ReformerEmbedding unclear behavior
## Environment info
- `transformers` version: 4.3.3
- Platform: Linux-4.15.0-136-generic-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyTorch version (GPU?): 1.7.1 (True)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>


### Who can help
@patrickvonplaten
## Information

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

The problem arises when using:
* [ ] the official example scripts: (give details below)
* [X] my own modified scripts: (give details below)

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [X] my own task or dataset: MNIST

## To reproduce

Steps to reproduce the behavior:

1. Leave max_position_embeddings undefined or different than axial_pos_embedding shape
2. Observe the assert get triggered for large sequence lengths (within limit of axial_pos_embedding) https://github.com/huggingface/transformers/blob/master/src/transformers/models/reformer/modeling_reformer.py#L255

## Expected behavior

* No assert

## Additional Details:

`max_position_embeddings` is only used by `PositionEmbeddings`, thus if we provide `axial_pos_embds`  in the configuration, the `max_position_embeddings` will be not used for positional embedding. Moreover it is considered for factorizing `num_buckets` in `LSHSelfAttention` layer. 

Thus in a scenario of using axial positional embedding, both the assert check will be useless as well as the [bucket factorization](https://github.com/huggingface/transformers/blob/master/src/transformers/models/reformer/modeling_reformer.py#L711)
I think I don't fully agree here...`max_position_embeddings` is important even if `axial_pos_embedding` is used. If a user makes use of `axial_pos_embeddings` then `max_position_embeddings` is clearly defined by the tuple of `axial_pos_embeddings`. IMO, it's important that the user fully understands how `axial_pos_embeddigs` work and should therefore also set `max_position_embeddigns`<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.