aha/docs/development.md

Development Guide

This guide covers contributing to AHA, including development setup, adding new models, and submitting contributions.

Table of Contents

• Development Setup
• Project Structure
• Adding a New Model
• Testing
• Code Style
• Submitting Contributions
• Release Process

Development Setup

Prerequisites

• Rust 1.85+ (edition 2024)
• Git
• Optional: CUDA Toolkit, Metal, FFmpeg (for feature development)

Clone and Build

# Clone the repository
git clone https://github.com/jhqxxx/aha.git
cd aha

# Build in debug mode (faster compilation)
cargo build

# Build in release mode (better performance)
cargo build --release

# Build with features
cargo build --features cuda,flash-attn
cargo build --features metal
cargo build --features ffmpeg

Development Workflow

# Run the CLI
cargo run -- -m qwen3-0.6b

# Run tests
cargo test

# Run specific test
cargo test test_qwen3vl_generate

# Run with logging
RUST_LOG=debug cargo run -- -m qwen3-0.6b

# Check code without building
cargo check

# Format code
cargo fmt

# Run linter
cargo clippy

Project Structure

aha/
├── Cargo.toml              # Build configuration
├── src/
│   ├── main.rs             # CLI entry point
│   ├── lib.rs              # Library exports
│   ├── api.rs              # HTTP API handlers
│   ├── exec/               # CLI command implementations
│   │   ├── mod.rs
│   │   ├── cli.rs
│   │   ├── serv.rs
│   │   ├── download.rs
│   │   └── run.rs
│   ├── models/             # Model implementations
│   │   ├── mod.rs          # Model factory and exports
│   │   ├── common/         # Shared model utilities
│   │   │   ├── config.rs
│   │   │   └── mod.rs
│   │   ├── qwen3vl/        # Qwen3-VL model
│   │   │   ├── config.rs
│   │   │   ├── model.rs
│   │   │   ├── generate.rs
│   │   │   ├── processor.rs
│   │   │   └── mod.rs
│   │   ├── voxcpm/         # VoxCPM model
│   │   └── ...             # Other models
│   ├── tokenizer/          # Tokenization utilities
│   ├── chat_template/      # Chat template handling
│   ├── position_embed/     # Positional embeddings
│   └── utils/              # Utility functions
│       ├── audio_utils.rs
│       ├── image_utils.rs
│       ├── tensor_utils.rs
│       └── mod.rs
├── tests/                  # Integration tests
│   ├── test_qwen2_5vl.rs
│   ├── test_qwen3vl.rs
│   └── ...
└── docs/                   # Documentation

Adding a New Model

This section provides a step-by-step guide for adding a new model to AHA.

Step 1: Create Model Directory

Create a new directory under src/models/:

mkdir -p src/models/newmodel

Step 2: Implement Model Files

Create the following files in src/models/newmodel/:

config.rs

Define model configuration:

use serde::{Deserialize, Serialize};

#[derive(Debug, Clone, Deserialize, Serialize)]
pub struct NewModelConfig {
    pub vocab_size: usize,
    pub hidden_size: usize,
    pub num_hidden_layers: usize,
    pub num_attention_heads: usize,
    // ... other config fields
}


#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GenerationConfig {
    pub temperature: f64,
    pub top_p: f64,
    pub max_tokens: usize,
}

model.rs

Implement the model architecture:

use candle::{Result, Tensor};
use candle_nn::{Module, VarBuilder};

use super::config::NewModelConfig;

pub struct NewModel {
    // Define model components
    config: NewModelConfig,
}

impl NewModel {
    pub fn load(vb: VarBuilder, config: &NewModelConfig) -> Result<Self> {
        // Load model weights
        Ok(Self {
            config: config.clone(),
        })
    }

    pub fn forward(&self, input_ids: &Tensor) -> Result<Tensor> {
        // Implement forward pass
        Ok(input_ids.clone())
    }
}

generate.rs

Implement the GenerateModel trait:

use std::path::Path;
use anyhow::Result;
use candle::Device;

use super::config::{GenerationConfig, NewModelConfig};
use aha::models::common::GenerateModel;

pub struct NewModelGenerate {
    // Define generate state
    model: super::model::NewModel,
    config: GenerationConfig,
    device: Device,
}

impl GenerateModel for NewModelGenerate {
    type Config = NewModelConfig;
    type GenerateConfig = GenerationConfig;

    fn init(
        model_path: &str,
        config: Option<Self::Config>,
        generate_config: Option<Self::GenerateConfig>,
    ) -> Result<Self> {
        // Load model from disk
        let device = Device::Cpu;
        let config = config.unwrap_or_default();
        let generate_config = generate_config.unwrap_or_default();

        // Load weights
        let model_path = Path::new(model_path);
        // ... load model implementation

        Ok(Self {
            model: todo!(),
            config: generate_config,
            device,
        })
    }

    fn generate(&mut self, prompt: &str) -> Result<String> {
        // Tokenize prompt
        // Run inference
        // Decode output
        Ok(prompt.to_string())
    }
}

processor.rs (optional)

For complex models with multimodal input:

use anyhow::Result;

pub struct NewModelProcessor {
    // Processing state
}

impl NewModelProcessor {
    pub fn new() -> Self {
        Self {}
    }

    pub fn process_text(&self, text: &str) -> Result<Vec<String>> {
        // Process text input
        Ok(vec![text.to_string()])
    }

    pub fn process_image(&self, image_path: &str) -> Result<Tensor> {
        // Process image input
        todo!()
    }
}

mod.rs

Export the model:

mod config;
mod model;
mod generate;
pub mod processor;

pub use generate::NewModelGenerate;
pub use config::{GenerationConfig, NewModelConfig};

Step 3: Register Model

Update src/models/mod.rs:

// Add to imports
pub mod newmodel;

// Add to WhichModel enum
#[derive(ValueEnum, Clone, Debug, PartialEq, Eq)]
pub enum WhichModel {
    // ... existing models
    NewModel,
}

// Add to model loading
pub fn load_model(
    model_type: &WhichModel,
    model_path: &str,
    device: &Device,
) -> Result<Box<dyn common::GenerateModel>> {
    match model_type {
        // ... existing models
        WhichModel::NewModel => {
            Ok(Box::new(newmodel::NewModelGenerate::init(model_path, None, None)?))
        }
    }
}

Step 4: Update CLI

Update src/main.rs to include the new model in help text and CLI options if needed.

Step 5: Add Tests

Create tests/test_newmodel.rs:

use anyhow::Result;

#[test]
fn test_newmodel_generate() -> Result<()> {
    let model_path = "path/to/test/model";
    let mut model = aha::models::newmodel::NewModelGenerate::init(model_path, None, None)?;
    
    let result = model.generate("Test prompt")?;
    assert!(!result.is_empty());
    
    Ok(())
}

Step 6: Update Documentation

Update the following files:

• README.md - Add model to supported models list
• docs/cli.md - Add model ID to model list
• docs/api.md - Add model to supported models section
• CHANGELOG.md - Add entry for new model

Testing

Unit Tests

# Run all tests
cargo test

# Run specific test
cargo test test_newmodel_generate

# Run with output
cargo test -- --nocapture

# Run tests in parallel
cargo test -- --test-threads=4

Integration Tests

Integration tests are located in tests/:

# Run specific integration test
cargo test --test test_qwen3vl

Manual Testing

Test the CLI and API manually:

# Test CLI
cargo run -- -m newmodel

# Test API
cargo run -- -m newmodel -p 8080
curl http://127.0.0.1:8080/chat/completions -d '{...}'

Model-Specific Tests

Each model should have tests covering:

• Model loading
• Basic inference
• Edge cases
• Error handling

Code Style

Formatting

# Format all code
cargo fmt

# Check formatting without making changes
cargo fmt --check

Linting

# Run clippy
cargo clippy

# Fix clippy warnings
cargo clippy --fix

Conventions

• Use 4 spaces for indentation
• Prefer Result<T> over Option<T> for errors
• Use anyhow::Result for application errors
• Document public APIs with rustdoc comments
• Keep functions focused and small
• Use meaningful variable names

Documentation

/// Generates text using the NewModel.
///
/// # Arguments
///
/// * `prompt` - The input prompt text
/// * `max_tokens` - Maximum tokens to generate
///
/// # Returns
///
/// Generated text as a String
///
/// # Examples
///
/// ```no_run
/// let result = model.generate("Hello")?;
/// ```
pub fn generate(&mut self, prompt: &str) -> Result<String> {
    // Implementation
}

Submitting Contributions

Pull Request Process

1. Fork the repository
2. Create a feature branch:

   git checkout -b feature/new-model
   

3. Make your changes
4. Add tests
5. Update documentation
6. Ensure all tests pass:

   cargo test
   cargo clippy
   cargo fmt --check
   

7. Commit and push:

   git commit -m "Add NewModel support"
   git push origin feature/new-model
   

8. Create a pull request on GitHub

Pull Request Checklist

• Tests added/updated
• Documentation updated
• CHANGELOG.md updated
• Code formatted (cargo fmt)
• Clippy warnings fixed (cargo clippy)
• All tests pass (cargo test)
• Commit messages follow conventions

Commit Message Conventions

Follow conventional commits:

feat: add NewModel support
fix: correct tensor dimensions in Qwen3VL
docs: update installation guide
test: add integration test for VoxCPM
refactor: simplify model loading logic
perf: improve inference speed by 20%

Release Process

Version Bump

Update Cargo.toml:

[package]
version = "0.1.9"  # Bump version

Update Changelog

Add entry to CHANGELOG.md:

## [0.1.9] - 2025-02-06

### Added
- NewModel support

### Fixed
- Memory leak in image processing

### Changed
- Improved error messages

Create Release

# Tag the release
git tag -a v0.1.9 -m "Release v0.1.9"
git push origin v0.1.9

# Build release artifacts
cargo build --release

# Publish to crates.io (if applicable)
cargo publish

Debugging

Logging

Enable debug logging:

RUST_LOG=debug cargo run -- -m qwen3-0.6b

Set specific module logging:

RUST_LOG=aha::models::qwen3vl=debug cargo run -- -m qwen3-0.6b

Debugging Tests

# Print test output
cargo test -- --nocapture

# Show backtrace
RUST_BACKTRACE=1 cargo test

Common Issues

Build Errors

• Linking errors: Install required system dependencies
• CUDA errors: Ensure CUDA toolkit is installed
• Metal errors: Check you're on Apple Silicon

Runtime Errors

• Model not found: Check model path and download
• Out of memory: Use smaller model or enable GPU
• Slow inference: Enable GPU acceleration

Resources

• Candle Documentation
• Rust Guidelines
• Conventional Commits

See Also

• Architecture & Design - How AHA works
• Installation - Setup guide
• API Reference - API documentation