[Bugfix] Fix Qwen3 MoE GPTQ inference (#23490)

Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>

Author: Isotr0py

vllm/model_executor/models/qwen3_moe.py

@@ -45,6 +45,9 @@
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.quantization.gptq import GPTQConfig
+from vllm.model_executor.layers.quantization.gptq_marlin import (
+    GPTQMarlinConfig)
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead, VocabParallelEmbedding)
@@ -146,11 +149,20 @@ def __init__(
                                 enable_eplb=self.enable_eplb,
                                 num_redundant_experts=self.n_redundant_experts)
 
-        self.gate = ReplicatedLinear(config.hidden_size,
-                                     config.num_experts,
-                                     bias=False,
-                                     quant_config=quant_config,
-                                     prefix=f"{prefix}.gate")
+        self.gate = ReplicatedLinear(
+            config.hidden_size,
+            config.num_experts,
+            bias=False,
+            quant_config=self._maybe_ignore_quant_config(quant_config),
+            prefix=f"{prefix}.gate")
+
+    def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig):
+        # GPTQ configs do not have a list of ignored modules, however AutoGPTQ
+        # seems to avoid gate quantization.
+        # See: https://huggingface.co/Qwen/Qwen3-30B-A3B-GPTQ-Int4
+        if isinstance(quant_config, (GPTQConfig, GPTQMarlinConfig)):
+            return None
+        return quant_config
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         # NOTE: hidden_states can have either 1D or 2D shape.
@@ -682,4 +694,4 @@ def load_weights(self, weights: Iterable[tuple[str,
         return loader.load_weights(weights)
 
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
-        return self.model.get_expert_mapping()
+        return self.model.get_expert_mapping()