[WIP] Adding support for `flax` for `pipelines`.
# What does this PR do?

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Fixes # (issue)

```python
from transformers import FlaxRobertaModel, RobertaTokenizerFast,
RobertaForMaskedLM, pipeline
import numpy as np
import datetime
import tqdm
import random

def data(n):
    for _ in range(n):
        yield "JAX/Flax is amazing <mask>"

def flax(n):
    print("----")
    print("Flax")
    start = datetime.datetime.now()
    pipe = pipeline(
        model="distilbert-base-uncased-finetuned-sst-2-english",
        device=0,
        framework="flax",
        model_kwargs={"from_pt": True},
    )
    print("Loading flax", datetime.datetime.now() - start)

    for out in tqdm.tqdm(pipe(data(n), batch_size=512)):
        pass

def tf(n):
    print("----")
    print("TF")
    start = datetime.datetime.now()
    pipe =
pipeline(model="distilbert-base-uncased-finetuned-sst-2-english",
device=0, framework="tf")
    print("Loading TF", datetime.datetime.now() - start)

    for out in tqdm.tqdm(pipe(data(n), batch_size=512)):
        pass

def pt(n):
    print("----")
    print("PT")
    start = datetime.datetime.now()
    pipe =
pipeline(model="distilbert-base-uncased-finetuned-sst-2-english",
device=0, framework="pt")
    print("Loading PT", datetime.datetime.now() - start)

    for out in tqdm.tqdm(pipe(data(n), batch_size=512)):
        pass
        # print(out)

if __name__ == "__main__":
    n = 20000
    # pt(n)
    # flax(n)
    tf(n)
```

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@Narsil - please ping me once you would like to have a review on this :-)<|||||>@patrickvonplaten I think this should be ready for a first pass review.

Let's focus on the big picture and overall architecture for this. The main pain points I am thinking of:

- Should we force padding to get compilation cache hit on jax.
- Should we enable JIT automatically or not? (If we want `pjit` or others maybe we should delegate that choice to users ?)<|||||>> @patrickvonplaten I think this should be ready for a first pass review.
> 
> Let's focus on the big picture and overall architecture for this. The main pain points I am thinking of:
> 
> * Should we force padding to get compilation cache hit on jax.
> * Should we enable JIT automatically or not? (If we want `pjit` or others maybe we should delegate that choice to users ?)

Good questions! 

IMO:

For a) -> yes I would definitely do this. The by far most important application will be to provide a simple and fast demo for large LM models, such as https://huggingface.co/Cedille/fr-boris?text=Mon+nom+est+Thomas+et+mon+principal (which currently doesn't work with PT on GPU). I woulcd actually force padding to a large multiple to something like 64 by default (cc @patil-suraj what do you think?)

For b) -> yeah good question. I think we should delegate it to the user meaning that there will be both a `jit=True/False` and a `parallel=True/False` input that the user can specify. IMO both should default to False, but for the inference widget we should default jit to True I think (if JAX is used for large models)

<|||||>> > @patrickvonplaten I think this should be ready for a first pass review.
> > Let's focus on the big picture and overall architecture for this. The main pain points I am thinking of:
> > 
> > * Should we force padding to get compilation cache hit on jax.
> > * Should we enable JIT automatically or not? (If we want `pjit` or others maybe we should delegate that choice to users ?)
> 
> Good questions!
> 
> IMO:
> 
> For a) -> yes I would definitely do this. The by far most important application will be to provide a simple and fast demo for large LM models, such as https://huggingface.co/Cedille/fr-boris?text=Mon+nom+est+Thomas+et+mon+principal (which currently doesn't work with PT on GPU). I woulcd actually force padding to a large multiple to something like 64 by default (cc @patil-suraj what do you think?)

Makes sense to me this was in the first iteration, and I removed it since during compilation during tests is still painfully slow because no cache hits there (we're mostly loading different models and sending a handful of inference at them).

> 
> For b) -> yeah good question. I think we should delegate it to the user meaning that there will be both a `jit=True/False` and a `parallel=True/False` input that the user can specify. IMO both should default to False, but for the inference widget we should default jit to True I think (if JAX is used for large models)

Ok, since this is specific to `jax` and I don't intend to add similar `torchscript` stuff to `pt` for instance, I am guessing I will aim for something that would look like

```python
pipe = pipeline(....)
pipe._enable_compilation(type="jit", )
# or
pipe._enable_compilation(type="pjit", **extra_mapping)
# Or add the jitting automatically if it wasn't called by the user with align to = 64
```<|||||>@Narsil - that looks good to me. Let's see what @patil-suraj thinks about the API<|||||>0Still valuable<|||||>Super sorry about being so late here, will take a look this week :) <|||||>Also think this would be a nice addition still - gently ping for @patil-suraj here.