def assert_skipped_parameters_are_frozen()

in training/utils/checkpoint_utils.py [0:0]

• 15 lines of code
• 6 McCabe index (conditional complexity)

def assert_skipped_parameters_are_frozen(model: nn.Module, patterns: List[str]):
    """
    Verifies that all the parameters matching the provided patterns
    are frozen - this acts as a safeguard when ignoring parameter
    when saving checkpoints - if the parameters are in fact trainable
    """
    if not patterns:
        return

    frozen_state_dict = filter_params_matching_unix_pattern(
        patterns=patterns, state_dict=model.state_dict()
    )
    non_frozen_keys = {
        n
        for n, p in model.named_parameters()
        if n in frozen_state_dict and p.requires_grad
    }
    if non_frozen_keys:
        raise ValueError(
            f"Parameters excluded with `skip_saving_parameters` should be frozen: {non_frozen_keys}"
        )