haven-conf 0.0.1

Simple and flexible dataclass configuration system

haven

A modular dataclass configuration system

Haven is system for configuring applications using dataclasses and YAML. It strives to be relatively simple while still scaling to complex use cases.

Key Features

• Builds plain dataclasses, so you can use all standard dataclass features, such as custom methods, __post_init__, etc.
• Doesn't take over your CLI or impose certain structure on your program.
• Support for parsing a wide variety of types and type hints, including optionals and unions.
• Scales to projects with many config variations or sub-components using choice and plugin fields.
• Easily link each config variation to the matching variation of your code using Component.

Tour

Basic example

@dataclass
class ModelConfig:
    num_layers: int = 5
    embed_dim: int = 512

@dataclass
class TrainConfig:
    workers: int = 5
    steps: list[int] = field(default_factory=lambda: [50, 100 150])
    model: ModelConfig = field(default_factory=ModelConfig)

# Load from yaml string
cfg = haven.load(TrainConfig, """
steps: [1,2,3]
model:
  num_layers: 16
""")
assert cfg.model.num_layers == 16

# Or load from file
with open("config.yaml") as f:
    cfg = haven.load(TrainConfig, f)

# Update using "dotlist" style overrides (e.g. from CLI args)
cfg = haven.update_from_dotlist(cfg, ["workers=3", "model.num_layers=2"])

# Print yaml
print(haven.dump(cfg))

Choice fields

More complex projects often want to support many variations for each application component. This can be accomplished through subclassing and choice fields.

@dataclass
class ModelConfig:
    name: str

# Two types of models
@dataclass
class GPT2Config(ModelConfig):
    num_layers: int

@dataclass
class Llama2Config(ModelConfig):
    embed_dim: int = 512

@dataclass
class TrainConfig:
    workers: int = 5
    steps: list[int] = field(default_factory=lambda: [50, 100 150])

    # Choose config class based on value of `ModelConfig.name`.
    model: ModelConfig = haven.choice(
        [GPT2Config, Llama2Config],
        key_field="name",
        default_factory=Llama2Config,
    )

# Load from yaml string
cfg = haven.load(TrainConfig, """
steps: [1,2,3]
model:
  name: GPT2Config
  num_layers: 16
""")
assert isinstance(cfg.model, GPT2Config)

Chocies can also be module + object paths that are imported lazily:

@dataclass
class TrainConfig:
    model: ModelConfig = haven.choice([
        "models.llama.Llama2Config",
        "models.gpt.GPT2Config",
    ])

The benefit of this style of configuration is that all of the available choices are documented directly in the config definition. This works well when there are a small to medium number of variations. For more flexibility, a plugin system is available:

@dataclass
class TrainConfig:
    model: ModelConfig = haven.plugin(
        discover_packages_path="mypackage.models",
        attr="MODEL_CONFIG",
    )

Each module under the mypackage.models namespace that contains the attribute MODEL_CONFIG will then be an available choice. The choice name is the same as the name of the module.

Components

The problem with choice fields alone is that typically, you want to run different code in your application depending on which variant of the config was selected. haven.Component provides a simple mechanism for linking each variation to a callable.

# Sample model definitions
class ModelBase(nn.Module):
    pass

class Llama2(Model):
    def __init__(self, cfg: Llama2Config):
        pass

class GPT(Model):
    def __init__(self, cfg: GPTConfig):
        pass

@dataclass
class TrainConfig:
    model: haven.Component[ModelConfig, ModelBase] = haven.choice([
        Llama2
        GPT,
    ])

cfg = haven.load(TrainConfig, "model: Llama2")

# Instantiate the chosen class, passing the appropriate config as the first arg.
model = cfg.model()
assert isinstance(model, Llama2)

The config dataclass to use for each variation is automatically derived from the type hint on the first argument of the callable.

More examples

See the examples directory in the source code for more complete examples.

API

Full documentation here.

Acknowledgements

This project is inspired by and borrows code from Pyrallis, SimpleParsing, and draccus, and Hydra