[FlaxBart] make sure no grads are computed an bias
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Makes sure that no gradients are computed on FlaxBart bias. Note that in PyTorch we don't compute a gradient because the bias logits are saved as a PyTorch buffer: https://discuss.pytorch.org/t/what-is-the-difference-between-register-buffer-and-register-parameter-of-nn-module/32723

Also see results of: https://discuss.huggingface.co/t/gradients-verification-between-jax-flax-models-and-pytorch/15970


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Models:

- albert, bert, xlm: @LysandreJik
- blenderbot, bart, marian, pegasus, encoderdecoder,  t5: @patrickvonplaten, @patil-suraj
- longformer, reformer, transfoxl, xlnet: @patrickvonplaten
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- tensorflow: @LysandreJik

Library:

- benchmarks: @patrickvonplaten
- deepspeed: @stas00
- ray/raytune: @richardliaw, @amogkam
- text generation: @patrickvonplaten
- tokenizers: @n1t0, @LysandreJik
- trainer: @sgugger
- pipelines: @LysandreJik

Documentation: @sgugger

HF projects:

- datasets: [different repo](https://github.com/huggingface/datasets)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

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The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_16345). All of your documentation changes will be reflected on that endpoint.<|||||>@patil-suraj @sanchit-gandhi - this surely makes a difference when training - I'm not sure how much of a difference though. Will be interesting to try out though.<|||||>The `stop_gradient` fix looks good - the results you cited verify nicely that the gradients are only frozen for the `final_logits_bias` parameters, and not for any other additional parameters upstream in the computation graph.

One small comment in regards to the testing script `check_gradients_pt_flax.py` that you used to generate the gradient comparison results. Currently, we are comparing the PyTorch gradients relative to the Flax ones (https://huggingface.co/patrickvonplaten/codesnippets/blob/main/check_gradients_pt_flax.py#L86):
```python
diff_rel = np.abs(ak_norm - bk_norm) / np.abs(ak_norm)
```
Since we are taking the PyTorch gradients as our ground truth values, we should compare the Flax gradients relative to the PyTorch ones:
```python
diff_rel = np.abs(ak_norm - bk_norm) / np.abs(bk_norm)
```