PyTorch DistributedTensor (DTensor) is a prototyping effort with distributed tensor primitives to allow easier distributed computation authoring in the SPMD (Single Program Multiple Devices) paradigm. The primitives are simple but powerful when used to express tensor distributions with both sharded and replicated parallelism strategies. PyTorch DTensor empowered PyTorch Tensor Parallelism along with other advanced parallelism explorations. In addition, it also offers a uniform way to save/load state_dict for distributed checkpointing purposes, even when there’re complex tensor distribution strategies such as combining tensor parallelism with parameter sharding in FSDP. More details can be found in this RFC and the DTensor examples notebook.