Add get_hidden_dim to qwen3.py for correct lora (#7312)

python/sglang/srt/models/qwen3.py

@@ -330,6 +330,30 @@ class Qwen3ForCausalLM(nn.Module):
     def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.model.get_input_embeddings(input_ids)
 
+    def get_hidden_dim(self, module_name: str) -> Tuple[int]:
+        # return input_dim, output_dim
+        if module_name in ["q_proj", "qkv_proj"]:
+            return (
+                self.config.hidden_size,
+                self.config.head_dim * self.config.num_attention_heads,
+            )
+        elif module_name in ["o_proj"]:
+            return (
+                self.config.head_dim * self.config.num_attention_heads,
+                self.config.hidden_size,
+            )
+        elif module_name in ["kv_proj"]:
+            return (
+                self.config.hidden_size,
+                self.config.head_dim * self.config.num_key_value_heads,
+            )
+        elif module_name == "gate_up_proj":
+            return self.config.hidden_size, self.config.intermediate_size
+        elif module_name == "down_proj":
+            return self.config.intermediate_size, self.config.hidden_size
+        else:
+            raise NotImplementedError()
+
     @torch.no_grad()
     def forward(
         self,

python/sglang/test/runners.py

@@ -134,10 +134,12 @@ class HFRunner:
         model_type: str = "generation",
         output_str_only: bool = False,
         trust_remote_code: bool = False,
+        patch_model_do_sample_false: bool = False,
     ):
         self.model_type = model_type
         self.output_str_only = output_str_only
         self.trust_remote_code = trust_remote_code
+        self.patch_model_do_sample_false = patch_model_do_sample_false
 
         self.in_queue = mp.Queue()
         self.out_queue = mp.Queue()
@@ -292,6 +294,7 @@ class HFRunner:
                             torch_dtype=torch_dtype,
                             output_str_only=self.output_str_only,
                             token_ids_logprob=token_ids_logprob,
+                            patch_model_do_sample_false=self.patch_model_do_sample_false,
                         )
                     )
                 elif self.model_type == "embedding":
@@ -380,6 +383,7 @@ class HFRunner:
         lora_paths: Optional[List[str]] = None,
         output_str_only: bool = False,
         token_ids_logprob: Optional[int] = None,
+        patch_model_do_sample_false: Optional[bool] = False,
     ) -> ModelOutput:
         output_strs = []
         top_input_logprobs = []
@@ -407,7 +411,8 @@ class HFRunner:
                 )
             else:
                 model = base_model
-
+            if patch_model_do_sample_false:
+                model.generation_config.do_sample = False
             outputs = model.generate(
                 input_ids=input_ids,
                 generation_config=GenerationConfig(