[FlaxBart] make sure no grads are computed an bias
Author: patrickvonplaten
Pull request: yes

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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:

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Comments:
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)
```