def get_all_params(self):
        """Get all the parameters of the models in all pp ranks

        Returns:
            params: List[List[Dict[str, Tensor]]]: a list of parameters in all pp, where each is a list of dict
                tensors of each model chunk

        """
        params = []
        for pp_rank in range(self.pp_size):
            params.append([])
            for model_chunk_idx in range(len(self.pp_models[pp_rank])):
                params[pp_rank].append({})
                pp_model = self.pp_models[pp_rank][model_chunk_idx]
                pp_model = unwrap_model(pp_model, ((torchDDP, LocalDDP, Float16Module)))  # not use Float16Module
                for name, param in pp_model.named_parameters():
                    # NOTE(gh) workaround: should not get lora params for inference
                    if 'lora' in name:
                        continue
                    params[pp_rank][model_chunk_idx][name] = param

        return params
Get all the parameters of the models in all pp ranks

        Returns:
            params: List[List[Dict[str, Tensor]]]: a list of parameters in all pp, where each is a list of dict
                tensors of each model chunk

        
verl/workers/sharding_manager/megatron_vllm.py