CompoConf

CompoConf is a Python library for compositional configuration management. It provides a type-safe way to define, parse, and instantiate configurations for complex, modular systems.

Features

• Type-safe configuration parsing with dataclass support
• Registry-based class instantiation
• Inheritance-based interface registration
• Support for nested configurations
• Optional OmegaConf integration
• Strict type checking and validation

Installation

pip install compoconf

Quick Start

Here's a simple example of how to use CompoConf:

from dataclasses import dataclass
from compoconf import (
    RegistrableConfigInterface,
    ConfigInterface,
    register_interface,
    register,
)

# Define an interface
@register_interface
class ModelInterface(RegistrableConfigInterface):
    pass

# Define a configuration
@dataclass
class MLPConfig(ConfigInterface):
    hidden_size: int = 128
    num_layers: int = 2

# Register a class with its configuration
@register
class MLPModel(ModelInterface):
    config_class = MLPConfig

    def __init__(self, config):
        self.config = config
        # Initialize model with config...

# Create and use configurations
config = MLPConfig(hidden_size=256)
model = config.instantiate(ModelInterface)

Advanced Usage

Nested Configurations

CompoConf supports nested configurations through type annotations:

@dataclass
class TrainerConfig(ConfigInterface):
    model: ModelInterface.cfgtype  # References the interface type
    learning_rate: float = 0.001

# Parse nested configuration
config = {
    "model": {
        "class_name": "MLPModel",
        "hidden_size": 256
    },
    "learning_rate": 0.01
}

trainer_config = parse_config(TrainerConfig, config)

Type Safety

The library provides comprehensive type checking:

• Validates configuration values against their type annotations
• Ensures registered classes match their interfaces
• Checks for missing required fields
• Supports strict mode for catching unknown configuration keys

OmegaConf Integration

CompoConf optionally integrates with OmegaConf for enhanced configuration handling:

from omegaconf import OmegaConf

# Load configuration from YAML
conf = OmegaConf.load('config.yaml')
config = parse_config(ModelConfig, conf)

Registry System

The registry system allows for dynamic class instantiation based on configuration:

# Register multiple implementations
@dataclass
class CNNConfig(ConfigInterface):
    kernel_size: int = 4

@register
class CNNModel(ModelInterface):
    config_class = CNNConfig

@dataclass
class TransformerConfig(ConfigInterface):
    hidden_size: int = 128
    num_heads: int = 4

@register
class TransformerModel(ModelInterface):
    config_class = TransformerConfig

# Configuration automatically creates correct instance
config = {
    "model": {
        "class_name": "TransformerModel",
        "num_heads": 8,
        "hidden_size": 512
    }
}

API Reference

Core Classes

• RegistrableConfigInterface: Base class for interfaces that can be configured
• ConfigInterface: Base class for configuration dataclasses
• Registry: Singleton managing registration of interfaces and implementations

Decorators

• @register_interface: Register a new interface
• @register: Register an implementation class

Functions

• parse_config(config_class, data, strict=True): Parse configuration data into typed objects

Enhanced Functionality

Parsing Module

The parsing module has been enhanced to provide more robust and flexible configuration parsing capabilities. Key improvements include:

• Improved handling of nested configurations and unions.
• Enhanced type validation and error reporting.
• Support for parsing configurations from various data sources (e.g., JSON, YAML).

Util Module

The util module now includes powerful utilities for dynamic configuration and validation:

• partial_call: Enables the creation of configurable classes from functions, allowing for dynamic modification of function arguments through configuration.
• from_annotations: Simplifies the creation of configurable classes by automatically extracting configuration parameters from class annotations.
• validate_literal_field and assert_check_literals: Provide mechanisms for validating Literal type annotations in dataclasses, ensuring that configuration values are within the allowed set of options.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License

Author

Korbinian Pöppel (korbip@korbip.de)