About IterableDateset with dataloader_num_workers>1

1. 如果使用IterableDataset, dataloader_num_workers=1会被执行：https://github.com/modelscope/ms-swift/blob/3bb1d21959687137b43d4d730424604dcaec68ee/swift/trainers/mixin.py#L74
2. 如果保证 IterableDataset.num_shards > world_size * dataloader_num_workers, transformers的IterableDatasets是支持dataloader_num_workers>1的；
3. 如果保证 IterableDataset.num_shards > world_size * dataloader_num_workers 并设置dataloader_num_workers>1，我用ms-swift在单机多卡下可以正常运行，并且训练速度有提升。
   我注意到ms-swift重写了dataloader相关实现：
   https://github.com/modelscope/ms-swift/blob/3bb1d21959687137b43d4d730424604dcaec68ee/swift/trainers/mixin.py#L1139
   我想知道这些部分均可以兼容多机多卡/单机多卡时IterableDataset使用dataloader_num_workers>1吗？另外，目前ms-swift的实现在dataloader_num_workers>1时是否存在一些其他的不兼容现象？

Start with swift/trainers/mixin.py at the references around lines 74 and 1139, then compare the IterableDataset sharding assumptions with the Transformers implementation. Run focused single-node and multi-node training checks using dataloader_num_workers greater than 1. Done means the supported configurations and any incompatibilities are documented or covered by tests.