[feature] add_rms_norm support bias (#5790)

### What this PR does / why we need it?
This PR is to replace addRmsNorm and Add With addRmsNormBias. This way
can lead to a more effecient result.

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
Full Test Pass

- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef

Signed-off-by: Chen_HaoWen <chenhaowen12@huawei.com>
Co-authored-by: Chen_HaoWen <chenhaowen12@huawei.com>

tests/ut/ops/test_layernorm.py

@@ -6,6 +6,8 @@ from vllm.config import set_current_vllm_config
 from vllm.model_executor.layers.layernorm import RMSNorm
 
 from vllm_ascend.utils import AscendDeviceType
+from vllm_ascend.utils import enable_custom_op
+enable_custom_op()
 
 
 @pytest.fixture
@@ -20,6 +22,13 @@ def mock_rms_norm(x, weight, eps):
 def mock_add_rms_norm(x, residual, weight, eps):
     return 2 * x, None, 2 * residual
 
+def mock_add_rms_norm_bias(x, residual, weight, bias, eps):
+    if bias is None:
+        return 2 * x, None, 2 * residual
+    else:
+        return 2 * x + bias, None, 2 * residual
+        
+
 
 @pytest.fixture(autouse=True)
 def default_vllm_config():
@@ -35,7 +44,8 @@ def default_vllm_config():
                          [None, torch.randn(4, 8, dtype=torch.float32)])
 @patch("torch_npu.npu_rms_norm", side_effect=mock_rms_norm)
 @patch("torch_npu.npu_add_rms_norm", side_effect=mock_add_rms_norm)
-def test_RMSNorm_forward(mock_add_rmsnorm, mock_rmsnorm, is_310p, residual,
+@patch("torch.ops._C_ascend.npu_add_rms_norm_bias", side_effect=mock_add_rms_norm_bias)
+def test_RMSNorm_forward(mock_add_rms_norm_bias, mock_add_rmsnorm, mock_rmsnorm, is_310p, residual,
                          dummy_tensor, default_vllm_config):
 
     with patch("vllm_ascend.utils.get_ascend_device_type",
@@ -56,7 +66,7 @@ def test_RMSNorm_forward(mock_add_rmsnorm, mock_rmsnorm, is_310p, residual,
             else:
                 expected_out_x = 2 * dummy_tensor
                 expected_out_residual = 2 * residual
-                mock_add_rmsnorm.assert_called_once()
+                mock_add_rms_norm_bias.assert_called_once()
                 assert torch.allclose(out_x, expected_out_x)
                 assert torch.allclose(out_residual, expected_out_residual)
         else: