[Fix] Fix SharedFusedMoE (#2817)

### What this PR does / why we need it?
Really strange that `register_oot` doesn't work with `SharedFusedMoE`,
so we have to add this patch, for now.

### Does this PR introduce _any_ user-facing change?
None.

### How was this patch tested?
This PR won't have any effect in DeepSeek since we currently still stick
with the old `CustomDeepseekV2`.

- vLLM version: v0.10.1.1
- vLLM main:
vllm-project/vllm@0cdd213

---------

Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>

Author: yiz-liu

vllm_ascend/ops/common_fused_moe.py

@@ -20,7 +20,8 @@
 import torch
 import torch_npu
 from vllm.config import CompilationLevel, get_current_vllm_config
-from vllm.distributed import get_dp_group, get_ep_group, get_tp_group
+from vllm.distributed import (get_dp_group, get_ep_group, get_tp_group,
+                              tensor_model_parallel_all_reduce)
 from vllm.forward_context import get_forward_context
 from vllm.model_executor.layers.fused_moe.config import \
     FusedMoEParallelConfig  # isort: skip
@@ -373,6 +374,21 @@ def __init__(
                 self, method.__name__.lower(),
                 method(moe_config=self.moe_config))  # type: ignore[abstract]
 
+    def maybe_all_reduce_tensor_model_parallel(
+            self, final_hidden_states: torch.Tensor):
+        """NOTE(Yizhou): This is to override the parent class method. In `mc2commimpl`,
+        and `alltoallcommimpl`, we do not need to all-reduce the final outputs since
+        the outputs are already aggregated across tensor parallel ranks in the
+        `finalize` function. In `allgathercommimpl`, we still need to all-reduce the
+        outputs since each rank only has partial outputs.
+        """
+        forward_context = get_forward_context()
+        moe_comm_method_name = forward_context.moe_comm_method_name
+        if moe_comm_method_name in {"alltoallcommimpl", "mc2commimpl"}:
+            return final_hidden_states
+        else:
+            return tensor_model_parallel_all_reduce(final_hidden_states)
+
     def forward_impl(self, hidden_states: torch.Tensor,
                      router_logits: torch.Tensor):
         assert self.quant_method is not None
@@ -415,6 +431,38 @@ def forward_impl(self, hidden_states: torch.Tensor,
         return final_hidden_states
 
 
+class AscendSharedFusedMoE(AscendFusedMoE):
+
+    def __init__(
+        self,
+        shared_experts: torch.nn.Module,
+        use_overlapped: bool = True,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self._shared_experts = shared_experts
+        self.use_overlapped = use_overlapped
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        router_logits: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        shared_out = self._shared_experts(hidden_states)
+
+        # NOTE: This is exactly the opposite of `maybe_all_reduce_tensor_model_parallel`
+        forward_context = get_forward_context()
+        moe_comm_method_name = forward_context.moe_comm_method_name
+        if moe_comm_method_name in {"alltoallcommimpl", "mc2commimpl"}:
+            shared_out = tensor_model_parallel_all_reduce(shared_out)
+
+        fused_out = super().forward(
+            hidden_states=hidden_states,
+            router_logits=router_logits,
+        )
+        return shared_out, fused_out
+
+
 UnquantizedFusedMoEMethod.__init__ = unquantized_fused_moe_init_func
 UnquantizedFusedMoEMethod.process_weights_after_loading = process_weights_after_loading
 

vllm_ascend/patch/platform/patch_common/__init__.py

@@ -16,3 +16,4 @@
 #
 
 import vllm_ascend.patch.platform.patch_common.patch_distributed  # noqa
+import vllm_ascend.patch.platform.patch_common.patch_shared_fused_moe  # noqa

vllm_ascend/patch/platform/patch_common/patch_shared_fused_moe.py

@@ -0,0 +1,21 @@
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2023 The vLLM team.
+#
+# 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 vllm.model_executor.models import deepseek_v2, llama4
+
+from vllm_ascend.ops.common_fused_moe import AscendSharedFusedMoE
+
+deepseek_v2.SharedFusedMoE = AscendSharedFusedMoE
+llama4.SharedFusedMoE = AscendSharedFusedMoE