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## Why are these changes needed?
This PR aims to port concurrency groups functionality with asyncio for Python.
### API
```python
@ray.remote(concurrency_groups={"io": 2, "compute": 4})
class AsyncActor:
def __init__(self):
pass
@ray.method(concurrency_group="io")
async def f1(self):
pass
@ray.method(concurrency_group="io")
def f2(self):
pass
@ray.method(concurrency_group="compute")
def f3(self):
pass
@ray.method(concurrency_group="compute")
def f4(self):
pass
def f5(self):
pass
```
The annotation above the actor class `AsyncActor` defines this actor will have 2 concurrency groups and defines their max concurrencies, and it has a default concurrency group. Every concurrency group has an async eventloop and a pythread to execute the methods which is defined on them.
Method `f1` will be invoked in the `io` concurrency group. `f2` in `io`, `f3` in `compute` and etc.
TO BE NOTICED, `f5` and `__init__` will be invoked in the default concurrency.
The following method `f2` will be invoked in the concurrency group `compute` since the dynamic specifying has a higher priority.
```python
a.f2.options(concurrency_group="compute").remote()
```
### Implementation
The straightforward implementation details are:
- Before we only have 1 eventloop binding 1 pythread for an asyncio actor. Now we create 1 eventloop binding 1 pythread for every concurrency group of the asyncio actor.
- Before we have 1 fiber state for every caller in the asyncio actor. Now we create a FiberStateManager for every caller in the asyncio actor. And the FiberStateManager manages the fiber states for concurrency groups.
## Related issue number
#16047
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Using Ray
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=========
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If you’re brand new to Ray, we recommend starting with our `tutorials <https://github.com/ray-project/tutorial>`_.
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Below, you'll find information ranging from how to `start Ray <starting-ray.html>`_ to `advanced usage <advanced.html>`_. There are also detailed instructions on how to work with Ray concepts such as Actors and managing GPUs.
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Finally, we've also included some content on using core Ray APIs with `Tensorflow <using-ray-with-tensorflow.html>`_, `PyTorch <using-ray-with-pytorch.html>`_ and `Jupyter Notebook <using-ray-with-jupyter.rst>`_.
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.. toctree::
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:maxdepth: -1
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starting-ray.rst
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actors.rst
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namespaces.rst
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async_api.rst
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concurrency_group_api.rst
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using-ray-with-gpus.rst
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serialization.rst
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memory-management.rst
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placement-group.rst
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troubleshooting.rst
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fault-tolerance.rst
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advanced.rst
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cross-language.rst
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using-ray-with-tensorflow.rst
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using-ray-with-pytorch.rst
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using-ray-with-jupyter.rst
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