Repository: huggingface/transformers
Pull request: revert behavior of _prepare_from_posids

# What does this PR do?

#39474 causes the grad norm to become NaN during training. We don't need to revert the whole thing, but reverting just this function seems to fix the regression.

@ArthurZucker @SunMarc 

pre-#39474 loss
<img width="957" height="496" alt="Screenshot 2025-07-23 at 5 28 24 PM" src="https://github.com/user-attachments/assets/f319791b-4f3b-489a-b7dd-dc0e2eb3112f" />

post-#39474 loss
<img width="1290" height="282" alt="Screenshot 2025-07-23 at 5 31 19 PM" src="https://github.com/user-attachments/assets/ee5fe4f9-5c42-4c7e-8f78-c6e14f960737" />

with this fix
<img width="1000" height="301" alt="Screenshot 2025-07-23 at 7 07 01 PM" src="https://github.com/user-attachments/assets/1dec04d5-5378-4f05-b57d-8a81a403ec28" />

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Changed files:
filename: src/transformers/modeling_flash_attention_utils.py
patch: @@ -222,16 +222,18 @@ def _prepare_from_posids(query, key, value, position_ids):
     query = query.contiguous().view(-1, query.size(-2), query.size(-1))
     key = key.contiguous().view(-1, key.size(-2), key.size(-1))
     value = value.contiguous().view(-1, value.size(-2), value.size(-1))
+
     cu_seqlens_k = torch.cat(
         [torch.tensor([0], dtype=torch.int32, device=query.device), position_ids[:, -1].cumsum(dim=0) + 1], dim=0
     )
     max_k = torch.max(position_ids, dim=1).values.max().item() + 1
+
     position_ids = position_ids.flatten()
     indices_q = torch.arange(position_ids.size(0), device=position_ids.device, dtype=torch.int32)
 
     cu_seq_lens = torch.cat(
         (
-            torch.tensor([0], device=position_ids.device, dtype=torch.int32),
+            indices_q[position_ids == 0],
             torch.tensor(position_ids.size(), device=position_ids.device, dtype=torch.int32),
         )
     )