def allreduce_position_embedding_grads(self, args):
        """
        All-reduce position_embeddings grad across first (encoder) and
        split (decoder) stages to ensure that position embeddings parameters
        stay in sync. This should only run for T5 models with pipeline
        parallelism.
        """
        if mpu.is_rank_in_position_embedding_group() and \
                mpu.get_pipeline_model_parallel_world_size() > 1 and \
                args.pipeline_model_parallel_split_rank is not None:
            unwrapped_model = self.models[0]
            unwrapped_model = unwrap_model(
                unwrapped_model, (torchDDP, LocalDDP, Float16Module))
            assert args.DDP_impl == 'local', \
                'T5 model is only supported with local DDP mode'
            grad = unwrapped_model.language_model.embedding.position_embeddings.weight.main_grad
            torch.distributed.all_reduce(grad, group=mpu.get_position_embedding_group())

        All-reduce position_embeddings grad across first (encoder) and
        split (decoder) stages to ensure that position embeddings parameters
        stay in sync. This should only run for T5 models with pipeline
        parallelism.
        
training/DeepSpeed-Domino/domino/optimizer/optimizer.py