code: def test_autocast_fused_upper_triangle_mask_softmax(self):
      for dtype in autocast_dtypes:
          msg = f"dtype: {dtype}"
          input_in_fp16 = dtype == torch.half
          input_in_bf16 = dtype == torch.bfloat16
          fused_fn, torch_fn = self._setup_fused_softmax(
              input_in_fp16,
              input_in_bf16,
              attn_mask_type=AttnMaskType.causal,
          )
  
          attn_weights_0 = (
              torch.randn((4, 12, 24, 24)).cuda().requires_grad_(True)
          )
          with torch.no_grad():
              attn_weights_1 = (
                  attn_weights_0.clone().to(dtype).requires_grad_(True)
              )
          total_mask = (
              ~(torch.tril(torch.randn((24, 24), device="cuda")).bool())
              .unsqueeze(0)
              .unsqueeze(0)
          )
  
          with torch.cuda.amp.autocast(dtype=dtype):
              actual = fused_fn(attn_weights_0, total_mask)
              self.assertEqual(actual.dtype, dtype, msg=msg)
          expected = torch_fn(attn_weights_1, total_mask)
          self.assertEqual(actual, expected, msg=msg)
  
          g0 = torch.randn_like(actual)
          with torch.no_grad():
              g1 = g0.clone()
          actual.backward(g0)
          expected.backward(g1)
  
imports: import itertools
  import torch
  from torch.testing._internal import common_utils
  from apex.transformer import AttnMaskType
  from apex.transformer.functional import FusedScaleMaskSoftmax
label_desc: Use image node_id 8 for calling the TestFusedScaleMaskSoftmax obj's underlying member method code with example usage: obj.test_autocast_fused_upper_triangle_mask_softmax() without return types