Can't create a dataset with `float16` features

## Describe the bug
I can't create a dataset with `float16` features.

I understand from the traceback that this is a `pyarrow` error, but I don't see anywhere in the `datasets` documentation about how to successfully do this.  Is it actually supported?  I've tried older versions of `pyarrow` as well with the same exact error.

The bug seems to arise from `datasets` casting the values to `double` and then `pyarrow` doesn't know how to convert those back to `float16`... does that sound right?  Is there a way to bypass this since it's not necessary in the `numpy` and `torch` cases?

Thanks!


## Steps to reproduce the bug
All of the following raise the following error with the same exact (as far as I can tell) traceback:
```python
ArrowNotImplementedError: Unsupported cast from double to halffloat using function cast_half_float
```
```python
from datasets import Dataset, Features, Value
Dataset.from_dict({"x": [0.0, 1.0, 2.0]}, features=Features(x=Value("float16")))

import numpy as np
Dataset.from_dict({"x": np.arange(3, dtype=np.float16)}, features=Features(x=Value("float16")))

import torch
Dataset.from_dict({"x": torch.arange(3).to(torch.float16)}, features=Features(x=Value("float16")))
```

## Expected results
A dataset with `float16` features is successfully created.

## Actual results

```python
---------------------------------------------------------------------------
ArrowNotImplementedError                  Traceback (most recent call last)
Cell In [14], line 1
----> 1 Dataset.from_dict({"x": [1.0, 2.0, 3.0]}, features=Features(x=Value("float16")))

File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py:870, in Dataset.from_dict(cls, mapping, features, info, split)
    865     mapping = features.encode_batch(mapping)
    866 mapping = {
    867     col: OptimizedTypedSequence(data, type=features[col] if features is not None else None, col=col)
    868     for col, data in mapping.items()
    869 }
--> 870 pa_table = InMemoryTable.from_pydict(mapping=mapping)
    871 if info.features is None:
    872     info.features = Features({col: ts.get_inferred_type() for col, ts in mapping.items()})

File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:750, in InMemoryTable.from_pydict(cls, *args, **kwargs)
    734 @classmethod
    735 def from_pydict(cls, *args, **kwargs):
    736     """
    737     Construct a Table from Arrow arrays or columns
    738 
   (...)
    748         :class:`datasets.table.Table`:
    749     """
--> 750     return cls(pa.Table.from_pydict(*args, **kwargs))

File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/table.pxi:3648, in pyarrow.lib.Table.from_pydict()

File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/table.pxi:5174, in pyarrow.lib._from_pydict()

File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:343, in pyarrow.lib.asarray()

File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:231, in pyarrow.lib.array()

File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:110, in pyarrow.lib._handle_arrow_array_protocol()

File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py:197, in TypedSequence.__arrow_array__(self, type)
    192     # otherwise we can finally use the user's type
    193     elif type is not None:
    194         # We use cast_array_to_feature to support casting to custom types like Audio and Image
    195         # Also, when trying type "string", we don't want to convert integers or floats to "string".
    196         # We only do it if trying_type is False - since this is what the user asks for.
--> 197         out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
    198     return out
    199 except (TypeError, pa.lib.ArrowInvalid) as e:  # handle type errors and overflows

File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1683, in _wrap_for_chunked_arrays.<locals>.wrapper(array, *args, **kwargs)
   1681     return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
   1682 else:
-> 1683     return func(array, *args, **kwargs)

File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1853, in cast_array_to_feature(array, feature, allow_number_to_str)
   1851     return array_cast(array, get_nested_type(feature), allow_number_to_str=allow_number_to_str)
   1852 elif not isinstance(feature, (Sequence, dict, list, tuple)):
-> 1853     return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
   1854 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")

File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1683, in _wrap_for_chunked_arrays.<locals>.wrapper(array, *args, **kwargs)
   1681     return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
   1682 else:
-> 1683     return func(array, *args, **kwargs)

File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1762, in array_cast(array, pa_type, allow_number_to_str)
   1760     if pa.types.is_null(pa_type) and not pa.types.is_null(array.type):
   1761         raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}")
-> 1762     return array.cast(pa_type)
   1763 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{pa_type}")

File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:919, in pyarrow.lib.Array.cast()

File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/compute.py:389, in cast(arr, target_type, safe, options)
    387     else:
    388         options = CastOptions.safe(target_type)
--> 389 return call_function("cast", [arr], options)

File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/_compute.pyx:560, in pyarrow._compute.call_function()

File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/_compute.pyx:355, in pyarrow._compute.Function.call()

File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status()

File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/error.pxi:121, in pyarrow.lib.check_status()

ArrowNotImplementedError: Unsupported cast from double to halffloat using function cast_half_float
```

## Environment info

- `datasets` version: 2.4.0
- Platform: macOS-12.5.1-arm64-arm-64bit
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.4


Hi @dconathan, thanks for reporting.

We rely on Arrow as a backend, and as far as I know currently support for `float16` in Arrow is not fully implemented in Python (C++), hence the `ArrowNotImplementedError` you get.

See, e.g.: https://arrow.apache.org/docs/status.html?highlight=float16#data-types

Thanks for the link…. didn’t realize arrow didn’t support it yet.  Should it be removed from https://huggingface.co/docs/datasets/v2.4.0/en/package_reference/main_classes#datasets.Value until Arrow supports it?

Yes, you are right: maybe we should either remove it from our docs or add a comment explaining the issue.

The thing is that in Arrow it is partially supported: you can create `float16` values, but you can't cast them from/to other types. And current implementation of `Value` always tries to perform a cast from `float64` to `float16`.

Maybe we can just add a note in the `Value` documentation ?

Would you accept a PR to fix this? @lhoestq Do you have an idea of how hard it would be to fix?

I think the issue comes mostly from pyarrow not supporting `float16` completely.

For example you stil can't cast from/to `float16`
```python
import numpy as np
import pyarrow as pa

pa.array(range(5)).cast(pa.float16())
# ArrowNotImplementedError: Unsupported cast from int64 to halffloat using function cast_half_float
pa.array(range(5), pa.float32()).cast(pa.float16())
# ArrowNotImplementedError: Unsupported cast from float to halffloat using function cast_half_float
pa.array(range(5), pa.float16())
# ArrowTypeError: Expected np.float16 instance
pa.array(np.arange(5, dtype=np.float16())).cast(pa.float32())
# ArrowNotImplementedError: Unsupported cast from halffloat to float using function cast_float
```

Hmm it seems like we can either:
1. try to fix pyarrow upstream
2. half-support float16 with some workaround to make sure we don't ever do casting internally