IndexError: index out of range in self while using longformers when i try to pass token_type_ids
     
- `transformers` version: 3.0.0
- Platform: windows
- Python version: 3.6.10 :: Anaconda, Inc.
- PyTorch version (GPU?): 1.7.0+cu101
- Tensorflow version (GPU?):
- Using GPU in script?:
- Using distributed or parallel set-up in script?:

### Who can help
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## To reproduce
import torch
from transformers import LongformerModel, LongformerTokenizer
model = LongformerModel.from_pretrained('allenai/longformer-base-4096')
tokenizer = LongformerTokenizer.from_pretrained('roberta-base')
SAMPLE_TEXT = ' '.join(['Hello world! '] * 100)  # long input document
input_ids = torch.tensor(tokenizer.encode(SAMPLE_TEXT)).unsqueeze(0)  # batch of size 1
attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=input_ids.device)
global_attention_mask = torch.zeros(input_ids.shape, dtype=torch.long, device=input_ids.device)
segment_ids = torch.ones(input_ids.shape, dtype=torch.long, device=input_ids.device)
outputs = model(input_ids=input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask,token_type_ids=segment_ids)

## Error info
IndexError                                Traceback (most recent call last)
<ipython-input-357-c7bf5dc7cbc9> in <module>
----> 1 outputs = model(input_ids=input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask,token_type_ids=segment_ids)

~\.conda\envs\env\lib\site-packages\torch\nn\modules\module.py in _call_impl(self, *input, **kwargs)
    725             result = self._slow_forward(*input, **kwargs)
    726         else:
--> 727             result = self.forward(*input, **kwargs)
    728         for hook in itertools.chain(
    729                 _global_forward_hooks.values(),

~\.conda\envs\env\lib\site-packages\transformers\modeling_longformer.py in forward(self, input_ids, attention_mask, global_attention_mask, token_type_ids, position_ids, inputs_embeds, output_attentions, output_hidden_states)
    675             encoder_attention_mask=None,
    676             output_attentions=output_attentions,
--> 677             output_hidden_states=output_hidden_states,
    678         )
    679 

~\.conda\envs\env\lib\site-packages\transformers\modeling_bert.py in forward(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, output_attentions, output_hidden_states)
    751 
    752         embedding_output = self.embeddings(
--> 753             input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
    754         )
    755         encoder_outputs = self.encoder(

~\.conda\envs\env\lib\site-packages\torch\nn\modules\module.py in _call_impl(self, *input, **kwargs)
    725             result = self._slow_forward(*input, **kwargs)
    726         else:
--> 727             result = self.forward(*input, **kwargs)
    728         for hook in itertools.chain(
    729                 _global_forward_hooks.values(),

~\.conda\envs\env\lib\site-packages\transformers\modeling_roberta.py in forward(self, input_ids, token_type_ids, position_ids, inputs_embeds)
     66 
     67         return super().forward(
---> 68             input_ids, token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds
     69         )
     70 

~\.conda\envs\env\lib\site-packages\transformers\modeling_bert.py in forward(self, input_ids, token_type_ids, position_ids, inputs_embeds)
    178             inputs_embeds = self.word_embeddings(input_ids)
    179         position_embeddings = self.position_embeddings(position_ids)
--> 180         token_type_embeddings = self.token_type_embeddings(token_type_ids)
    181 
    182         embeddings = inputs_embeds + position_embeddings + token_type_embeddings

~\.conda\envs\env\lib\site-packages\torch\nn\modules\module.py in _call_impl(self, *input, **kwargs)
    725             result = self._slow_forward(*input, **kwargs)
    726         else:
--> 727             result = self.forward(*input, **kwargs)
    728         for hook in itertools.chain(
    729                 _global_forward_hooks.values(),

~\.conda\envs\env\lib\site-packages\torch\nn\modules\sparse.py in forward(self, input)
    124         return F.embedding(
    125             input, self.weight, self.padding_idx, self.max_norm,
--> 126             self.norm_type, self.scale_grad_by_freq, self.sparse)
    127 
    128     def extra_repr(self) -> str:

~\.conda\envs\env\lib\site-packages\torch\nn\functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
   1850         # remove once script supports set_grad_enabled
   1851         _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
-> 1852     return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
   1853 
   1854 

IndexError: index out of range in self


## Expected behavior

If i am not passing a segment_id (token_type_ids) then model is executing or if i am passing segment_id's as zeros then its getting executed when i am passing segment_id's 1 or 0's and 1's then i am getting an error as index out of range in self.

Can we assume that "token_type_ids" will not support in longformer?<|||||>Yes, actually yesterday a PR (#9152) was merged to update the docs stating the LongFormer does not support token type ids. <|||||>@NielsRogge Thank you<|||||>This issue has been automatically marked as stale and been closed because it has not had recent activity. Thank you for your contributions.

If you think this still needs to be addressed please comment on this thread.