code: def forward(
      ctx,
      x,
      scale,
      rpe,
      key_padding_mask,
      attn_mask,
      kp_mask_mode,
      attn_mask_mode,
      spdims,
      block,
      lut,
      num_blocks,
      maxlut,
      bench,
      time,
  ):
      apply_scale = False if scale == 1.0 else True
  
      # handle None rpe
      if rpe is None:
          apply_rpe = False
          stride_zrpe, stride_hrpe, stride_srpe = 0, 0, 0
          rpe = torch.empty(0, dtype=x.dtype, device=x.device)
      else:
          apply_rpe = True
          stride_zrpe, stride_hrpe, stride_srpe = (
              rpe.stride(0),
              rpe.stride(1),
              rpe.stride(2),
          )
  
      # handle None key_padding_mask
      if key_padding_mask is None:
          apply_kp_mask = False
          stride_zkpm = 0
          key_padding_mask = torch.empty(
              0, dtype=x.dtype, device=x.device
          )
      else:
          apply_kp_mask = True
          stride_zkpm = key_padding_mask.stride(0)
  
      # handle None attention_mask
      if attn_mask is None:
          apply_attn_mask = False
          stride_zattnm = 0
          attn_mask = torch.empty(0, dtype=x.dtype, device=x.device)
      else:
          apply_attn_mask = True
          stride_zattnm = attn_mask.stride(0)
  
      # run kernel
      M = x.shape[0]
      meta = {
          "BLOCK": block,
          "APPLY_SCALE": apply_scale,
          "APPLY_RPE": apply_rpe,
          "APPLY_KP_MASK": apply_kp_mask,
          "APPLY_ATTN_MASK": apply_attn_mask,
          "KP_MASK_MUL": kp_mask_mode == "mul",
          "ATTN_MASK_MUL": attn_mask_mode == "mul",
      }
      grid = lambda opt: [spdims[0] * spdims[1] * block, M]
      _forward[grid](
          x,
          scale,
          lut,
          rpe,
          key_padding_mask,
          attn_mask,
          maxlut,
          x.stride(0),
          stride_zrpe,
          stride_hrpe,
          stride_srpe,
          stride_zkpm,
          stride_zattnm,
          **meta,
      )
  
      # save to context
      ctx.mark_dirty(x)
      ctx.save_for_backward(x, lut)
      ctx.spdims = spdims
      ctx.block = block
      ctx.maxlut = maxlut
      ctx.scale = scale
      ctx.apply_scale = apply_scale
      ctx.apply_rpe = apply_rpe
      ctx.apply_kp_mask = apply_kp_mask
      ctx.apply_attn_mask = apply_attn_mask
      ctx.kp_mask_mode = kp_mask_mode
      ctx.attn_mask_mode = attn_mask_mode
      return x
  
file_path: H2O/h2o_flexgen/benchmark/third_party/DeepSpeed/deepspeed/ops/sparse_attention/softmax.py
imports: import torch
  import triton
  import triton.language
label_desc: Use image node_id 2 for calling the _sparse_softmax obj's underlying member method code with example usage: obj.forward(ctx, x, scale, rpe, key_padding_mask, attn_mask, kp_mask_mode, attn_mask_mode, spdims, block, lut, num_blocks, maxlut, bench, time) and returns: x