code: def generate_qkv(
      q,
      k,
      v,
      query_padding_mask=None,
      key_padding_mask=None,
      kvpacked=False,
      qkvpacked=False,
  ):
      """
      Arguments:
          q: (batch_size, seqlen_q, nheads, d)
          k: (batch_size, seqlen_k, nheads_k, d)
          v: (batch_size, seqlen_k, nheads_k, d)
          query_padding_mask: (batch_size, seqlen), bool
          key_padding_mask: (batch_size, seqlen), bool
      """
      assert not (kvpacked and qkvpacked)
      batch_size, seqlen_q, nheads, d = q.shape
      _, seqlen_k, nheads_k, _ = k.shape
      assert k.shape == (batch_size, seqlen_k, nheads_k, d)
      assert v.shape == (batch_size, seqlen_k, nheads_k, d)
  
      if query_padding_mask is not None:
          q_unpad, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(
              q, query_padding_mask
          )
          output_pad_fn = lambda output_unpad: pad_input(
              output_unpad, indices_q, batch_size, seqlen_q
          )
      else:
          q_unpad = rearrange(q, "b s h d -> (b s) h d")
          cu_seqlens_q = torch.arange(
              0,
              (batch_size + 1) * seqlen_q,
              step=seqlen_q,
              dtype=torch.int32,
              device=q_unpad.device,
          )
          max_seqlen_q = seqlen_q
          output_pad_fn = lambda output_unpad: rearrange(
              output_unpad, "(b s) h d -> b s h d", b=batch_size
          )
  
      if key_padding_mask is not None:
          k_unpad, indices_k, cu_seqlens_k, max_seqlen_k = unpad_input(
              k, key_padding_mask
          )
          v_unpad, _, _, _ = unpad_input(v, key_padding_mask)
      else:
          k_unpad = rearrange(k, "b s h d -> (b s) h d")
          v_unpad = rearrange(v, "b s h d -> (b s) h d")
          cu_seqlens_k = torch.arange(
              0,
              (batch_size + 1) * seqlen_k,
              step=seqlen_k,
              dtype=torch.int32,
              device=k_unpad.device,
          )
          max_seqlen_k = seqlen_k
  
      if qkvpacked:
          assert (query_padding_mask == key_padding_mask).all()
          assert nheads == nheads_k
          qkv_unpad = torch.stack([q_unpad, k_unpad, v_unpad], dim=1)
          qkv = torch.stack([q, k, v], dim=2)
          if query_padding_mask is not None:
              dqkv_pad_fn = lambda dqkv_unpad: pad_input(
                  dqkv_unpad, indices_q, batch_size, seqlen_q
              )
          else:
              dqkv_pad_fn = lambda dqkv_unpad: rearrange(
                  dqkv_unpad, "(b s) t h d -> b s t h d", b=batch_size
              )
          return (
              qkv_unpad.detach().requires_grad_(),
              cu_seqlens_q,
              max_seqlen_q,
              qkv.detach().requires_grad_(),
              output_pad_fn,
              dqkv_pad_fn,
          )
      elif kvpacked:
          kv_unpad = torch.stack([k_unpad, v_unpad], dim=1)
          kv = torch.stack([k, v], dim=2)
          dq_pad_fn = output_pad_fn
          if key_padding_mask is not None:
              dkv_pad_fn = lambda dkv_unpad: pad_input(
                  dkv_unpad, indices_k, batch_size, seqlen_k
              )
          else:
              dkv_pad_fn = lambda dkv_unpad: rearrange(
                  dkv_unpad, "(b s) t h d -> b s t h d", b=batch_size
              )
          return (
              q_unpad.detach().requires_grad_(),
              kv_unpad.detach().requires_grad_(),
              cu_seqlens_q,
              cu_seqlens_k,
              max_seqlen_q,
              max_seqlen_k,
              q.detach().requires_grad_(),
              kv.detach().requires_grad_(),
              output_pad_fn,
              dq_pad_fn,
              dkv_pad_fn,
          )
      else:
          dq_pad_fn = output_pad_fn
          if key_padding_mask is not None:
              dk_pad_fn = lambda dk_unpad: pad_input(
                  dk_unpad, indices_k, batch_size, seqlen_k
              )
          else:
              dk_pad_fn = lambda dk_unpad: rearrange(
                  dk_unpad, "(b s) h d -> b s h d", b=batch_size
              )
          return (
              q_unpad.detach().requires_grad_(),
              k_unpad.detach().requires_grad_(),
              v_unpad.detach().requires_grad_(),
              cu_seqlens_q,
              cu_seqlens_k,
              max_seqlen_q,
              max_seqlen_k,
              q.detach().requires_grad_(),
              k.detach().requires_grad_(),
              v.detach().requires_grad_(),
              output_pad_fn,
              dq_pad_fn,
              dk_pad_fn,
          )
  
imports: import math
  import pytest
  import torch
  import torch.nn.functional
  from einops import rearrange, repeat
  from flash_attn import flash_attn_func, flash_attn_kvpacked_func, flash_attn_qkvpacked_func, flash_attn_varlen_func, flash_attn_varlen_kvpacked_func, flash_attn_varlen_qkvpacked_func, flash_attn_with_kvcache
  from flash_attn.bert_padding import pad_input, unpad_input
  from flash_attn.flash_attn_interface import _get_block_size_n
  from flash_attn.layers.rotary import apply_rotary_emb
label_desc: Use image node_id 3 for calling a global function with example usage: generate_qkv(q, k, v, query_padding_mask, key_padding_mask, kvpacked, qkvpacked) and returns: qkv_unpad,  cu_seqlens_q,  max_seqlen_q,  qkv,  output_pad_fn,  dqkv_pad_fn, q_unpad,  kv_unpad,  cu_seqlens_q,  cu_seqlens_k,  max_seqlen_q,  max_seqlen_k,  q,  kv,  output_pad_fn,  dq_pad_fn,  dkv_pad_fn, q_unpad,  k_unpad,  v_unpad,  cu_seqlens_q,  cu_seqlens_k,  max_seqlen_q,  max_seqlen_k,  q,  k,  v,  output_pad_fn,  dq_pad_fn,  dk_pad_fn