CUDA out of memory when using Trainer with compute_metrics

[tahmid1234]

@morenolq

I don’t think it solves the issue, it only moves it to RAM instead of GPU. The real solution is introduced with preprocess_logits_for_metrics function (here).

I leave here my specific solution (both functions):

def compute_metrics(pred):

    labels_ids = pred.label_ids
    pred_ids = pred.predictions[0]

    pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
    labels_ids[labels_ids == -100] = tokenizer.pad_token_id
    label_str = tokenizer.batch_decode(labels_ids, skip_special_tokens=True)

    rouge_output = rouge.compute(
        predictions=pred_str,
        references=label_str,
        rouge_types=["rouge1", "rouge2", "rougeL", "rougeLsum"],
    )

    return {
        "R1": round(rouge_output["rouge1"], 4),
        "R2": round(rouge_output["rouge2"], 4),
        "RL": round(rouge_output["rougeL"], 4),
        "RLsum": round(rouge_output["rougeLsum"], 4),
    }

def preprocess_logits_for_metrics(logits, labels):
    """
    Original Trainer may have a memory leak. 
    This is a workaround to avoid storing too many tensors that are not needed.
    """
    pred_ids = torch.argmax(logits[0], dim=-1)
    return pred_ids, labels

BTW, proceeding in this way, you may not need to use eval_accumulation_steps=1 (that slows down the evaluation significantly).

But will this work if compute_metrics calculates AUPR or AUROC? If I use sigmoid, will I stop getting this error?