Repository: huggingface/transformers
Pull request: Fix Causality Handling in Flash Attention to Support Bidirectional Attention

# What does this PR do?
Fixes #39554 

The original implementation of the `flash_attention_forward` function is restricted to performing **causal attention** and does not support **bidirectional attention**. This behavior stems from how the function handles causality:

- The function relies on the `Attention.is_causal` attribute, which belongs to the `Attention` class in the model.
- By default, `Attention.is_causal` is set to `True`, enforcing causal attention (where the model only attends to previous tokens in a sequence).
- This attribute is never modified in the code, meaning the setting is effectively fixed.
- Additionally, while the function removes the `is_causal` key from the keyword arguments (`kwargs`) passed to it, this value is not used. Instead, it always defers to the hardcoded `Attention.is_causal` value.

As a result, even if a user attempts to pass `is_causal=False` through `kwargs` to enable bidirectional attention (where the model can attend to both previous and future tokens), the input is ignored. Consequently, the current setup makes it impossible to perform bidirectional attention when using flash attention.

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Changed files:
filename: src/transformers/integrations/flash_attention.py
patch: @@ -58,16 +58,18 @@ def flash_attention_forward(
         else:
             target_dtype = next(layer for layer in module.modules() if isinstance(layer, torch.nn.Linear)).weight.dtype
 
-    # FA2 always relies on the value set in the module, so remove it if present in kwargs to avoid passing it twice
-    kwargs.pop("is_causal", None)
+    # Instead of relying on the value set in the module directly, we use the is_causal passed in kwargs if it is presented
+    is_causal = kwargs.pop("is_causal", None)
+    if is_causal is None:
+        is_causal = module.is_causal
 
     attn_output = _flash_attention_forward(
         query,
         key,
         value,
         attention_mask,
         query_length=seq_len,
-        is_causal=module.is_causal,
+        is_causal=is_causal,
         dropout=dropout,
         softmax_scale=scaling,
         sliding_window=sliding_window,