    def forward(self, qkv, key_padding_mask=None, causal=False, cu_seqlens=None,
                max_s=None, need_weights=False):
        """Implements the multihead softmax attention.
        Arguments
        ---------
            qkv: The tensor containing the query, key, and value. (B, S, 3, H, D) if key_padding_mask is None
                if unpadded: (nnz, 3, h, d)
            key_padding_mask: a bool tensor of shape (B, S)
        """
        assert not need_weights
        assert qkv.dtype in [torch.float16, torch.bfloat16]
        assert qkv.is_cuda

        if cu_seqlens is None:
            batch_size = qkv.shape[0]
            seqlen = qkv.shape[1]
            if key_padding_mask is None:
                qkv = rearrange(qkv, 'b s ... -> (b s) ...')
                max_s = seqlen
                cu_seqlens = torch.arange(0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32,
                                          device=qkv.device)
                output = flash_attn_unpadded_qkvpacked_func(
                    qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
                    softmax_scale=self.softmax_scale, causal=causal
                )
                output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
            else:
                nheads = qkv.shape[-2]
                x = rearrange(qkv, 'b s three h d -> b s (three h d)')
                x_unpad, indices, cu_seqlens, max_s = unpad_input(x, key_padding_mask)
                x_unpad = rearrange(x_unpad, 'nnz (three h d) -> nnz three h d', three=3, h=nheads)
                output_unpad = flash_attn_unpadded_qkvpacked_func(
                    x_unpad, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
                    softmax_scale=self.softmax_scale, causal=causal
                )
                output = rearrange(pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'),
                                             indices, batch_size, seqlen),
                                   'b s (h d) -> b s h d', h=nheads)
        else:
            assert max_s is not None
            output = flash_attn_unpadded_qkvpacked_func(
                qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
                softmax_scale=self.softmax_scale, causal=causal
            )

        return output, None
Implements the multihead softmax attention.
Arguments
---------
qkv: The tensor containing the query, key, and value. (B, S, 3, H, D) if key_padding_mask is None
if unpadded: (nnz, 3, h, d)
key_padding_mask: a bool tensor of shape (B, S)
projects/llava_sam2/hf/models/flash_attention.py