[Model] Support common fused moe ops for moe model, such as Qwen3Moe (#709)

vllm-ascend now only support moe for deepseek. We should add common moe
support back

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

Author: wangxiyuan

vllm_ascend/ops/__init__.py

@@ -19,6 +19,7 @@
 import torch_npu  # noqa: F401
 
 import vllm_ascend.ops.activation  # noqa
+import vllm_ascend.ops.common_fused_moe  # noqa
 import vllm_ascend.ops.fused_moe  # noqa
 import vllm_ascend.ops.layernorm  # noqa
 import vllm_ascend.ops.rotary_embedding  # noqa

vllm_ascend/ops/common_fused_moe.py

@@ -0,0 +1,67 @@
+#
+# 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.
+#
+
+from typing import Callable, Optional
+
+import torch
+from vllm.model_executor.layers.fused_moe.layer import \
+    UnquantizedFusedMoEMethod
+
+from vllm_ascend.ops.fused_moe import fused_experts, select_experts
+
+
+def forward_oot(
+    self,
+    layer: torch.nn.Module,
+    x: torch.Tensor,
+    use_grouped_topk: bool,
+    top_k: int,
+    router_logits: torch.Tensor,
+    renormalize: bool,
+    topk_group: Optional[int] = None,
+    num_expert_group: Optional[int] = None,
+    custom_routing_function: Optional[Callable] = None,
+    scoring_func: str = "softmax",
+    e_score_correction_bias: Optional[torch.Tensor] = None,
+    global_num_experts: Optional[int] = None,
+    expert_map: Optional[torch.Tensor] = None,
+    apply_router_weight_on_input: bool = False,
+    activation: str = "silu",
+) -> torch.Tensor:
+    topk_weights, topk_ids = select_experts(
+        hidden_states=x,
+        router_logits=router_logits,
+        top_k=top_k,
+        use_grouped_topk=use_grouped_topk,
+        renormalize=renormalize,
+        topk_group=topk_group,
+        num_expert_group=num_expert_group,
+        custom_routing_function=custom_routing_function,
+        scoring_func=scoring_func,
+        e_score_correction_bias=e_score_correction_bias,
+    )
+
+    return fused_experts(hidden_states=x,
+                         w1=layer.w13_weight,
+                         w2=layer.w2_weight,
+                         topk_weights=topk_weights,
+                         topk_ids=topk_ids,
+                         top_k=top_k,
+                         expert_map=expert_map)
+
+
+UnquantizedFusedMoEMethod.forward_oot = forward_oot