Index

optimus_dl.recipe.mixins

Shared mixins that can be used across different recipe types.

ModelBuilder

Mixin for building models and applying transformations.

Encapsulates the logic for: 1. Instantiating a BaseModel from a configuration object. 2. Sequentially applying a list of ModelTransforms (e.g., FSDP, DDP, compile). 3. Logging model statistics (parameter count).

Parameters:

Name	Type	Description	Default
cfg	ModelBuilderConfig	Builder configuration.	required
model_transforms	list[ModelTransformConfig] | None	List of configurations for transforms to apply.	None

Source code in optimus_dl/recipe/mixins/model_builder.py

class ModelBuilder:
    """Mixin for building models and applying transformations.

    Encapsulates the logic for:
    1.  Instantiating a `BaseModel` from a configuration object.
    2.  Sequentially applying a list of `ModelTransforms` (e.g., FSDP, DDP, compile).
    3.  Logging model statistics (parameter count).

    Args:
        cfg: Builder configuration.
        model_transforms: List of configurations for transforms to apply.
    """

    def __init__(
        self,
        cfg: ModelBuilderConfig,
        model_transforms: list[ModelTransformConfig] | None = None,
        **kwargs: Any,
    ):
        self.model_transforms = model_transforms or []

    def build_model(
        self, model_config: ModelConfig | None, collective: Collective, **kwargs
    ) -> BaseModel:
        """Build the model and apply all configured transforms.

        Args:
            model_config: Configuration for the model architecture.
            collective: Distributed collective for transforms that need it.
            **kwargs: Additional arguments passed to model constructor and transforms.

        Returns:
            The fully constructed and transformed model.
        """
        if model_config is None:
            raise ValueError(
                "model_config is None. Use build_model_from_checkpoint for evaluation."
            )

        model = build_model(model_config, **kwargs)
        num_param_before = get_num_parameters(model)
        logger.info(f"Params num (before model transforms): {num_param_before:,}")
        log_averaged("model/num_params_before_transforms", num_param_before)
        assert isinstance(model, BaseModel)

        # Apply model transforms (including distributed setup)
        model = self._apply_model_transforms(
            model, collective=collective, device=collective.default_device, **kwargs
        )
        num_param_after = get_num_parameters(model)
        logger.info(f"Model \n{model}")
        logger.info(f"Params num (after model transforms): {num_param_after:,}")
        log_averaged("model/num_params_after_transforms", num_param_after)

        return model

    def _apply_model_transforms(self, model: BaseModel, **kwargs) -> BaseModel:
        """Iteratively apply all configured model transforms.

        Args:
            model: The base model.
            **kwargs: Context arguments (device, collective, etc.).

        Returns:
            The transformed model.
        """
        for transform_cfg in self.model_transforms:
            try:
                transform = build_model_transform(transform_cfg, **kwargs)
                if transform is not None:
                    logger.info(f"Applying model transform: {transform}")
                    model = transform.apply(model, **kwargs)
                else:
                    logger.warning(
                        f"Failed to build model transform from config: {transform_cfg}"
                    )
            except Exception as e:
                logger.error(f"Failed to apply model transform {transform_cfg}: {e}")
                raise

        return model

build_model(model_config, collective, **kwargs)

Build the model and apply all configured transforms.

Parameters:

Name	Type	Description	Default
model_config	ModelConfig | None	Configuration for the model architecture.	required
collective	Collective	Distributed collective for transforms that need it.	required
**kwargs		Additional arguments passed to model constructor and transforms.	{}

Returns:

Type	Description
BaseModel	The fully constructed and transformed model.

Source code in optimus_dl/recipe/mixins/model_builder.py

def build_model(
    self, model_config: ModelConfig | None, collective: Collective, **kwargs
) -> BaseModel:
    """Build the model and apply all configured transforms.

    Args:
        model_config: Configuration for the model architecture.
        collective: Distributed collective for transforms that need it.
        **kwargs: Additional arguments passed to model constructor and transforms.

    Returns:
        The fully constructed and transformed model.
    """
    if model_config is None:
        raise ValueError(
            "model_config is None. Use build_model_from_checkpoint for evaluation."
        )

    model = build_model(model_config, **kwargs)
    num_param_before = get_num_parameters(model)
    logger.info(f"Params num (before model transforms): {num_param_before:,}")
    log_averaged("model/num_params_before_transforms", num_param_before)
    assert isinstance(model, BaseModel)

    # Apply model transforms (including distributed setup)
    model = self._apply_model_transforms(
        model, collective=collective, device=collective.default_device, **kwargs
    )
    num_param_after = get_num_parameters(model)
    logger.info(f"Model \n{model}")
    logger.info(f"Params num (after model transforms): {num_param_after:,}")
    log_averaged("model/num_params_after_transforms", num_param_after)

    return model

Modules and Sub-packages

• model_builder: Model builder mixin for building and transforming models with checkpoint loading.