[LED] fix global_attention_mask not being passed for generation and docs clarification about grad checkpointing
Author: caesar-one
Pull request: yes

# What does this PR do?
There are two changes:
1. The `global_attention_mask` parameter was not passed in `.prepare_inputs_for_generation(...)`. So, when you call the `.generate(...)` method it never reaches the `LEDModel.forward(...)` (even though `global_attention_mask` is passed in `kwargs`). So local attention is used for all tokens. This problem was mentioned also in [this pull request](https://github.com/huggingface/transformers/pull/16485), but not completely fixed.
2. The [documentation of LED](https://huggingface.co/docs/transformers/model_doc/led) states: 
> To fine-tune LED on all 16384, it is necessary to enable gradient checkpointing by executing model.gradient_checkpointing_enable(). 

This could be misleading since gradient checkpointing _can_ be used if training leads to OOM errors, but it is _not actually necessary_, as mentioned in [this issue](https://github.com/huggingface/transformers/issues/16541). Some hints on the use of the `use_cache` flag were also added, as suggested in [this notebook](https://colab.research.google.com/drive/12LjJazBl7Gam0XBPy_y0CTOJZeZ34c2v?usp=sharing) by @patrickvonplaten (who initializes the model using `AutoModelForSeq2SeqLM.from_pretrained("allenai/led-base-16384", gradient_checkpointing=True, use_cache=False)`.

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Fixes # (issue)


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@patrickvonplaten @sgugger

Comments:
_The documentation is not available anymore as the PR was closed or merged._

Thanks for catching this @caesar-one, looking forward to it getting merged as I am also trying to use LED-based models with a global attention mask!

@patrickvonplaten could this issue be the reason for the [lower performances of Primera](https://huggingface.co/allenai/PRIMERA) (LED-based model) in the hf implementation vs the original implementation? After all, global attention was not considered during generation.

Anyway, thanks for your fast feedback :-)

Thanks a lot for the PR @caesar-one !