[RAG] Add Ray implementation for distributed retrieval
# What does this PR do?
This PR adds a new distributed retriever implementation for RAG built on Ray, as an alternative to the current retriever implementation that uses torch.distributed. With Ray it's possible to load the index on multiple processes instead of just the rank 0 training worker, allowing fine tuning to scale out better to multiple GPUs, and also allowing the index to potentially be fit in GPU memory. This also removes a core dependency on Pytorch, allowing a Tensorflow implementation of `finetune.py`.

This PR also makes changes to support finetune.py with Pytorch Lightning >v1.0.

A benchmark of Pytorch distribtued retrieval vs. Ray distributed retrieval
![image](https://user-images.githubusercontent.com/8068268/99327675-4ae49380-282f-11eb-8126-6250558fd795.png)

## Implementation Details
In the current Pytorch retrieval implementation, the index is loaded once on just the rank 0 training workers. Training worker 0 gathers the inputs from all other workers, performs the index lookup, and scatters the results back to the other workers.
![image](https://user-images.githubusercontent.com/8068268/99328533-238ec600-2831-11eb-9d26-e153cd0de74f.png)

With the Ray implementation, the index is loaded on *separate* processes, which are referred to as Ray actors. Each training worker randomly selects a retrieval actor to query for documents and Ray handles all the communication between the processes. Because the index can be loaded in *multiple* processes, training can scale up since no synchronization needs to happen for the index lookup.
![image](https://user-images.githubusercontent.com/8068268/99328651-618bea00-2831-11eb-990b-f1727df15190.png)

Note that Pytorch Lightning is still handling distributed *training*, but Ray manages distributed *retrieval*. Because PTL calls the entire training script under the hood multiple times, we have to use Ray's named actors feature (https://docs.ray.io/en/master/actors.html?highlight=named%20actors#named-actors) allowing the retrieval actors to be referenced by all training processes. The use of named actors is necessitated by how PTL handles distributed training, and a simpler approach could probably be used for a Tensorflow implentation.

## Testing Strategy
Unit tests were added to `test_distributed_retriever.py`. Note that the local Ray cluster for the tests had to be started with `local_mode=True` because the test file modifies `sys.path` and these changes are not propagated to remote processes. See https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder for more info.
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cc @sgugger @patrickvonplaten @LysandreJik @lhoestq <|||||>Nice, good to merge then!<|||||>Awesome, thank you so much for the reviews @lhoestq @patrickvonplaten -- happy holidays!<|||||>Thanks guys!<|||||>@amogkam @patrickvonplaten  I need some help to implement an end-to-end retrieval training feature for the rag with Ray.

How can I run document encoding and indexing with an updated doc-encoder (context encoder network that kept frozen in the original RAG)  using a Ray actor separated from the main training process? 

How can I access the document index inside Ray actors during the training incase I want to update the index, say in every 5000 steps. 

<|||||>@shamanez could you open a new issue to track this?<|||||>@richardliaw  

I have already opened one a few weeks ago. Please refer to this [issue](https://github.com/huggingface/transformers/issues/9646)

I added a new issue explaining the exact problem in [this](https://github.com/huggingface/transformers/issues/10135)