Adding Bigscience 176B parameters model
# What does this PR do?

This PR add BigScience 176B parameters model to transformers.

The model architecture is detailed here: https://bigscience.notion.site/BigScience-176B-Model-Training-ad073ca07cdf479398d5f95d88e218c4

The training of the model can be followed here: https://twitter.com/BigScienceLLM

The model is trained using 3D parallelism with a combination of data (DP), pipeline (PP) and tensor (TP) parallelisms (see an illustration [here](https://twitter.com/BigScienceLLM/status/1506588988278198273?s=20&t=mxyFZQvVTM3YFDaQeHvlHQ)). The training code is a fork of NVIDIA/Microsoft Megatron-DeepSpeed and can be found here: https://github.com/bigscience-workshop/Megatron-DeepSpeed)

For integration in transformers, the following approach is being currently taken:
- focus on using the model in inference only (will impact PP questions - no backward pass, no bubble - may also impact the need for TP - we can also focus on batch-size=1 for now => no DP)
- try to be able to use the model on a rather small workstation (with all caveats but goal is to get the integration to work on less than the 48 GPUs currently used for training)
- be very pragmatic - we can always develop optimized version of the model/code later

# Current status:
This PR is still a not working draft.

During the integration a potential bug in the original training code was discovered where the layer norms on the parallel branches of the TP direction were not identical unlike expected. The integration was then stop to investigate the situation and should start again when the issue is solved on the training side.

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.