code: def forward(ctx, q, k, v, causal, sm_scale):
      # shape constraints
      Lq, Lk, Lv = q.shape[-1], k.shape[-1], v.shape[-1]
      assert Lq == Lk and Lk == Lv
      assert Lk in {16, 32, 64, 128}
      o = torch.empty_like(q)
      BLOCK_M = 128
      BLOCK_N = 64 if Lk <= 64 else 32
      num_stages = 4 if Lk <= 64 else 3
      num_warps = 4
      stage = 3 if causal else 1
      # Tuning for H100
      if torch.cuda.get_device_capability()[0] == 9:
          num_warps = 8
          num_stages = 7 if Lk >= 64 else 3
      grid = (
          triton.cdiv(q.shape[2], BLOCK_M),
          q.shape[0] * q.shape[1],
          1,
      )
      M = torch.empty(
          (q.shape[0], q.shape[1], q.shape[2]),
          device=q.device,
          dtype=torch.float32,
      )
      _attn_fwd[grid](
          q,
          k,
          v,
          sm_scale,
          M,
          o,  #
          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),  #
          o.stride(0),
          o.stride(1),
          o.stride(2),
          o.stride(3),  #
          q.shape[0],
          q.shape[1],  #
          N_CTX=q.shape[2],  #
          BLOCK_M=BLOCK_M,  #
          BLOCK_N=BLOCK_N,  #
          BLOCK_DMODEL=Lk,  #
          STAGE=stage,  #
          num_warps=num_warps,  #
          num_stages=num_stages,  #
      )
  
      ctx.save_for_backward(q, k, v, o, M)
      ctx.grid = grid
      ctx.sm_scale = sm_scale
      ctx.BLOCK_DMODEL = Lk
      ctx.causal = causal
      return o
  
file_path: triton/python/tutorials/06-fused-attention.py
imports: import pytest
  import torch
  import triton
  import triton.language
label_desc: Use image node_id 1 for calling the _attention obj's underlying member method code with example usage: obj.forward(ctx, q, k, v, causal, sm_scale) and returns: o