Lightning-AI/pytorch-lightning · error · MisconfigurationException

When requesting `ln_structured` pruning, the `pruning_norm`

Error message

When requesting `ln_structured` pruning, the `pruning_norm` should be provided.

What it means

The 'ln_structured' (ln-norm) PyTorch pruning method requires the norm order n in addition to the dimension. If pruning_fn == 'ln_structured' and pruning_norm is None, ModelPruning raises MisconfigurationException in __init__.

Source

Thrown at src/lightning/pytorch/callbacks/pruning.py:194

                    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:
                        raise MisconfigurationException(
                            "When requesting `ln_structured` pruning, the `pruning_norm` should be provided."
                        )
                    pruning_kwargs["n"] = pruning_norm
                pruning_kwargs["dim"] = pruning_dim
            pruning_fn = self._create_pruning_fn(pruning_fn, **pruning_kwargs)
        elif self._is_pruning_method(pruning_fn):
            if not use_global_unstructured:
                raise MisconfigurationException(
                    "PyTorch `BasePruningMethod` is currently only supported with `use_global_unstructured=True`."
                )
        else:
            raise MisconfigurationException(
                f"`pruning_fn` is expected to be a str in {list(_PYTORCH_PRUNING_FUNCTIONS.keys())}"
                f" or a PyTorch `BasePruningMethod`. Found: {pruning_fn}."
                " HINT: if passing a `BasePruningMethod`, pass the class, not an instance"
            )

        # need to ignore typing here since pytorch base class does not define the PRUNING_TYPE attribute

View on GitHub (pinned to 9fed5c27d2)

Solutions

Set pruning_norm to the desired norm order, e.g., pruning_norm=1 (L1) or 2 (L2)
Double-check that pruning_fn really needs to be ln_structured; l1-like selection with random_structured needs no norm

Example fix

# before
ModelPruning(pruning_fn='ln_structured', pruning_dim=0)
# after
ModelPruning(pruning_fn='ln_structured', pruning_norm=1, pruning_dim=0)

Defensive patterns

Strategy: validation

Validate before calling

if pruning_fn == 'ln_structured':
    assert pruning_norm is not None and pruning_dim is not None

Prevention

Check ln_structured requirements (n and dim) together
Prefer l1_unstructured unless channel pruning is required

When it happens

Trigger: ModelPruning(pruning_fn='ln_structured', pruning_dim=0) without pruning_norm; supplying dim but assuming a default norm exists (there is none in this callback).

Common situations: Mirroring torch.nn.utils.prune.ln_structured docs but omitting n; switching from random_structured where no norm is needed.

Understand the failure class

Background: "missing required argument" and "the following required arguments were not provided": what required-argument errors mean and how to fix them — this error's family across 20 libraries.

Related errors

AI-assisted analysis of Lightning-AI/pytorch-lightning@9fed5c27d2 (2026-08-28). Data as JSON: /api/errors/94ec4b2474a8b132. Report an issue: GitHub.