Lightning-AI/pytorch-lightning · error · MisconfigurationException

The provided `parameter_names` name: {name} isn't in {self.P

Error message

The provided `parameter_names` name: {name} isn't in {self.PARAMETER_NAMES}

What it means

ModelPruning prunes parameters by name (e.g., 'weight' or 'bias') on the modules you list. Each name in parameter_names must exist in the callback's PARAMETER_NAMES set; otherwise __init__ raises MisconfigurationException listing the accepted names.

Source

Thrown at src/lightning/pytorch/callbacks/pruning.py:175

                if ``pruning_norm`` is not provided when ``"ln_structured"``,
                if ``pruning_fn`` is neither ``str`` nor :class:`torch.nn.utils.prune.BasePruningMethod`, or
                if ``amount`` is none of ``int``, ``float`` and ``Callable``.

        """

        self._use_global_unstructured = use_global_unstructured
        self._parameters_to_prune = parameters_to_prune
        self._use_lottery_ticket_hypothesis = use_lottery_ticket_hypothesis
        self._resample_parameters = resample_parameters
        self._prune_on_train_epoch_end = prune_on_train_epoch_end
        self._parameter_names = parameter_names or self.PARAMETER_NAMES
        self._global_kwargs: dict[str, Any] = {}
        self._original_layers: Optional[dict[int, _LayerRef]] = None
        self._pruning_method_name: Optional[str] = None

        for name in self._parameter_names:
            if name not in self.PARAMETER_NAMES:
                raise MisconfigurationException(
                    f"The provided `parameter_names` name: {name} isn't in {self.PARAMETER_NAMES}"
                )

        if isinstance(pruning_fn, str):
            pruning_kwargs = {}
            pruning_fn = pruning_fn.lower()
            if pruning_fn not in _PYTORCH_PRUNING_FUNCTIONS:
                raise MisconfigurationException(
                    f"The provided `pruning_fn` {pruning_fn} isn't available in PyTorch's"
                    f" built-in functions: {list(_PYTORCH_PRUNING_FUNCTIONS.keys())} "
                )
            if pruning_fn.endswith("_structured"):
                if pruning_dim is None:
                    raise MisconfigurationException(
                        "When requesting `structured` pruning, the `pruning_dim` should be provided."
                    )
                if pruning_fn == "ln_structured":
                    if pruning_norm is None:

View on GitHub (pinned to 9fed5c27d2)

Solutions

1. Restrict parameter_names to the supported set: ['weight'] and/or ['bias']
2. Check available names via the callback's PARAMETER_NAMES attribute before constructing
3. For custom-named parameters, subclass ModelPruning and override filter_parameters_to_prune to map to the real attributes

Example fix

# before
ModelPruning(parameter_names=['weights'])
# after
ModelPruning(parameter_names=['weight'])

from lightning.pytorch.callbacks.pruning import ModelPruning
assert set(names).issubset(ModelPruning.PARAMETER_NAMES), f'allowed: {ModelPruning.PARAMETER_NAMES}'

def valid_param_names(names) -> bool:
    return set(names).issubset({'weight', 'bias'})

When it happens

Trigger: Passing parameter_names=['weights'], ['keras_weight'], or any string not in PARAMETER_NAMES (typically {'weight','bias'}) to ModelPruning; singular/plural or casing mistakes in config.

Common situations: Assuming arbitrary attribute names work; configs written for other pruning tools; misspelling 'weight'.

Understand the failure class

Background: "Must be a positive integer", "Invalid value", "Unsupported": the invalid-argument-value error family, when a library rejects the value you pass — this error's family across 35 libraries.

Related errors

• The provided `pruning_fn` {pruning_fn} isn't available in Py
• `verbose` must be any of (0, 1, 2)
• The provided `parameters_to_prune` should either be list of
• Invalid value for every_n_train_steps={self._every_n_train_s
• Invalid value for every_n_epochs={self._every_n_epochs}. Mus

AI-assisted analysis of Lightning-AI/pytorch-lightning@9fed5c27d2 (2026-08-28). Data as JSON: /api/errors/b28e8a0e2dfc885c. Report an issue: GitHub.