llmcompressor.core.helpers

def log_model_info(state: State, current_log_step):
    """
    Log model level info to the metrics
    Relies on `state.model` having a `loggable_items` method
    that returns a generator of tuples of the loggable item
    name and value. Also relies on `state.loggers` being a
    `LoggerManager` instance.

    :param state: The current state of sparsification
    :param current_log_step: The current log step to log
        model info at
    """
    _log_current_step(logger_manager=state.loggers, current_log_step=current_log_step)
    _log_model_loggable_items(
        logger_manager=state.loggers,
        loggable_items=state.model.loggable_items(),
        epoch=current_log_step,
    )

def should_log_model_info(
    model: Any,
    loggers: LoggerManager,
    current_log_step: float,
    last_log_step: Optional[float] = None,
) -> bool:
    """
    Check if we should log model level info
    Criteria:
        - model has a loggable_items method
        - state has a metrics manager
        - metrics manager is ready to log based on cadence and last log epoch

    :param model: The model whose info we want to log
    :param loggers: The metrics manager to log to
    :param current_log_step: The current epoch
    :param last_log_step: The last step we logged model info at
    :return: True if we should log model level info, False otherwise
    """
    return (
        hasattr(model, "loggable_items")
        and isinstance(loggers, LoggerManager)
        and loggers.log_ready(
            current_log_step=current_log_step, last_log_step=last_log_step
        )
    )