def compute_log_prob(self, data: DataProto) -> torch.Tensor:
        """Compute the log probability of the responses given input_ids, attention_mask and position_ids

        Args:
            data (DataProto): a DataProto containing keys

                ``input_ids``: tensor of shape [batch_size, sequence_length]. torch.int64. Note that input_ids is the
                concatenation of prompt and response. Note that ``sequence_length = prompt_length + response_length``.

                ``attention_mask``: tensor of shape [batch_size, sequence_length]. torch.int64.

                ``position_ids``: tensor of shape [batch_size, sequence_length]. torch.int64.

                ``responses``:  tensor of shape [batch_size, response_length]. torch.int64.

        Returns:
            torch.Tensor: the log_prob tensor
        """
        # set to eval
        self.actor_module.eval()

        micro_batch_size = data.meta_info['micro_batch_size']
        temperature = data.meta_info['temperature']  # temperature must be in the data.meta_info to avoid slient error
        use_dynamic_bsz = data.meta_info['use_dynamic_bsz']

        select_keys = ['responses', 'input_ids', 'attention_mask', 'position_ids']
        batch = data.select(batch_keys=select_keys).batch

        if use_dynamic_bsz:
            # split using dynamic bsz
            max_token_len = data.meta_info['max_token_len'] * self.ulysses_sequence_parallel_size
            micro_batches, indices = rearrange_micro_batches(batch=batch, max_token_len=max_token_len)
        else:
            micro_batches = batch.split(micro_batch_size)

        log_probs_lst = []
        for micro_batch in micro_batches:
            with torch.no_grad():
                _, log_probs = self._forward_micro_batch(micro_batch, temperature=temperature)
            log_probs_lst.append(log_probs)
        log_probs = torch.concat(log_probs_lst, dim=0)

        if use_dynamic_bsz:
            indices = list(itertools.chain.from_iterable(indices))
            assert len(indices) == log_probs.size(0), f"{len(indices)} vs. {log_probs.size()}"
            revert_indices = torch.tensor(get_reverse_idx(indices), dtype=torch.long)
            log_probs = log_probs[revert_indices]

        return log_probs
Compute the log probability of the responses given input_ids, attention_mask and position_ids

        Args:
            data (DataProto): a DataProto containing keys

                ``input_ids``: tensor of shape [batch_size, sequence_length]. torch.int64. Note that input_ids is the
                concatenation of prompt and response. Note that ``sequence_length = prompt_length + response_length``.

                ``attention_mask``: tensor of shape [batch_size, sequence_length]. torch.int64.

                ``position_ids``: tensor of shape [batch_size, sequence_length]. torch.int64.

                ``responses``:  tensor of shape [batch_size, response_length]. torch.int64.

        Returns:
            torch.Tensor: the log_prob tensor
        
verl/workers/actor/dp_actor.py