pegasus xsum won't train on xsum dataset
## Environment info
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- `transformers` version: 5.0dev
- Platform: linux
- Python version: 3.6.9
- PyTorch version (GPU?): pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html
- Tensorflow version (GPU?): 2.4.1
- Using GPU in script?: Both - 
- Using distributed or parallel set-up in script?: no


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## Information

Model I am using (Bert, XLNet ...): Pegasus XSUM

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

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

## To reproduce

Steps to reproduce the behavior:

I am using run_summarization.py to retrain a fine-tuned model (before I try it on my own data).  I first fine-tuned on gigaword for a few thousand iterations, tested it on gigaword, then switched to evaluate on the xsum dataset.  The xsum eval dataset produces the following error on the CPU (similar error on GPU, just with a lot of extra fluff)

```
 File "run_summarization.py", line 593, in <module>
    main()
  File "run_summarization.py", line 550, in main
    max_length=data_args.val_max_target_length, num_beams=data_args.num_beams, metric_key_prefix="eval"
  File "/disk1/work/marc/pegasus/venv/lib/python3.6/site-packages/transformers/trainer_seq2seq.py", line 74, in evaluate
    return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)
  File "/disk1/work/marc/pegasus/venv/lib/python3.6/site-packages/transformers/trainer.py", line 1707, in evaluate
    metric_key_prefix=metric_key_prefix,
  File "/disk1/work/marc/pegasus/venv/lib/python3.6/site-packages/transformers/trainer.py", line 1838, in prediction_loop
    loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys)
  File "/disk1/work/marc/pegasus/venv/lib/python3.6/site-packages/transformers/trainer_seq2seq.py", line 167, in prediction_step
    **gen_kwargs,
  File "/disk1/work/marc/pegasus/venv/lib/python3.6/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/disk1/work/marc/pegasus/venv/lib/python3.6/site-packages/transformers/generation_utils.py", line 927, in generate
    model_kwargs = self._prepare_encoder_decoder_kwargs_for_generation(input_ids, model_kwargs)
  File "/disk1/work/marc/pegasus/venv/lib/python3.6/site-packages/transformers/generation_utils.py", line 412, in _prepare_encoder_decoder_kwargs_for_generation
    model_kwargs["encoder_outputs"]: ModelOutput = encoder(input_ids, return_dict=True, **encoder_kwargs)
  File "/disk1/work/marc/pegasus/venv/lib/python3.6/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/disk1/work/marc/pegasus/venv/lib/python3.6/site-packages/transformers/models/pegasus/modeling_pegasus.py", line 725, in forward
    embed_pos = self.embed_positions(input_shape)
  File "/disk1/work/marc/pegasus/venv/lib/python3.6/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/disk1/work/marc/pegasus/venv/lib/python3.6/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/disk1/work/marc/pegasus/venv/lib/python3.6/site-packages/transformers/models/pegasus/modeling_pegasus.py", line 139, in forward
    return super().forward(positions)
  File "/disk1/work/marc/pegasus/venv/lib/python3.6/site-packages/torch/nn/modules/sparse.py", line 147, in forward
    self.norm_type, self.scale_grad_by_freq, self.sparse)
  File "/disk1/work/marc/pegasus/venv/lib/python3.6/site-packages/torch/nn/functional.py", line 1913, in embedding
    return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
```
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## Expected behavior

<!-- A clear and concise description of what you would expect to happen. -->

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