[Graph][Fusion] Add MatmulAllReduceAddRMSNorm graph fusion for npugraph_ex. (#6006)

### What this PR does / why we need it?
This PR builds upon PR
https://github.com/vllm-project/vllm-ascend/pull/5011 and aims to
further enhance the npu_graph_ex_passes module. Based on prior work, we
have added graph optimization support for the add_rms_quant fused
operator in scenarios where a bias term is present—ensuring the fusion
pattern is correctly registered and matched into the computation graph.

This time, we performed the operator fusion of MatmulAllReduceAddRMSNorm
and added corresponding ST test cases for regression monitoring.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?

- vLLM version: v0.13.0
- vLLM main:
2c24bc6996

---------

Signed-off-by: cjian <2318164299@qq.com>

vllm_ascend/compilation/npu_graph_ex_pass_manager.py

@@ -48,4 +48,18 @@ class NpuGraphEXPassManager:
 
     def configure(self, config: VllmConfig):
         # By default, we enable the graph fusion and quantization fusion pass.
-        self.ascend_compilation_config: dict = config.additional_config.get("ascend_compilation_config", {})
+        self.npugraph_ex_config: dict = config.additional_config.get("npugraph_ex_config", {})
+        if self.npugraph_ex_config.get("fuse_norm_quant", True):
+            from .npugraph_ex_passes.graphex_norm_quant_fusion_pass import GraphEXAddRMSNormFusionPass
+
+            self.passes.append(GraphEXAddRMSNormFusionPass(config))
+
+        if self.npugraph_ex_config.get("fuse_qknorm_rope", True):
+            from .npugraph_ex_passes.graphex_qknorm_rope_fusion_pass import GraphEXQKNormRopeFusionPass
+
+            self.passes.append(GraphEXQKNormRopeFusionPass(config))
+
+        if self.npugraph_ex_config.get("fuse_allreduce_rms", True):
+            from .npugraph_ex_passes.graphex_allreduce_rmsnorm_fusion_pass import GraphEXMatmulAllReduceAddRMSNormPass
+
+            self.passes.append(GraphEXMatmulAllReduceAddRMSNormPass(config))

vllm_ascend/compilation/npugraph_ex_passes/graphex_allreduce_rmsnorm_fusion_pass.py

@@ -0,0 +1,150 @@
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# This file is a part of the vllm-ascend project.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import torch
+import torchair
+from vllm.config import VllmConfig
+from vllm.config.compilation import Range
+from vllm.distributed import get_tensor_model_parallel_world_size, tensor_model_parallel_all_reduce
+from vllm.distributed.parallel_state import get_tp_group
+
+from vllm_ascend.compilation.npugraph_ex_passes.utils.npugraph_ex_utils_check import extra_stream_scope_check
+
+# computation-communication tiling block is 512
+ALLREDUCE_NORM_FUSE_THREHOLD = 512
+
+
+class GraphEXMiddleLayerMatmulAllReduceAddRMSNormPattern:
+    """
+    recognizing the Matmul + AllReduce + AddRMSNorm computation pattern
+    AllReduce is optimized in the fusion operator to a two-stage communication of ReduceScatter+AllGather
+    """
+
+    def __init__(self, vllm_config, eps=1e-6):
+        self.vllm_config = vllm_config
+        self.eps = eps
+        device_group = get_tp_group().device_group
+        backend = device_group._get_backend(torch.device("npu"))
+        self.local_rank = torch.distributed.get_rank(group=device_group)
+        self.tp_group_name = backend.get_hccl_comm_name(self.local_rank)
+        self.tp_size = get_tensor_model_parallel_world_size()
+
+    def get_inputs(self):
+        batch_size, seq_len = 2, 4
+        hidden_size = 4096
+        x = torch.randn(batch_size, seq_len, hidden_size, device="npu")
+        weight = torch.randn(hidden_size, hidden_size, device="npu")
+        residual = torch.randn(batch_size, seq_len, hidden_size, device="npu")
+        rms_norm_weight = torch.randn(hidden_size, device="npu")
+        return [x, weight, residual, rms_norm_weight]
+
+    def register(self):
+        def pattern(x, weight, residual, rms_norm_weight):
+            mm = torch.ops.vllm.unquantized_gemm(x, weight, None)
+            all_reduce_ = tensor_model_parallel_all_reduce(mm)
+            output = torch.ops._C_ascend.npu_add_rms_norm_bias(all_reduce_, residual, rms_norm_weight, None)
+            out0 = output[0]
+            out1 = output[2]
+
+            return out0, out1
+
+        def replacement(x, weight, residual, rms_norm_weight):
+            out0, out1 = torch.ops._C_ascend.matmul_allreduce_add_rmsnorm(
+                x,
+                weight,
+                residual,
+                rms_norm_weight,
+                self.tp_group_name,
+                self.tp_size,
+                self.local_rank,
+                self.eps,
+                True,
+                False,
+            )
+            return out0, out1
+
+        torchair.register_replacement(
+            search_fn=pattern,
+            replace_fn=replacement,
+            example_inputs=self.get_inputs(),
+            extra_check=extra_stream_scope_check,
+        )
+
+
+class GraphEXLastLayerMatmulAllReduceAddRMSNormPattern:
+    def __init__(self, vllm_config, eps=1e-6):
+        self.vllm_config = vllm_config
+        self.eps = eps
+        device_group = get_tp_group().device_group
+        backend = device_group._get_backend(torch.device("npu"))
+        self.local_rank = torch.distributed.get_rank(group=device_group)
+        self.tp_group_name = backend.get_hccl_comm_name(self.local_rank)
+        self.tp_size = get_tensor_model_parallel_world_size()
+
+    def get_inputs(self):
+        batch_size, seq_len = 2, 4
+        hidden_size = 4096
+        x = torch.randn(batch_size, seq_len, hidden_size, device="npu")
+        weight = torch.randn(hidden_size, hidden_size, device="npu")
+        residual = torch.randn(batch_size, seq_len, hidden_size, device="npu")
+        rms_norm_weight = torch.randn(hidden_size, device="npu")
+        return [x, weight, residual, rms_norm_weight]
+
+    def register(self):
+        def pattern(x, weight, residual, rms_norm_weight):
+            mm = torch.ops.vllm.unquantized_gemm(x, weight, None)
+            all_reduce_ = tensor_model_parallel_all_reduce(mm)
+            output = torch.ops._C_ascend.npu_add_rms_norm_bias(all_reduce_, residual, rms_norm_weight, None)
+
+            return output[0]
+
+        def replacement(x, weight, residual, rms_norm_weight):
+            out0, _ = torch.ops._C_ascend.matmul_allreduce_add_rmsnorm(
+                x,
+                weight,
+                residual,
+                rms_norm_weight,
+                self.tp_group_name,
+                self.tp_size,
+                self.local_rank,
+                self.eps,
+                True,
+                False,
+            )
+            return out0
+
+        torchair.register_replacement(
+            search_fn=pattern,
+            replace_fn=replacement,
+            example_inputs=self.get_inputs(),
+            extra_check=extra_stream_scope_check,
+        )
+
+
+class GraphEXMatmulAllReduceAddRMSNormPass:
+    def __init__(self, vllm_config: VllmConfig):
+        GraphEXMiddleLayerMatmulAllReduceAddRMSNormPattern(vllm_config).register()
+        GraphEXLastLayerMatmulAllReduceAddRMSNormPattern(vllm_config).register()
+
+    def __call__(self, graph: torch.fx.Graph):
+        pass
+
+    def is_applicable_for_range(self, compile_range: Range) -> bool:
+        """
+        Check if the pass is applicable for the current configuration.
+        """
+        applicable = compile_range.start > ALLREDUCE_NORM_FUSE_THREHOLD
+        return applicable

vllm_ascend/compilation/passes/allreduce_rmsnorm_fusion_pass.py

@@ -56,7 +56,7 @@ class MiddleLayerMatmulAllReduceAddRMSNormPattern:
         def pattern(x, weight, residual, rms_norm_weight):
             mm = torch.ops.vllm.unquantized_gemm(x, weight, None)
             all_reduce_ = tensor_model_parallel_all_reduce(mm)
-            output = torch.ops.npu.npu_add_rms_norm(all_reduce_, residual, rms_norm_weight)
+            output = torch.ops._C_ascend.npu_add_rms_norm_bias(all_reduce_, residual, rms_norm_weight, None)
             out0 = output[0]
             out1 = output[2]
 
@@ -103,7 +103,7 @@ class LastLayerMatmulAllReduceAddRMSNormPattern:
         def pattern(x, weight, residual, rms_norm_weight):
             mm = torch.ops.vllm.unquantized_gemm(x, weight, None)
             all_reduce_ = tensor_model_parallel_all_reduce(mm)
-            output = torch.ops.npu.npu_add_rms_norm(all_reduce_, residual, rms_norm_weight)
+            output = torch.ops._C_ascend.npu_add_rms_norm_bias(all_reduce_, residual, rms_norm_weight, None)
 
             return output[0]