Wrap each metric update in try/except. #3562
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arnavgarg1
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Aug 30, 2023
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| try: | ||
| if prediction_key == RESPONSE: | ||
| if tokenizer is not None: | ||
| # RESPONSE metrics cannot be computed if decoded texts are not provided. | ||
| # Decoded texts are only provided using the LLM model type. | ||
| if decoded_targets is not None and decoded_predictions is not None: | ||
| if metric_name == "bleu": | ||
| # BLEU takes in targets as a list. | ||
| metric_fn.update(decoded_predictions, [decoded_targets]) | ||
| else: | ||
| metric_fn.update(decoded_predictions, decoded_targets) | ||
| else: | ||
| metric_fn = metric_fn.to(predictions[prediction_key].device) | ||
| metric_fn.update(predictions[prediction_key].detach(), targets) | ||
| except Exception as e: | ||
| logger.info(f"Ran into error when calculating metric {metric_name}. Skipping. The error is: {e}") |
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Hmm, so will the metric function have no value then that we can collect later? Will that cause an error?
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I think we should be ok. get_metrics() is already wrapped in a try/except:
def get_metrics(self):
metric_vals = {}
for metric_name, metric_fn in self._metric_functions.items():
try:
computed_metric = metric_fn.compute()
except Exception as e:
logger.exception(f"Caught exception computing metric: {metric_name} with error: {e}.")
continueAnd it also looks like there's default values for torchmetrics, even if no update() was called.
from torchmetrics.text import BLEUScore
bleu = BLEUScore()
bleu.compute()
/Users/justinzhao/ludwig/env/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: The ``compute`` method of metric BLEUScore was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.
warnings.warn(*args, **kwargs)
tensor(0.)
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