Lightning-AI/pytorch-lightning · error · MisconfigurationException

The provided `parameters_to_prune` should either be list of

Error message

The provided `parameters_to_prune` should either be list of tuple with 2 elements: (nn.Module, parameter_name_to_prune) or None

What it means

parameters_to_prune must be a list of 2-element tuples (nn.Module instance, parameter_name string) or empty/None. If the value is not such a list (a list of 3-tuples, strings, or a non-list), sanitize_parameters_to_prune raises MisconfigurationException stating the expected shape.

Source

Thrown at src/lightning/pytorch/callbacks/pruning.py:487

            and len(parameters_to_prune) > 0
            and all(len(p) == 2 for p in parameters_to_prune)
            and all(isinstance(a, nn.Module) and isinstance(b, str) for a, b in parameters_to_prune)
        ):
            missing_modules, missing_parameters = [], []
            for module, name in parameters_to_prune:
                if module not in current_modules:
                    missing_modules.append(module)
                    continue
                if not hasattr(module, name):
                    missing_parameters.append(name)

            if missing_modules or missing_parameters:
                raise MisconfigurationException(
                    "Some provided `parameters_to_prune` don't exist in the model."
                    f" Found missing modules: {missing_modules} and missing parameters: {missing_parameters}"
                )
        else:
            raise MisconfigurationException(
                "The provided `parameters_to_prune` should either be list of tuple"
                " with 2 elements: (nn.Module, parameter_name_to_prune) or None"
            )

        return parameters_to_prune

    @staticmethod
    def _is_pruning_method(method: Any) -> bool:
        if not inspect.isclass(method):
            return False
        return issubclass(method, pytorch_prune.BasePruningMethod)

View on GitHub (pinned to 9fed5c27d2)

Solutions

1. Pass module objects with names: parameters_to_prune=[(model.fc1, 'weight'), (model.fc2, 'weight')]
2. Or leave it None/[] and override filter_parameters_to_prune in a subclass to select modules dynamically
3. Wrap generators with list() and ensure each item is a 2-tuple

Example fix

# before
ModelPruning(pruning_fn='l1_unstructured', amount=0.5, parameters_to_prune=['fc1.weight'])
# after
ModelPruning(pruning_fn='l1_unstructured', amount=0.5, parameters_to_prune=[(model.fc1, 'weight')])

def is_valid_prune_list(p) -> bool:
    return p is None or (isinstance(p, list) and all(
        isinstance(t, tuple) and len(t) == 2 and isinstance(t[1], str) for t in p
    ))
assert is_valid_prune_list(parameters_to_prune)

def is_valid_prune_list(p) -> bool:
    return p is None or (isinstance(p, list) and all(
        isinstance(t, tuple) and len(t) == 2 for t in p
    ))

When it happens

Trigger: Passing parameters_to_prune=['model.layer1.weight'] (dotted name strings instead of module objects); a list of (module, name, extra) 3-tuples; a dict or generator instead of a list.

Common situations: Assuming string module paths like torch.nn.utils.prune examples that use named_modules lookups; generating tuples with a comprehension bug that yields wrong shapes; passing a generator that the isinstance(list) check rejects.

Understand the failure class

Background: "Wrong argument type", "must be a string", "expected Array or Prism::Scope": TypeError and ArgumentError when a library receives a value of the wrong type — this error's family across 28 libraries.

Related errors

• The provided `parameter_names` name: {name} isn't in {self.P
• The provided `pruning_fn` {pruning_fn} isn't available in Py
• `verbose` must be any of (0, 1, 2)
• Some provided `parameters_to_prune` don't exist in the model
• Invalid value for every_n_train_steps={self._every_n_train_s

AI-assisted analysis of Lightning-AI/pytorch-lightning@9fed5c27d2 (2026-08-28). Data as JSON: /api/errors/02a9da0c654fa3c1. Report an issue: GitHub.