Multi-GPU / Multi-Node Worker Crash / Deadlock When Returning {"loss": None, "lr": None}
**Describe the bug**

When running multi-GPU / multi-node training of NLP Entity Linking model against full UMLS dataset, a return of None for loss & lr from training_step results in a crash of a worker and deadlock (at 100% GPU / CPU utilization). It appears this is incompatible with multi-GPU, but entity linking notebooks reference multi-gpu training.

**Steps/Code to reproduce bug**

Training fails at around 75% of first epoch, which is ~18 hours in on 4x A100s, or more than double that on single GPU setups.

Error message is "pytorch_lightning.utilities.exceptions.MisconfigurationException: In automatic_optimization, when `training_step` returns a dict, the 'loss' key needs to be present"

Relevant code in training_step: https://github.com/NVIDIA/NeMo/blob/69385daa999907a15f86e461b670a866424f5bb9/nemo/collections/nlp/models/entity_linking/entity_linking_model.py#L83-L103

Note PyTorch Lightning docs on [training_step](https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html#training-step) return value:
```
None - Training will skip to the next batch. This is only for automatic optimization.
**This is not supported for multi-GPU, TPU, IPU, or DeepSpeed.**
```

Thus this will work on single GPU, but not multi-GPU according to the docs.

**Expected behavior**

Training completes without crashing.

**Environment overview (please complete the following information)**

 - Environment location: Docker 
 - Method of NeMo install: Nemo NGC v.1.6.1
 - If method of install is [Docker], provide `docker pull` & `docker run` commands used
 Pull: docker pull nvcr.io/nvidia/nemo:1.6.1
Run: docker run --gpus all -it -v /opt/nvidia/nemo:/workspace/nemo/local --shm-size=16g --ulimit memlock=-1 --ulimit stack=67108864 -v /dev/infiniband --uts=host --net=host --ipc=host --cap-add IPC_LOCK --device=/dev/snd --device /dev/infiniband/uverbs0 --device /dev/infiniband/rdma_cm --device /dev/infiniband/umad0 nvcr.io/nvidia/nemo:1.6.1 /bin/bash

**Environment details**

If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version

**Additional context**

Add any other context about the problem here.
Tested on A100 & Titan RTX - same results
Single vs. Multi-node does not appear to make a difference either.
