Training metrics currently not logged?

[SirRob1997]

I'm trying to replicate the training process, is it expected that there are no metrics logged during eval such as automatic translation quality metrics e.g. using sacrebleu?

Seems like https://github.com/fe1ixxu/ALMA/blob/b92304c6548b7d0af0cdadca9d63c07c70d19cd5/run_llmmt.py#L152C5-L152C11 is never used. There should probably be a compute_metrics function that is passed to the trainer?

[fe1ixxu]

Thanks for your interest!

Yes, metric will not be logged during eval and only eval loss will show if you enable --do_eval. metric = evaluate.load("sacrebleu") is an leftover line and will be deleted in the future.

[SirRob1997]

@fe1ixxu given the diverging behaviour between chrF / COMET it might be worth to track both during validation. How would you integrate that into the LlmmtTrainer ? There seems to be custom code for prediction_step if we disable --prediction_loss_only but I've ran into some issues with that (likely because it's never used) during the prediction_step when calling self.model.generate(**inputs, **gen_kwargs).

*** RuntimeError: probability tensor contains either `inf`, `nan` or element < 0

Likely, something along these lines should be passed to the trainer to track chrF:

def compute_metrics_chrf_comet(eval_preds):
        def postprocess_text(preds, labels):
            preds = [pred.strip() for pred in preds]
            labels = [[label.strip()] for label in labels]

            return preds, labels

        chrf_metric = evaluate.load("chrf")
        preds, labels = eval_preds
        if isinstance(preds, tuple):
            preds = preds[0]

        preds = np.where(preds != -100, preds, tokenizer.pad_token_id)
        decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
        decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)

        # Some simple post-processing
        decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
 
        logger.info(f"Sample decoded prediction {decoded_preds[0]}")
        logger.info(f"Sample decoded label {decoded_labels[0]}")

        result_chrf = chrf_metric.compute(predictions=decoded_preds, references=decoded_labels)
        result = {"chrF": result_chrf["score"]}
        return result