Debug mode for distributed problems

Nobody has claimed this yet.

## 🚀 Feature

Implement an environment flag where collective ops print information about their use. This can be for example the barrier and its name per process.

### Motivation

Improve the debugging experience for distributed problems.

cc @borda @awaelchli @rohitgr7 @akihironitta @tchaton
______________________________________________________________________

#### If you enjoy Lightning, check out our other projects! ⚡

- [**Metrics**](https://github.com/PyTorchLightning/metrics): Machine learning metrics for distributed, scalable PyTorch applications.

- [**Flash**](https://github.com/PyTorchLightning/lightning-flash): The fastest way to get a Lightning baseline! A collection of tasks for fast prototyping, baselining, finetuning and solving problems with deep learning

- [**Bolts**](https://github.com/PyTorchLightning/lightning-bolts): Pretrained SOTA Deep Learning models, callbacks and more for research and production with PyTorch Lightning and PyTorch

- [**Lightning Transformers**](https://github.com/PyTorchLightning/lightning-transformers): Flexible interface for high performance research using SOTA Transformers leveraging Pytorch Lightning, Transformers, and Hydra.

No files, tests, or entry points are named. Start by locating the collective-operation implementations and the environment-configuration path, then determine how an environment flag could report operations such as barriers and their names per process. Done means the intended collective operations provide useful debug information when the flag is enabled, with coverage for the resulting behavior.

Written by the indexing model from the issue text.