code: def backward(ctx, do):
      BLOCK = 128
      q, k, v, o, l, m = ctx.saved_tensors
      do = do.contiguous()
      dq = torch.zeros_like(q, dtype=torch.float32)
      dk = torch.empty_like(k)
      dv = torch.empty_like(v)
      do_scaled = torch.empty_like(do)
      delta = torch.empty_like(l)
      _bwd_preprocess[(ctx.grid[0] * ctx.grid[1],)](
          o,
          do,
          l,  #
          do_scaled,
          delta,  #
          BLOCK_M=BLOCK,
          D_HEAD=ctx.BLOCK_DMODEL,
      )
      _bwd_kernel[(ctx.grid[1],)](
          q,
          k,
          v,
          ctx.sm_scale,  #
          o,
          do_scaled,  #
          dq,
          dk,
          dv,  #
          l,
          m,  #
          delta,  #
          q.stride(0),
          q.stride(1),
          q.stride(2),
          q.stride(3),  #
          k.stride(0),
          k.stride(1),
          k.stride(2),
          k.stride(3),  #
          v.stride(0),
          v.stride(1),
          v.stride(2),
          v.stride(3),  #
          q.shape[0],
          q.shape[1],
          q.shape[2],  #
          ctx.grid[0],  #
          BLOCK_M=BLOCK,
          BLOCK_N=BLOCK,  #
          BLOCK_DMODEL=ctx.BLOCK_DMODEL,  #
          num_warps=8,
          num_stages=1,  #
      )
      return dq, dk, dv, None
  
file_path: triton/python/test/unit/hopper/test_persistent_warp_specialized_fused-attention.py
imports: import pytest
  import torch
  import triton
  import triton.language
label_desc: Use image node_id 2 for calling the _attention obj's underlying member method code with example usage: obj.backward(ctx, do) and returns: dq,  dk,  dv,  None