataloader - 3. For each batch: - - Zero gradients - - Forward pass - - Compute loss - - Backward pass - - Update parameters - - Track metrics - 4. Return averaged metrics for the epoch - - LEARNING CONNECTIONS: - - **Training Loop Foundation**: Core pattern used in all deep learning frameworks - - **Gradient Accumulation**: Optimizer.zero_grad() prevents gradient accumulation bugs - - **Backpropagation**: loss.backward() computes gradients through entire network - - **Parameter Updates**: optimizer.step() applies computed gradients to model weights - - HINTS: - - Use optimizer.zero_grad() before each batch - - Call loss.backward() for gradient computation - - Use optimizer.step() for parameter updates - - Track running averages for metrics - """ - ### BEGIN SOLUTION - epoch_metrics = {'loss': 0.0} - - # Initialize metric tracking - for metric in self.metrics: - metric_name = metric.__class__.__name__.lower() - epoch_metrics[metric_name] = 0.0 - - batch_count = 0 - - for batch_x, batch_y in dataloader: - # Zero gradients - self.optimizer.zero_grad() - - # Forward pass - predictions = self.model(batch_x) - - # Compute loss - loss = self.loss_function(predictions, batch_y) - - # Backward pass - now that loss functions support autograd! - if hasattr(loss, 'backward'): - loss.backward() - - # Update parameters - self.optimizer.step() - - # Track metrics - if hasattr(loss, 'data'): - if hasattr(loss.data, 'data'): - epoch_metrics['loss'] += loss.data.data # Variable with Tensor data - else: - epoch_metrics['loss'] += loss.data # Variable with numpy data - else: - epoch_metrics['loss'] += loss # Direct value - - for metric in self.metrics: - metric_name = metric.__class__.__name__.lower() - metric_value = metric(predictions, batch_y) - epoch_metrics[metric_name] += metric_value - - batch_count += 1 - self.current_step += 1 - - # Average metrics over all batches - for key in epoch_metrics: - epoch_metrics[key] /= batch_count - - return epoch_metrics - ### END SOLUTION - - def validate_epoch(self, dataloader): - """ - Validate for one epoch on the given dataloader. - - Args: - dataloader: DataLoader containing validation data - - Returns: - Dictionary with epoch validation metrics - - TODO: Implement single epoch validation logic. - - STEP-BY-STEP IMPLEMENTATION: - 1. Initialize epoch metrics tracking - 2. Iterate through batches in dataloader - 3. For each batch: - - Forward pass (no gradient computation) - - Compute loss - - Track metrics - 4. Return averaged metrics for the epoch - - LEARNING CONNECTIONS: - - **Model Evaluation**: Validation measures generalization to unseen data - - **Overfitting Detection**: Comparing train vs validation metrics reveals overfitting - - **Model Selection**: Validation metrics guide hyperparameter tuning and architecture choices - - **Early Stopping**: Validation loss plateaus indicate optimal training duration - - HINTS: - - No gradient computation needed for validation - - No parameter updates during validation - - Similar to train_epoch but simpler - """ - ### BEGIN SOLUTION - epoch_metrics = {'loss': 0.0} - - # Initialize metric tracking - for metric in self.metrics: - metric_name = metric.__class__.__name__.lower() - epoch_metrics[metric_name] = 0.0 - - batch_count = 0 - - for batch_x, batch_y in dataloader: - # Forward pass only (no gradients needed) - predictions = self.model(batch_x) - - # Compute loss - loss = self.loss_function(predictions, batch_y) - - # Track metrics - if hasattr(loss, 'data'): - if hasattr(loss.data, 'data'): - epoch_metrics['loss'] += loss.data.data # Variable with Tensor data - else: - epoch_metrics['loss'] += loss.data # Variable with numpy data - else: - epoch_metrics['loss'] += loss # Direct value - - for metric in self.metrics: - metric_name = metric.__class__.__name__.lower() - metric_value = metric(predictions, batch_y) - epoch_metrics[metric_name] += metric_value - - batch_count += 1 - - # Average metrics over all batches - for key in epoch_metrics: - epoch_metrics[key] /= batch_count - - return epoch_metrics - ### END SOLUTION - - def fit(self, train_dataloader, val_dataloader=None, epochs=10, verbose=True, save_best=False, checkpoint_path="best_model.pkl"): - """ - Train the model for specified number of epochs. - - Args: - train_dataloader: Training data - val_dataloader: Validation data (optional) - epochs: Number of training epochs - verbose: Whether to print training progress - - Returns: - Training history dictionary - - TODO: Implement complete training loop. - - STEP-BY-STEP IMPLEMENTATION: - 1. Loop through epochs - 2. For each epoch: - - Train on training data - - Validate on validation data (if provided) - - Update history - - Print progress (if verbose) - 3. Return complete training history - - LEARNING CONNECTIONS: - - **Epoch Management**: Organizing training into discrete passes through the dataset - - **Learning Curves**: History tracking enables visualization of training progress - - **Hyperparameter Tuning**: Training history guides learning rate and architecture decisions - - **Production Monitoring**: Training logs provide debugging and optimization insights - - HINTS: - - Use train_epoch() and validate_epoch() methods - - Update self.history with results - - Print epoch summary if verbose=True - """ - ### BEGIN SOLUTION - print(f"Starting training for {epochs} epochs...") - best_val_loss = float('inf') - - for epoch in range(epochs): - self.current_epoch = epoch - - # Training phase - train_metrics = self.train_epoch(train_dataloader) - - # Validation phase - val_metrics = {} - if val_dataloader is not None: - val_metrics = self.validate_epoch(val_dataloader) - - # Update history - self.history['epoch'].append(epoch) - self.history['train_loss'].append(train_metrics['loss']) - - if val_dataloader is not None: - self.history['val_loss'].append(val_metrics['loss']) - - # Update metric history - for metric in self.metrics: - metric_name = metric.__class__.__name__.lower() - self.history[f'train_{metric_name}'].append(train_metrics[metric_name]) - if val_dataloader is not None: - self.history[f'val_{metric_name}'].append(val_metrics[metric_name]) - - # Save best model checkpoint - if save_best and val_dataloader is not None: - if val_metrics['loss'] < best_val_loss: - best_val_loss = val_metrics['loss'] - self.save_checkpoint(checkpoint_path) - if verbose: - print(f" 💾 Saved best model (val_loss: {best_val_loss:.4f})") - - # Print progress - if verbose: - train_loss = train_metrics['loss'] - print(f"Epoch {epoch+1}/{epochs} - train_loss: {train_loss:.4f}", end="") - - if val_dataloader is not None: - val_loss = val_metrics['loss'] - print(f" - val_loss: {val_loss:.4f}", end="") - - for metric in self.metrics: - metric_name = metric.__class__.__name__.lower() - train_metric = train_metrics[metric_name] - print(f" - train_{metric_name}: {train_metric:.4f}", end="") - - if val_dataloader is not None: - val_metric = val_metrics[metric_name] - print(f" - val_{metric_name}: {val_metric:.4f}", end="") - - print() # New line - - print("Training completed!") - return self.history - ### END SOLUTION - - def save_checkpoint(self, filepath): - """Save model checkpoint.""" - checkpoint = { - 'epoch': self.current_epoch, - 'model_state': self._get_model_state(), - 'history': self.history - } - - with open(filepath, 'wb') as f: - pickle.dump(checkpoint, f) - - def load_checkpoint(self, filepath): - """Load model checkpoint.""" - with open(filepath, 'rb') as f: - checkpoint = pickle.load(f) - - self.current_epoch = checkpoint['epoch'] - self.history = checkpoint['history'] - self._set_model_state(checkpoint['model_state']) - - print(f"✅ Loaded checkpoint from epoch {self.current_epoch}") - - def _get_model_state(self): - """Extract model parameters.""" - state = {} - for i, layer in enumerate(self.model.layers): - if hasattr(layer, 'weight'): - state[f'layer_{i}_weight'] = layer.weight.data.copy() - state[f'layer_{i}_bias'] = layer.bias.data.copy() - return state - - def _set_model_state(self, state): - """Restore model parameters.""" - for i, layer in enumerate(self.model.layers): - if hasattr(layer, 'weight'): - layer.weight.data = state[f'layer_{i}_weight'] - layer.bias.data = state[f'layer_{i}_bias'] - -# %% ../../modules/source/10_training/training_dev.ipynb 24 -class TrainingPipelineProfiler: - """ - Production Training Pipeline Analysis and Optimization - - Monitors end-to-end training performance and identifies bottlenecks - across the complete training infrastructure. - """ - - def __init__(self, warning_threshold_seconds=5.0): - """ - Initialize training pipeline profiler. - - Args: - warning_threshold_seconds: Warn if any pipeline step exceeds this time - """ - self.warning_threshold = warning_threshold_seconds - self.profiling_data = defaultdict(list) - self.resource_usage = defaultdict(list) - - def profile_complete_training_step(self, model, dataloader, optimizer, loss_fn, batch_size=32): - """ - Profile complete training step including all pipeline components. - - TODO: Implement comprehensive training step profiling. - - STEP-BY-STEP IMPLEMENTATION: - 1. Time each component: data loading, forward pass, loss computation, backward pass, optimization - 2. Monitor memory usage throughout the pipeline - 3. Calculate throughput metrics (samples/second, batches/second) - 4. Identify pipeline bottlenecks and optimization opportunities - 5. Generate performance recommendations - - EXAMPLE: - profiler = TrainingPipelineProfiler() - step_metrics = profiler.profile_complete_training_step(model, dataloader, optimizer, loss_fn) - - LEARNING CONNECTIONS: - - **Performance Optimization**: Identifying bottlenecks in training pipeline - - **Resource Planning**: Understanding memory and compute requirements - - **Hardware Selection**: Data guides GPU vs CPU trade-offs - - **Production Scaling**: Optimizing training throughput for large models - print(f"Training throughput: {step_metrics['samples_per_second']:.1f} samples/sec") - - HINTS: - - Use time.time() for timing measurements - - Monitor before/after memory usage - - Calculate ratios: compute_time / total_time - - Identify which step is the bottleneck - """ - ### BEGIN SOLUTION - import time - - # Initialize timing and memory tracking - step_times = {} - memory_usage = {} - - # Get initial memory baseline (simplified - in production would use GPU monitoring) - baseline_memory = self._estimate_memory_usage() - - # 1. Data Loading Phase - data_start = time.time() - try: - batch_x, batch_y = next(iter(dataloader)) - data_time = time.time() - data_start - step_times['data_loading'] = data_time - except: - # Handle case where dataloader is not iterable for testing - data_time = 0.001 # Minimal time for testing - step_times['data_loading'] = data_time - batch_x = Tensor(np.random.randn(batch_size, 10)) - batch_y = Tensor(np.random.randint(0, 2, batch_size)) - - memory_usage['after_data_loading'] = self._estimate_memory_usage() - - # 2. Forward Pass Phase - forward_start = time.time() - try: - predictions = model(batch_x) - forward_time = time.time() - forward_start - step_times['forward_pass'] = forward_time - except: - # Handle case for testing with simplified model - forward_time = 0.002 - step_times['forward_pass'] = forward_time - predictions = Tensor(np.random.randn(batch_size, 2)) - - memory_usage['after_forward_pass'] = self._estimate_memory_usage() - - # 3. Loss Computation Phase - loss_start = time.time() - loss = loss_fn(predictions, batch_y) - loss_time = time.time() - loss_start - step_times['loss_computation'] = loss_time - - memory_usage['after_loss_computation'] = self._estimate_memory_usage() - - # 4. Backward Pass Phase (simplified for testing) - backward_start = time.time() - # In real implementation: loss.backward() - backward_time = 0.003 # Simulated backward pass time - step_times['backward_pass'] = backward_time - - memory_usage['after_backward_pass'] = self._estimate_memory_usage() - - # 5. Optimization Phase - optimization_start = time.time() - try: - optimizer.step() - optimization_time = time.time() - optimization_start - step_times['optimization'] = optimization_time - except: - # Handle case for testing - optimization_time = 0.001 - step_times['optimization'] = optimization_time - - memory_usage['after_optimization'] = self._estimate_memory_usage() - - # Calculate total time and throughput - total_time = sum(step_times.values()) - samples_per_second = batch_size / total_time if total_time > 0 else 0 - - # Identify bottleneck - bottleneck_step = max(step_times.items(), key=lambda x: x[1]) - - # Calculate component percentages - component_percentages = { - step: (time_taken / total_time * 100) if total_time > 0 else 0 - for step, time_taken in step_times.items() - } - - # Generate performance analysis - performance_analysis = self._analyze_pipeline_performance(step_times, memory_usage, component_percentages) - - # Store profiling data - self.profiling_data['total_time'].append(total_time) - self.profiling_data['samples_per_second'].append(samples_per_second) - self.profiling_data['bottleneck_step'].append(bottleneck_step[0]) - - return { - 'step_times': step_times, - 'total_time': total_time, - 'samples_per_second': samples_per_second, - 'bottleneck_step': bottleneck_step[0], - 'bottleneck_time': bottleneck_step[1], - 'component_percentages': component_percentages, - 'memory_usage': memory_usage, - 'performance_analysis': performance_analysis - } - ### END SOLUTION - - def _estimate_memory_usage(self): - """Estimate current memory usage (simplified implementation).""" - # In production: would use psutil.Process().memory_info().rss or GPU monitoring - import sys - return sys.getsizeof({}) * 1024 # Simplified estimate - - def _analyze_pipeline_performance(self, step_times, memory_usage, component_percentages): - """Analyze training pipeline performance and generate recommendations.""" - analysis = [] - - # Identify performance bottlenecks - max_step = max(step_times.items(), key=lambda x: x[1]) - if max_step[1] > self.warning_threshold: - analysis.append(f"⚠️ BOTTLENECK: {max_step[0]} taking {max_step[1]:.3f}s (>{self.warning_threshold}s threshold)") - - # Analyze component balance - forward_pct = component_percentages.get('forward_pass', 0) - backward_pct = component_percentages.get('backward_pass', 0) - data_pct = component_percentages.get('data_loading', 0) - - if data_pct > 30: - analysis.append("📊 Data loading is >30% of total time - consider data pipeline optimization") - - if forward_pct > 60: - analysis.append("🔄 Forward pass dominates (>60%) - consider model optimization or batch size tuning") - - # Memory analysis - memory_keys = list(memory_usage.keys()) - if len(memory_keys) > 1: - memory_growth = memory_usage[memory_keys[-1]] - memory_usage[memory_keys[0]] - if memory_growth > 1024 * 1024: # > 1MB growth - analysis.append("💾 Significant memory growth during training step - monitor for memory leaks") - - return analysis - -# %% ../../modules/source/10_training/training_dev.ipynb 27 -class ProductionTrainingOptimizer: - """ - Production Training Pipeline Optimization - - Optimizes training pipelines for production deployment with focus on - throughput, resource utilization, and system stability. - """ - - def __init__(self): - """Initialize production training optimizer.""" - self.optimization_history = [] - self.baseline_metrics = None - - def optimize_batch_size_for_throughput(self, model, loss_fn, optimizer, initial_batch_size=32, max_batch_size=512): - """ - Find optimal batch size for maximum training throughput. - - TODO: Implement batch size optimization for production throughput. - - STEP-BY-STEP IMPLEMENTATION: - 1. Test range of batch sizes from initial to maximum - 2. For each batch size, measure: - - Training throughput (samples/second) - - Memory usage - - Time per step - 3. Find optimal batch size balancing throughput and memory - 4. Handle memory limitations gracefully - 5. Return recommendations with trade-off analysis - - EXAMPLE: - optimizer = ProductionTrainingOptimizer() - optimal_config = optimizer.optimize_batch_size_for_throughput(model, loss_fn, optimizer) - print(f"Optimal batch size: {optimal_config['batch_size']}") - - LEARNING CONNECTIONS: - - **Memory vs Throughput**: Larger batches improve GPU utilization but use more memory - - **Hardware Optimization**: Optimal batch size depends on GPU memory and compute units - - **Training Dynamics**: Batch size affects gradient noise and convergence behavior - - **Production Cost**: Throughput optimization directly impacts cloud computing costs - print(f"Expected throughput: {optimal_config['throughput']:.1f} samples/sec") - - HINTS: - - Test powers of 2: 32, 64, 128, 256, 512 - - Monitor memory usage to avoid OOM - - Calculate samples_per_second for each batch size - - Consider memory efficiency (throughput per MB) - """ - ### BEGIN SOLUTION - print("🔧 Optimizing batch size for production throughput...") - - # Test batch sizes (powers of 2 for optimal GPU utilization) - test_batch_sizes = [] - current_batch = initial_batch_size - while current_batch <= max_batch_size: - test_batch_sizes.append(current_batch) - current_batch *= 2 - - optimization_results = [] - profiler = TrainingPipelineProfiler() - - for batch_size in test_batch_sizes: - print(f" Testing batch size: {batch_size}") - - try: - # Create test data for this batch size - test_x = Tensor(np.random.randn(batch_size, 10)) - test_y = Tensor(np.random.randint(0, 2, batch_size)) - - # Create mock dataloader - class MockDataLoader: - def __init__(self, x, y): - self.x, self.y = x, y - def __iter__(self): - return self - def __next__(self): - return self.x, self.y - - dataloader = MockDataLoader(test_x, test_y) - - # Profile training step - metrics = profiler.profile_complete_training_step( - model, dataloader, optimizer, loss_fn, batch_size - ) - - # Estimate memory usage (simplified) - estimated_memory_mb = batch_size * 10 * 4 / (1024 * 1024) # 4 bytes per float - memory_efficiency = metrics['samples_per_second'] / estimated_memory_mb if estimated_memory_mb > 0 else 0 - - optimization_results.append({ - 'batch_size': batch_size, - 'throughput': metrics['samples_per_second'], - 'total_time': metrics['total_time'], - 'estimated_memory_mb': estimated_memory_mb, - 'memory_efficiency': memory_efficiency, - 'bottleneck_step': metrics['bottleneck_step'] - }) - - except Exception as e: - print(f" ⚠️ Batch size {batch_size} failed: {e}") - # In production, this would typically be OOM - break - - # Find optimal configuration - if not optimization_results: - return {'error': 'No valid batch sizes found'} - - # Optimal = highest throughput that doesn't exceed memory limits - best_config = max(optimization_results, key=lambda x: x['throughput']) - - # Generate optimization analysis - analysis = self._generate_batch_size_analysis(optimization_results, best_config) - - # Store optimization history - self.optimization_history.append({ - 'optimization_type': 'batch_size', - 'results': optimization_results, - 'best_config': best_config, - 'analysis': analysis - }) - - return { - 'optimal_batch_size': best_config['batch_size'], - 'expected_throughput': best_config['throughput'], - 'estimated_memory_usage': best_config['estimated_memory_mb'], - 'all_results': optimization_results, - 'optimization_analysis': analysis - } - ### END SOLUTION - - def _generate_batch_size_analysis(self, results, best_config): - """Generate analysis of batch size optimization results.""" - analysis = [] - - # Throughput analysis - throughputs = [r['throughput'] for r in results] - max_throughput = max(throughputs) - min_throughput = min(throughputs) - - analysis.append(f"📈 Throughput range: {min_throughput:.1f} - {max_throughput:.1f} samples/sec") - analysis.append(f"🎯 Optimal batch size: {best_config['batch_size']} ({max_throughput:.1f} samples/sec)") - - # Memory efficiency analysis - memory_efficiencies = [r['memory_efficiency'] for r in results] - most_efficient = max(results, key=lambda x: x['memory_efficiency']) - - analysis.append(f"💾 Most memory efficient: batch size {most_efficient['batch_size']} ({most_efficient['memory_efficiency']:.2f} samples/sec/MB)") - - # Bottleneck analysis - bottleneck_counts = {} - for r in results: - step = r['bottleneck_step'] - bottleneck_counts[step] = bottleneck_counts.get(step, 0) + 1 - - common_bottleneck = max(bottleneck_counts.items(), key=lambda x: x[1]) - analysis.append(f"🔍 Common bottleneck: {common_bottleneck[0]} ({common_bottleneck[1]}/{len(results)} configurations)") - - return analysis diff --git a/tinytorch/core/transformers.py b/tinytorch/core/transformers.py deleted file mode 100644 index dd4a0f56..00000000 --- a/tinytorch/core/transformers.py +++ /dev/null @@ -1,1067 +0,0 @@ -# ╔═══════════════════════════════════════════════════════════════════════════════╗ -# ║ 🚨 CRITICAL WARNING 🚨 ║ -# ║ AUTOGENERATED! DO NOT EDIT! ║ -# ║ ║ -# ║ This file is AUTOMATICALLY GENERATED from source modules. ║ -# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║ -# ║ ║ -# ║ ✅ TO EDIT: modules/source/XX_transformers/transformers_dev.py ║ -# ║ ✅ TO EXPORT: Run 'tito module complete ' ║ -# ║ ║ -# ║ 🛡️ STUDENT PROTECTION: This file contains optimized implementations. ║ -# ║ Editing it directly may break module functionality and training. ║ -# ║ ║ -# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║ -# ║ happens! The tinytorch/ directory is just the compiled output. ║ -# ╚═══════════════════════════════════════════════════════════════════════════════╝ - -# %% auto 0 -__all__ = ['LayerNorm', 'PositionwiseFeedForward', 'TransformerBlock', 'Transformer', 'TransformerProfiler', - 'analyze_transformer_system_design'] - -# %% ../../modules/14_transformers/transformers_dev.ipynb 1 -import math -import numpy as np -import os -import sys -from typing import Union, List, Optional, Tuple, Dict - -# Import our Tensor class - try from package first, then from local module -try: - from tinytorch.core.tensor import Tensor -except ImportError: - # For development, import from local tensor module - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '02_tensor')) - from tensor_dev import Tensor - -# Try to import attention classes -try: - from tinytorch.core.attention import ScaledDotProductAttention, MultiHeadAttention, KVCache -except ImportError: - # For development, import from local module - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '13_attention')) - try: - from attention_dev import ScaledDotProductAttention, MultiHeadAttention, KVCache - except ImportError: - # Create minimal mock classes if not available - class MultiHeadAttention: - def __init__(self, embed_dim, num_heads): - self.embed_dim = embed_dim - self.num_heads = num_heads - def forward(self, q, k, v, mask=None): - return q # Mock implementation - class ScaledDotProductAttention: - def __init__(self): - pass - class KVCache: - def __init__(self, *args, **kwargs): - pass - -# Try to import embedding classes -try: - from tinytorch.core.embeddings import Embedding, PositionalEncoding -except ImportError: - # For development, import from local module - sys.path.append(os.path.join(os.path.dirname(__file__), '..', '12_embeddings')) - try: - from embeddings_dev import Embedding, PositionalEncoding - except ImportError: - # Create minimal mock classes if not available - class Embedding: - def __init__(self, vocab_size, embedding_dim): - self.vocab_size = vocab_size - self.embedding_dim = embedding_dim - class PositionalEncoding: - def __init__(self, embedding_dim, max_seq_length=5000): - self.embedding_dim = embedding_dim - -# %% ../../modules/14_transformers/transformers_dev.ipynb 6 -class LayerNorm: - """ - Layer Normalization for transformers. - - Normalizes across the feature dimension (last axis) for each sample, - making training more stable and enabling deeper networks. - """ - - def __init__(self, normalized_shape: Union[int, Tuple[int]], eps: float = 1e-5): - """ - Initialize layer normalization with learnable parameters. - - TODO: Implement layer normalization initialization. - - STEP-BY-STEP IMPLEMENTATION: - 1. Store normalization configuration - 2. Initialize learnable scale (gamma) and shift (beta) parameters - 3. Set epsilon for numerical stability - 4. Set up parameter tracking for optimization - - MATHEMATICAL FOUNDATION: - LayerNorm(x) = γ * (x - μ) / σ + β - - Where: - - μ = mean across feature dimensions - - σ = std across feature dimensions - - γ = learnable scale parameter - - β = learnable shift parameter - - Args: - normalized_shape: Shape of features to normalize (e.g., embedding_dim) - eps: Small value for numerical stability - """ - ### BEGIN SOLUTION - if isinstance(normalized_shape, int): - self.normalized_shape = (normalized_shape,) - else: - self.normalized_shape = normalized_shape - - self.eps = eps - - # Initialize learnable parameters - # Gamma (scale): initialized to ones - # Beta (bias): initialized to zeros - self.gamma = Tensor(np.ones(self.normalized_shape)) - self.beta = Tensor(np.zeros(self.normalized_shape)) - - # Track parameters for optimization - self.parameters = [self.gamma, self.beta] - ### END SOLUTION - - def forward(self, x: Tensor) -> Tensor: - """ - Apply layer normalization to input tensor. - - TODO: Implement layer normalization forward pass. - - STEP-BY-STEP IMPLEMENTATION: - 1. Calculate mean across feature dimensions - 2. Calculate standard deviation across feature dimensions - 3. Normalize: (x - mean) / (std + eps) - 4. Apply learnable scale and shift: gamma * normalized + beta - - NUMERICAL STABILITY: - - Add eps to variance before taking sqrt - - Use unbiased variance calculation - - EXAMPLE: - layer_norm = LayerNorm(256) - x = Tensor(np.random.randn(32, 128, 256)) # (batch, seq, features) - normalized = layer_norm.forward(x) # Same shape as input - - Args: - x: Input tensor with shape (..., *normalized_shape) - - Returns: - Normalized tensor with same shape as input - """ - ### BEGIN SOLUTION - # Calculate mean and variance across the feature dimensions (last axes) - # For shape (..., *normalized_shape), we want to normalize over the last len(normalized_shape) axes - - # Determine axes to normalize over - axes_to_normalize = tuple(range(len(x.shape) - len(self.normalized_shape), len(x.shape))) - - # Calculate mean - mean = np.mean(x.data, axis=axes_to_normalize, keepdims=True) - - # Calculate variance - variance = np.var(x.data, axis=axes_to_normalize, keepdims=True) - - # Normalize - normalized = (x.data - mean) / np.sqrt(variance + self.eps) - - # Apply learnable scale and shift - # Reshape gamma and beta to be broadcastable - gamma_broadcasted = self.gamma.data.reshape([1] * (len(x.shape) - len(self.normalized_shape)) + list(self.normalized_shape)) - beta_broadcasted = self.beta.data.reshape([1] * (len(x.shape) - len(self.normalized_shape)) + list(self.normalized_shape)) - - output = gamma_broadcasted * normalized + beta_broadcasted - - return Tensor(output) - ### END SOLUTION - - def __call__(self, x: Tensor) -> Tensor: - """Make the class callable.""" - return self.forward(x) - - def get_memory_usage(self) -> Dict[str, float]: - """ - Calculate memory usage of layer normalization parameters. - - This function is PROVIDED to show memory analysis. - """ - # Parameter memory - param_memory_mb = sum(param.data.nbytes for param in self.parameters) / (1024 * 1024) - - return { - 'parameter_memory_mb': param_memory_mb, - 'total_parameters': sum(param.data.size for param in self.parameters), - 'normalized_shape': self.normalized_shape - } - -# %% ../../modules/14_transformers/transformers_dev.ipynb 10 -class PositionwiseFeedForward: - """ - Position-wise feed-forward network used in transformer blocks. - - Applies the same feed-forward network to each position in the sequence: - FFN(x) = max(0, xW₁ + b₁)W₂ + b₂ - """ - - def __init__(self, embed_dim: int, hidden_dim: int, dropout: float = 0.0): - """ - Initialize position-wise feed-forward network. - - TODO: Implement feed-forward network initialization. - - STEP-BY-STEP IMPLEMENTATION: - 1. Store network configuration - 2. Initialize weight matrices and bias vectors for two linear layers - 3. Set up parameter tracking for optimization - 4. Store dropout rate for training - - ARCHITECTURE: - - Input: (batch, seq_len, embed_dim) - - Linear 1: embed_dim → hidden_dim - - ReLU activation - - Linear 2: hidden_dim → embed_dim - - Output: (batch, seq_len, embed_dim) - - PARAMETER INITIALIZATION: - Use Xavier/Glorot initialization for stable training - - Args: - embed_dim: Embedding dimension (input and output size) - hidden_dim: Hidden layer dimension (typically 4 * embed_dim) - dropout: Dropout rate for regularization - """ - ### BEGIN SOLUTION - self.embed_dim = embed_dim - self.hidden_dim = hidden_dim - self.dropout = dropout - - # Initialize weights using Xavier initialization - # W1: embed_dim → hidden_dim - xavier_bound_1 = math.sqrt(6.0 / (embed_dim + hidden_dim)) - self.w1 = Tensor(np.random.uniform(-xavier_bound_1, xavier_bound_1, (embed_dim, hidden_dim))) - self.b1 = Tensor(np.zeros(hidden_dim)) - - # W2: hidden_dim → embed_dim - xavier_bound_2 = math.sqrt(6.0 / (hidden_dim + embed_dim)) - self.w2 = Tensor(np.random.uniform(-xavier_bound_2, xavier_bound_2, (hidden_dim, embed_dim))) - self.b2 = Tensor(np.zeros(embed_dim)) - - # Track parameters for optimization - self.parameters = [self.w1, self.b1, self.w2, self.b2] - ### END SOLUTION - - def forward(self, x: Tensor) -> Tensor: - """ - Apply position-wise feed-forward transformation. - - TODO: Implement feed-forward forward pass. - - STEP-BY-STEP IMPLEMENTATION: - 1. Apply first linear transformation: x @ W1 + b1 - 2. Apply ReLU activation: max(0, linear1) - 3. Apply second linear transformation: relu @ W2 + b2 - 4. Return result with same shape as input - - MATHEMATICAL FORMULATION: - hidden = ReLU(x @ W1 + b1) - output = hidden @ W2 + b2 - - Args: - x: Input tensor with shape (batch_size, seq_len, embed_dim) - - Returns: - Output tensor with shape (batch_size, seq_len, embed_dim) - """ - ### BEGIN SOLUTION - # Reshape input for matrix multiplication if needed - original_shape = x.shape - if len(x.shape) == 3: - batch_size, seq_len, embed_dim = x.shape - # Reshape to (batch_size * seq_len, embed_dim) for efficient computation - x_reshaped = x.data.reshape(-1, embed_dim) - else: - x_reshaped = x.data - - # First linear transformation: x @ W1 + b1 - hidden = np.matmul(x_reshaped, self.w1.data) + self.b1.data - - # ReLU activation - hidden_relu = np.maximum(0, hidden) - - # Second linear transformation: hidden @ W2 + b2 - output = np.matmul(hidden_relu, self.w2.data) + self.b2.data - - # Reshape back to original shape - if len(original_shape) == 3: - output = output.reshape(original_shape) - - return Tensor(output) - ### END SOLUTION - - def __call__(self, x: Tensor) -> Tensor: - """Make the class callable.""" - return self.forward(x) - - def get_memory_usage(self) -> Dict[str, float]: - """ - Calculate memory usage of feed-forward parameters. - - This function is PROVIDED to show memory analysis. - """ - # Parameter memory - param_memory_mb = sum(param.data.nbytes for param in self.parameters) / (1024 * 1024) - - # Calculate parameter counts - w1_params = self.embed_dim * self.hidden_dim - w2_params = self.hidden_dim * self.embed_dim - bias_params = self.hidden_dim + self.embed_dim - total_params = w1_params + w2_params + bias_params - - return { - 'parameter_memory_mb': param_memory_mb, - 'total_parameters': total_params, - 'w1_parameters': w1_params, - 'w2_parameters': w2_params, - 'bias_parameters': bias_params, - 'embed_dim': self.embed_dim, - 'hidden_dim': self.hidden_dim - } - -# %% ../../modules/14_transformers/transformers_dev.ipynb 14 -class TransformerBlock: - """ - Complete transformer block with self-attention and feed-forward layers. - - Combines multi-head self-attention, layer normalization, residual connections, - and position-wise feed-forward networks into the standard transformer architecture. - """ - - def __init__(self, embed_dim: int, num_heads: int, hidden_dim: int, - dropout: float = 0.0, pre_norm: bool = True): - """ - Initialize transformer block with all components. - - TODO: Implement transformer block initialization. - - STEP-BY-STEP IMPLEMENTATION: - 1. Store block configuration - 2. Create multi-head attention layer - 3. Create two layer normalization layers (for attention and FFN) - 4. Create position-wise feed-forward network - 5. Set up parameter tracking from all sub-components - - ARCHITECTURE CHOICE: Pre-norm vs Post-norm - - Pre-norm: LayerNorm → Attention → Residual (more stable) - - Post-norm: Attention → LayerNorm → Residual (original paper) - - Args: - embed_dim: Embedding dimension - num_heads: Number of attention heads - hidden_dim: Feed-forward hidden dimension (typically 4 * embed_dim) - dropout: Dropout rate for regularization - pre_norm: Whether to use pre-normalization (recommended) - """ - ### BEGIN SOLUTION - self.embed_dim = embed_dim - self.num_heads = num_heads - self.hidden_dim = hidden_dim - self.dropout = dropout - self.pre_norm = pre_norm - - # Multi-head self-attention - self.attention = MultiHeadAttention(embed_dim=embed_dim, num_heads=num_heads) - - # Layer normalization layers - self.norm1 = LayerNorm(embed_dim) # For attention - self.norm2 = LayerNorm(embed_dim) # For feed-forward - - # Position-wise feed-forward network - self.ffn = PositionwiseFeedForward(embed_dim=embed_dim, hidden_dim=hidden_dim, dropout=dropout) - - # Collect all parameters from sub-components - self.parameters = [] - if hasattr(self.attention, 'parameters'): - self.parameters.extend(self.attention.parameters) - self.parameters.extend(self.norm1.parameters) - self.parameters.extend(self.norm2.parameters) - self.parameters.extend(self.ffn.parameters) - ### END SOLUTION - - def forward(self, x: Tensor, mask: Optional[Tensor] = None, - return_attention_weights: bool = False) -> Union[Tensor, Tuple[Tensor, Tensor]]: - """ - Process input through complete transformer block. - - TODO: Implement transformer block forward pass. - - STEP-BY-STEP IMPLEMENTATION (Pre-norm): - 1. Self-attention with residual: x + attention(norm1(x)) - 2. Feed-forward with residual: attn_out + ffn(norm2(attn_out)) - 3. Return final output (and optionally attention weights) - - RESIDUAL CONNECTIONS: - Essential for training deep networks - allow gradients to flow directly - - Args: - x: Input tensor with shape (batch_size, seq_len, embed_dim) - mask: Optional attention mask - return_attention_weights: Whether to return attention weights - - Returns: - Transformer block output with same shape as input - Optionally also attention weights - """ - ### BEGIN SOLUTION - if self.pre_norm: - # Pre-normalization: LayerNorm before attention/FFN - - # Self-attention with residual connection - norm1_x = self.norm1(x) - if return_attention_weights: - attn_output, attn_weights = self.attention.forward( - norm1_x, norm1_x, norm1_x, mask=mask, return_attention_weights=True - ) - else: - attn_output = self.attention.forward(norm1_x, norm1_x, norm1_x, mask=mask) - - # Residual connection - x = Tensor(x.data + attn_output.data) - - # Feed-forward with residual connection - norm2_x = self.norm2(x) - ffn_output = self.ffn.forward(norm2_x) - - # Residual connection - output = Tensor(x.data + ffn_output.data) - - else: - # Post-normalization: LayerNorm after attention/FFN (original transformer) - - # Self-attention with residual connection - if return_attention_weights: - attn_output, attn_weights = self.attention.forward( - x, x, x, mask=mask, return_attention_weights=True - ) - else: - attn_output = self.attention.forward(x, x, x, mask=mask) - - # Residual + LayerNorm - attn_residual = Tensor(x.data + attn_output.data) - norm1_output = self.norm1(attn_residual) - - # Feed-forward with residual connection - ffn_output = self.ffn.forward(norm1_output) - - # Residual + LayerNorm - ffn_residual = Tensor(norm1_output.data + ffn_output.data) - output = self.norm2(ffn_residual) - - if return_attention_weights: - return output, attn_weights - else: - return output - ### END SOLUTION - - def __call__(self, x: Tensor, mask: Optional[Tensor] = None, - return_attention_weights: bool = False) -> Union[Tensor, Tuple[Tensor, Tensor]]: - """Make the class callable.""" - return self.forward(x, mask, return_attention_weights) - - def get_memory_usage(self) -> Dict[str, float]: - """ - Calculate memory usage of transformer block components. - - This function is PROVIDED to show memory analysis. - """ - # Get memory usage from components - if hasattr(self.attention, 'get_memory_usage'): - attention_memory = self.attention.get_memory_usage()['total_parameter_memory_mb'] - else: - attention_memory = 0.0 - - norm1_memory = self.norm1.get_memory_usage()['parameter_memory_mb'] - norm2_memory = self.norm2.get_memory_usage()['parameter_memory_mb'] - ffn_memory = self.ffn.get_memory_usage()['parameter_memory_mb'] - - total_memory = attention_memory + norm1_memory + norm2_memory + ffn_memory - total_params = len(self.parameters) if hasattr(self, 'parameters') else 0 - - return { - 'total_memory_mb': total_memory, - 'attention_memory_mb': attention_memory, - 'norm_memory_mb': norm1_memory + norm2_memory, - 'ffn_memory_mb': ffn_memory, - 'total_parameters': sum(p.data.size for p in self.parameters) if hasattr(self, 'parameters') else 0, - 'embed_dim': self.embed_dim, - 'num_heads': self.num_heads, - 'hidden_dim': self.hidden_dim, - 'pre_norm': self.pre_norm - } - -# %% ../../modules/14_transformers/transformers_dev.ipynb 18 -class Transformer: - """ - Complete transformer model for language processing. - - Stacks multiple transformer blocks with token embeddings and positional - encoding to create a complete language model architecture. - """ - - def __init__(self, vocab_size: int, embed_dim: int, num_heads: int, - num_layers: int, hidden_dim: int, max_seq_length: int = 1024, - dropout: float = 0.0, pre_norm: bool = True): - """ - Initialize complete transformer model. - - TODO: Implement transformer model initialization. - - STEP-BY-STEP IMPLEMENTATION: - 1. Store model configuration - 2. Create token embedding layer - 3. Create positional encoding - 4. Create stack of transformer blocks - 5. Create output projection layer (for language modeling) - 6. Set up parameter tracking from all components - - LANGUAGE MODELING HEAD: - Final linear layer that projects hidden states to vocabulary logits - - Args: - vocab_size: Size of vocabulary - embed_dim: Embedding dimension - num_heads: Number of attention heads per layer - num_layers: Number of transformer blocks - hidden_dim: Feed-forward hidden dimension - max_seq_length: Maximum sequence length for positional encoding - dropout: Dropout rate - pre_norm: Whether to use pre-normalization - """ - ### BEGIN SOLUTION - self.vocab_size = vocab_size - self.embed_dim = embed_dim - self.num_heads = num_heads - self.num_layers = num_layers - self.hidden_dim = hidden_dim - self.max_seq_length = max_seq_length - self.dropout = dropout - self.pre_norm = pre_norm - - # Token embedding layer - self.token_embedding = Embedding(vocab_size=vocab_size, embedding_dim=embed_dim) - - # Positional encoding - self.pos_encoding = PositionalEncoding(embedding_dim=embed_dim, max_seq_length=max_seq_length) - - # Stack of transformer blocks - self.transformer_blocks = [] - for _ in range(num_layers): - block = TransformerBlock( - embed_dim=embed_dim, - num_heads=num_heads, - hidden_dim=hidden_dim, - dropout=dropout, - pre_norm=pre_norm - ) - self.transformer_blocks.append(block) - - # Final layer normalization (for pre-norm architecture) - if pre_norm: - self.final_norm = LayerNorm(embed_dim) - else: - self.final_norm = None - - # Language modeling head (projects to vocabulary) - xavier_bound = math.sqrt(6.0 / (embed_dim + vocab_size)) - self.lm_head = Tensor(np.random.uniform(-xavier_bound, xavier_bound, (embed_dim, vocab_size))) - - # Collect all parameters - self.parameters = [] - if hasattr(self.token_embedding, 'parameters'): - self.parameters.extend(self.token_embedding.parameters) - - for block in self.transformer_blocks: - if hasattr(block, 'parameters'): - self.parameters.extend(block.parameters) - - if self.final_norm: - self.parameters.extend(self.final_norm.parameters) - - self.parameters.append(self.lm_head) - ### END SOLUTION - - def forward(self, input_ids: Tensor, mask: Optional[Tensor] = None, - return_attention_weights: bool = False) -> Union[Tensor, Tuple[Tensor, List[Tensor]]]: - """ - Process input through complete transformer model. - - TODO: Implement transformer model forward pass. - - STEP-BY-STEP IMPLEMENTATION: - 1. Convert token IDs to embeddings - 2. Add positional encoding - 3. Process through all transformer blocks - 4. Apply final normalization (if pre-norm) - 5. Apply language modeling head - 6. Return logits (and optionally attention weights) - - Args: - input_ids: Token indices with shape (batch_size, seq_len) - mask: Optional attention mask - return_attention_weights: Whether to return all attention weights - - Returns: - Logits with shape (batch_size, seq_len, vocab_size) - Optionally also list of attention weights from each layer - """ - ### BEGIN SOLUTION - # Token embeddings - embeddings = self.token_embedding.forward(input_ids) - - # Add positional encoding - x = self.pos_encoding.forward(embeddings) - - # Process through transformer blocks - all_attention_weights = [] - - for block in self.transformer_blocks: - if return_attention_weights: - x, attn_weights = block.forward(x, mask=mask, return_attention_weights=True) - all_attention_weights.append(attn_weights) - else: - x = block.forward(x, mask=mask) - - # Final layer normalization (for pre-norm) - if self.final_norm: - x = self.final_norm.forward(x) - - # Language modeling head - # x: (batch_size, seq_len, embed_dim) - # lm_head: (embed_dim, vocab_size) - # output: (batch_size, seq_len, vocab_size) - - batch_size, seq_len, embed_dim = x.shape - x_reshaped = x.data.reshape(-1, embed_dim) # (batch_size * seq_len, embed_dim) - logits_reshaped = np.matmul(x_reshaped, self.lm_head.data) # (batch_size * seq_len, vocab_size) - logits = logits_reshaped.reshape(batch_size, seq_len, self.vocab_size) - - if return_attention_weights: - return Tensor(logits), all_attention_weights - else: - return Tensor(logits) - ### END SOLUTION - - def __call__(self, input_ids: Tensor, mask: Optional[Tensor] = None, - return_attention_weights: bool = False) -> Union[Tensor, Tuple[Tensor, List[Tensor]]]: - """Make the class callable.""" - return self.forward(input_ids, mask, return_attention_weights) - - def generate(self, input_ids: Tensor, max_new_tokens: int = 50, - temperature: float = 1.0) -> Tensor: - """ - Generate text autoregressively. - - This function is PROVIDED to show text generation capability. - """ - batch_size, current_seq_len = input_ids.shape - - if current_seq_len >= self.max_seq_length: - raise ValueError(f"Input sequence length {current_seq_len} exceeds max {self.max_seq_length}") - - generated_ids = input_ids.data.copy() - - for _ in range(max_new_tokens): - # Create causal mask - seq_len = generated_ids.shape[1] - causal_mask = np.triu(np.ones((seq_len, seq_len)), k=1) - causal_mask = 1 - causal_mask - - # Forward pass - logits = self.forward(Tensor(generated_ids), mask=Tensor(causal_mask)) - - # Get logits for last position - last_logits = logits.data[:, -1, :] # (batch_size, vocab_size) - - # Apply temperature - last_logits = last_logits / temperature - - # Sample next token (using simple sampling) - # Convert to probabilities - exp_logits = np.exp(last_logits - np.max(last_logits, axis=-1, keepdims=True)) - probs = exp_logits / np.sum(exp_logits, axis=-1, keepdims=True) - - # Sample from distribution - next_tokens = [] - for i in range(batch_size): - next_token = np.random.choice(self.vocab_size, p=probs[i]) - next_tokens.append(next_token) - - next_tokens = np.array(next_tokens).reshape(batch_size, 1) - - # Append to sequence - generated_ids = np.concatenate([generated_ids, next_tokens], axis=1) - - # Stop if we reach max sequence length - if generated_ids.shape[1] >= self.max_seq_length: - break - - return Tensor(generated_ids) - - def get_memory_usage(self) -> Dict[str, float]: - """ - Calculate memory usage of complete transformer model. - - This function is PROVIDED to show memory analysis. - """ - # Token embedding memory - if hasattr(self.token_embedding, 'get_memory_usage'): - embedding_memory = self.token_embedding.get_memory_usage()['total_memory_mb'] - else: - embedding_memory = self.vocab_size * self.embed_dim * 4 / (1024 * 1024) - - # Transformer blocks memory - block_memory = 0 - if self.transformer_blocks: - single_block_memory = self.transformer_blocks[0].get_memory_usage()['total_memory_mb'] - block_memory = single_block_memory * self.num_layers - - # Final norm memory - final_norm_memory = 0 - if self.final_norm: - final_norm_memory = self.final_norm.get_memory_usage()['parameter_memory_mb'] - - # Language modeling head memory - lm_head_memory = self.lm_head.data.nbytes / (1024 * 1024) - - total_memory = embedding_memory + block_memory + final_norm_memory + lm_head_memory - total_params = sum(p.data.size for p in self.parameters) if hasattr(self, 'parameters') else 0 - - return { - 'total_memory_mb': total_memory, - 'embedding_memory_mb': embedding_memory, - 'transformer_blocks_memory_mb': block_memory, - 'lm_head_memory_mb': lm_head_memory, - 'total_parameters': total_params, - 'vocab_size': self.vocab_size, - 'embed_dim': self.embed_dim, - 'num_layers': self.num_layers, - 'num_heads': self.num_heads, - 'hidden_dim': self.hidden_dim - } - -# %% ../../modules/14_transformers/transformers_dev.ipynb 22 -import time - -class TransformerProfiler: - """ - Performance profiling toolkit for transformer architectures. - - Helps ML engineers understand computational costs, memory scaling, - and architectural trade-offs in transformer-based models. - """ - - def __init__(self): - self.results = {} - - def measure_scaling_with_depth(self, base_config: Dict, layer_counts: List[int]) -> Dict: - """ - Measure how transformer performance scales with number of layers. - - TODO: Implement transformer depth scaling measurement. - - STEP-BY-STEP IMPLEMENTATION: - 1. Create transformers with different layer counts - 2. Measure memory usage and computation time for each - 3. Calculate scaling patterns (should be linear with depth) - 4. Analyze parameter growth and memory requirements - 5. Return comprehensive scaling analysis - - EXPECTED SCALING: - - Parameters: Linear with depth - - Memory: Linear with depth - - Computation: Linear with depth - - Quality: Generally improves with depth (to a point) - - Args: - base_config: Base transformer configuration - layer_counts: List of layer counts to test - - Returns: - Dictionary with scaling analysis results - """ - ### BEGIN SOLUTION - scaling_results = {} - - # Test input - batch_size = 4 - seq_len = 32 - vocab_size = base_config['vocab_size'] - test_input = Tensor(np.random.randint(0, vocab_size, (batch_size, seq_len))) - - for num_layers in layer_counts: - # Create transformer with this depth - transformer = Transformer( - vocab_size=base_config['vocab_size'], - embed_dim=base_config['embed_dim'], - num_heads=base_config['num_heads'], - num_layers=num_layers, - hidden_dim=base_config['hidden_dim'], - max_seq_length=base_config.get('max_seq_length', 128) - ) - - # Measure memory usage - memory_stats = transformer.get_memory_usage() - - # Measure computation time - start_time = time.time() - logits = transformer.forward(test_input) - end_time = time.time() - - computation_time_ms = (end_time - start_time) * 1000 - - # Calculate throughput - total_tokens = batch_size * seq_len - tokens_per_second = total_tokens / (end_time - start_time) if end_time > start_time else 0 - - scaling_results[num_layers] = { - 'num_layers': num_layers, - 'total_parameters': memory_stats['total_parameters'], - 'total_memory_mb': memory_stats['total_memory_mb'], - 'computation_time_ms': computation_time_ms, - 'tokens_per_second': tokens_per_second, - 'memory_per_layer_mb': memory_stats['transformer_blocks_memory_mb'] / num_layers if num_layers > 0 else 0, - 'parameters_per_layer': (memory_stats['total_parameters'] - - base_config['vocab_size'] * base_config['embed_dim'] * 2) // num_layers if num_layers > 0 else 0 - } - - return scaling_results - ### END SOLUTION - - def analyze_width_vs_depth_tradeoffs(self, base_params: int, configurations: List[Dict]) -> Dict: - """ - Compare different ways to allocate a fixed parameter budget. - - This function is PROVIDED to show parameter allocation analysis. - """ - print(f"📊 WIDTH vs DEPTH TRADE-OFF ANALYSIS") - print(f"Target parameter budget: ~{base_params:,} parameters") - print("=" * 70) - - results = {} - - # Test input - batch_size = 4 - seq_len = 32 - test_input = Tensor(np.random.randint(0, 1000, (batch_size, seq_len))) - - print(f"{'Config':<15} {'Layers':<7} {'Embed':<6} {'Heads':<6} {'Hidden':<7} {'Params':<12} {'Time (ms)':<10} {'Memory'}") - print("-" * 80) - - for i, config in enumerate(configurations): - try: - # Create transformer - transformer = Transformer( - vocab_size=1000, # Fixed vocab size - embed_dim=config['embed_dim'], - num_heads=config['num_heads'], - num_layers=config['num_layers'], - hidden_dim=config['hidden_dim'], - max_seq_length=128 - ) - - # Get actual parameter count - memory_stats = transformer.get_memory_usage() - actual_params = memory_stats['total_parameters'] - - # Measure performance - start_time = time.time() - logits = transformer.forward(test_input) - computation_time = (time.time() - start_time) * 1000 - - config_name = f"Config_{i+1}" - results[config_name] = { - 'config': config, - 'actual_parameters': actual_params, - 'computation_time_ms': computation_time, - 'memory_mb': memory_stats['total_memory_mb'], - 'parameter_efficiency': abs(actual_params - base_params) / base_params - } - - print(f"{config_name:<15} {config['num_layers']:<7} {config['embed_dim']:<6} " - f"{config['num_heads']:<6} {config['hidden_dim']:<7} {actual_params:<12,} " - f"{computation_time:<10.2f} {memory_stats['total_memory_mb']:.1f}MB") - - except Exception as e: - print(f"{config_name:<15} ERROR: {str(e)[:50]}") - - # Analysis - print(f"\n💡 TRADE-OFF INSIGHTS:") - print(f" - Deeper models: Better at learning complex patterns, more sequential") - print(f" - Wider models: More parallelizable, can capture diverse features") - print(f" - More heads: Richer attention patterns, more computation") - print(f" - Hidden dimension: Affects FFN capacity, major parameter contributor") - - return results - - def simulate_production_scaling(self, model_sizes: List[str]) -> Dict: - """ - Simulate memory and computation requirements for production model sizes. - - This function is PROVIDED to show production scaling analysis. - """ - print(f"\n🏭 PRODUCTION MODEL SCALING SIMULATION") - print("=" * 60) - - # Production model configurations (simplified) - size_configs = { - 'Small': {'vocab_size': 50000, 'embed_dim': 512, 'num_heads': 8, 'num_layers': 6, 'hidden_dim': 2048}, - 'Medium': {'vocab_size': 50000, 'embed_dim': 768, 'num_heads': 12, 'num_layers': 12, 'hidden_dim': 3072}, - 'Large': {'vocab_size': 50000, 'embed_dim': 1024, 'num_heads': 16, 'num_layers': 24, 'hidden_dim': 4096}, - 'XL': {'vocab_size': 50000, 'embed_dim': 1280, 'num_heads': 20, 'num_layers': 36, 'hidden_dim': 5120} - } - - results = {} - - print(f"{'Model Size':<12} {'Parameters':<12} {'Memory (GB)':<12} {'Training GPU':<12} {'Inference'}") - print("-" * 70) - - for size in model_sizes: - if size not in size_configs: - continue - - config = size_configs[size] - - # Estimate parameters - # Embedding: vocab_size * embed_dim * 2 (input + output) - embedding_params = config['vocab_size'] * config['embed_dim'] * 2 - - # Per layer: - # - Attention: 4 * embed_dim^2 (Q, K, V, O projections) - # - FFN: 2 * embed_dim * hidden_dim + embed_dim + hidden_dim (weights + biases) - # - LayerNorm: 2 * embed_dim * 2 (two norms per layer) - attention_params_per_layer = 4 * config['embed_dim'] ** 2 - ffn_params_per_layer = 2 * config['embed_dim'] * config['hidden_dim'] + config['embed_dim'] + config['hidden_dim'] - norm_params_per_layer = 4 * config['embed_dim'] - - layer_params = attention_params_per_layer + ffn_params_per_layer + norm_params_per_layer - total_params = embedding_params + layer_params * config['num_layers'] - - # Estimate memory (parameters + activations + gradients for training) - param_memory_gb = total_params * 4 / (1024**3) # 4 bytes per float32 - - # Training memory: parameters + gradients + optimizer states + activations - training_memory_gb = param_memory_gb * 4 # Rough estimate (param + grad + 2x optimizer states) - - # Inference memory: just parameters + activations - inference_memory_gb = param_memory_gb * 1.5 # Parameters + activation memory - - # GPU requirements (very rough estimates) - if training_memory_gb < 24: - training_gpu = "Single RTX 4090" - elif training_memory_gb < 80: - training_gpu = "Single A100" - else: - training_gpu = "Multi-GPU" - - if inference_memory_gb < 12: - inference_req = "RTX 4060 Ti" - elif inference_memory_gb < 24: - inference_req = "RTX 4090" - else: - inference_req = "A100+" - - results[size] = { - 'config': config, - 'total_parameters': total_params, - 'training_memory_gb': training_memory_gb, - 'inference_memory_gb': inference_memory_gb, - 'training_gpu_req': training_gpu, - 'inference_gpu_req': inference_req - } - - print(f"{size:<12} {total_params/1e6:.1f}M {training_memory_gb:.1f} {training_gpu:<12} {inference_req}") - - print(f"\n📈 SCALING OBSERVATIONS:") - print(f" - Model size grows super-linearly with dimension increases") - print(f" - Memory requirements dominate deployment decisions") - print(f" - Training requires 3-4x more memory than inference") - print(f" - Multi-GPU becomes necessary for large models") - - return results - -def analyze_transformer_system_design(): - """ - Comprehensive analysis of transformer system design choices and trade-offs. - - This function is PROVIDED to show systems-level design thinking. - """ - print("🏗️ TRANSFORMER SYSTEM DESIGN ANALYSIS") - print("=" * 60) - - # Architecture decision analysis - design_choices = { - 'Layer Normalization': { - 'Pre-norm': {'stability': 'High', 'training': 'Easier', 'performance': 'Good'}, - 'Post-norm': {'stability': 'Lower', 'training': 'Harder', 'performance': 'Potentially better'} - }, - 'Attention Patterns': { - 'Full attention': {'complexity': 'O(N²)', 'quality': 'Best', 'scalability': 'Limited'}, - 'Sparse attention': {'complexity': 'O(N√N)', 'quality': 'Good', 'scalability': 'Better'}, - 'Linear attention': {'complexity': 'O(N)', 'quality': 'Reduced', 'scalability': 'Excellent'} - }, - 'Feed-Forward Size': { - '2x embed_dim': {'parameters': 'Low', 'capacity': 'Limited', 'speed': 'Fast'}, - '4x embed_dim': {'parameters': 'Standard', 'capacity': 'Good', 'speed': 'Medium'}, - '8x embed_dim': {'parameters': 'High', 'capacity': 'High', 'speed': 'Slow'} - } - } - - print("🎯 ARCHITECTURAL DESIGN CHOICES:") - for category, choices in design_choices.items(): - print(f"\n{category}:") - for choice, properties in choices.items(): - prop_str = ", ".join([f"{k}: {v}" for k, v in properties.items()]) - print(f" - {choice}: {prop_str}") - - # Memory scaling analysis - print(f"\n📊 MEMORY SCALING PATTERNS:") - print(f"Component breakdown for typical transformer:") - print(f" - Token embeddings: vocab_size × embed_dim parameters") - print(f" - Position encodings: 0 parameters (sinusoidal) or seq_len × embed_dim (learned)") - print(f" - Attention layers: 4 × embed_dim² parameters per layer") - print(f" - Feed-forward: 2 × embed_dim × hidden_dim parameters per layer") - print(f" - Layer normalization: 2 × embed_dim parameters per layer") - print(f" - Output projection: embed_dim × vocab_size parameters") - - print(f"\n🔧 OPTIMIZATION STRATEGIES:") - optimization_techniques = [ - "Gradient checkpointing: Trade computation for memory", - "Mixed precision training: Use FP16 for 2x memory reduction", - "Parameter sharing: Share weights across layers", - "Sparse attention: Reduce quadratic scaling", - "Model parallelism: Distribute layers across GPUs", - "Pipeline parallelism: Process different batch elements on different GPUs", - "Activation checkpointing: Recompute activations instead of storing" - ] - - for technique in optimization_techniques: - print(f" - {technique}") - - print(f"\n🎯 PRODUCTION DEPLOYMENT CONSIDERATIONS:") - deployment_factors = [ - "Batch size: Larger batches improve GPU utilization but increase memory", - "Sequence length: Quadratic impact on attention memory", - "Model depth: Linear impact on memory and computation", - "Model width: Quadratic impact on attention parameters", - "Precision: FP32 vs FP16 vs INT8 trade-offs", - "Hardware: GPU memory and compute capabilities", - "Latency requirements: Real-time vs batch processing", - "Throughput requirements: Tokens per second targets" - ] - - for factor in deployment_factors: - print(f" - {factor}") diff --git a/tinytorch/nn/functional.py b/tinytorch/nn/functional.py deleted file mode 100644 index 8ed34363..00000000 --- a/tinytorch/nn/functional.py +++ /dev/null @@ -1,216 +0,0 @@ -# ╔═══════════════════════════════════════════════════════════════════════════════╗ -# ║ 🚨 CRITICAL WARNING 🚨 ║ -# ║ AUTOGENERATED! DO NOT EDIT! ║ -# ║ ║ -# ║ This file is AUTOMATICALLY GENERATED from source modules. ║ -# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║ -# ║ ║ -# ║ ✅ TO EDIT: modules/source/XX_functional/functional_dev.py ║ -# ║ ✅ TO EXPORT: Run 'tito module complete ' ║ -# ║ ║ -# ║ 🛡️ STUDENT PROTECTION: This file contains critical fixes for Variable/ ║ -# ║ Tensor compatibility. Editing it directly WILL break CIFAR-10 training. ║ -# ║ ║ -# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║ -# ║ happens! The tinytorch/ directory is just the compiled output. ║ -# ╚═══════════════════════════════════════════════════════════════════════════════╝ -""" -Functional interface for TinyTorch operations. - -This module provides function-based implementations of common operations -that can be used independently or within Module classes. This matches -PyTorch's functional interface pattern. - -Functions here are stateless - they don't hold parameters, just compute. -""" - -import numpy as np -from typing import Tuple - - -def relu(x): - """ - Rectified Linear Unit activation function. - - Args: - x: Input tensor - - Returns: - Tensor with ReLU applied element-wise - - Example: - >>> x = Tensor([-1, 0, 1, 2]) - >>> F.relu(x) # Returns [0, 0, 1, 2] - """ - from ..core.tensor import Tensor - from ..core.autograd import Variable - - # Handle both Tensor and Variable inputs - if hasattr(x, 'data'): - input_data = x.data - # If data is still a Tensor, get its numpy array - if hasattr(input_data, 'data'): - input_data = input_data.data - else: - input_data = x - - # Ensure we have a numpy array, not a memoryview - input_data = np.asarray(input_data) - - # Apply ReLU: max(0, x) - output_data = np.maximum(0, input_data) - - # Preserve input type - if isinstance(x, Variable): - # For Variables, preserve gradient tracking - def relu_grad_fn(grad_output): - if x.requires_grad: - # ReLU derivative: 1 where x > 0, 0 elsewhere - grad_input = grad_output.data * (input_data > 0) - x.backward(Variable(grad_input)) - - return Variable(output_data, requires_grad=x.requires_grad, grad_fn=relu_grad_fn) - else: - return Tensor(output_data) - - -def flatten(x, start_dim=1): - """ - Flatten tensor preserving batch dimension. - - Args: - x: Input tensor with shape (batch_size, ...) - start_dim: Dimension to start flattening from (default: 1) - - Returns: - Flattened tensor with shape (batch_size, -1) - - Example: - >>> x = Tensor([[[[1, 2], [3, 4]], [[5, 6], [7, 8]]]]) # (1, 2, 2, 2) - >>> F.flatten(x) # Returns shape (1, 8) - """ - from ..core.tensor import Tensor - from ..core.autograd import Variable - - # Handle both Tensor and Variable inputs - if hasattr(x, 'data'): - input_data = x.data - # If data is still a Tensor, get its numpy array - if hasattr(input_data, 'data'): - input_data = input_data.data - else: - input_data = x - - # Ensure we have a numpy array, not a memoryview - input_data = np.asarray(input_data) - - # Calculate new shape - original_shape = input_data.shape - if start_dim >= len(original_shape): - raise ValueError(f"start_dim {start_dim} is out of range for tensor with {len(original_shape)} dimensions") - - # Keep dimensions before start_dim, flatten the rest - new_shape = original_shape[:start_dim] + (-1,) - output_data = input_data.reshape(new_shape) - - # Preserve input type - if isinstance(x, Variable): - def flatten_grad_fn(grad_output): - if x.requires_grad: - # Reshape gradient back to original shape - grad_input = grad_output.data.reshape(original_shape) - x.backward(Variable(grad_input)) - - return Variable(output_data, requires_grad=x.requires_grad, grad_fn=flatten_grad_fn) - else: - return Tensor(output_data) - - -def max_pool2d(x, kernel_size, stride=None): - """ - Apply 2D max pooling operation. - - Args: - x: Input tensor with shape (..., H, W) - kernel_size: Size of pooling window (int or tuple) - stride: Stride of pooling (defaults to kernel_size) - - Returns: - Pooled tensor - - Example: - >>> x = Tensor([[[[1, 2, 3, 4]]]]) # (1, 1, 1, 4) - >>> F.max_pool2d(x, kernel_size=2) # Pool 2x2 regions - """ - from ..core.tensor import Tensor - from ..core.autograd import Variable - - # Handle both Tensor and Variable inputs - if hasattr(x, 'data'): - input_data = x.data - # If data is still a Tensor, get its numpy array - if hasattr(input_data, 'data'): - input_data = input_data.data - else: - input_data = x - - # Ensure we have a numpy array, not a memoryview - input_data = np.asarray(input_data) - - # Handle kernel_size as int or tuple - if isinstance(kernel_size, int): - kH = kW = kernel_size - else: - kH, kW = kernel_size - - # Default stride to kernel_size (non-overlapping) - if stride is None: - stride = kernel_size - if isinstance(stride, int): - sH = sW = stride - else: - sH, sW = stride - - # Get input dimensions - *batch_dims, H, W = input_data.shape - - # Calculate output dimensions - out_H = (H - kH) // sH + 1 - out_W = (W - kW) // sW + 1 - - # Initialize output - output_shape = tuple(batch_dims) + (out_H, out_W) - output_data = np.zeros(output_shape, dtype=input_data.dtype) - - # Apply max pooling - for i in range(out_H): - for j in range(out_W): - h_start = i * sH - h_end = h_start + kH - w_start = j * sW - w_end = w_start + kW - - # Extract pooling region and take max - region = input_data[..., h_start:h_end, w_start:w_end] - output_data[..., i, j] = np.max(region, axis=(-2, -1)) - - # Preserve input type - if isinstance(x, Variable): - def maxpool_grad_fn(grad_output): - if x.requires_grad: - # Simplified gradient - just distribute back - # In full implementation, would track max locations - grad_input = np.zeros_like(input_data) - for i in range(out_H): - for j in range(out_W): - h_start = i * sH - h_end = h_start + kH - w_start = j * sW - w_end = w_start + kW - grad_input[..., h_start:h_end, w_start:w_end] += grad_output.data[..., i, j, np.newaxis, np.newaxis] / (kH * kW) - - x.backward(Variable(grad_input)) - - return Variable(output_data, requires_grad=x.requires_grad, grad_fn=maxpool_grad_fn) - else: - return Tensor(output_data) \ No newline at end of file diff --git a/tinytorch/nn/modules.py b/tinytorch/nn/modules.py deleted file mode 100644 index 67d55264..00000000 --- a/tinytorch/nn/modules.py +++ /dev/null @@ -1,107 +0,0 @@ -# ╔═══════════════════════════════════════════════════════════════════════════════╗ -# ║ 🚨 CRITICAL WARNING 🚨 ║ -# ║ AUTOGENERATED! DO NOT EDIT! ║ -# ║ ║ -# ║ This file is AUTOMATICALLY GENERATED from source modules. ║ -# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║ -# ║ ║ -# ║ ✅ TO EDIT: modules/source/XX_modules/modules_dev.py ║ -# ║ ✅ TO EXPORT: Run 'tito module complete ' ║ -# ║ ║ -# ║ 🛡️ STUDENT PROTECTION: This file contains critical fixes for Variable/ ║ -# ║ Tensor compatibility. Editing it directly WILL break CIFAR-10 training. ║ -# ║ ║ -# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║ -# ║ happens! The tinytorch/ directory is just the compiled output. ║ -# ╚═══════════════════════════════════════════════════════════════════════════════╝ -""" -Base Module class for TinyTorch neural network layers. - -This module provides the foundational Module class that enables: -- Automatic parameter registration -- Recursive parameter collection -- Clean composition of neural networks -- PyTorch-compatible interface - -Students implement the core algorithms while this infrastructure -provides the clean API patterns they expect. -""" - -from typing import Iterator, List - - -class Module: - """ - Base class for all neural network modules. - - Your models should subclass this class to automatically get: - - Parameter registration when you set attributes - - Recursive parameter collection via parameters() - - Clean callable interface model(x) instead of model.forward(x) - - This matches PyTorch's nn.Module interface for familiar patterns. - """ - - def __init__(self): - """Initialize module with parameter and submodule tracking.""" - # Use object.__setattr__ to avoid triggering our custom __setattr__ - object.__setattr__(self, '_parameters', []) - object.__setattr__(self, '_modules', []) - object.__setattr__(self, '_initialized', True) - - def __setattr__(self, name: str, value): - """ - Automatically register parameters and submodules. - - When you do: self.weight = Parameter(...), it gets auto-registered. - When you do: self.layer = Linear(...), it gets auto-registered. - """ - if not hasattr(self, '_initialized'): - # Still in __init__, use normal assignment - object.__setattr__(self, name, value) - return - - # Check if this is a Parameter (has requires_grad attribute and is True) - if hasattr(value, 'requires_grad') and value.requires_grad: - if value not in self._parameters: - self._parameters.append(value) - - # Check if this is a Module subclass - elif isinstance(value, Module): - if value not in self._modules: - self._modules.append(value) - - # Normal attribute assignment - object.__setattr__(self, name, value) - - def parameters(self) -> Iterator: - """ - Return an iterator over module parameters. - - This is used by optimizers to find all trainable parameters: - optimizer = Adam(model.parameters()) - """ - # Return our direct parameters - for param in self._parameters: - yield param - - # Recursively collect parameters from submodules - for module in self._modules: - for param in module.parameters(): - yield param - - def __call__(self, *args, **kwargs): - """ - Make modules callable: model(x) calls model.forward(x). - - This is the standard PyTorch pattern that students expect. - """ - return self.forward(*args, **kwargs) - - def forward(self, *args, **kwargs): - """ - Define the forward pass computation. - - Subclasses must implement this method. - """ - raise NotImplementedError("Subclasses must implement forward()") \ No newline at end of file diff --git a/tinytorch/profiling/__init__.py b/tinytorch/profiling/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tinytorch/profiling/profiler.py b/tinytorch/profiling/profiler.py new file mode 100644 index 00000000..f87c5764 --- /dev/null +++ b/tinytorch/profiling/profiler.py @@ -0,0 +1,49 @@ +# ╔═══════════════════════════════════════════════════════════════════════════════╗ +# ║ 🚨 CRITICAL WARNING 🚨 ║ +# ║ AUTOGENERATED! DO NOT EDIT! ║ +# ║ ║ +# ║ This file is AUTOMATICALLY GENERATED from source modules. ║ +# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║ +# ║ ║ +# ║ ✅ TO EDIT: modules/source/XX_profiler/profiler_dev.py ║ +# ║ ✅ TO EXPORT: Run 'tito module complete ' ║ +# ║ ║ +# ║ 🛡️ STUDENT PROTECTION: This file contains optimized implementations. ║ +# ║ Editing it directly may break module functionality and training. ║ +# ║ ║ +# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║ +# ║ happens! The tinytorch/ directory is just the compiled output. ║ +# ╚═══════════════════════════════════════════════════════════════════════════════╝ +# %% auto 0 +__all__ = [] + +# %% ../../modules/source/15_profiling/profiling_dev.ipynb 1 +import time +import numpy as np +import tracemalloc +from typing import Dict, List, Any, Optional, Tuple +from collections import defaultdict +import gc + +# Import our TinyTorch components for profiling +import sys +import os +sys.path.append(os.path.join(os.path.dirname(__file__), '..', '01_tensor')) +sys.path.append(os.path.join(os.path.dirname(__file__), '..', '03_layers')) +sys.path.append(os.path.join(os.path.dirname(__file__), '..', '09_spatial')) + +# For testing purposes - in real package these would be proper imports +try: + from tensor_dev import Tensor + from layers_dev import Linear, Sequential + from spatial_dev import Conv2d +except ImportError: + # Fallback - create minimal implementations for testing + class Tensor: + def __init__(self, data): + self.data = np.array(data) + self.shape = self.data.shape + def __mul__(self, other): + return Tensor(self.data * other.data) + def sum(self): + return Tensor(np.sum(self.data)) diff --git a/tinytorch/tinygpt.py b/tinytorch/tinygpt.py deleted file mode 100644 index 18f2a1af..00000000 --- a/tinytorch/tinygpt.py +++ /dev/null @@ -1,1094 +0,0 @@ -# ╔═══════════════════════════════════════════════════════════════════════════════╗ -# ║ 🚨 CRITICAL WARNING 🚨 ║ -# ║ AUTOGENERATED! DO NOT EDIT! ║ -# ║ ║ -# ║ This file is AUTOMATICALLY GENERATED from source modules. ║ -# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║ -# ║ ║ -# ║ ✅ TO EDIT: modules/source/[unknown]/[unknown]_dev.py ║ -# ║ ✅ TO EXPORT: Run 'tito module complete ' ║ -# ║ ║ -# ║ 🛡️ STUDENT PROTECTION: This file contains optimized implementations. ║ -# ║ Editing it directly may break module functionality and training. ║ -# ║ ║ -# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║ -# ║ happens! The tinytorch/ directory is just the compiled output. ║ -# ╚═══════════════════════════════════════════════════════════════════════════════╝ - -# %% auto 0 -__all__ = ['CrossEntropyLoss', 'Trainer', 'no_grad', 'CharTokenizer', 'MultiHeadAttention', 'create_causal_mask', 'LayerNorm', - 'TransformerBlock', 'PositionalEncoding', 'TinyGPT', 'LanguageModelLoss', 'LanguageModelAccuracy', - 'LanguageModelTrainer', 'shakespeare_demo', 'live_demo'] - -# %% ../modules/source/temp_holding/16_tinygpt/tinygpt_dev.ipynb 6 -import numpy as np -import time -from typing import Dict, List, Tuple, Any, Optional -from dataclasses import dataclass -import json - -# Import TinyTorch components - the foundation we've built -from .core.tensor import Tensor -from .core.layers import Dense -from .core.activations import ReLU, Softmax -from .core.optimizers import Adam, SGD - -# Define minimal classes for missing components -class CrossEntropyLoss: - def forward(self, logits, targets): - return 0.5 # Simplified for integration testing - -class Trainer: - def __init__(self, *args, **kwargs): - pass - -def no_grad(): - """Context manager for disabling gradients (simplified).""" - return None - -# %% ../modules/source/temp_holding/16_tinygpt/tinygpt_dev.ipynb 7 -class CharTokenizer: - """ - Character-level tokenizer for TinyGPT. - Converts text to token sequences and back. - """ - - def __init__(self, vocab_size: Optional[int] = None, - special_tokens: Optional[List[str]] = None): - self.vocab_size = vocab_size - self.special_tokens = special_tokens or ['', ''] - - # Core vocabulary mappings - self.char_to_idx: Dict[str, int] = {} - self.idx_to_char: Dict[int, str] = {} - - # Special token indices - self.unk_token = '' - self.pad_token = '' - self.unk_idx = 0 - self.pad_idx = 1 - - self.is_fitted = False - self.character_counts: Dict[str, int] = {} - - def fit(self, text: str) -> None: - """Build vocabulary from training text.""" - if not text: - raise ValueError("Cannot fit tokenizer on empty text") - - print(f"🔍 Analyzing text for vocabulary...") - print(f" Text length: {len(text):,} characters") - - # Count character frequencies - self.character_counts = {} - for char in text: - self.character_counts[char] = self.character_counts.get(char, 0) + 1 - - unique_chars = len(self.character_counts) - print(f" Unique characters found: {unique_chars}") - - # Build vocabulary with special tokens first - self.char_to_idx = {} - self.idx_to_char = {} - - for i, token in enumerate(self.special_tokens): - self.char_to_idx[token] = i - self.idx_to_char[i] = token - - self.unk_idx = self.char_to_idx[self.unk_token] - self.pad_idx = self.char_to_idx[self.pad_token] - - # Add characters by frequency - sorted_chars = sorted(self.character_counts.items(), - key=lambda x: x[1], reverse=True) - - current_idx = len(self.special_tokens) - chars_added = 0 - - for char, count in sorted_chars: - if char in self.char_to_idx: - continue - if self.vocab_size and current_idx >= self.vocab_size: - break - - self.char_to_idx[char] = current_idx - self.idx_to_char[current_idx] = char - current_idx += 1 - chars_added += 1 - - self.is_fitted = True - - print(f"✅ Vocabulary built:") - print(f" Final vocab size: {len(self.char_to_idx)}") - print(f" Characters included: {chars_added}") - print(f" Most frequent: {sorted_chars[:10]}") - - def encode(self, text: str) -> List[int]: - """Convert text to sequence of token indices.""" - if not self.is_fitted: - raise RuntimeError("Tokenizer must be fitted before encoding") - - if not text: - return [] - - indices = [] - unk_count = 0 - - for char in text: - if char in self.char_to_idx: - indices.append(self.char_to_idx[char]) - else: - indices.append(self.unk_idx) - unk_count += 1 - - if unk_count > 0: - unk_rate = unk_count / len(text) * 100 - print(f"⚠️ Encoding: {unk_count} unknown chars ({unk_rate:.1f}%)") - - return indices - - def decode(self, indices: List[int]) -> str: - """Convert sequence of token indices back to text.""" - if not self.is_fitted: - raise RuntimeError("Tokenizer must be fitted before decoding") - - if not indices: - return "" - - chars = [] - invalid_count = 0 - - for idx in indices: - if idx in self.idx_to_char: - char = self.idx_to_char[idx] - if char not in [self.pad_token]: # Skip padding - chars.append(char) - else: - invalid_count += 1 - - if invalid_count > 0: - print(f"⚠️ Decoding: {invalid_count} invalid indices skipped") - - return ''.join(chars) - - def get_vocab_size(self) -> int: - """Get current vocabulary size.""" - return len(self.char_to_idx) - - def encode_batch(self, texts: List[str], max_length: Optional[int] = None, - padding: bool = True) -> np.ndarray: - """Encode batch of texts with padding.""" - if not self.is_fitted: - raise RuntimeError("Tokenizer must be fitted before encoding") - - if not texts: - return np.array([]) - - encoded_texts = [self.encode(text) for text in texts] - - if max_length is None: - max_length = max(len(encoded) for encoded in encoded_texts) - - batch_size = len(texts) - batch_array = np.full((batch_size, max_length), self.pad_idx, dtype=np.int32) - - for i, encoded in enumerate(encoded_texts): - seq_len = min(len(encoded), max_length) - batch_array[i, :seq_len] = encoded[:seq_len] - - return batch_array - -# %% ../modules/source/temp_holding/16_tinygpt/tinygpt_dev.ipynb 11 -class MultiHeadAttention: - """ - Multi-head self-attention mechanism using TinyTorch Dense layers. - This is the key component that enables language understanding. - """ - - def __init__(self, d_model: int, num_heads: int, dropout: float = 0.1): - """ - Initialize multi-head attention. - - Args: - d_model: Model dimension (embedding size) - num_heads: Number of attention heads - dropout: Dropout rate (not implemented yet) - """ - assert d_model % num_heads == 0, "d_model must be divisible by num_heads" - - self.d_model = d_model - self.num_heads = num_heads - self.d_k = d_model // num_heads # Dimension per head - self.dropout = dropout - - # Linear projections using TinyTorch Dense layers! - self.w_q = Dense(d_model, d_model) # Query projection - self.w_k = Dense(d_model, d_model) # Key projection - self.w_v = Dense(d_model, d_model) # Value projection - self.w_o = Dense(d_model, d_model) # Output projection - - print(f"🔀 MultiHeadAttention initialized:") - print(f" Model dim: {d_model}, Heads: {num_heads}, Head dim: {self.d_k}") - - def forward(self, query: Tensor, key: Tensor, value: Tensor, - mask: Tensor = None) -> Tensor: - """ - Forward pass of multi-head attention. - - Educational Process: - 1. Project Q, K, V using Dense layers (reusing TinyTorch!) - 2. Split into multiple heads for parallel attention - 3. Compute scaled dot-product attention for each head - 4. Concatenate heads and project to output - """ - batch_size, seq_len, d_model = query.shape - - # Reshape for Dense layers (expects 2D input) - query_2d = Tensor(query.data.reshape(-1, d_model)) - key_2d = Tensor(key.data.reshape(-1, d_model)) - value_2d = Tensor(value.data.reshape(-1, d_model)) - - # Linear projections using TinyTorch Dense layers - Q_2d = self.w_q.forward(query_2d) - K_2d = self.w_k.forward(key_2d) - V_2d = self.w_v.forward(value_2d) - - # Reshape back to 3D - Q = Tensor(Q_2d.data.reshape(batch_size, seq_len, d_model)) - K = Tensor(K_2d.data.reshape(batch_size, seq_len, d_model)) - V = Tensor(V_2d.data.reshape(batch_size, seq_len, d_model)) - - # Reshape for multi-head attention - Q = self._reshape_for_attention(Q) # (batch, heads, seq_len, d_k) - K = self._reshape_for_attention(K) - V = self._reshape_for_attention(V) - - # Scaled dot-product attention - attention_output = self._scaled_dot_product_attention(Q, K, V, mask) - - # Combine heads and project output - attention_output = self._combine_heads(attention_output) - - # Final projection using Dense layer - attention_2d = Tensor(attention_output.data.reshape(-1, d_model)) - output_2d = self.w_o.forward(attention_2d) - output = Tensor(output_2d.data.reshape(batch_size, seq_len, d_model)) - - return output - - def _reshape_for_attention(self, x: Tensor) -> Tensor: - """Reshape tensor for multi-head attention.""" - batch_size, seq_len, d_model = x.shape - # Reshape to (batch, seq_len, num_heads, d_k) - reshaped = Tensor(x.data.reshape(batch_size, seq_len, self.num_heads, self.d_k)) - # Transpose to (batch, num_heads, seq_len, d_k) - return Tensor(reshaped.data.transpose(0, 2, 1, 3)) - - def _combine_heads(self, x: Tensor) -> Tensor: - """Combine attention heads back into single tensor.""" - batch_size, num_heads, seq_len, d_k = x.shape - # Transpose to (batch, seq_len, num_heads, d_k) - transposed = Tensor(x.data.transpose(0, 2, 1, 3)) - # Reshape to (batch, seq_len, d_model) - return Tensor(transposed.data.reshape(batch_size, seq_len, self.d_model)) - - def _scaled_dot_product_attention(self, Q: Tensor, K: Tensor, V: Tensor, - mask: Tensor = None) -> Tensor: - """Compute scaled dot-product attention.""" - # Compute attention scores: Q @ K^T - K_T = K.data.transpose(0, 1, 3, 2) # Transpose last two dims - scores = Tensor(np.matmul(Q.data, K_T)) - scores = scores * (1.0 / np.sqrt(self.d_k)) # Scale by sqrt(d_k) - - # Apply causal mask if provided - if mask is not None: - scores = scores + (mask * -1e9) # Large negative for masked positions - - # Apply softmax for attention weights - scores_max = np.max(scores.data, axis=-1, keepdims=True) - scores_shifted = scores.data - scores_max - exp_scores = np.exp(scores_shifted) - attention_weights = exp_scores / np.sum(exp_scores, axis=-1, keepdims=True) - attention_weights = Tensor(attention_weights) - - # Apply attention to values: attention_weights @ V - output = Tensor(np.matmul(attention_weights.data, V.data)) - - return output - -def create_causal_mask(seq_len: int) -> Tensor: - """ - Create causal mask for preventing attention to future tokens. - - Returns lower triangular matrix where: - - 0 = can attend (past/present) - - 1 = cannot attend (future) - """ - mask = np.triu(np.ones((seq_len, seq_len)), k=1) # Upper triangular - return Tensor(mask) - -# %% ../modules/source/temp_holding/16_tinygpt/tinygpt_dev.ipynb 15 -class LayerNorm: - """Layer normalization for transformer models.""" - - def __init__(self, d_model: int, eps: float = 1e-6): - self.d_model = d_model - self.eps = eps - - # Learnable parameters (simplified) - self.gamma = Tensor(np.ones(d_model)) - self.beta = Tensor(np.zeros(d_model)) - - def forward(self, x: Tensor) -> Tensor: - """Apply layer normalization.""" - # Compute mean and variance along last dimension - mean = np.mean(x.data, axis=-1, keepdims=True) - var = np.var(x.data, axis=-1, keepdims=True) - - # Normalize and scale - normalized = (x.data - mean) / np.sqrt(var + self.eps) - output = normalized * self.gamma.data + self.beta.data - - return Tensor(output) - -class TransformerBlock: - """ - Complete transformer block: Multi-head attention + feedforward network. - Uses TinyTorch Dense layers for the feedforward component! - """ - - def __init__(self, d_model: int, num_heads: int, d_ff: int, dropout: float = 0.1): - self.d_model = d_model - self.num_heads = num_heads - self.d_ff = d_ff - self.dropout = dropout - - # Multi-head self-attention - self.self_attention = MultiHeadAttention(d_model, num_heads, dropout) - - # Feedforward network using TinyTorch Dense layers! - self.ff_layer1 = Dense(d_model, d_ff) - self.ff_activation = ReLU() - self.ff_layer2 = Dense(d_ff, d_model) - - # Layer normalization - self.ln1 = LayerNorm(d_model) - self.ln2 = LayerNorm(d_model) - - print(f"🧱 TransformerBlock initialized:") - print(f" d_model: {d_model}, d_ff: {d_ff}, heads: {num_heads}") - - def forward(self, x: Tensor, mask: Tensor = None) -> Tensor: - """ - Forward pass of transformer block. - - Educational Process: - 1. Self-attention with residual connection and layer norm - 2. Feedforward network with residual connection and layer norm - 3. Both use the Add & Norm pattern from the original Transformer paper - """ - # Self-attention with residual connection - attn_output = self.self_attention.forward(x, x, x, mask) - x = self.ln1.forward(x + attn_output) # Add & Norm - - # Feedforward network with residual connection - # Reshape for Dense layers - batch_size, seq_len, d_model = x.shape - x_2d = Tensor(x.data.reshape(-1, d_model)) - - # Apply feedforward layers (using TinyTorch Dense!) - ff_output = self.ff_layer1.forward(x_2d) - ff_output = self.ff_activation.forward(ff_output) - ff_output = self.ff_layer2.forward(ff_output) - - # Reshape back and add residual - ff_output_3d = Tensor(ff_output.data.reshape(batch_size, seq_len, d_model)) - x = self.ln2.forward(x + ff_output_3d) # Add & Norm - - return x - -class PositionalEncoding: - """Sinusoidal positional encoding for sequence order.""" - - def __init__(self, d_model: int, max_length: int = 5000): - self.d_model = d_model - self.max_length = max_length - - # Create positional encoding matrix - pe = np.zeros((max_length, d_model)) - position = np.arange(0, max_length).reshape(-1, 1) - - # Compute sinusoidal encoding - div_term = np.exp(np.arange(0, d_model, 2) * -(np.log(10000.0) / d_model)) - - pe[:, 0::2] = np.sin(position * div_term) # Even positions - if d_model % 2 == 0: - pe[:, 1::2] = np.cos(position * div_term) # Odd positions - else: - pe[:, 1::2] = np.cos(position * div_term[:-1]) - - self.pe = Tensor(pe) - - def forward(self, x: Tensor) -> Tensor: - """Add positional encoding to embeddings.""" - batch_size, seq_len, d_model = x.shape - pos_encoding = Tensor(self.pe.data[:seq_len, :]) - return x + pos_encoding - -# %% ../modules/source/temp_holding/16_tinygpt/tinygpt_dev.ipynb 19 -class TinyGPT: - """ - Complete GPT-style transformer model using TinyTorch components. - - This model demonstrates that the same mathematical foundation used for - vision models can power language understanding and generation! - """ - - def __init__(self, vocab_size: int, d_model: int = 256, num_heads: int = 8, - num_layers: int = 6, d_ff: int = None, max_length: int = 1024, - dropout: float = 0.1): - """ - Initialize TinyGPT model. - - Args: - vocab_size: Size of the character vocabulary - d_model: Model dimension (embedding size) - num_heads: Number of attention heads - num_layers: Number of transformer layers - d_ff: Feedforward dimension (default: 4 * d_model) - max_length: Maximum sequence length - dropout: Dropout rate - """ - self.vocab_size = vocab_size - self.d_model = d_model - self.num_heads = num_heads - self.num_layers = num_layers - self.d_ff = d_ff or 4 * d_model - self.max_length = max_length - self.dropout = dropout - - # Token embeddings using TinyTorch Dense layer! - self.token_embedding = Dense(vocab_size, d_model) - - # Positional encoding - self.positional_encoding = PositionalEncoding(d_model, max_length) - - # Stack of transformer blocks - self.blocks = [ - TransformerBlock(d_model, num_heads, self.d_ff, dropout) - for _ in range(num_layers) - ] - - # Final layer norm and output projection - self.ln_final = LayerNorm(d_model) - self.output_projection = Dense(d_model, vocab_size) - - print(f"🤖 TinyGPT initialized:") - print(f" Vocab: {vocab_size}, Model dim: {d_model}") - print(f" Heads: {num_heads}, Layers: {num_layers}") - print(f" Parameters: ~{self.count_parameters():,}") - - def forward(self, input_ids: Tensor, use_cache: bool = False) -> Tensor: - """ - Forward pass of TinyGPT. - - Educational Process: - 1. Convert token indices to embeddings (using Dense layer!) - 2. Add positional encoding for sequence order - 3. Pass through stack of transformer blocks - 4. Project to vocabulary for next-token predictions - """ - batch_size, seq_len = input_ids.shape - - # Convert token indices to one-hot for embedding - one_hot = np.zeros((batch_size, seq_len, self.vocab_size)) - for b in range(batch_size): - for s in range(seq_len): - token_id = int(input_ids.data[b, s]) - if 0 <= token_id < self.vocab_size: - one_hot[b, s, token_id] = 1.0 - - # Token embeddings using TinyTorch Dense layer - one_hot_2d = Tensor(one_hot.reshape(-1, self.vocab_size)) - x_2d = self.token_embedding.forward(one_hot_2d) - x = Tensor(x_2d.data.reshape(batch_size, seq_len, self.d_model)) - - # Add positional encoding - x = self.positional_encoding.forward(x) - - # Create causal mask for autoregressive generation - mask = create_causal_mask(seq_len) - - # Pass through transformer blocks - for block in self.blocks: - x = block.forward(x, mask) - - # Final layer norm - x = self.ln_final.forward(x) - - # Project to vocabulary using TinyTorch Dense layer - x_2d = Tensor(x.data.reshape(-1, self.d_model)) - logits_2d = self.output_projection.forward(x_2d) - logits = Tensor(logits_2d.data.reshape(batch_size, seq_len, self.vocab_size)) - - return logits - - def generate(self, input_ids: Tensor, max_new_tokens: int = 50, - temperature: float = 1.0, do_sample: bool = True) -> Tensor: - """ - Generate text autoregressively. - - Educational Process: - 1. Start with input tokens - 2. For each new position: - a. Run forward pass to get next-token logits - b. Apply temperature scaling - c. Sample or choose most likely token - d. Append to sequence and repeat - """ - generated = input_ids.data.copy() - - for _ in range(max_new_tokens): - # Forward pass - logits = self.forward(Tensor(generated)) - - # Get logits for last token (next prediction) - next_token_logits = logits.data[0, -1, :] # (vocab_size,) - - # Apply temperature scaling - if temperature != 1.0: - next_token_logits = next_token_logits / temperature - - # Sample next token - if do_sample: - # Convert to probabilities and sample - probs = np.exp(next_token_logits) / np.sum(np.exp(next_token_logits)) - next_token = np.random.choice(len(probs), p=probs) - else: - # Greedy decoding - next_token = np.argmax(next_token_logits) - - # Append to sequence - generated = np.concatenate([ - generated, - np.array([[next_token]]) - ], axis=1) - - # Stop if we hit max length - if generated.shape[1] >= self.max_length: - break - - return Tensor(generated) - - def count_parameters(self) -> int: - """Estimate number of parameters.""" - params = 0 - - # Token embedding - params += self.vocab_size * self.d_model - - # Transformer blocks - for _ in range(self.num_layers): - # Multi-head attention (Q, K, V, O projections) - params += 4 * self.d_model * self.d_model - # Feedforward (2 layers) - params += 2 * self.d_model * self.d_ff - # Layer norms (2 per block) - params += 4 * self.d_model - - # Final layer norm and output projection - params += 2 * self.d_model + self.d_model * self.vocab_size - - return params - -# %% ../modules/source/temp_holding/16_tinygpt/tinygpt_dev.ipynb 23 -class LanguageModelLoss: - """Cross-entropy loss for language modeling with proper target shifting.""" - - def __init__(self, ignore_index: int = -100): - self.ignore_index = ignore_index - self.cross_entropy = CrossEntropyLoss() - - def forward(self, logits: Tensor, targets: Tensor) -> float: - """ - Compute language modeling loss. - - Educational Note: - Language models predict the NEXT token, so we shift targets: - Input: [1, 2, 3, 4] - Target: [2, 3, 4, ?] (predict token i+1 from tokens 0..i) - """ - batch_size, seq_len, vocab_size = logits.shape - - # Shift for next-token prediction - shifted_targets = targets.data[:, 1:] # Remove first token - shifted_logits = logits.data[:, :-1, :] # Remove last prediction - - # Reshape for cross-entropy - logits_2d = Tensor(shifted_logits.reshape(-1, vocab_size)) - targets_1d = Tensor(shifted_targets.reshape(-1)) - - return self.cross_entropy.forward(logits_2d, targets_1d) - -class LanguageModelAccuracy: - """Next-token prediction accuracy.""" - - def forward(self, logits: Tensor, targets: Tensor) -> float: - """Compute next-token prediction accuracy.""" - batch_size, seq_len, vocab_size = logits.shape - - # Shift for next-token prediction - shifted_targets = targets.data[:, 1:] - shifted_logits = logits.data[:, :-1, :] - - # Get predictions and compute accuracy - predictions = np.argmax(shifted_logits, axis=-1) - correct = np.sum(predictions == shifted_targets) - total = shifted_targets.size - - return correct / total - -class LanguageModelTrainer: - """Training infrastructure for TinyGPT models.""" - - def __init__(self, model, tokenizer, optimizer=None, loss_fn=None, metrics=None): - self.model = model - self.tokenizer = tokenizer - - # Default components (reusing TinyTorch!) - self.optimizer = optimizer or Adam([], learning_rate=0.001) # Empty params list for now - self.loss_fn = loss_fn or LanguageModelLoss() - self.metrics = metrics or [LanguageModelAccuracy()] - - print(f"🎓 LanguageModelTrainer initialized:") - print(f" Model: {type(model).__name__}") - print(f" Tokenizer vocab: {tokenizer.get_vocab_size()}") - print(f" Optimizer: {type(self.optimizer).__name__}") - - def create_training_data(self, text: str, seq_length: int, - batch_size: int) -> Tuple[np.ndarray, np.ndarray]: - """ - Create training batches from text. - - Educational Process: - 1. Tokenize the entire text - 2. Split into overlapping sequences - 3. Input = tokens[:-1], Target = tokens[1:] (next token prediction) - 4. Group into batches - """ - # Tokenize text - tokens = self.tokenizer.encode(text) - - if len(tokens) < seq_length + 1: - raise ValueError(f"Text too short ({len(tokens)} tokens) for sequence length {seq_length}") - - # Create overlapping sequences - sequences = [] - for i in range(len(tokens) - seq_length): - seq = tokens[i:i + seq_length + 1] # +1 for target - sequences.append(seq) - - sequences = np.array(sequences) - - # Split input and targets - inputs = sequences[:, :-1] # All but last token - targets = sequences[:, 1:] # All but first token (shifted) - - # Create batches - num_batches = len(sequences) // batch_size - if num_batches == 0: - raise ValueError(f"Not enough sequences for batch size {batch_size}") - - # Trim to even batches - total_samples = num_batches * batch_size - inputs = inputs[:total_samples] - targets = targets[:total_samples] - - # Reshape into batches - input_batches = inputs.reshape(num_batches, batch_size, seq_length) - target_batches = targets.reshape(num_batches, batch_size, seq_length) - - return input_batches, target_batches - - def fit(self, text: str, epochs: int = 5, seq_length: int = 64, - batch_size: int = 8, val_split: float = 0.2, - verbose: bool = True) -> Dict[str, List[float]]: - """ - Train the language model. - - This follows the same pattern as TinyTorch vision model training! - """ - if verbose: - print(f"🚀 Starting TinyGPT training:") - print(f" Text length: {len(text):,} chars") - print(f" Epochs: {epochs}, Seq length: {seq_length}") - print(f" Batch size: {batch_size}, Val split: {val_split}") - - # Split data - split_idx = int(len(text) * (1 - val_split)) - train_text = text[:split_idx] - val_text = text[split_idx:] - - # Create training data - try: - train_inputs, train_targets = self.create_training_data( - train_text, seq_length, batch_size) - val_inputs, val_targets = self.create_training_data( - val_text, seq_length, batch_size) - except ValueError as e: - print(f"❌ Data preparation failed: {e}") - return { - 'train_loss': [2.0] * epochs, - 'val_loss': [2.1] * epochs, - 'train_accuracy': [0.1] * epochs, - 'val_accuracy': [0.09] * epochs - } - - if verbose: - print(f" Train batches: {len(train_inputs)}") - print(f" Val batches: {len(val_inputs)}") - print() - - # Training history - history = { - 'train_loss': [], - 'val_loss': [], - 'train_accuracy': [], - 'val_accuracy': [] - } - - # Training loop (same pattern as TinyTorch!) - for epoch in range(epochs): - epoch_start = time.time() - - # Training phase - train_losses = [] - train_accuracies = [] - - for batch_idx in range(len(train_inputs)): - inputs = Tensor(train_inputs[batch_idx]) - targets = Tensor(train_targets[batch_idx]) - - # Forward pass - logits = self.model.forward(inputs) - - # Compute loss and metrics - loss = self.loss_fn.forward(logits, targets) - train_losses.append(loss) - - for metric in self.metrics: - acc = metric.forward(logits, targets) - train_accuracies.append(acc) - - # Backward pass (simplified) - self.optimizer.zero_grad() - self.optimizer.step() - - # Validation phase - val_losses = [] - val_accuracies = [] - - for batch_idx in range(len(val_inputs)): - inputs = Tensor(val_inputs[batch_idx]) - targets = Tensor(val_targets[batch_idx]) - - logits = self.model.forward(inputs) - loss = self.loss_fn.forward(logits, targets) - val_losses.append(loss) - - for metric in self.metrics: - acc = metric.forward(logits, targets) - val_accuracies.append(acc) - - # Record results - history['train_loss'].append(np.mean(train_losses)) - history['val_loss'].append(np.mean(val_losses)) - history['train_accuracy'].append(np.mean(train_accuracies)) - history['val_accuracy'].append(np.mean(val_accuracies)) - - epoch_time = time.time() - epoch_start - - if verbose: - print(f" Epoch {epoch + 1}/{epochs} ({epoch_time:.1f}s):") - print(f" Train: Loss {history['train_loss'][-1]:.4f}, Acc {history['train_accuracy'][-1]:.3f}") - print(f" Val: Loss {history['val_loss'][-1]:.4f}, Acc {history['val_accuracy'][-1]:.3f}") - - if verbose: - print(f"\n✅ Training completed!") - - return history - - def generate_text(self, prompt: str, max_length: int = 50, - temperature: float = 1.0) -> str: - """Generate text from a prompt.""" - if not prompt: - return "" - - # Encode prompt - prompt_tokens = self.tokenizer.encode(prompt) - if not prompt_tokens: - return prompt - - # Generate - input_ids = Tensor(np.array([prompt_tokens])) - - try: - generated_tensor = self.model.generate( - input_ids, - max_new_tokens=max_length - len(prompt_tokens), - temperature=temperature, - do_sample=True - ) - - # Decode - generated_tokens = generated_tensor.data[0].tolist() - return self.tokenizer.decode(generated_tokens) - - except Exception as e: - print(f"⚠️ Generation failed: {e}") - # Fallback - fallback_tokens = prompt_tokens + [np.random.randint(0, self.tokenizer.get_vocab_size()) - for _ in range(min(10, max_length - len(prompt_tokens)))] - return self.tokenizer.decode(fallback_tokens) - -# %% ../modules/source/temp_holding/16_tinygpt/tinygpt_dev.ipynb 27 -def shakespeare_demo(): - """Complete Shakespeare demo showing TinyGPT in action""" - print("🎭 TinyGPT Shakespeare Demo") - print("=" * 60) - print("Training a character-level GPT on Shakespeare using TinyTorch!") - print() - - # Extended Shakespeare text for better training - shakespeare_text = """To be, or not to be, that is the question: -Whether 'tis nobler in the mind to suffer -The slings and arrows of outrageous fortune, -Or to take arms against a sea of troubles -And by opposing end them. To die—to sleep, -No more; and by a sleep to say we end -The heart-ache and the thousand natural shocks -That flesh is heir to: 'tis a consummation -Devoutly to be wish'd. To die, to sleep; -To sleep, perchance to dream—ay, there's the rub: -For in that sleep of death what dreams may come, -When we have shuffled off this mortal coil, -Must give us pause—there's the respect -That makes calamity of so long life. - -Shall I compare thee to a summer's day? -Thou art more lovely and more temperate: -Rough winds do shake the darling buds of May, -And summer's lease hath all too short a date: -Sometime too hot the eye of heaven shines, -And often is his gold complexion dimmed; -And every fair from fair sometime declines, -By chance, or nature's changing course, untrimmed; -But thy eternal summer shall not fade, -Nor lose possession of that fair thou ow'st, -Nor shall death brag thou wander'st in his shade, -When in eternal lines to time thou grow'st: -So long as men can breathe or eyes can see, -So long lives this, and this gives life to thee.""" - - print(f"📚 Shakespeare text: {len(shakespeare_text):,} characters") - print(f" Words: {len(shakespeare_text.split()):,}") - print(f" Lines: {len(shakespeare_text.split(chr(10)))}") - print() - - # Create and fit tokenizer - print("🔤 Creating character tokenizer...") - tokenizer = CharTokenizer(vocab_size=80) - tokenizer.fit(shakespeare_text) - vocab_size = tokenizer.get_vocab_size() - print(f" Final vocabulary size: {vocab_size}") - print() - - # Create TinyGPT model - print("🤖 Creating TinyGPT model...") - model = TinyGPT( - vocab_size=vocab_size, - d_model=128, # Model dimension - num_heads=8, # Attention heads - num_layers=4, # Transformer layers - d_ff=512, # Feedforward dimension - max_length=256, # Max sequence length - dropout=0.1 - ) - print() - - # Create trainer - print("🎓 Setting up trainer...") - trainer = LanguageModelTrainer(model, tokenizer) - print() - - # Generate text BEFORE training - print("📝 Text generation BEFORE training (should be random):") - pre_prompts = ["To be", "Shall I", "The"] - for prompt in pre_prompts: - generated = trainer.generate_text(prompt, max_length=30, temperature=1.0) - print(f" '{prompt}' → '{generated[:50]}...'") - print() - - # Train the model - print("🚀 Training TinyGPT on Shakespeare...") - start_time = time.time() - - history = trainer.fit( - text=shakespeare_text, - epochs=5, - seq_length=32, - batch_size=4, - val_split=0.2, - verbose=True - ) - - training_time = time.time() - start_time - print(f"\n⏱️ Training completed in {training_time:.1f} seconds") - print() - - # Analyze training results - print("📈 Training Analysis:") - final_train_loss = history['train_loss'][-1] - final_val_loss = history['val_loss'][-1] - final_train_acc = history['train_accuracy'][-1] - final_val_acc = history['val_accuracy'][-1] - - print(f" Final train loss: {final_train_loss:.4f}") - print(f" Final val loss: {final_val_loss:.4f}") - print(f" Final train acc: {final_train_acc:.3f}") - print(f" Final val acc: {final_val_acc:.3f}") - - if final_train_loss < final_val_loss * 0.8: - print(" ⚠️ Possible overfitting detected") - else: - print(" ✅ Training looks healthy") - print() - - # Generate text AFTER training - print("📝 Text generation AFTER training:") - post_prompts = ["To be", "Shall I", "The", "And", "But"] - - for prompt in post_prompts: - for temp in [0.3, 0.7, 1.0]: - generated = trainer.generate_text(prompt, max_length=40, temperature=temp) - print(f" '{prompt}' (T={temp}) → '{generated}'") - print() - - # Shakespeare completion test - print("🎯 Shakespeare Completion Test:") - completions = [ - "To be, or not to", - "Shall I compare thee", - "The slings and arrows", - "When in eternal lines" - ] - - for completion_prompt in completions: - generated = trainer.generate_text(completion_prompt, max_length=35, temperature=0.5) - print(f" '{completion_prompt}' → '{generated}'") - print() - - # Performance analysis - print("⚡ Performance Analysis:") - total_params = model.count_parameters() - tokens_processed = len(tokenizer.encode(shakespeare_text)) * history['train_loss'].__len__() - - print(f" Model parameters: {total_params:,}") - print(f" Training time: {training_time:.1f}s") - print(f" Tokens processed: {tokens_processed:,}") - print(f" Memory estimate: ~{total_params * 4 / 1024 / 1024:.1f} MB") - print() - - return trainer, model, tokenizer - -# Only run demo if executed directly -if __name__ == "__main__": - demo_results = shakespeare_demo() - -# %% ../modules/source/temp_holding/16_tinygpt/tinygpt_dev.ipynb 37 -def live_demo(): - """ - Live TinyGPT demonstration with typewriter effect. - Shows real-time text generation character by character. - """ - import time - - def typewriter_effect(text, delay=0.05): - """Print text with typewriter effect""" - for char in text: - print(char, end='', flush=True) - time.sleep(delay) - print() - - print("🤖 TinyGPT Live Demo") - print("=" * 40) - print("Watch TinyGPT learn and generate text!") - print() - - # Shakespeare training text - text = """To be, or not to be, that is the question: -Whether 'tis nobler in the mind to suffer -The slings and arrows of outrageous fortune, -Or to take arms against a sea of troubles -And by opposing end them. To die—to sleep, -No more; and by a sleep to say we end -The heart-ache and the thousand natural shocks -That flesh is heir to: 'tis a consummation -Devoutly to be wish'd.""" - - print(f"📚 Training text: {len(text)} characters") - - # Setup - typewriter_effect("🔤 Creating tokenizer...") - tokenizer = CharTokenizer(vocab_size=80) - tokenizer.fit(text) - vocab_size = tokenizer.get_vocab_size() - print(f" ✅ Vocabulary: {vocab_size} characters") - - typewriter_effect("🧠 Building TinyGPT...") - model = TinyGPT( - vocab_size=vocab_size, - d_model=64, - num_heads=4, - num_layers=2, - d_ff=256, - max_length=100, - dropout=0.1 - ) - print(f" ✅ Model: {model.count_parameters():,} parameters") - - typewriter_effect("🎓 Training neural network...") - trainer = LanguageModelTrainer(model, tokenizer) - - # Pre-training generation - print("\n📝 BEFORE training:") - prompt = "To be" - print(f"🎯 '{prompt}' → ", end='', flush=True) - pre_gen = trainer.generate_text(prompt, max_length=20, temperature=1.0) - typewriter_effect(pre_gen[len(prompt):], delay=0.08) - - # Train - print("\n🚀 Training...") - trainer.fit(text=text, epochs=2, seq_length=16, batch_size=2, verbose=False) - - # Post-training generation - print("\n📝 AFTER training:") - for temp in [0.5, 0.8]: - print(f"🎯 '{prompt}' (T={temp}) → ", end='', flush=True) - post_gen = trainer.generate_text(prompt, max_length=25, temperature=temp) - typewriter_effect(post_gen[len(prompt):], delay=0.1) - - print("\n✨ Demo complete! TinyGPT generated text character by character.") - print("🔥 Built entirely from scratch with TinyTorch components!") - -# Only run tests if executed directly -if __name__ == "__main__": - print("🎭 TinyGPT Module Complete!") - print() - print("Available demos:") - print("• shakespeare_demo() - Full training and generation demo") - print("• live_demo() - Live typing effect demonstration") - print("• run_comprehensive_tests() - Complete test suite") - print() - print("Running live demo...") - live_demo() diff --git a/tito/commands/export.py b/tito/commands/export.py index c40c45b9..3d2fd459 100644 --- a/tito/commands/export.py +++ b/tito/commands/export.py @@ -5,11 +5,15 @@ Export command for TinyTorch CLI: exports notebook code to Python package using import subprocess import sys import re +import stat from argparse import ArgumentParser, Namespace from pathlib import Path from typing import Optional, Dict from rich.panel import Panel from rich.text import Text +import logging + +logger = logging.getLogger(__name__) from .base import BaseCommand from .checkpoint import CheckpointSystem @@ -544,9 +548,11 @@ class ExportCommand(BaseCommand): def run(self, args: Namespace) -> int: console = self.console + logger.info("Starting export command") # Determine what to export if hasattr(args, 'modules') and args.modules: + logger.info(f"Exporting specific modules: {args.modules}") # Export multiple specific modules modules_to_export = args.modules @@ -557,6 +563,7 @@ class ExportCommand(BaseCommand): # Process each module for module_name in modules_to_export: + logger.debug(f"Processing module: {module_name}") module_path = Path(f"modules/source/{module_name}") if not module_path.exists(): console.print(Panel(f"[red]❌ Module '{module_name}' not found in modules/source/[/red]", @@ -579,21 +586,35 @@ class ExportCommand(BaseCommand): console.print(f"📝 Converting {module_name} Python file to notebook...") if not self._convert_py_to_notebook(module_path): - console.print(Panel(f"[red]❌ Failed to convert .py file to notebook for {module_name}. Is jupytext installed?[/red]", - title="Conversion Error", border_style="red")) + logger.error(f"Failed to convert .py file to notebook for {module_name}") return 1 exported_notebooks.append(str(notebook_file)) - console.print(f"🔄 Exporting {len(exported_notebooks)} notebooks to tinytorch package...") + logger.info(f"Exporting {len(exported_notebooks)} notebooks to tinytorch package") # Export all notebooks success_count = 0 - for notebook_path in exported_notebooks: + for notebook_path_str in exported_notebooks: try: - notebook_name = Path(notebook_path).name + notebook_path = Path(notebook_path_str) + notebook_name = notebook_path.name console.print(f"[dim]🔄 Exporting {notebook_name} to tinytorch package...[/dim]") + + # --- FIX: Ensure target file is writable before exporting --- + module_path = notebook_path.parent + export_target = self._get_export_target(module_path) + if export_target != "unknown": + target_file_rel_path = export_target.replace('.', '/') + '.py' + target_file = Path("tinytorch") / target_file_rel_path + + if target_file.exists(): + try: + # Add write permission for the owner to overwrite the file + target_file.chmod(target_file.stat().st_mode | stat.S_IWUSR) + except Exception as e: + console.print(f"[yellow]⚠️ Could not make {target_file} writable: {e}[/yellow]") - cmd = ["nbdev_export", "--path", notebook_path] + cmd = ["nbdev_export", "--path", notebook_path_str] console.print(f"[dim]⚙️ Running: nbdev_export --path {notebook_name}[/dim]") result = subprocess.run(cmd, capture_output=True, text=True, cwd=Path.cwd()) @@ -613,6 +634,7 @@ class ExportCommand(BaseCommand): console.print(f"❌ Error exporting {Path(notebook_path).name}: {e}") if success_count == len(exported_notebooks): + logger.info("All notebooks exported successfully") # ALWAYS add auto-generated warnings immediately after export self._add_autogenerated_warnings(console) @@ -623,10 +645,12 @@ class ExportCommand(BaseCommand): title="Export Success", border_style="green")) return 0 else: + logger.warning(f"Exported {success_count}/{len(exported_notebooks)} modules. Some exports failed.") console.print(Panel(f"[yellow]⚠️ Exported {success_count}/{len(exported_notebooks)} modules. Some exports failed.[/yellow]", title="Partial Success", border_style="yellow")) return 1 elif hasattr(args, 'all') and args.all: + logger.info("Exporting all modules") console.print(Panel("🔄 Exporting All Modules to Package", title="Complete Export Workflow", border_style="bright_cyan")) @@ -634,6 +658,7 @@ class ExportCommand(BaseCommand): console.print("📝 Converting all Python files to notebooks...") converted = self._convert_all_modules() if not converted: + logger.error("No modules converted. Check if jupytext is installed and .py files exist.") console.print(Panel("[red]❌ No modules converted. Check if jupytext is installed and .py files exist.[/red]", title="Conversion Error", border_style="red")) return 1 @@ -644,6 +669,7 @@ class ExportCommand(BaseCommand): # Step 2: Use nbdev_export for all modules cmd = ["nbdev_export"] else: + logger.error("Must specify either module names or --all") console.print(Panel("[red]❌ Must specify either module names or --all[/red]\n\n" "[dim]Examples:[/dim]\n" "[dim] tito module export 01_tensor[/dim]\n" @@ -656,6 +682,7 @@ class ExportCommand(BaseCommand): result = subprocess.run(cmd, capture_output=True, text=True, cwd=Path.cwd()) if result.returncode == 0: + logger.info("Export command completed successfully") # ALWAYS add auto-generated warnings immediately after export self._add_autogenerated_warnings(console) @@ -678,6 +705,7 @@ class ExportCommand(BaseCommand): self._show_export_details(console, None) else: + logger.error(f"Export failed with return code {result.returncode}") error_msg = result.stderr.strip() if result.stderr else "Unknown error" console.print(Panel(f"[red]❌ Export failed:\n{error_msg}[/red]", title="Export Error", border_style="red")) @@ -695,8 +723,10 @@ class ExportCommand(BaseCommand): return result.returncode except FileNotFoundError: - console.print(Panel("[red]❌ nbdev not found. Install with: pip install nbdev[/red]", - title="Missing Dependency", border_style="red")) + logger.exception("nbdev not found. Install with: pip install nbdev") + return 1 + except Exception as e: + logger.exception(f"Unexpected error during export: {e}") return 1 def _auto_enable_protection(self, console): diff --git a/tito/commands/reset.py b/tito/commands/reset.py index 02596d3c..2a6ef833 100644 --- a/tito/commands/reset.py +++ b/tito/commands/reset.py @@ -52,25 +52,19 @@ class ResetCommand(BaseCommand): return 0 reset_text = Text() - reset_text.append("🗑️ Removing generated files:\n", style="bold red") + reset_text.append("🗑️ Removing all exported files:\n", style="bold red") - # Remove generated Python files but keep __init__.py files and directory structure + # Simple approach: remove all .py files except __init__.py files_removed = 0 for py_file in tinytorch_path.rglob("*.py"): if py_file.name != "__init__.py": - # Check if it's an auto-generated file try: - with open(py_file, 'r') as f: - # Check first 5 lines for AUTOGENERATED marker - first_lines = ''.join(f.readline() for _ in range(5)) - if "AUTOGENERATED" in first_lines or "_modidx.py" in str(py_file): - rel_path = py_file.relative_to(tinytorch_path) - reset_text.append(f" 🗑️ tinytorch/{rel_path}\n", style="red") - py_file.unlink() - files_removed += 1 - except Exception: - # If we can't read the file, skip it for safety - pass + rel_path = py_file.relative_to(tinytorch_path) + reset_text.append(f" 🗑️ tinytorch/{rel_path}\n", style="red") + py_file.unlink() + files_removed += 1 + except Exception as e: + reset_text.append(f" ❌ Failed to remove {py_file}: {e}\n", style="red") # Remove __pycache__ directories for pycache in tinytorch_path.rglob("__pycache__"):