Repository: huggingface/transformers
Pull request: fix document masking for chunked attention

# What does this PR do?

The chunked attention was clobbering the document_ids meaning that the info to distinguish documents within the same chunk is gone. The proper way to do this is to simply generate a chunk mask and combine with the existing causal mask.

## 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#create-a-pull-request),
      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?
@ArthurZucker @SunMarc 
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
- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
- graph models: @clefourrier

Library:

- flax: @gante and @Rocketknight1
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @zach-huggingface and @SunMarc
- chat templates: @Rocketknight1

Integrations:

- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber

Documentation: @stevhliu

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: @Rocketknight1
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

 -->


Changed files:
filename: src/transformers/integrations/flex_attention.py
patch: @@ -122,7 +122,7 @@ def make_flex_block_causal_mask(
 
     if attention_chunk_size is not None:
         # we create an arange, then we just // by chunk size to get [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3]
-        document_ids = (document_ids.fill_(1).cumsum(-1) - 1) // (attention_chunk_size)
+        chunk_idxs = (document_ids.clone().fill_(1).cumsum(-1) - 1) // (attention_chunk_size)
 
     # Instead of passing a tensor mask, flex attention requires a mask_mod function
     # that determines which elements of QK^T should be included in the attention
@@ -143,16 +143,26 @@ def causal_mask_mod(batch_idx, head_idx, q_idx, kv_idx):
         final_mask = causal_mask & padding_mask & document_mask
         return final_mask
 
+    def chunk_causal_mask_mod(batch_idx, head_idx, q_idx, kv_idx):
+        """
+        Combines the chunk mask with the causal mask for chunked attention.
+        """
+        chunk_mask = chunk_idxs[batch_idx, q_idx] == chunk_idxs[batch_idx, kv_idx]
+        causal_doc_mask = causal_mask_mod(batch_idx, head_idx, q_idx, kv_idx)
+        return chunk_mask & causal_doc_mask
+
+    mask_mod_maybe_combined = causal_mask_mod if attention_chunk_size is None else chunk_causal_mask_mod
+
     if offsets is not None:
         q_offset = offsets[0]
         kv_offset = offsets[1]
 
         def mask_mod(batch_idx, head_idx, q_idx, kv_idx):
             offset_q = q_idx + q_offset
             offset_kv = kv_idx + kv_offset
-            return causal_mask_mod(batch_idx, head_idx, offset_q, offset_kv)
+            return mask_mod_maybe_combined(batch_idx, head_idx, offset_q, offset_kv)
     else:
-        mask_mod = causal_mask_mod
+        mask_mod = mask_mod_maybe_combined
     return create_block_causal_mask_flex(
         mask_mod=mask_mod,
         B=batch_size,