def unpad_input(hidden_states, attention_mask):
    """
    Arguments:
        hidden_states: (batch, seqlen, dim)
        attention_mask: (batch, seqlen), bool / int, 1 means valid and 0 means not valid.
    Return:
        hidden_states: (total_nnz, dim), where total_nnz = number of tokens in selected in attention_mask.
        cu_seqlens: (batch + 1), the cumulative sequence lengths, used to index into hidden_states.
        max_seqlen_in_batch: int
    """
    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
    max_seqlen_in_batch = seqlens_in_batch.max().item()
    cu_seqlens = F.pad(
        torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)
    )
    return (
        index_first_axis(rearrange(hidden_states, "b s d -> (b s) d"), indices),
        indices,
        cu_seqlens,
        max_seqlen_in_batch,
    )
Arguments:
hidden_states: (batch, seqlen, dim)
attention_mask: (batch, seqlen), bool / int, 1 means valid and 0 means not valid.
Return:
hidden_states: (total_nnz, dim), where total_nnz = number of tokens in selected in attention_mask.
cu_seqlens: (batch + 1), the cumulative sequence lengths, used to index into hidden_states.
max_seqlen_in_batch: int
tests/unittest/ops/test_attention.py