Fix ReduceLROnPlateau scheduler error when validation doesn't run every epoch

[Copilot]

What does this PR do?

Fixes a MisconfigurationException that occurs when using ReduceLROnPlateau scheduler with check_val_every_n_epoch > 1. The scheduler was attempting to access validation metrics on epochs where validation didn't run, causing an error.

Issue

When check_val_every_n_epoch is set to a value greater than 1, validation only runs on specific epochs (e.g., every 2nd epoch). However, the ReduceLROnPlateau scheduler was being updated at the end of every epoch, attempting to access the monitored metric (e.g., val/loss) even when it wasn't available.

Example error:

MisconfigurationException: ReduceLROnPlateau conditioned on metric val/loss which is not available. 
Available metrics are: ['lr-AdamW/pg1', 'lr-AdamW/pg2', 'train/a_pcc', 'train/loss']. 
Condition can be set using `monitor` key in lr scheduler dict

Before submitting

• Was this discussed/approved via a GitHub issue? (not for typos and docs)
• Did you read the contributor guideline?
• Did you make sure your PR does only one thing, instead of bundling different changes together?
• Did you make sure to update the documentation with your changes? (if necessary)
• Did you write any new necessary tests?
• Did you verify new and existing tests pass locally with your changes?
• Did you list all the breaking changes introduced by this pull request?
• Did you update the CHANGELOG? (not for typos, docs, test updates, or internal changes)

Solution

Modified the scheduler update logic in fit_loop.py to only update plateau schedulers when validation actually runs. This ensures the monitored metrics are available when the scheduler needs them.

Before:

self.epoch_loop.update_lr_schedulers("epoch", update_plateau_schedulers=not self.restarting)

After:

if (
    not self.restarting
    and self.epoch_loop._num_ready_batches_reached()
    and self.epoch_loop._should_check_val_epoch()
):
    self.epoch_loop.update_lr_schedulers("epoch", update_plateau_schedulers=True)

Testing

Added test case test_reducelronplateau_with_check_val_every_n_epoch that verifies the fix works correctly when validation runs every N epochs.

trainer = Trainer(
    max_epochs=3,
    check_val_every_n_epoch=2,  # Validation only on epochs 0, 2
)
trainer.fit(model)  # No longer raises MisconfigurationException

Impact

• Behavior change: Plateau schedulers are now only updated on epochs when validation runs
• Non-plateau schedulers: Unchanged behavior - still updated every epoch
• Backward compatibility: Maintained - default behavior (validation every epoch) works as before
• User workaround: Users no longer need to set "strict": False to avoid the error

Fixes #<issue_number>

Original prompt

---

This section details on the original issue you should resolve

<issue_title>ReduceLROnPlateu within configure_optimizers behave abnormally</issue_title>
<issue_description>### Bug description

Got error

  File "c:\Users\sean\miniconda3\envs\keras+torch+pl\Lib\site-packages\lightning\pytorch\loops\training_epoch_loop.py", line 459, in _update_learning_rates
    raise MisconfigurationException(
lightning.fabric.utilities.exceptions.MisconfigurationException: ReduceLROnPlateau conditioned on metric val/loss which is not available. Available metrics are: ['lr-AdamW/pg1', 'lr-AdamW/pg2', 'train/a_pcc', 'train/loss']. Condition can be set using `monitor` key in lr scheduler dict

Here is the configure_optimizers function:

    @final
    def configure_optimizers(self):

        decay, no_decay = [], []
        for name, param in self.named_parameters():
            if not param.requires_grad:
                continue
            if "bias" in name or "Norm" in name:
                no_decay.append(param)
            else:
                decay.append(param)

        grouped_params = [
            {"params": decay, "weight_decay": self.weight_decay, "lr": self.lr * 0.3},
            {
                "params": no_decay,
                "weight_decay": self.weight_decay,
                "lr": self.lr * 1.7,
            },
        ]

        optimizer = self.optmizer_class(
            grouped_params, lr=self.lr, weight_decay=self.weight_decay
        )

        scheduler = self.lr_scheduler_class(
            optimizer, **self.lr_scheduler_args if self.lr_scheduler_args else {}
        )
        scheduler = {
            "scheduler": self.lr_scheduler_class(
                optimizer, **self.lr_scheduler_args if self.lr_scheduler_args else {}
            ),
            "monitor": "val/loss",
            "interval": "epoch",
            "frequency": 1,
            # "strict": False,
        }
        return {"optimizer": optimizer, "lr_scheduler": scheduler}

The lr_scheduler_class is passed in as

  lr_scheduler_class: torch.optim.lr_scheduler.ReduceLROnPlateau
  lr_scheduler_args:
    mode: min
    factor: 0.5
    patience: 10
    threshold: 0.0001
    threshold_mode: rel
    cooldown: 5
    min_lr: 1.e-9
    eps: 1.e-08

(using yaml and CLI, which, I think, is not the case here)

It seems that I got the error at the end of the training epoch, as I just see the progress bar reports train/loss. The validation epoch is not finished, but the scheduler is called.

I am quite sure that val/loss is available after validation epoch is finished, because progress bar can correctly display it.

What version are you seeing the problem on?

v2.5

Reproduced in studio

No response

How to reproduce the bug

Error messages and logs

# Error messages and logs here please

Environment

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More info

No response</issue_description>

Comments on the Issue (you are @copilot in this section)

Fixes #20829

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📚 Documentation preview 📚: https://pytorch-lightning--21266.org.readthedocs.build/en/21266/