ReformerForQuestionAnswering : int() argument must be a string, a bytes-like object or a number, not 'NoneType'
## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->

- `transformers` version:
- Platform: 
- Python version: 3.7.10
- PyTorch version (GPU?): 1.7
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No

### Who can help
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Models: 

- albert, bert, xlm: @LysandreJik
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- benchmarks: @patrickvonplaten
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Documentation: @sgugger

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@patrickvonplaten 
## Information

Model I am using (Bert, XLNet ...): Reformer

The problem arises when using:
* [ ] my own modified scripts: performing a backward() after passing the query and text to the `ReformerForQuestionAnswering` model.

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: a subset of SQuAD

## To reproduce

Steps to reproduce the behavior:

Performing backward on the loss throwing an error.

Minimal code to reproduce the error.

```
from transformers import ReformerTokenizer, ReformerForQuestionAnswering
import torch

tokenizer = ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment')
model = ReformerForQuestionAnswering.from_pretrained('google/reformer-crime-and-punishment')

question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
inputs = tokenizer(question, text, return_tensors='pt')
start_positions = torch.tensor([1])
end_positions = torch.tensor([3])

outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
loss = outputs.loss
loss.backward()
```

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Error Traceback
```
create_graph)
    219                 retain_graph=retain_graph,
    220                 create_graph=create_graph)
--> 221         torch.autograd.backward(self, gradient, retain_graph, create_graph)
    222 
    223     def register_hook(self, hook):

/usr/local/lib/python3.7/dist-packages/torch/autograd/__init__.py in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables)
    130     Variable._execution_engine.run_backward(
    131         tensors, grad_tensors_, retain_graph, create_graph,
--> 132         allow_unreachable=True)  # allow_unreachable flag
    133 
    134 

/usr/local/lib/python3.7/dist-packages/torch/autograd/function.py in apply(self, *args)
     87     def apply(self, *args):
     88         # _forward_cls is defined by derived class
---> 89         return self._forward_cls.backward(self, *args)  # type: ignore
     90 
     91 

/usr/local/lib/python3.7/dist-packages/transformers/models/reformer/modeling_reformer.py in backward(***failed resolving arguments***)
   1673                 head_mask=head_mask[len(layers) - idx - 1],
   1674                 attention_mask=attention_mask,
-> 1675                 buckets=buckets,
   1676             )
   1677 

/usr/local/lib/python3.7/dist-packages/transformers/models/reformer/modeling_reformer.py in backward_pass(self, next_attn_output, hidden_states, grad_attn_output, grad_hidden_states, attention_mask, head_mask, buckets)
   1527 
   1528             # set seed to have correct dropout
-> 1529             torch.manual_seed(self.feed_forward_seed)
   1530             # g(Y_1)
   1531             res_hidden_states = self.feed_forward(next_attn_output)

/usr/local/lib/python3.7/dist-packages/torch/random.py in manual_seed(seed)
     30             `0xffff_ffff_ffff_ffff + seed`.
     31     """
---> 32     seed = int(seed)
     33     import torch.cuda
     34 

TypeError: int() argument must be a string, a bytes-like object or a number, not 'NoneType'

```
From debugging, I believe that the error was caused because the `self.feed_forward_seed` in `ReformerLayer` class is `None`.

I have tried the same code with Longformer and it was working perfectly.

## Expected behavior

<!-- A clear and concise description of what you would expect to happen. -->
`loss.backward()` running properly.
Hey @harikc456,

The problem is that the model is not put into training mode. If you run the following code:

```python
from transformers import ReformerTokenizer, ReformerForQuestionAnswering
from transformers.models.reformer.modeling_reformer import PositionEmbeddings
import torch

tokenizer = ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment')
model = ReformerForQuestionAnswering.from_pretrained('google/reformer-crime-and-punishment')

# change to position embeddings to prevent error
model.reformer.embeddings.position_embeddings = PositionEmbeddings(model.config)

question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
inputs = tokenizer(question, text, return_tensors='pt')
start_positions = torch.tensor([1])
end_positions = torch.tensor([3])

outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
loss = outputs.loss
loss.backward()
```

you can see that the code runs without error.<|||||>@patrickvonplaten 

Hello, I've just come across the same issue.

I tried the code below,

``` python
from transformers import ReformerTokenizer, ReformerForQuestionAnswering
from transformers.models.reformer.modeling_reformer import PositionEmbeddings
import torch

tokenizer = ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment')
model = ReformerForQuestionAnswering.from_pretrained('google/reformer-crime-and-punishment')

# change to position embeddings to prevent error
model.reformer.embeddings.position_embeddings = PositionEmbeddings(model.config)

question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
inputs = tokenizer(question, text, return_tensors='pt')
start_positions = torch.tensor([1])
end_positions = torch.tensor([3])

outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
loss = outputs.loss
loss.backward()
```

and got the following error message.

```
Some weights of the model checkpoint at google/reformer-crime-and-punishment were not used when initializing ReformerForQuestionAnswering: ['lm_head.bias', 'lm_head.decoder.weight', 'lm_head.decoder.bias']
- This IS expected if you are initializing ReformerForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing ReformerForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of ReformerForQuestionAnswering were not initialized from the model checkpoint at google/reformer-crime-and-punishment and are newly initialized: ['reformer.encoder.layers.0.attention.self_attention.mask_value_float16', 'reformer.encoder.layers.0.attention.self_attention.mask_value_float32', 'reformer.encoder.layers.1.attention.self_attention.self_mask_value_float16', 'reformer.encoder.layers.1.attention.self_attention.self_mask_value_float32', 'reformer.encoder.layers.1.attention.self_attention.mask_value_float16', 'reformer.encoder.layers.1.attention.self_attention.mask_value_float32', 'reformer.encoder.layers.2.attention.self_attention.mask_value_float16', 'reformer.encoder.layers.2.attention.self_attention.mask_value_float32', 'reformer.encoder.layers.3.attention.self_attention.self_mask_value_float16', 'reformer.encoder.layers.3.attention.self_attention.self_mask_value_float32', 'reformer.encoder.layers.3.attention.self_attention.mask_value_float16', 'reformer.encoder.layers.3.attention.self_attention.mask_value_float32', 'reformer.encoder.layers.4.attention.self_attention.mask_value_float16', 'reformer.encoder.layers.4.attention.self_attention.mask_value_float32', 'reformer.encoder.layers.5.attention.self_attention.self_mask_value_float16', 'reformer.encoder.layers.5.attention.self_attention.self_mask_value_float32', 'reformer.encoder.layers.5.attention.self_attention.mask_value_float16', 'reformer.encoder.layers.5.attention.self_attention.mask_value_float32', 'qa_outputs.weight', 'qa_outputs.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
/path/to/.pyenv/versions/anaconda3-2020.07/lib/python3.8/site-packages/torch/nn/modules/container.py:435: UserWarning: Setting attributes on ParameterList is not supported.
  warnings.warn("Setting attributes on ParameterList is not supported.")
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-1-60eb084822c0> in <module>
     16 outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
     17 loss = outputs.loss
---> 18 loss.backward()

~/.pyenv/versions/anaconda3-2020.07/lib/python3.8/site-packages/torch/tensor.py in backward(self, gradient, retain_graph, create_graph)
    219                 retain_graph=retain_graph,
    220                 create_graph=create_graph)
--> 221         torch.autograd.backward(self, gradient, retain_graph, create_graph)
    222 
    223     def register_hook(self, hook):

~/.pyenv/versions/anaconda3-2020.07/lib/python3.8/site-packages/torch/autograd/__init__.py in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables)
    128         retain_graph = create_graph
    129 
--> 130     Variable._execution_engine.run_backward(
    131         tensors, grad_tensors_, retain_graph, create_graph,
    132         allow_unreachable=True)  # allow_unreachable flag

~/.pyenv/versions/anaconda3-2020.07/lib/python3.8/site-packages/torch/autograd/function.py in apply(self, *args)
     87     def apply(self, *args):
     88         # _forward_cls is defined by derived class
---> 89         return self._forward_cls.backward(self, *args)  # type: ignore
     90 
     91 

~/.pyenv/versions/anaconda3-2020.07/lib/python3.8/site-packages/transformers/models/reformer/modeling_reformer.py in backward(***failed resolving arguments***)
   1666 
   1667             # backprop
-> 1668             output = layer.backward_pass(
   1669                 next_attn_output=output.attn_output,
   1670                 hidden_states=output.hidden_states,

~/.pyenv/versions/anaconda3-2020.07/lib/python3.8/site-packages/transformers/models/reformer/modeling_reformer.py in backward_pass(self, next_attn_output, hidden_states, grad_attn_output, grad_hidden_states, attention_mask, head_mask, buckets)
   1527 
   1528             # set seed to have correct dropout
-> 1529             torch.manual_seed(self.feed_forward_seed)
   1530             # g(Y_1)
   1531             res_hidden_states = self.feed_forward(next_attn_output)

~/.pyenv/versions/anaconda3-2020.07/lib/python3.8/site-packages/torch/random.py in manual_seed(seed)
     30             `0xffff_ffff_ffff_ffff + seed`.
     31     """
---> 32     seed = int(seed)
     33     import torch.cuda
     34 

TypeError: int() argument must be a string, a bytes-like object or a number, not 'NoneType'
```

I first tried to use:
```python
    tokenizer = AutoTokenizer.from_pretrained("google/reformer-crime-and-punishment")
    model = AutoModelForSequenceClassification.from_pretrained(
        "google/reformer-crime-and-punishment", return_dict=True
    )
```
It failed, then I found this issue and added:
```
    # change to position embeddings to prevent error
    model.reformer.embeddings.position_embeddings = PositionEmbeddings(model.config)
```
However, the same error occurs.

- `transformers` version: 4.1.1
- Platform: Linux-4.15.0-135-generic-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyTorch version (GPU?): 1.7.1 (True)
- Tensorflow version (GPU?): 2.4.1 (False)
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>

Maybe the problem is that the version of Transformers I am using for this is old?

Thank you in advance.<|||||>It seems that the same issue occurs when I updated the transformers to the latest stable version via pip.

- `transformers` version: 4.4.1
- Platform: Linux-4.15.0-135-generic-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyTorch version (GPU?): 1.7.1 (True)
- Tensorflow version (GPU?): 2.4.1 (False)
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>

Is the problem depending on the version of some other library?<|||||>Excuse me for my frequent posting.

Instead of overwriting `position_embeddings`,
inserting `model.train()` seems to work (but with another issue).

```python 
from transformers import ReformerTokenizer, ReformerForQuestionAnswering
from transformers.models.reformer.modeling_reformer import PositionEmbeddings
import torch

tokenizer = ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment')
model = ReformerForQuestionAnswering.from_pretrained('google/reformer-crime-and-punishment')

# # change to position embeddings to prevent error
# model.reformer.embeddings.position_embeddings = PositionEmbeddings(model.config)

model.train()

question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
inputs = tokenizer(question, text, return_tensors='pt')
start_positions = torch.tensor([1])
end_positions = torch.tensor([3])

outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
loss = outputs.loss

loss.backward()
```

The different error message is shown, but it seems can be treated by just doing padding.

```
~/.pyenv/versions/anaconda3-2020.07/lib/python3.8/site-packages/transformers/models/reformer/modeling_reformer.py in forward(self, position_ids)
    154 
    155         if self.training is True:
--> 156             assert (
    157                 reduce(mul, self.axial_pos_shape) == sequence_length
    158             ), "If training, make sure that config.axial_pos_shape factors: {} multiply to sequence length. Got prod({}) != sequence_length: {}. You might want to consider padding your sequence length to {} or changing config.axial_pos_shape.".format(

AssertionError: If training, make sure that config.axial_pos_shape factors: (512, 1024) multiply to sequence length. Got prod((512, 1024)) != sequence_length: 28. You might want to consider padding your sequence length to 524288 or changing config.axial_pos_shape.
```

I'm now trying padding the input, and it seems working.

```
tokenizer.pad_token = tokenizer.eos_token

inputs = tokenizer(question, text, padding='max_length', truncation=True, max_length=524288, return_tensors='pt')
```

I apologize if this is not an appropriate solution.<|||||>We could maybe add a better error message that fires when Reformer is not in training mode, but one runs `.backward()`. @forest1988 if you want feel free to open a PR :-)<|||||>@patrickvonplaten 
Thanks, I'll open a PR!
I'm a little busy right now, but I'll make time to work on it soon.<|||||>Hi @patrickvonplaten,
Sorry to be late. I've just opened PR #11117 regarding this issue. All checks have passed.
Could you please have a look at it when you have time?