Repository: huggingface/diffusers
File: src/diffusers/models/attention_dispatch.py (Python)

Code before:
        attn_mask = attn_mask.any(dim=(1, 2))  # [B, K]

    else:
        raise ValueError(f"Unsupported attention mask shape: {attn_mask.shape}")

    if attn_mask.shape != (batch_size, seq_len_k):
        raise ValueError(
            f"Normalized attention mask shape mismatch: got {attn_mask.shape}, expected ({batch_size}, {seq_len_k})"
        )

    return attn_mask


def _flex_attention_causal_mask_mod(batch_idx, head_idx, q_idx, kv_idx):
    return q_idx >= kv_idx


# ===== torch op registrations =====
# Registrations are required for fullgraph tracing compatibility
# Version guard for PyTorch compatibility - custom_op was added in PyTorch 2.4
# TODO: this is only required because the beta release FA3 does not have it. There is a PR adding
# this but it was never merged: https://github.com/Dao-AILab/flash-attention/pull/1590

if torch.__version__ >= "2.4.0":
    custom_op = torch.library.custom_op
    register_fake = torch.library.register_fake
else:
    def custom_op_no_op(name, fn=None, /, *, mutates_args, device_types=None, schema=None):
        def wrap(func):
            return func
        return wrap if fn is None else fn
    
    def register_fake_no_op(op, fn=None, /, *, lib=None, _stacklevel=1):
        def wrap(func):
            return func
        return wrap if fn is None else fn
    
    custom_op = custom_op_no_op
    register_fake = register_fake_no_op


@custom_op("flash_attn_3::_flash_attn_forward", mutates_args=(), device_types="cuda")
def _wrapped_flash_attn_3_original(
    query: torch.Tensor, key: torch.Tensor, value: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
    out, lse = flash_attn_3_func(query, key, value)
    lse = lse.permute(0, 2, 1)
    return out, lse


@register_fake("flash_attn_3::_flash_attn_forward")

Review comment (suggestion):
```suggestion
    _custom_op = torch.library.custom_op
    _register_fake = torch.library.register_fake
```

Code after:
                f"attn_mask.shape[0] ({attn_mask.shape[0]}) must be 1 or {batch_size} for 2D attention mask."
            )
        attn_mask = attn_mask.expand(batch_size, seq_len_k)

    elif attn_mask.ndim == 3:
        # [batch_size, seq_len_q, seq_len_k] -> reduce over query dimension
        # We do this reduction because we know that arbitrary QK masks is not supported in Flash/Sage varlen.
        if attn_mask.size(0) not in [1, batch_size]:
            raise ValueError(
                f"attn_mask.shape[0] ({attn_mask.shape[0]}) must be 1 or {batch_size} for 3D attention mask."
            )
        attn_mask = attn_mask.any(dim=1)
        attn_mask = attn_mask.expand(batch_size, seq_len_k)

    elif attn_mask.ndim == 4:
        # [batch_size, num_heads, seq_len_q, seq_len_k] or broadcastable versions
        if attn_mask.size(0) not in [1, batch_size]:
            raise ValueError(
                f"attn_mask.shape[0] ({attn_mask.shape[0]}) must be 1 or {batch_size} for 4D attention mask."
            )
        attn_mask = attn_mask.expand(batch_size, -1, -1, seq_len_k)  # [B, H, Q, K]
        attn_mask = attn_mask.any(dim=(1, 2))  # [B, K]

    else:
        raise ValueError(f"Unsupported attention mask shape: {attn_mask.shape}")

    if attn_mask.shape != (batch_size, seq_len_k):
        raise ValueError(
            f"Normalized attention mask shape mismatch: got {attn_mask.shape}, expected ({batch_size}, {seq_len_k})"
        )

    return attn_mask


def _flex_attention_causal_mask_mod(batch_idx, head_idx, q_idx, kv_idx):
    return q_idx >= kv_idx


# ===== torch op registrations =====
# Registrations are required for fullgraph tracing compatibility
# TODO: this is only required because the beta release FA3 does not have it. There is a PR adding
# this but it was never merged: https://github.com/Dao-AILab/flash-attention/pull/1590


@_custom_op("flash_attn_3::_flash_attn_forward", mutates_args=(), device_types="cuda")
def _wrapped_flash_attn_3_original(
    query: torch.Tensor, key: torch.Tensor, value: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
    out, lse = flash_attn_3_func(query, key, value)
    lse = lse.permute(0, 2, 1)
    return out, lse