Model2Model: RuntimeError: expected device cpu and dtype Float but got device cpu and dtype Bool
## ❓ Questions & Help

I'm going to try the new Model2Model feature:

```
import torch
import numpy as np
from transformers import Model2Model, BertTokenizer, BertModel

# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device = torch.device("cpu")

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = Model2Model.from_pretrained('bert-base-uncased').to(device)

source_ids = torch.tensor([tokenizer.encode("this is source sentence", add_special_tokens=True)], 
                          dtype=torch.long).to(device)
target_ids = torch.tensor([tokenizer.encode("this is target sentence", add_special_tokens=True)], 
                          dtype=torch.long).to(device)

model(source_ids, target_ids)
```

This is the output:
```
RuntimeError                              Traceback (most recent call last)
<ipython-input-10-885d1d4b847f> in <module>
----> 1 model(source_ids, target_ids)

/users/tr.amirhj/anaconda3/envs/genv/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    545             result = self._slow_forward(*input, **kwargs)
    546         else:
--> 547             result = self.forward(*input, **kwargs)
    548         for hook in self._forward_hooks.values():
    549             hook_result = hook(self, input, result)

/users/tr.amirhj/anaconda3/envs/genv/lib/python3.6/site-packages/transformers/modeling_encoder_decoder.py in forward(self, encoder_input_ids, decoder_input_ids, **kwargs)
    229             "attention_mask", None
    230         )
--> 231         decoder_outputs = self.decoder(decoder_input_ids, **kwargs_decoder)
    232 
    233         return decoder_outputs + encoder_outputs

/users/tr.amirhj/anaconda3/envs/genv/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    545             result = self._slow_forward(*input, **kwargs)
    546         else:
--> 547             result = self.forward(*input, **kwargs)
    548         for hook in self._forward_hooks.values():
    549             hook_result = hook(self, input, result)

/users/tr.amirhj/anaconda3/envs/genv/lib/python3.6/site-packages/transformers/modeling_bert.py in forward(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, masked_lm_labels, encoder_hidden_states, encoder_attention_mask, lm_labels)
    871                             inputs_embeds=inputs_embeds,
    872                             encoder_hidden_states=encoder_hidden_states,
--> 873                             encoder_attention_mask=encoder_attention_mask)
    874 
    875         sequence_output = outputs[0]

/users/tr.amirhj/anaconda3/envs/genv/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    545             result = self._slow_forward(*input, **kwargs)
    546         else:
--> 547             result = self.forward(*input, **kwargs)
    548         for hook in self._forward_hooks.values():
    549             hook_result = hook(self, input, result)

/users/tr.amirhj/anaconda3/envs/genv/lib/python3.6/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)
    677                 seq_ids = torch.arange(seq_length, device=device)
    678                 causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
--> 679                 extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
    680             else:
    681                 extended_attention_mask = attention_mask[:, None, None, :]

RuntimeError: expected device cpu and dtype Float but got device cpu and dtype Bool
```

If I missed something?
In my environment, the code you've posted **works as expected**.
- Python: 3.6.9
- Transformers: 2.2.1 (installed from PyPi with pip install transformers)
- PyTorch: 1.3.1
- TensorFlow: 2.0
- OS: Ubuntu 16.04

Here the stack trace:
```
Python 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31) 
[GCC 7.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> import numpy as np
>>> from transformers import Model2Model, BertTokenizer, BertModel
/home/<user>/anaconda3/envs/huggingface/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/<user>/anaconda3/envs/huggingface/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/<user>/anaconda3/envs/huggingface/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/<user>/anaconda3/envs/huggingface/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/<user>/anaconda3/envs/huggingface/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/<user>/anaconda3/envs/huggingface/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
2019-12-10 17:40:40.255384: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-12-10 17:40:40.277896: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3408000000 Hz
2019-12-10 17:40:40.279096: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x558b757804f0 executing computations on platform Host. Devices:
2019-12-10 17:40:40.279146: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Host, Default Version
>>> device = torch.device("cpu")
>>> device
device(type='cpu')
>>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
>>> model = Model2Model.from_pretrained('bert-base-uncased').to(device)
>>> source_ids = torch.tensor([tokenizer.encode("this is source sentence", add_special_tokens=True)], 
...                           dtype=torch.long).to(device)
>>> target_ids = torch.tensor([tokenizer.encode("this is target sentence", add_special_tokens=True)], 
...                           dtype=torch.long).to(device)
>>> 
>>> source_ids
tensor([[ 101, 2023, 2003, 3120, 6251,  102]])
>>> target_ids
tensor([[ 101, 2023, 2003, 4539, 6251,  102]])
>>> model(source_ids, target_ids)
(tensor([[[ -6.3390,  -6.3664,  -6.4600,  ...,  -5.5354,  -4.1787,  -5.8384],
         [ -7.9225,  -7.7588,  -7.9552,  ...,  -6.6068,  -5.5835,  -6.9365],
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         [-13.0414, -12.5687, -12.3714,  ..., -10.1630, -11.1963,  -9.3892]]],
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         -4.9402e-01,  5.9927e-01,  3.1836e-01,  9.9176e-01,  2.0806e-01,
          2.6599e-01, -1.2025e-01, -2.1694e-01,  3.5750e-01, -2.4472e-01,
         -1.0000e+00,  4.2622e-01,  2.0912e-01, -8.1241e-02,  7.5354e-02,
         -2.0835e-01,  1.8585e-01, -9.7545e-01, -1.5719e-01,  1.4028e-01,
         -1.8040e-01, -5.1406e-01, -3.6387e-01,  3.6267e-01,  5.9451e-01,
          3.2176e-01,  9.0730e-01, -4.6973e-02,  5.9712e-01,  4.5915e-01,
         -5.8261e-02, -6.2097e-01,  9.1518e-01]], grad_fn=<TanhBackward>))
>>> 
```<|||||>It seems that it was a problem with pytorch. Upgrading to 1.3 solve the problem. Thanks @TheEdoardo93 