Exclude the load balancing loss of padding tokens in Mixtral-8x7B
Author: khaimt
Pull request: yes

# What does this PR do?
This PR implements excluding the load balancing loss of padding tokens in Mixtral-8x7B
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Fixes #28505


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Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28517). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

> Thanks for the update. Can you add a good test to make sure this behaves as expected? 🤗

Sure, I will add a test to make sure that the implementation is correct

> Thanks for the update. Can you add a good test to make sure this behaves as expected? 🤗

Hi @ArthurZucker, I have just added the tests for load balancing loss 

@ArthurZucker I don't know why The test is failed on github but was passed in my local test.
In my local test, I only changed the test_mixtral.py so I ran:
pytest --picked
And test was passed, but was failed on github.
It is as if the modeling_mixtral.py was not updated on github test 

> I don't know why The test is failed on github but was passed in my local test. 

From details of [check_code_quality](https://circleci.com/gh/huggingface/transformers/1066145):
tests/models/mixtral/test_modeling_mixtral.py:19:8: F401 [*] `random` imported but unused

Unused import of the random module in the test file.

> > I don't know why The test is failed on github but was passed in my local test.
> 
> From details of [check_code_quality](https://circleci.com/gh/huggingface/transformers/1066145): tests/models/mixtral/test_modeling_mixtral.py:19:8: F401 [*] `random` imported but unused
> 
> Unused import of the random module in the test file.

I mean this assert: https://app.circleci.com/pipelines/github/huggingface/transformers/82724/workflows/61975ffc-0bdb-4aa7-935e-70f69f11e748/jobs/1066148/parallel-runs/0/steps/0-117

Some thoughts:
- Regarding `∣actual−expected∣>atol+rtol⋅∣expected∣`.. You aren't getting the fabs() of result.aux_loss before multiplying. 
- There's also an [assertNotAlmostEqual](https://github.com/huggingface/transformers/blob/3f69f415adcbdaedec154ba8eac220ef3276975d/tests/models/encoder_decoder/test_modeling_encoder_decoder.py#L817) function for comparing if they're not close.
- Specifying the device to use might give different results on different systems

Thank you for doing this btw!

Edit for clarity

> Some thoughts:
> 
>     * Regarding `∣actual−expected∣>atol+rtol⋅∣expected∣`.. You aren't getting the fabs() of result.aux_loss before multiplying.
> 
>     * There's also an [assertNotAlmostEqual](https://github.com/huggingface/transformers/blob/3f69f415adcbdaedec154ba8eac220ef3276975d/tests/models/encoder_decoder/test_modeling_encoder_decoder.py#L817) function for comparing if they're not close.
> 
>     * Specifying the device to use might give different results on different systems
> 
> 
> Thank you for doing this btw!
> 
> Edit for clarity

Hi @congruency Thank you for your comments
Actually we no need to get the fabs() of result.aux_loss, because it is already positive, and actually the value is around 2. You can see the value from previous assert: 
https://github.com/khaimt/transformers/blob/exclude_padding_tokens_in_aux_loss_Mixtral_Moe/tests/models/mixtral/test_modeling_mixtral.py#L478 
Oh, thankyou for reminding me of: assertNotAlmostEqual, I will fix my code with this suggestion

@ArthurZucker The tests have been fully added and passed, can you review ?

Am a business 

UC eye care center 

Sorry for the delay will review! 

@ArthurZucker I have just fixed my PR:
+ move attention_mask to the last position
+ pad_leng --> pad_length
+ reduce the tol for assert_close