[bugfix] adapt bugfix for norm_quant_fusion_pass to npugraph_ex (#6726)

### What this PR does / why we need it?

This PR adapts bugfixes from `norm_quant_fusion_pass` to
`graphex_norm_quant_fusion_pass` for the `npugraph_ex` backend.

The main changes are:
- Replaced `torch.ops.npu.npu_add_rms_norm` with
`torch.ops._C_ascend.npu_add_rms_norm_bias`.
- For patterns without bias, `None` is passed as the bias argument.
- For patterns with bias, the separate `add` operation for bias is
removed and the bias is passed directly to `npu_add_rms_norm_bias`. This
improves fusion.

These changes ensure consistency and correctness for RMSNorm and
quantization fusion patterns when using `npugraph_ex`.

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.15.0
- vLLM main:
9562912cea

Signed-off-by: huyuanquan1 <huyuanquan1@huawei.com>
Co-authored-by: huyuanquan1 <huyuanquan1@huawei.com>

vllm_ascend/compilation/npugraph_ex_passes/graphex_norm_quant_fusion_pass.py

@@ -58,7 +58,9 @@ class GraphEXAddRMSNormQuantPattern:
             """
             Pattern for AddRMSNormQuant fusion.
             """
-            output = torch.ops.npu.npu_add_rms_norm(rms_norm_input, residual, rms_norm_weight, self.eps)
+            output = torch.ops._C_ascend.npu_add_rms_norm_bias(
+                rms_norm_input, residual, rms_norm_weight, None, self.eps
+            )
             out0 = output[0]
             out1 = output[2]
             quantized_output = torch.ops.vllm.quantize(out0, scale, scale_reciprocal, offset)
@@ -123,10 +125,11 @@ class GraphEXAddRMSNormQuantPatternWithBias:
             """
             Pattern for AddRMSNormQuantWithBias fusion.
             """
-            output = torch.ops.npu.npu_add_rms_norm(rms_norm_input, residual, rms_norm_weight, self.eps)
+            output = torch.ops._C_ascend.npu_add_rms_norm_bias(
+                rms_norm_input, residual, rms_norm_weight, bias, self.eps
+            )
             out0 = output[0]
             out1 = output[2]
-            out0 = out0 + bias
             quantized_output = torch.ops.vllm.quantize(out0, scale, scale_reciprocal, offset)
             return quantized_output, out1
 
@@ -188,7 +191,9 @@ class GraphEXAddRMSNormQuantSPPattern:
             """
             Pattern for AddRMSNormQuantSPPattern fusion.
             """
-            output = torch.ops.npu.npu_add_rms_norm(rms_norm_input, residual, rms_norm_weight, self.eps)
+            output = torch.ops._C_ascend.npu_add_rms_norm_bias(
+                rms_norm_input, residual, rms_norm_weight, None, self.eps
+            )
             out0 = output[0]
             out1 = output[2]
             out0 = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(out0, True)
@@ -255,10 +260,11 @@ class GraphEXAddRMSNormQuantSPPatternWithBias:
             """
             Pattern for AddRMSNormQuantSPPatternWithBias fusion.
             """
-            output = torch.ops.npu.npu_add_rms_norm(rms_norm_input, residual, rms_norm_weight, self.eps)
+            output = torch.ops._C_ascend.npu_add_rms_norm_bias(
+                rms_norm_input, residual, rms_norm_weight, bias, self.eps
+            )
             out0 = output[0]
             out1 = output[2]
-            out0 = out0 + bias
             out0 = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(out0, True)
             quantized_output = torch.ops.vllm.quantize(out0, scale, scale_reciprocal, offset)
             return quantized_output, out1