Account for mpirun use case in get_rank

[janekl]

What does this PR do ?

Some distributed workloads can possibly be launched "manually" with mpirun -n N python .... I'm adapting nemo.utils.get_rank.py module to take it into account and produce correct rank information in such cases.

Currently the below rank_example.py script (with the project pattern for setting up distributed environment) when launched with, for example, mpirun -n 2 python rank_example.py outputs

RANKS (NeMo, PyT): (0, 0)
RANKS (NeMo, PyT): (0, 1)

while the expected output is

RANKS (NeMo, PyT): (0, 0)
RANKS (NeMo, PyT): (1, 1)

The MR fixes this.

rank_example.py:

import torch.distributed as dist
from pytorch_lightning.trainer.trainer import Trainer

from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy
from nemo.utils.get_rank import get_rank

trainer = Trainer(
    strategy=NLPDDPStrategy(),
    devices=-1,
    num_nodes=1,
    accelerator="gpu",
    logger=False,
    precision="bf16",
    enable_checkpointing=False,
)


def dummy():
    return


if trainer.strategy.launcher is not None:
    trainer.strategy.launcher.launch(dummy, trainer=trainer)
trainer.strategy.setup_environment()

print("RANKS (NeMo, PyT):", (get_rank(), dist.get_rank()))

Collection: [ALL]

Changelog

• Fixing is_global_rank_zero and get_rank helper functions in case of using mpirun by considering OMPI_COMM_WORLD_RANK env var.

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