Got pyarrow error when loading a dataset while adding special tokens into the tokenizer

I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:

Traceback (most recent call last):
  File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1687, in _map_single
    writer.write(example)
  File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_writer.py", line 296, in write
    self.write_on_file()
  File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_writer.py", line 270, in write_on_file
    pa_array = pa.array(typed_sequence)
  File "pyarrow/array.pxi", line 222, in pyarrow.lib.array
  File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
  File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_writer.py", line 108, in __arrow_array__
    out = out.cast(pa.list_(self.optimized_int_type))
  File "pyarrow/array.pxi", line 810, in pyarrow.lib.Array.cast
  File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/pyarrow/compute.py", line 281, in cast
    return call_function("cast", [arr], options)
  File "pyarrow/_compute.pyx", line 465, in pyarrow._compute.call_function
  File "pyarrow/_compute.pyx", line 294, in pyarrow._compute.Function.call
  File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
  File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Integer value 50259 not in range: -128 to 127

Do you have any idea about it?

Comments:
Hi,

the output of the tokenizers is treated specially in the lib to optimize the dataset size (see the code [here](https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_writer.py#L138-L141)). It looks like that one of the values in a dictionary returned by the tokenizer is out of the assumed range.
Can you please provide a minimal reproducible example for more help?

Hi @yana-xuyan, thanks for reporting.

As clearly @mariosasko explained, `datasets` performs some optimizations in order to reduce the size of the dataset cache files. And one of them is storing the field `special_tokens_mask` as `int8`, which means that this field can only contain integers between `-128` to `127`. As your message error states, one of the values of this field is `50259`, and therefore it cannot be stored as an `int8`.

Maybe we could implement a way to disable this optimization and allow using any integer value; although the size of the cache files would be much larger.

I'm facing same issue @mariosasko @albertvillanova 

```
ArrowInvalid: Integer value 50260 not in range: -128 to 127
```

To reproduce:
```python
SPECIAL_TOKENS = ['<bos>','<eos>','<speaker1>','<speaker2>','<pad>']
ATTR_TO_SPECIAL_TOKEN = {
    'bos_token': '<bos>', 
    'eos_token': '<eos>', 
    'pad_token': '<pad>',
    'additional_special_tokens': ['<speaker1>', '<speaker2>']
    }

tokenizer = AutoTokenizer.from_pretrained("gpt2", use_fast=False)
num_added_tokens =tokenizer.add_special_tokens(ATTR_TO_SPECIAL_TOKEN)
vocab_size = len(self.tokenizer.encoder) + num_added_tokens
vocab =tokenizer.get_vocab()

pad_index = tokenizer.pad_token_id
eos_index = tokenizer.eos_token_id
bos_index = tokenizer.bos_token_id
speaker1_index = vocab["<speaker1>"]
speaker2_index = vocab["<speaker2>"]
```

```python
tokenizer.decode(['50260'])
'<speaker1>'
```

@mariosasko 
I am hitting this bug in the Bert tokenizer too.  I see that @albertvillanova labeled this as a bug back in April.  Has there been a fix released yet?
What I did for now is to just disable the optimization in the HF library.  @yana-xuyan and @thomas-happify, is that what you did and did that work for you?



Hi @gregg-ADP, 

This is still a bug.

As @albertvillanova has suggested, maybe it's indeed worth adding a variable to `config.py` to have a way to disable this behavior.

In the meantime, this forced optimization can be disabled by specifying `features` (of the returned examples) in the `map` call:
```python
from datasets import *
... # dataset init
ds.map(process_example, features=Features({"special_tokens_mask": Sequence(Value("int32")), ... rest of the features}) 
```

cc @lhoestq so he is also aware of this issue

Thanks for the quick reply @mariosasko.  What I did was to changed the optimizer to use int32 instead of int8. 
What you're suggesting specifies the type for each feature explicitly without changing the HF code. This is definitely a better option.  However, we are hitting a new error later:
```
  File "/Users/ccccc/PycharmProjects/aaaa-ml/venv-source/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
TypeError: forward() got an unexpected keyword argument 'pos'

```
Where 'pos' is the name of a new feature we added.  Do you agree that your way of fixing the optimizer issue will not fix our new issue?  If not, I will continue with this optimizer fix until we resolve our other issue.


Hi @gwc4github,

the fix was merged a few minutes ago, and it doesn't require any changes on the user side (e.g. no need for specifying `features`). If you find time, feel free to install `datasets` from master with:
```
pip install git+https://github.com/huggingface/datasets.git
```
and let us know if it works for your use case!