[Bugfix][Qwen3-Next] add prefixes to shared_expert in qwen3-next and mlp in...

[Bugfix][Qwen3-Next] add prefixes to shared_expert in qwen3-next and mlp in qwen2moe to successfully load ignored params in quantized models (#24960)
Signed-off-by: toncao <cpatonn@gmail.com>
Co-authored-by: toncao <cpatonn@gmail.com>
Co-authored-by: Jee Jee Li <pandaleefree@gmail.com>

vllm/model_executor/models/qwen2_moe.py

@@ -72,17 +72,20 @@ class Qwen2MoeMLP(nn.Module):
         hidden_act: str,
         quant_config: Optional[QuantizationConfig] = None,
         reduce_results: bool = True,
+        prefix: str = "",
     ) -> None:
         super().__init__()
         self.gate_up_proj = MergedColumnParallelLinear(
             hidden_size, [intermediate_size] * 2,
             bias=False,
-            quant_config=quant_config)
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj")
         self.down_proj = RowParallelLinear(intermediate_size,
                                            hidden_size,
                                            bias=False,
                                            quant_config=quant_config,
-                                           reduce_results=reduce_results)
+                                           reduce_results=reduce_results,
+                                           prefix=f"{prefix}.down_proj")
         if hidden_act != "silu":
             raise ValueError(f"Unsupported activation: {hidden_act}. "
                              "Only silu is supported for now.")
@@ -123,7 +126,8 @@ class Qwen2MoeSparseMoeBlock(nn.Module):
         self.gate = ReplicatedLinear(config.hidden_size,
                                      config.num_experts,
                                      bias=False,
-                                     quant_config=None)
+                                     quant_config=None,
+                                     prefix=f"{prefix}.gate")
         if config.shared_expert_intermediate_size > 0:
             self.shared_expert = Qwen2MoeMLP(
                 hidden_size=config.hidden_size,
@@ -132,6 +136,7 @@ class Qwen2MoeSparseMoeBlock(nn.Module):
                 quant_config=quant_config,
                 reduce_results=self.experts.must_reduce_shared_expert_outputs(
                 ),
+                prefix=f"{prefix}.shared_expert",
             )
         else:
             self.shared_expert = None
@@ -203,21 +208,19 @@ class Qwen2MoeAttention(nn.Module):
         self.max_position_embeddings = max_position_embeddings
         self.dual_chunk_attention_config = dual_chunk_attention_config
 
-        self.qkv_proj = QKVParallelLinear(
-            hidden_size,
-            self.head_dim,
-            self.total_num_heads,
-            self.total_num_kv_heads,
-            bias=True,
-            quant_config=quant_config,
-        )
+        self.qkv_proj = QKVParallelLinear(hidden_size,
+                                          self.head_dim,
+                                          self.total_num_heads,
+                                          self.total_num_kv_heads,
+                                          bias=True,
+                                          quant_config=quant_config,
+                                          prefix=f"{prefix}.qkv_proj")
 
-        self.o_proj = RowParallelLinear(
-            self.total_num_heads * self.head_dim,
-            hidden_size,
-            bias=False,
-            quant_config=quant_config,
-        )
+        self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
+                                        hidden_size,
+                                        bias=False,
+                                        quant_config=quant_config,
+                                        prefix=f"{prefix}.o_proj")
 
         self.rotary_emb = get_rope(
             self.head_dim,
@@ -296,12 +299,11 @@ class Qwen2MoeDecoderLayer(nn.Module):
                                               quant_config=quant_config,
                                               prefix=f"{prefix}.mlp")
         else:
-            self.mlp = Qwen2MoeMLP(
-                hidden_size=config.hidden_size,
-                intermediate_size=config.intermediate_size,
-                hidden_act=config.hidden_act,
-                quant_config=quant_config,
-            )
+            self.mlp = Qwen2MoeMLP(hidden_size=config.hidden_size,
+                                   intermediate_size=config.intermediate_size,
+                                   hidden_act=config.hidden_act,
+                                   quant_config=quant_config,
+                                   prefix=f"{prefix}.mlp")
         self.input_layernorm = RMSNorm(config.hidden_size,
                                        eps=config.rms_norm_eps)
         self.post_attention_layernorm = RMSNorm(config.hidden_size,

vllm/model_executor/models/qwen3_next.py

@@ -138,6 +138,7 @@ class Qwen3NextSparseMoeBlock(nn.Module):
                 quant_config=quant_config,
                 reduce_results=self.experts.must_reduce_shared_expert_outputs(
                 ),
+                prefix=f"{prefix}.shared_expert",
             )
         else:
             self.shared_expert = None