[`FA-2`] Add Flash Attention to `Phi`
Author: susnato
Pull request: yes

# What does this PR do?

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This PR adds Flash Attention to `Phi`.


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @muellerzr and @pacman100

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc and @younesbelkada

Documentation: @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

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cc: @younesbelkada, @ArthurZucker  


Comments:
All the FA tests pass except the `test_flash_attn_2_generate_padding_right`.

![Screenshot from 2023-11-23 00-31-04](https://github.com/huggingface/transformers/assets/56069179/46386b35-f7eb-4190-b16e-e3cca96a09ba)

This is odd given that the `flash_attn_2_inference_padding_right` test is passing as does the `test_flash_attn_2_generate_left_padding` test.


@susnato can you try to run that test multiple times? sometimes it is flaky - apart from that the changes look great on my end !

Hi @younesbelkada, I ran that test 30 times and every time it failed!

Shouldn't the inference test fail too, if the generation test fails? :sweat_smile:
 

Hmm yes correct, what I did for llama was to overwrite the test as can be see here: https://github.com/huggingface/transformers/blob/main/tests/models/llama/test_modeling_llama.py#L392 using a real checkpoint. It would be great if you can do the same and test the next 10 tokens are the same (make sure to use `do_sample=False`)

Hi @younesbelkada, thanks a lot for the advice! All the flash attention tests are passing now. :hugs: 

 
![Screenshot from 2023-11-23 17-57-49](https://github.com/huggingface/transformers/assets/56069179/78a6f2dd-77d4-430d-abd8-597d20a9a30d)


Hi @younesbelkada, I have pushed the suggestion you asked. 

BTW when is the next release date for `transformers`?

Just force-pushed the branch along with the changes. @younesbelkada 

yep