Fix PaliGemma Pad Token Masking During Training #35855
Author: sambhavnoobcoder
Pull request: yes

## Problem Statement
In PaliGemma model's `_update_causal_mask` function, padding tokens were being incorrectly unmasked during training mode. This occurred because the order of operations first applied padding masks and then unmasked prefix tokens (including pad tokens) during training, leading to inconsistent behavior especially with left padding.

Fixes : #35855 

## Approach
After analyzing the masking logic, we identified that the issue stemmed from the sequence of operations in mask application. The solution was to reorder the masking operations to:
1. First unmask prefix tokens during training mode
2. Then apply padding masks

This ensures pad tokens remain masked regardless of their position or training state.

## Implementation
The fix involved reordering the masking operations in the `_update_causal_mask` function while maintaining the same mathematical operations. This approach ensures:
- No changes to the underlying logic
- Minimal code changes
- Preserved backward compatibility
- Consistent behavior across training and inference modes

### Test Coverage
The new test validates:
1. Pad tokens remain masked (value = dtype.min) during training
2. Non-pad tokens are properly masked/unmasked based on their position
3. Behavior is consistent across different batch sizes and sequence lengths

### Screenshots

<img width="617" alt="Screenshot 2025-01-24 at 12 57 47 AM" src="https://github.com/user-attachments/assets/6d3644ab-8515-4b23-9637-7328d4e19f63" />

**cc:** @amyeroberts @molbap @zucchini-nlp  kindly review this whenever you find the time .


Comments:
Thanks for the feedback! I've moved the test into `PaliGemmaForConditionalGenerationModelTest` class and am now using the existing dummy model setup instead of creating a separate test class. Instead of directly testing `_update_causal_mask` , I've switched to testing the full model forward pass with `output_attentions=True` which better reflects real usage. The test now covers both scenarios - with and without token_type_ids - to ensure attention masking works correctly in all cases. It verifies that pad tokens have zero attention weights across all attention layers and that the masking behavior remains consistent regardless of token type presence. Let me know if you'd like me to make any additional adjustments!

P.s also made the style fixup to make this ready to merge . 

Thank you for the review @zucchini-nlp . I have removed `PaliGemmaAttentionMaskTest` in [a015a90](https://github.com/huggingface/transformers/pull/35859/commits/a015a90b12f5d3d524bbef20ba10cb2644edfc51) commit .


**cc:** @ArthurZucker for final review / merging.

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_35859). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Thanks a lot! gentle ping @ArthurZucker for this! 
I'm sorry for not looking earlier, I thought we already moved that method around within `_update_causal_mask`. It turns out we are calling this method once more in `prepare_inputs_for_generation` when it's a PaliGemma2 model with HybridCache, any possible interaction with the training?

Thank you for the careful review @molbap  ! I've double-checked the interaction between `_update_causal_mask` and its usage in `prepare_inputs_for_generation` with HybridCache.

The current implementation maintains correct masking behavior in both contexts because:

1. The reordered masking operations (first prefix unmasking, then padding masks) ensure pad tokens stay masked regardless of where the method is called from
2. The `is_training` flag is properly propagated in both cases
3. Our test coverage in `test_attention_mask_with_token_types` verifies the masking behavior works correctly with and without token_type_ids

So the masking logic remains consistent whether called during regular training/inference or during generation with HybridCache, so no additional changes should be needed.