def upgrade_state_dict_named(self, state_dict, name):
        """Upgrade a (possibly old) state dict for new versions of fairseq."""
        if isinstance(self.embed_positions, SinusoidalPositionalEmbedding):
            weights_key = "{}.embed_positions.weights".format(name)
            if weights_key in state_dict:
                del state_dict[weights_key]
            state_dict[
                "{}.embed_positions._float_tensor".format(name)
            ] = torch.FloatTensor(1)

        for i in range(len(self.layers)):
            # update layer norms
            layer_norm_map = {
                "0": "self_attn_layer_norm",
                "1": "encoder_attn_layer_norm",
                "2": "final_layer_norm",
            }
            for old, new in layer_norm_map.items():
                for m in ("weight", "bias"):
                    k = "{}.layers.{}.layer_norms.{}.{}".format(name, i, old, m)
                    if k in state_dict:
                        state_dict[
                            "{}.layers.{}.{}.{}".format(name, i, new, m)
                        ] = state_dict[k]
                        del state_dict[k]

        version_key = "{}.version".format(name)
        if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) <= 2:
            # earlier checkpoints did not normalize after the stack of layers
            self.layer_norm = None
            self.normalize = False
            state_dict[version_key] = torch.Tensor([1])

        return state_dict
Upgrade a (possibly old) state dict for new versions of fairseq.
understand_icl/fairseq/fairseq/model_parallel/models/pipeline_parallel_transformer/model.py