Repository: huggingface/transformers
Pull request: fix flex attn when optional args aren't passed

# What does this PR do?
#37307 (llama4) introduced a regression in make_flex_block_causal_mask with new optional args that llama4 uses. It also added padding, but padding only works if the `key_length` isn't None. For all models except llama4, flex attention is now broken since none of them pass key_length, and currently they error with.

```
[rank3]:   File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/torch/nn/functional.py", line 5209, in pad                                                                                                                                        
[rank3]:     return torch._C._nn.pad(input, pad, mode, value)                                                                                                                                                                                                
[rank3]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^                                                                                                                                                                                                
[rank3]: TypeError: pad(): argument 'pad' failed to unpack the object at pos 2 with error "type must be tuple of ints,but got NoneType"                                                                                                                      
```

Also, the batch size was likely mistakenly hard-coded to 1, which probably shouldn't be the case.  

Fixes # (issue)


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@ArthurZucker 

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Changed files:
filename: src/transformers/integrations/flex_attention.py
patch: @@ -100,6 +100,11 @@ def make_flex_block_causal_mask(
     Returns:
         BlockMask
     """
+    batch_size, total_seq_len = attention_mask_2d.shape
+    if not key_length:
+        key_length = total_seq_len
+    if not query_length:
+        query_length = total_seq_len
     attention_mask_2d = torch.nn.functional.pad(attention_mask_2d, value=0, pad=(0, key_length))
     device = attention_mask_2d.device
     document_ids = attention_mask_2d.clone()
@@ -139,7 +144,7 @@ def mask_mod(batch_idx, head_idx, q_idx, kv_idx):
         mask_mod = causal_mask_mod
     return create_block_causal_mask_flex(
         mask_mod=mask_mod,
-        B=1,
+        B=batch_size,
         H=None,  # attention head
         Q_LEN=query_length,
         KV_LEN=key_length,