Accessing labels in the compute_metrics function
Hi everyone,I’m fine-tuning an amd/AMD-Llama-135, a small llama2 based model. I have a dataset of short sequences separated as “input” and “output”. Here’s how I tokenize my dataset:def tokenize(examples):
    input_texts = examples["input"]
    output_texts = examples["output"]

    modified_input_texts = []
    modified_output_texts = []
    for input_text, output_text in zip(input_texts, output_texts):
        modified_input_texts.append(f"{input_text.strip()}</s></s>")
        modified_output_texts.append(f"<s>{output_text.strip()}</s>")

    tokenized = tokenizer(modified_input_texts, max_length=64, padding="max_length", truncation=True)
    labels = tokenizer(modified_output_texts, max_length=64, padding="max_length", truncation=True)
    tokenized["labels"] = labels["input_ids"]
    return tokenizedAfter tokenizing my dataset, I can decode the labels as expected:tokenizer.decode(tokenized_dataset["test"][0]["labels"])I’m launching a training and have a custom compute_metrics function and before that I have a preprocess_logits_for_metrics function, here’s how they look like:exact_match = load("exact_match")

def compute_metrics(eval_pred):
    labels = eval_pred.label_ids
    pred_ids = eval_pred.predictions
    labels[labels == -100] = tokenizer.pad_token_id
    pred_ids[0][pred_ids[0] == -100] = tokenizer.pad_token_id
    
    decoded_preds = tokenizer.batch_decode(pred_ids[0], skip_special_tokens=False)
    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=False)

    decoded_preds = [pred.strip() for pred in decoded_preds]
    decoded_labels = [label.strip() for label in decoded_labels]
    
    
    def extract_between_tokens(text):
        match = re.search(r'<s><s>(.*?)<\/s>', text)
        if match:
            extracted = match.group(1)
            return text
        else:
            return text

    preds_between_tokens = [extract_between_tokens(pred) for pred in decoded_preds]


    wrong_predictions = [
        (pred, label) for pred, label in zip(preds_between_tokens, decoded_labels) if pred != label
    ]
    for pred, label in wrong_predictions:
        print(f"Prediction: {pred}")
        print(f"Ground Truth: {label}")
        print("-------")

    results = exact_match.compute(predictions=preds_between_tokens, references=decoded_labels)
    
    return {"exact_match": results["exact_match"]}


def preprocess_logits_for_metrics(logits, labels): 
    
    labels[labels == -100] = tokenizer.pad_token_id
    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=False)
    print(f"Dec labels preprocess: {decoded_labels}")
    pred_ids = torch.argmax(logits, dim=-1) 
    	
    return pred_ids, labelsWhen I print out the decoded labels in the preprocess logits function (doing this only for debug purposes), which is before the compute_metrics. I’m seeing the inputs rather than the labels. The same happens in the compute_metrics function.I searched a lot for this particular issues but could not find any relevant topics. Can anyone share any ideas on what might be the reason for this?Thanks in advance!
2025-01-14T20:21:42Z
Going through the same thing right now. Did you find any solution?
2025-01-15T16:26:32Z
Soo appearantly as ChatGPT puts it:“If the dataset is correct, the issue may lie in the way the DataCollatorForLanguageModeling  handles the labels.The DataCollatorForLanguageModeling is designed for causal language modeling, where labels are typically just a shifted version of input_ids. If the dataset already has separate labels, the collator might overwrite them.”Took me ages and many debug prints to find the problem and it was the data collator for me that fked me over. Still a lot of problems to fix but at least this isn’t one of them anymore.
2025-01-15T20:39:23Z
Nice tip to check the data collator, I need to revisit this code and check whether I’m using it as well. What did you end up using instead of DataCollatorForLanguageModelling?I ended up tokenizing my dataset in a different way and concatenate my input to my output and the model still learns how to handle it, because it’s a decoder only model it bases its next tokens on the previous ones. It’s a bit counter intuitive to include the labels in the input strings, but it actually worked out fine.That said I’m still keen on doing as described in the original post and will give it a try.
2025-01-15T21:28:31Z
I simply tried to use this data collator data_collector = DataCollatorWithPadding(tokenizer)and while that solved the switch up my model still doesn’t work properly. Likely because as you said these models base their prediction on the previous ones. I also got some issues that the grad_norm is always 0. Gonna try a few more things in the next days