Lightning-AI/pytorch-lightning · error · MisconfigurationException

Could not find the `LightningModule` attribute for the `torc

Error message

Could not find the `LightningModule` attribute for the `torchmetrics.Metric` logged. You can fix this by calling `self.log({name}, ..., metric_attribute=name)` where `name` is one of {list(self._metric_attributes.values)}

What it means

The logged torchmetrics.Metric object was not found among the module's registered metric attributes (id lookup in _metric_attributes failed). Lightning needs an attribute path to checkpoint/restore the metric, so you must tell it where the metric lives via metric_attribute.

Source

Thrown at src/lightning/pytorch/core/module.py:507

            # if we started a new epoch (running its first batch) the hook name has changed
            # reset any tensors for the new hook name
            results.reset(metrics=False, fx=self._current_fx_name)

        if metric_attribute is None and isinstance(value, Metric):
            if self._metric_attributes is None:
                # compute once
                self._metric_attributes = {
                    id(module): name for name, module in self.named_modules() if isinstance(module, Metric)
                }
                if not self._metric_attributes:
                    raise MisconfigurationException(
                        "Could not find the `LightningModule` attribute for the `torchmetrics.Metric` logged."
                        " You can fix this by setting an attribute for the metric in your `LightningModule`."
                    )
            # try to find the passed metric in the LightningModule
            metric_attribute = self._metric_attributes.get(id(value), None)
            if metric_attribute is None:
                raise MisconfigurationException(
                    "Could not find the `LightningModule` attribute for the `torchmetrics.Metric` logged."
                    f" You can fix this by calling `self.log({name}, ..., metric_attribute=name)` where `name` is one"
                    f" of {list(self._metric_attributes.values())}"
                )

        if (
            trainer.training
            and is_param_in_hook_signature(self.training_step, "dataloader_iter", explicit=True)
            and batch_size is None
        ):
            raise MisconfigurationException(
                "With `def training_step(self, dataloader_iter)`, `self.log(..., batch_size=...)` should be provided."
            )

        if logger and trainer.logger is None:
            rank_zero_warn(
                f"You called `self.log({name!r}, ..., logger=True)` but have no logger configured. You can enable one"
                " by doing `Trainer(logger=ALogger(...))`"

View on GitHub (pinned to 9fed5c27d2)

Solutions

1. Log the exact attribute object: self.log('acc', self.my_metric)
2. Pass the attribute name: self.log('acc', metric, metric_attribute='my_metric')
3. Store metrics in nn.ModuleDict so they are discoverable by named_modules

Example fix

# before
self.log('acc', my_local_metric)  # not the registered attribute object

# after
self.log('acc', my_local_metric, metric_attribute='my_metric')
# where self.my_metric was defined in __init__

attr = next((n for n, m in self.named_modules() if m is metric), None)
if attr is None:
    self.log(name, metric.compute(), batch_size=bs)  # log value instead
else:
    self.log(name, metric)

def metric_attribute_of(module, metric):
    return next((n for n, m in module.named_modules() if m is metric), None)

When it happens

Trigger: Calling self.log('acc', metric) where metric is a duplicate object, a copy, or held in a structure whose attribute name doesn't match any registered Metric (e.g. created ad hoc but other metrics exist on the module).

Common situations: User deep-copied a metric, wrapped it in a custom container, or logs a metric stored in a plain python dict attribute; mismatch after reload/mutation of metric objects.

Related errors

• Could not find the `LightningModule` attribute for the `torc
• Device should be CPU, got {device} instead.
• `devices` selected with `CPUAccelerator` should be an int >
• You are trying to `self.log()` but the loop's result collect
• You are trying to `self.log()` but it is not managed by the

AI-assisted analysis of Lightning-AI/pytorch-lightning@9fed5c27d2 (2026-08-28). Data as JSON: /api/errors/2b896a9ae37d798d. Report an issue: GitHub.