A question of the calculatePerplexity

[Ethan-Chen-plus]

I know that the following can calculate loss

However, why labels be input_id? After read the paper, maybe I think the code should be:

def calculatePerplexity(sentence, model1, model2, tokenizer):
    """
    exp(loss)
    """
    input_ids = torch.tensor(tokenizer.encode(sentence)).unsqueeze(0)
    input_ids = input_ids.to(device)
    outputs_ids = model2.generate(**input_ids, **gen_kwargs).to(device)
    with torch.no_grad():
        outputs = model1(input_ids, labels=output_ids)
    loss, logits = outputs[:2]
    return torch.exp(loss)

this can test and verify whether the output of the two models is the same. If different, maybe one of the model memorizes the train data.

[ftramer]

This code is just the standard way of calculating a model's loss over a given set of tokens.
Say you have a sentence of tokens (t1, ..., tn). Then if you feed this into the model (model(input_ids)), the model will output n prediction vectors where the i-th prediction vector contains the model's prediction for the i-th token, given the (i-1) first tokens.
Then, to calculate a loss, we feed in the same tokens as ground truth. So the model's prediction for the i-th token gets compared with the actual i-th token of the sentence.

In the paper, we just compare two models by looking at the perplexities they assign to the same sentence.

[Ethan-Chen-plus]

Thanks a lot!
but I am wondering this:

    def compute_loss(self, model, inputs, return_outputs=False):
        return model(
            input_ids=inputs["input_ids"],
            attention_mask=inputs["attention_mask"],
            position_ids=inputs["position_ids"],
            labels=inputs["labels"],
        ).loss

but in this repo, the code use input_ids labels, I thought that we might use inputs["labels"] as labels.
@ftramer

[ftramer]

Where is this code from?
The code we use is a standard way of calculating perplexity in huggingface.