Title: `with_format("numpy")` silently downcasts float64 to float32 features
Author: ernestum (NONE)
State: open
Created: 2023-02-09T14:18:00Z

### Describe the bug

When I create a dataset with a `float64` feature, then apply numpy formatting the returned numpy arrays are silently downcasted to `float32`.

### Steps to reproduce the bug

```python
import datasets
dataset = datasets.Dataset.from_dict({'a': [1.0, 2.0, 3.0]}).with_format("numpy")
print("feature dtype:", dataset.features['a'].dtype)
print("array dtype:", dataset['a'].dtype)
```
output:
```
feature dtype: float64
array dtype: float32
```

### Expected behavior

```
feature dtype: float64
array dtype: float64
```

### Environment info

- `datasets` version: 2.8.0
- Platform: Linux-5.4.0-135-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 10.0.1
- Pandas version: 1.4.4

### Suggested Fix

Changing [the `_tensorize` function of the numpy formatter](https://github.com/huggingface/datasets/blob/b065547654efa0ec633cf373ac1512884c68b2e1/src/datasets/formatting/np_formatter.py#L32) to 
```python
    def _tensorize(self, value):

        if isinstance(value, (str, bytes, type(None))):
            return value
        elif isinstance(value, (np.character, np.ndarray)) and np.issubdtype(value.dtype, np.character):
            return value
        elif isinstance(value, np.number):
            return value

        return np.asarray(value, **self.np_array_kwargs)
```
fixes this particular issue for me. Not sure if this would break other tests. This should also avoid unnecessary copying of the array.

Comments:
Hi! This behavior stems from these lines:

https://github.com/huggingface/datasets/blob/b065547654efa0ec633cf373ac1512884c68b2e1/src/datasets/formatting/np_formatter.py#L45-L46

I agree we should preserve the original type whenever possible and downcast explicitly with a warning.

@lhoestq Do you remember why we need this "default dtype" logic in our formatters?

I was also wondering why the default type logic is needed. Me just deleting it is probably too naive of a solution.

Hmm I think the idea was to end up with the usual default precision for deep learning models - no matter how the data was stored or where it comes from.

For example in NLP we store tokens using an optimized low precision to save disk space, but when we set the format to `torch` we actually need to get `int64`. Although the need for a default for integers also comes from numpy not returning the same integer precision depending on your machine. Finally I guess we added a default for floats as well for consistency.

I'm a bit embarrassed by this though, as a user I'd have expected to get the same precision indeed as well and get a zero copy view.

Will you fix this or should I open a PR?

Unfortunately removing it for integers is a breaking change for most `transformers` + `datasets` users for NLP (which is a common case). Removing it for floats is a breaking change for `transformers` + `datasets` for ASR as well. And it also is a breaking change for the other users relying on this behavior.

Therefore I think that the only short term solution is for the user to provide `dtype=` manually and document better this behavior. We could also extend `dtype` to accept a value that means "return the same dtype as the underlying storage" and make it easier to do zero copy.

@lhoestq It should be fine to remove this conversion in Datasets 3.0, no? For now, we can warn the user (with a log message) about the future change when the default type is changed.

Let's see with the transformers team if it sounds reasonable ? We'd have to fix multiple example scripts though.

If it's not ok we can also explore keeping this behavior only for tokens and audio data.

IMO being coupled with Transformers can lead to unexpected behavior when one tries to use our lib without pairing it with Transformers, so I think it's still important to "fix" this, even if it means we will need to update Transformers' example scripts afterward.


Ideally let's update the `transformers` example scripts before the change :P

For others that run into the same issue: A temporary workaround for me is this:
```python
def numpy_transform(batch):
    return {key: np.asarray(val) for key, val in batch.items()}

dataset = dataset.with_transform(numpy_transform)
```

This behavior (silent upcast from `int32` to `int64`) is also unexpected for the user in https://discuss.huggingface.co/t/standard-getitem-returns-wrong-data-type-for-arrays/62470/2

Hi, I stumbled on a variation that upcasts uint8 to int64. I would expect the dtype to be the same as it was when I generated the dataset.

```
import numpy as np
import datasets as ds

foo = np.random.randint(0, 256, size=(5, 10, 10), dtype=np.uint8)

features = ds.Features({"foo": ds.Array2D((10, 10), "uint8")})
dataset = ds.Dataset.from_dict({"foo": foo}, features=features)
dataset.set_format("torch")
print("feature dtype:", dataset.features["foo"].dtype)
print("array dtype:", dataset["foo"].dtype)

# feature dtype: uint8
# array dtype: torch.int64
```


workaround to remove torch upcasting

```
import datasets as ds
import torch

class FixedTorchFormatter(ds.formatting.TorchFormatter):
    def _tensorize(self, value):
        return torch.from_numpy(value)


ds.formatting._register_formatter(FixedTorchFormatter, "torch")
```