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llmcompressor.core.helpers

Helper functions for core compression operations.

Provides utility functions for logging model information and state management during compression workflows. Includes functionality for conditional logging and parameter tracking.

Functions:

log_model_info 

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.

Parameters:

  • state

    (State) –

    The current state of sparsification

  • current_log_step

    –

    The current log step to log model info at

Source code in llmcompressor/core/helpers.py 
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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,
    )

should_log_model_info 

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

Parameters:

  • model

    (Any) –

    The model whose info we want to log

  • loggers

    (LoggerManager) –

    The metrics manager to log to

  • current_log_step

    (float) –

    The current epoch

  • last_log_step

    (Optional[float], default: None ) –

    The last step we logged model info at

Returns:

  • bool –

    True if we should log model level info, False otherwise

Source code in llmcompressor/core/helpers.py 
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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
        )
    )