[CustomOp] Register AscendSharedFusedMoE custom op

vllm_ascend/ops/common_fused_moe.py

@@ -27,6 +27,7 @@
     FusedMoEParallelConfig  # isort: skip
 from vllm.model_executor.layers.fused_moe.layer import (
     FusedMoE, UnquantizedFusedMoEMethod, determine_expert_map)
+from vllm.model_executor.layers.shared_fused_moe import SharedFusedMoE
 
 from vllm_ascend.ascend_config import get_ascend_config
 from vllm_ascend.distributed.parallel_state import get_mc2_group
@@ -415,15 +416,15 @@ def _load_w2(self,
         expert_data.copy_(loaded_weight)
 
 
-class AscendSharedFusedMoE(AscendFusedMoE):
+class AscendSharedFusedMoE(SharedFusedMoE, AscendFusedMoE):
 
     def __init__(
         self,
         shared_experts: torch.nn.Module,
         use_overlapped: bool = True,
         **kwargs,
     ):
-        super().__init__(**kwargs)
+        AscendFusedMoE.__init__(self, **kwargs)
         self._shared_experts = shared_experts
         self.use_overlapped = use_overlapped
         self.shared_expert_stream = None
@@ -452,7 +453,8 @@ def forward(
             if moe_comm_method_name in {"alltoallcommimpl", "mc2commimpl"}:
                 shared_out = tensor_model_parallel_all_reduce(shared_out)
 
-        fused_out = super().forward(
+        _, fused_out = AscendFusedMoE.forward(
+            self,
             hidden_states=hidden_states,
             router_logits=router_logits,
         )
@@ -461,6 +463,16 @@ def forward(
             torch.npu.current_stream().wait_stream(self.shared_expert_stream)
         return shared_out, fused_out
 
+    def forward_impl(self, hidden_states: torch.Tensor,
+                     router_logits: torch.Tensor):
+        shared_output = torch.empty(1)
+        fused_output = AscendFusedMoE.forward_impl(
+            self,
+            hidden_states=hidden_states,
+            router_logits=router_logits,
+        )
+        return shared_output, fused_output
+
 
 UnquantizedFusedMoEMethod.__init__ = unquantized_fused_moe_init_func
 UnquantizedFusedMoEMethod.process_weights_after_loading = process_weights_after_loading

vllm_ascend/patch/worker/patch_common/__init__.py

@@ -18,4 +18,3 @@
 import vllm_ascend.patch.worker.patch_common.patch_distributed  # noqa
 import vllm_ascend.patch.worker.patch_common.patch_logits  # noqa
 import vllm_ascend.patch.worker.patch_common.patch_minicpm  # noqa
-import vllm_ascend.patch.worker.patch_common.patch_shared_fused_moe  # noqa

vllm_ascend/utils.py

@@ -498,7 +498,8 @@ def register_ascend_customop(vllm_config: Optional[VllmConfig] = None):
 
     from vllm_ascend.models.layers.mla import AscendMultiHeadLatentAttention
     from vllm_ascend.ops.activation import AscendQuickGELU, AscendSiluAndMul
-    from vllm_ascend.ops.common_fused_moe import AscendFusedMoE
+    from vllm_ascend.ops.common_fused_moe import (AscendFusedMoE,
+                                                  AscendSharedFusedMoE)
     from vllm_ascend.ops.layernorm import AscendQuantRMSNorm, AscendRMSNorm
     from vllm_ascend.ops.linear import (AscendColumnParallelLinear,
                                         AscendMergedColumnParallelLinear,
@@ -525,6 +526,7 @@ def register_ascend_customop(vllm_config: Optional[VllmConfig] = None):
         "LogitsProcessor": AscendLogitsProcessor,
         "RMSNorm": AscendRMSNorm,
         "FusedMoE": AscendFusedMoE,
+        "SharedFusedMoE": AscendSharedFusedMoE,
         "MultiHeadLatentAttention": AscendMultiHeadLatentAttention,
     }