[Graph][Fusion] Add AddRMSNorm(with bias) and Quant Fusion Pattern (#5011)

### What this PR does / why we need it?
AddRMSNorm(with bias) and Quant Fusion Pattern

### Does this PR introduce _any_ user-facing change?
N/A

### How was this patch tested?
CI passed with new added/existing test.

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

---------

Signed-off-by: wxsIcey <1790571317@qq.com>

vllm_ascend/compilation/passes/norm_quant_fusion_pass.py

@@ -79,6 +79,64 @@ class AddRMSNormQuantPattern:
                                 pm.fwd_only, pm_pass)
 
 
+class AddRMSNormQuantPatternWithBias:
+
+    def __init__(self, vllm_config: VllmConfig, eps: float = 1e-6):
+        self.vllm_config = vllm_config
+        self.eps = eps
+
+    def get_inputs(self):
+        """
+        Generate example inputs for the AddRMSNormQuant fusion pattern.
+        """
+        rms_norm_input = torch.randn(2, 4, device="npu")
+        residual = torch.randn(2, 4, device="npu")
+        rms_norm_weight = torch.randn(4, device="npu")
+        scale = torch.tensor([1.0], device="npu")
+        offset = torch.tensor([0.0], device="npu")
+        bias = torch.randn(4, device="npu")
+        return [rms_norm_input, residual, rms_norm_weight, scale, offset, bias]
+
+    def register(self, pm_pass: PatternMatcherPass):
+
+        def pattern(rms_norm_input: torch.Tensor, residual: torch.Tensor,
+                    rms_norm_weight: torch.Tensor, scale: torch.Tensor,
+                    offset: torch.Tensor, bias: torch.Tensor):
+            """
+            Pattern for AddRMSNormQuant fusion.
+            """
+            output = torch.ops.npu.npu_add_rms_norm(rms_norm_input, residual,
+                                                    rms_norm_weight, self.eps)
+            out0 = output[0]
+            out1 = output[2]
+            out0 = out0 + bias
+            quantized_output = torch.ops.npu.npu_quantize(
+                out0, scale, offset, torch.qint8, -1, False)
+            return quantized_output, out1
+
+        def replacement(rms_norm_input: torch.Tensor, residual: torch.Tensor,
+                        rms_norm_weight: torch.Tensor, scale: torch.Tensor,
+                        offset: torch.Tensor, bias: torch.Tensor):
+            """
+            Replacement for the AddRMSNormQuant fusion.
+            """
+            output = torch.ops.npu.npu_add_rms_norm_quant(
+                rms_norm_input,
+                residual,
+                rms_norm_weight,
+                1. /
+                scale,  # The inverse of scale is required by npu_add_rms_norm_quant kernel which is opposite to the npu_quantize kernel.
+                offset,
+                epsilon=self.eps,
+                beta=bias)
+            quantized_output = output[0]
+            out1 = output[2]
+            return quantized_output, out1
+
+        pm.register_replacement(pattern, replacement, self.get_inputs(),
+                                pm.fwd_only, pm_pass)
+
+
 class AddRMSNormQuantFusionPass(VllmInductorPass):
     """
     A pass for fusing AddRMSNorm and W8A8 quantization operations on Ascend.
@@ -99,6 +157,8 @@ class AddRMSNormQuantFusionPass(VllmInductorPass):
         for eps in common_epsilons:
             AddRMSNormQuantPattern(vllm_config,
                                    eps=eps).register(self.pattern_match_passes)
+            AddRMSNormQuantPatternWithBias(vllm_config, eps=eps).register(
+                self.pattern_match_passes)
 
     def __call__(self, graph: torch.fx.Graph):
         self.begin()