[BUGFIX][v0.9.1] fix enable_multistream_moe bug when DBO is enabled (…

vllm_ascend/models/deepseek_dbo.py

@@ -154,47 +154,6 @@ def __init__(
         CustomDeepseekDBOMoE.top_k = config.num_experts_per_tok
         self.config = config
 
-    def forward(
-            self,
-            hidden_states: torch.Tensor,
-            attn_metadata: Optional[AttentionMetadata] = None) -> torch.Tensor:
-        forward_context = get_forward_context()
-        if attn_metadata is None:
-            attn_metadata = forward_context.attn_metadata
-
-        # when profile runs, force experts to load balanced tokens
-        # to avoid high memory consumption on a single rank.
-        enable_force_load_balance = forward_context.in_profile_run
-
-        is_prefill = forward_context.with_prefill
-        # If this node is kv_consumer, we force the moe always runs in decode path to make sure
-        # the behaviour aligned between dummy_run and normal model_execute.
-        if self.kv_consumer:
-            is_prefill = False
-
-        # router_logits: (num_tokens, n_experts)
-        router_logits, _ = self.gate(hidden_states)
-
-        experts_hidden_states = self.experts(
-            hidden_states=hidden_states,
-            router_logits=router_logits,
-            is_prefill=is_prefill,
-            top_k=CustomDeepseekDBOMoE.top_k,
-            enable_force_load_balance=enable_force_load_balance,
-            shared_experts=self.shared_experts)
-
-        shared_experts_hidden = experts_hidden_states[1]
-        if not (self.shared_experts.down_proj.reduce_results
-                and self.shared_experts.down_proj.tp_size > 1):
-            shared_experts_hidden = tensor_model_parallel_all_reduce(
-                shared_experts_hidden)
-
-        hidden_states = (
-            experts_hidden_states[0] * self.routed_scaling_factor +
-            shared_experts_hidden)
-
-        return hidden_states
-
     # ----------------------------------------- TBO-related --------------------------------------------
     def _forward_ms_op_shared_expert(
         self,

vllm_ascend/ops/fused_moe.py

@@ -1266,6 +1266,9 @@ def forward(
         if shared_experts:
             if not self.enable_multistream_moe or fused_moe_state != FusedMoEState.MC2:
                 shared_hidden_states = shared_experts(hidden_states)
+                if not shared_experts.down_proj.reduce_results and shared_experts.down_proj.tp_size > 1:
+                    shared_hidden_states = tensor_model_parallel_all_reduce(
+                        shared_hidden_states)
 
         mc2_mask = forward_context.mc2_mask
         tp_size = get_tensor_model_parallel_world_size()