This method is too long. I would suggest creating two separate methods like:

if is_multi_dataloader:
    self._process_multi_dataloader_validation()
else:
    self._process_single_dataloader_validation()

Instead of maintaining 4 lists
all_losses, all_codebook_losses, all_alignment_losses, all_aligner_encoder_losses, all_local_transformer_losses switch to a dict. Keeps it clean, something like:

for metric_name, metric_value in dataloader_logs.items():
                self.log(f"Loss:{dataloader_prefix}/{metric_name}", metric_value, prog_bar=False, sync_dist=True)
                aggregated_metrics.setdefault(metric_name, []).append(metric_value)

1. Using a dict instead of 4 separate lists: This is a good suggestion! will make changes.

2. Splitting into two methods: Reasonable for readability, though the method is currently ~115 lines which is manageable. The split would reduce cognitive load. After refactoring by item 1, 115 lines are reduced to 50 lines.

Just a suggestion. I see a the metrics being hard coded everywhere and loosely used. Instead we should define them in a constant Enum.

I may not follow what are the "hard coded" metrics in this context. Could you pls elaborate?

a second thought on your suggestion, maybe we can get something like below,

from enum import Enum

class ValidationMetric(Enum):
    """Validation metric keys used in validation_step outputs and logging."""
    LOSS = 'loss'
    CODEBOOK_LOSS = 'codebook_loss'
    ALIGNMENT_LOSS = 'alignment_loss'
    ALIGNER_ENCODER_LOSS = 'aligner_encoder_loss'
    LOCAL_TRANSFORMER_LOSS = 'local_transformer_loss'

# Usage:
val_loss = collect_required_metric(outputs, f'val_{ValidationMetric.LOSS.value}')
log_dict = {
    ValidationMetric.LOSS.value: val_loss,
    ValidationMetric.CODEBOOK_LOSS.value: val_codebook_loss,
}

If my understanding aligns with yours, I would leave it for future cleanup because we are not adding new metrics frequently. It is a good suggestion, and we can track as a follow-up imrpovement.

Sure, let's take it up for future cleanup.

Can something like this be done?

for metric in VAL_METRIC_LIST:
   log_dict[metric] = collect_optional_metric(outputs, metric)

We need to check for Nones.

Nones are already managed in validation_step. No need to check again here. Besides, collect_optional_metric would return None if any metric does not exist in outputs.

Can something like this be done?

for metric in VAL_METRIC_LIST:
   log_dict[metric] = collect_optional_metric(outputs, metric)

We need to check for Nones.

we only have two required losses, so i believe no need to wrap up as a for loop. Similar to optional losses, we only have 3. So I would leave it if we want to access many more losses in the future.

I meant do something like the following. So if we in the future need to add any additional loss we do not need to touch this code, also makes it much cleaner.

VAL_METRIC_LIST = ['val_loss', 'val_codebook_loss', 'val_alignment_loss', ...]
# This is where a global list/Enum of metrics would help, so we can just iterate over them.
# But for now a local list would be fine.

for metric in VAL_METRIC_LIST:
   metric_value = collect_optional_metric(outputs, metric)
   if metric_value is not None:
       log_dict[metric] = metric_value

I am not a fan of passing dataclasses to functions especially if you do not re-use them. It hides the underlying datatypes of the class and it's not intuitive from the docstring what you are supposed to pass to this function. Is there an argument for why you want to do it this way?