fix qwen3-235b run error and remove update_environment_variables of hip

vllm/executor/mp_distributed_executor.py

@@ -52,9 +52,6 @@ class MultiprocessingDistributedExecutor(DistributedExecutorBase):
             update_environment_variables({
                 "CUDA_VISIBLE_DEVICES": (",".join(map(str, range(world_size))))
             })
-            update_environment_variables({
-                "HIP_VISIBLE_DEVICES": (",".join(map(str, range(world_size))))
-            })
 
     def _init_executor(self) -> None:
 

vllm/model_executor/model_loader/utils.py

@@ -89,7 +89,8 @@ def get_model_architecture(
         model_config: ModelConfig) -> Tuple[Type[nn.Module], str]:
     architectures = getattr(model_config.hf_config, "architectures", [])
     visions = getattr(model_config.hf_config, "visual", []) or getattr(model_config.hf_config, "vision_config", [])
-    support_nn_architectures = ['LlamaForCausalLM', 'QWenLMHeadModel', 'Qwen2ForCausalLM', 'Qwen2VLForConditionalGeneration', 'Qwen2_5_VLForConditionalGeneration', 'Qwen2MoeForCausalLM', 'Qwen3ForCausalLM', 'Qwen3MoeForCausalLM',
+    # TODO: 'Qwen2_5_VLForConditionalGeneration', 
+    support_nn_architectures = ['LlamaForCausalLM', 'QWenLMHeadModel', 'Qwen2ForCausalLM', 'Qwen2VLForConditionalGeneration', 'Qwen2MoeForCausalLM', 'Qwen3ForCausalLM', 'Qwen3MoeForCausalLM',
                                 'ChatGLMModel', 'Glm4ForCausalLM', 'ChatGLMForConditionalGeneration', 'BaichuanForCausalLM', 'BloomForCausalLM', 'TeleChat2ForCausalLM', 'MixtralForCausalLM', 'FalconForCausalLM',
                                 'MedusaModel', 'MLPSpeculatorPreTrainedModel', 'DeepseekV2ForCausalLM', 'DeepseekV3ForCausalLM', 'DeepSeekMTPModel']  
     if any(arch in architectures for arch in support_nn_architectures): 

vllm/model_executor/models/qwen3_moe.py

@@ -414,6 +414,9 @@ class Qwen3MoeModel(nn.Module):
         params_dict = dict(self.named_parameters())
         loaded_params: Set[str] = set()
         for name, loaded_weight in weights:
+            if self.use_llama_nn:
+                current_count = loaded_weight.current_count 
+                total_count = loaded_weight.total_count
             for (param_name, weight_name, shard_id) in stacked_params_mapping:
                 # Skip non-stacked layers and experts (experts handled below).
                 if weight_name not in name:
@@ -490,7 +493,7 @@ class Qwen3MoeModel(nn.Module):
                     weight_loader(param, loaded_weight)
             loaded_params.add(name)
         
-        if self.use_llama_nn and self.quant_method is None:
+        if self.use_llama_nn and self.quant_method is None and current_count==total_count:
             lay_key_words = [
                 "gate_up_proj.weight",
                 "down_proj.weight",

vllm/utils.py

@@ -690,7 +690,7 @@ def update_environment_variables(envs: dict[str, str]):
             logger.warning(
                 "Overwriting environment variable %s "
                 "from '%s' to '%s'", k, os.environ[k], v)
-        # os.environ[k] = v
+        os.environ[k] = v
 
 
 def chunk_list(lst: list[T], chunk_size: int):