update comment

                torch.distributed.all_reduce(self.word_embeddings_weight().data,

                                             group=mpu.get_embedding_group())

            # All-reduce other embeddings as well as necessary. The last stage

            # does not have these other embeddings, so just create placeholder

            # tensors of the right shape with all zeros.

            # Ensure that encoder(first stage) and decoder(split stage) position 

            # embeddings have the same initial parameter values

            # NOTE: We don't currently support T5 with the interleaved schedule.

            if mpu.is_rank_in_position_embedding_group() and \

                    args.pipeline_model_parallel_split_rank is not None: