def loglikelihood_rolling()

in src/lighteval/models/endpoints/endpoint_model.py [0:0]

• 31 lines of code
• 9 McCabe index (conditional complexity)

    def loglikelihood_rolling(self, requests: list[Doc], override_bs=None) -> list[ModelResponse]:
        """This function is used to compute the log likelihood of the context for perplexity metrics."""
        dataset = LoglikelihoodDataset(requests=requests, num_dataset_splits=self.DATASET_SPLITS)
        batch_size = override_bs if override_bs is not None else BATCH_SIZE
        results: list[ModelResponse] = []

        for split in tqdm(
            dataset.splits_iterator(),
            total=dataset.num_dataset_splits,
            desc="Splits",
            position=0,
            disable=self.disable_tqdm,
        ):
            dataloader = DataLoader(split, batch_size=batch_size, collate_fn=lambda batch: batch)

            for batch in tqdm(
                dataloader, desc="Loglikelihoods, rolling", position=1, leave=False, disable=self.disable_tqdm
            ):
                if self.use_async:
                    responses = asyncio.run(self._async_process_batch_logprob(batch, rolling=True))
                else:
                    responses = self._process_batch_logprob(batch, rolling=True)

                for response in responses:
                    logits = [t.logprob for t in response.details.tokens[:-1]]

                    results.append(
                        ModelResponse(
                            result=sum(logits),
                            input_tokens=[t.id for t in response.details.prefill],
                            generated_tokens=[t.id for t in response.details.tokens[:-1]],
                            truncated_tokens_count=-1,
                            padded_tokens_count=-1,
                        )
                    )

        return dataset.get_original_order(results)