[FLAX] Whisper
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Models:

- albert, bert, xlm: @LysandreJik
- blenderbot, bart, marian, pegasus, encoderdecoder,  t5: @patrickvonplaten, @patil-suraj
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The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_19512). All of your documentation changes will be reflected on that endpoint.<|||||>Hi,

I need little clarification about implementing the `FlaxWhisperDecoder` Module.

What would be the best way to pass `past_key_values_length` to the module?
 
Reference in Pytorch implementation. 
https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/modeling_whisper.py#L863-L873 

@patrickvonplaten @ydshieh @patil-suraj <|||||>Whisper on TPU will make :fire: colab demos<|||||>![Screenshot 2022-10-16 at 8 29 27 PM](https://user-images.githubusercontent.com/17096858/196042609-971f30b8-a96c-4df8-8524-c3c6ef379ae6.png)
<|||||>Awesome work here! Feel free to ping me for a review once it is ready 😄 <|||||>Hi,

I have finished the model and working on the test cases now. 
The pt<->flax equivalence test is failing, even though the `model.generate` produce the exact speech-to-text like the PyTorch model. 

![Screenshot 2022-10-19 at 10 15 39 PM](https://user-images.githubusercontent.com/17096858/196753624-b0941182-401d-443c-a0b2-fdc32ed57112.png)


I have attached steps to reproduce the issue in this notebook - https://colab.research.google.com/drive/1KmO8OBUpHfs1uYA_eSwamQAXnjsdbkRS?usp=sharing

Any pointers will be helpful.

Thanks

@patrickvonplaten @patil-suraj @ydshieh @ArthurZucker <|||||>Hi @kamalkraj First, thank you for this awesome PR!

Regarding the PT/Flax tests, I probably need to improve that PT/Flax equivalence tests to make it (a bit) easier to find out which layers gives the larger difference.

In the meantime, I have to say there is no easy way to debug such issue. We need patience to find out at which layer(s) we have the first large difference (greater than the tolerance) and see what's wrong inside that layer.

This is usually tedious and involving manually debugging process.

Anyway, I can open a PR to make the process (a bit) easier - if you want to wait a bit. But notice that we still need similar process even that PR is merged.<|||||>Will try to get #18420 merged so that we can maybe use the `find_pt_fx_differences(pt_outputs, fx_outputs)` function! But in the mean time, you should set `output_hidden_states=True` and check where the lists differ 🤗 <|||||>Hi @kamalkraj Actually that test is quite good enough, but we need to change a bit to debug more.

The last 2 commit in this [branch](https://github.com/huggingface/transformers/commits/temp-debug-whisper-flax) could log more information.

If you run the tests like

```bash
RUN_PT_FLAX_CROSS_TESTS=true python3 -m pytest -v tests/models/whisper/test_modeling_flax_whisper.py -k "test_equivalence_pt_to_flax"
```
it logs something
```
max diff. in outputs.logits: 0.0020506680011749268
```
but it doesn't fail the test -> it continues. So far, I got

```bash
E   AssertionError: <class 'list'> != <class 'tuple'> : outputs.decoder_hidden_states: Output types differ between Flax and PyTorch
```
so you will have to look the output type of `decoder_hidden_states` and make sure the type is the same as the PyTorch one.
Continue this process will eventually show you all the difference, and you can get a better idea where to debug in the modeling code.

Also, it seems when running the tests from `tests/models/whisper/test_modeling_whisper.py`, we have some shape issue. This is another thing to debug.

Hopefully this gives you some idea of how we can debug here 🤗 

<|||||>Thanks, @ydshieh and @ArthurZucker

<|||||>To make for a more consistent API across models, couldn't we swap out `past_key_values_length` and instead compute `position_ids` to get the current positional embeddings for the decoder? It feels like this would make it easier to fit Whisper in with other finetuning codebases (no need to create custom logic for computing `past_key_values_length` when dealing with Whisper). As the code currently stands, I think it would actually give incorrect outputs when decoding a batch when each element of the batch has different decoder prefix/prompt tokens. Computing position ids from the attention mask would also allow for either left or right padding. 

I have another Flax Whisper implementation with .from_pretrained(..., from_pt=True) working correctly and it giving correct outputs for variable length prompts that I'd be happy to share (or create a separate PR for). It also adds some stuff to the generation utilities to support prompt tokens to the decoder that already exist in the PyTorch utilities (using prompt tokens instead of `model.config.decoder_start_token_id` if specified).<|||||>I haven't look into this. But @andyehrenberg do you suggest a different way of computation in Flax Whisper than the one implemented in our PyTorch/TensorFlow Whisper?

It's also better for @kamalkraj to express if he would like to continue this PR before we go ahead.<|||||>@ydshieh @andyehrenberg

If there is already a working implementation, please continue.
I am closing this one. 

Thanks<|||||>@ydshieh I guess what I'm suggesting for this could also be helpful for the PyTorch/TF implementations to improve flexibility/compatibility with existing codebases that use `position_ids` for other models (such as when finetuning).

For example, the use-case I'm working on is fine-tuning Whisper with RL (trying to expose it to its own outputs to reduce hallucinations). At each step when collecting rollouts, it is given a batch of audio features and decoder prompts (from previous audio snippets) - these prompts are of varying lengths, so padding/attention masks are needed, and the position embeddings need to adjust accordingly. And then when doing PPO updates on these steps, the position embeddings need to be computed correctly based off of which timesteps (tokens) are padding.

The implementation in this PR wouldn't accommodate this scenario as it assumes the same `past_key_values_length` for each sequence in the batch, whereas the implementation I've worked on uses `position_ids` to keep track of where we are in each sequence of the batch. Earlier I had use a different method that only used the attention mask along with another caching method in the decoder, but using position_ids is much simpler and accommodates multiple padding schemes more simply.