Beam search sometimes fails this assert error
# 🐛 Bug

## Information

Model I am using: 
GPT2 with custom config (vocab=27)

Language I am using the model on (English, Chinese ...):
Molecules... (see https://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system)

The problem arises when using:
Using the .generate(beam=2) function

The tasks I am working on is:
Generating molecules

## The Problem
Essentially, every so 

## To reproduce

Steps to reproduce the behavior:

Just do the generation with these args:

```args = { "num_beams": 3, "max_length": 50, "temperature": 1, "repetition_penalty": 1, "length_penalty": 1, "do_sample": true, "top_k": 50, "top_p": 1} ```

The generation runs fine for several batches, but then after like 100s of iterations, it sometimes bugs out with this error:

```File "/Users/laithani/anaconda3/envs/TransformerVAE/lib/python3.7/site-packages/transformers/modeling_utils.py", line 979, in _generate_beam_search
    assert len(next_batch_beam) == num_beams * (batch_ex + 1), f"{next_batch_beam}, {num_beams}, {batch_ex}"
```

I then added print statements to modeling_utils to try and see what is going on. I changed the assert line to: 

```                
assert len(next_batch_beam) == num_beams * (batch_ex + 1), f"{next_batch_beam}, {num_beams}, {batch_ex}"
```

And with this I got: 
```
AssertionError: [(tensor(-19.8421), tensor(26), tensor(0)), (tensor(-20.9710), tensor(26), tensor(0)), (tensor(-30.5064), tensor(5), tensor(0)), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (tensor(-17.4236), tensor(11), tensor(9)), (tensor(-26.3645), tensor(16), tensor(9)), (tensor(-23.9410), tensor(16), tensor(9)), (0, 0, 0), (0, 0, 0), (0, 0, 0), (tensor(-58.0648), tensor(0), tensor(15))], 3, 5
```

If you count up the length of the list, it is length 16, which =/= 3 * (5 + 1) 

Not sure what is going on here, looking into the code now to try and figure out what is going on.


- `transformers` version: 2.4.1
- Platform: Ubuntu and Mac (problem occurs in both)
- Python version: 3.6 and 3.7 (problem occurs in both)
- PyTorch version (GPU?): 1.3.0
- Tensorflow version (GPU?): N/A
- Using GPU in script?: Yes, either V100 or K80
- Using distributed or parallel set-up in script?: No

@Laksh1997  thanks a lot for reporting this error. Can you provide a code snippet and maybe a link to your data to easily reproduce this error? 

In the meantime: 

- There has been a lot of changes recently in the beam search decoding -> I would recommend using the master branch of beam search decoding! 
- Beam search is not really made for top_p_top_k sampling, when using beam search I recommend setting do_sample=False<|||||>Right, I'll try the master branch and inform any further problems.

For generation of samples (without any context or input), there is no point of having sample set to False, as one will always generate the same sample as a result.<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
<|||||>I'm running into this issue somewhat commonly as well.

- Python 3.7
- transformers 3.0.2
- torch 1.5.1
- macOS 10.15
- Running on CPU

Code to reproduce error:
```python
import torch
from transformers import MarianMTModel, MarianTokenizer

torch.manual_seed(15)
phrase = "Ich verstehe nicht, was du sagen willst. Sprich doch Deutsch!"
model = MarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-de-ZH")
tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-de-ZH")

# Nucleus sampling as per https://github.com/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb
input_ids = tokenizer.prepare_translation_batch([phrase])
token_ids_p = model.generate(
    **input_ids,
    do_sample=True,
    top_p=0.9,
)

translated_p = [tokenizer.decode(string, skip_special_tokens=True) for string in token_ids_p]
print(translated_p)
```

Error:
```
Traceback (most recent call last):
  File "temp.py", line 14, in <module>
    top_p=0.9,
  File "/Users/kaz/envs/venv-3.7/lib/python3.7/site-packages/torch/autograd/grad_mode.py", line 15, in decorate_context
    return func(*args, **kwargs)
  File "/Users/kaz/envs/venv-3.7/lib/python3.7/site-packages/transformers/generation_utils.py", line 459, in generate
    model_specific_kwargs=model_specific_kwargs,
  File "/Users/kaz/envs/venv-3.7/lib/python3.7/site-packages/transformers/generation_utils.py", line 757, in _generate_beam_search
    assert len(next_sent_beam) == num_beams, "Beam should always be full"
```
@patrickvonplaten Is it possible to revive this issue?