[REQUEST] Expose TP/DP/PP Rank as Environment Variable for integration with low Level Profiling Tools

First, thank you for providing such a powerful and scalable framework for distributed training.

I would like to propose a small but practical feature that would help with system-level profiling using external tracing tools like CUPTI.

While torchrun already sets RANK, LOCAL_RANK, and WORLD_SIZE as environment variables, DeepSpeed
internally computes parallelism ranks: TP / DP/ PP Rank using APIs like topo.get_tensor_model_parallel_rank( )

However, these ranks are not exposed as environment variables, which makes it difficult to access them from low level tools such as CUPTI.

Would you consider adding the following environment variables after initialize parallelism?

like

    os.environ["TP_RANK"] = str(self._topo.get_tensor_model_parallel_rank(rank))
    os.environ["PP_RANK"] = str(self._topo.get_pipeline_model_parallel_rank(rank))
    os.environ["DP_RANK"] = str(self._topo.get_data_parallel_rank(rank))

Would you consider accepting a PR for this?

Thank you again for your work and consideration!
Looking forward to your thoughts.

Best Regards.
[Woosung Myung]

Start by locating the topology initialization code that calls get_tensor_model_parallel_rank(), get_pipeline_model_parallel_rank(), and get_data_parallel_rank(). Verify how ranks are computed after parallelism initialization, then expose the three requested environment variables and confirm they are available to external profiling tools after initialization.