Implementation Documentation

This directory is for documenting implementation reasoning, design decisions, and algorithm principles. These documents are an important part of the educational value of the Hahaha project.

📁 Directory Structure

The structure of the explains/ directory should correspond to the code directory structure:

explains/
├── index.md              # This file
├── math/                 # Corresponds to core/include/math/
│   ├── tensor.md        # Tensor implementation reasoning
│   └── tensor-wrapper.md
├── compute/              # Corresponds to core/include/compute/
│   ├── graph.md         # Computational graph implementation reasoning
│   └── autograd.md      # Automatic differentiation implementation reasoning
├── ml/                   # Corresponds to core/include/ml/
│   ├── optimizer/       # Optimizer implementation reasoning
│   │   ├── sgd.md
│   │   └── adam.md
│   ├── model/           # Model implementation reasoning
│   │   ├── linear.md
│   │   └── knn.md
│   └── loss/            # Loss function implementation reasoning
│       └── mse.md
├── backend/             # Corresponds to core/include/backend/
│   ├── device.md
│   └── vectorize.md
└── display/             # Corresponds to core/include/display/
    └── visualizer.md

✍️ Suggested Content

Each implementation documentation can include the following:

1. Feature Overview

What does this feature/module do?
What problem does it solve?

2. Design Reasoning

Why was this implementation approach chosen?
What alternatives existed? Why weren't they chosen?
What factors were considered during design?

3. Algorithm Principles

Mathematical principles of the core algorithm
Detailed explanation of algorithm steps
Key formulas and derivations (if applicable)

4. Implementation Details

Explanation of key data structures
Implementation logic of important functions
How edge cases are handled

5. Performance Considerations

Time complexity analysis
Space complexity analysis
Optimization points and trade-offs

6. Usage Examples

Simple code examples
Common usage scenarios

📝 Writing Guidelines

Document Naming

Use lowercase letters and hyphens
File names should clearly describe the content
Examples: adam-optimizer.md, tensor-broadcast.md

Document Format

Use Markdown format
Use code blocks, formulas, and diagrams appropriately
Keep structure clear, use heading hierarchy to organize content

Content Requirements

Clear and understandable: express ideas in concise, clear language
Logically complete: include necessary background information and reasoning
Rich examples: provide code examples or pseudocode
Personal thinking: reflect your design reasoning and understanding

AI Assistance

We fully support using AI to help write documentation, for example:

Use AI to polish your verbal descriptions
Use AI to generate mathematical formulas
Use AI to check grammar and formatting

However, we prefer to see:

Documentation with personal thinking and understanding
Reflection of your design decision process
Avoid purely AI-generated content

How to Contribute - Learn how to write implementation documentation
Code Style - Learn about coding standards
Architecture Design - View system-level design documents

💡 Example

Here is an example structure for an implementation documentation:

# Adam Optimizer Implementation Reasoning

## Feature Overview

Adam (Adaptive Moment Estimation) is an adaptive learning rate optimization algorithm...

## Design Reasoning

### Why Adam?

Adam combines the advantages of Momentum and RMSprop...

### Implementation Approach

We chose to implement using template classes to support different data types...

## Algorithm Principles

### Mathematical Formulas

Adam's core formulas include:
- First moment estimate: ...
- Second moment estimate: ...
- Parameter update: ...

### Algorithm Steps

1. Initialize...
2. Compute gradients...
3. Update parameters...

## Implementation Details

### Key Data Structures

```cpp
class AdamOptimizer {
    // ...
};

Important Functions

step(): Execute one optimization step
zeroGrad(): Zero out gradients

Performance Considerations

Time complexity: O(n), where n is the number of parameters
Space complexity: O(n), need to store momentum and variance

Usage Examples

auto optimizer = AdamOptimizer(parameters, 0.001);
optimizer.zeroGrad();
loss.backward();
optimizer.step();


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**Remember**: Good implementation documentation not only helps others understand the code, but also helps you better understand design decisions. Let's build a valuable knowledge base together! 📚✨

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