Fix incorrect default get_hidden_dim logic (#8987)

python/sglang/srt/lora/utils.py

@@ -92,11 +92,30 @@ def get_hidden_dim(
         Please implement the function in the model class if it is not.
         You can reference this function in llama.py.
         """
-        if module_name in ["q_proj", "o_proj", "qkv_proj"]:
-            return config.hidden_size, config.hidden_size
-        elif module_name in ["kv_proj"]:
-            return config.hidden_size, config.hidden_size // (
-                config.num_attention_heads // config.num_key_value_heads
+        head_dim = getattr(
+            config, "head_dim", config.hidden_size // config.num_attention_heads
+        )
+
+        # TODO: the special handling of qkv will be addressed in #8940.
+        if module_name == "qkv_proj":
+            return (
+                config.hidden_size,
+                None,  # qkv_proj is only used in LoRA A
+            )
+        elif module_name == "kv_proj":
+            return (
+                None,  # kv_proj is only used in LoRA B
+                head_dim * config.num_key_value_heads,
+            )
+        elif module_name == "q_proj":
+            return (
+                None,  # q_proj is only used in LoRA B
+                head_dim * config.num_attention_heads,
+            )
+        elif module_name == "o_proj":
+            return (
+                head_dim * config.num_attention_heads,
+                config.hidden_size,
             )
         elif module_name == "gate_up_proj":
             return config.hidden_size, config.intermediate_size

python/sglang/srt/models/gemma2.py

@@ -432,40 +432,6 @@ class Gemma2ForCausalLM(nn.Module):
 
         return result
 
-    def get_hidden_dim(self, module_name):
-        # 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()
-
-    def get_module_name(self, name):
-        params_mapping = {
-            "q_proj": "qkv_proj",
-            "k_proj": "qkv_proj",
-            "v_proj": "qkv_proj",
-            "gate_proj": "gate_up_proj",
-            "up_proj": "gate_up_proj",
-        }
-        return params_mapping.get(name, name)
-
     def get_attention_sliding_window_size(self):
         return get_attention_sliding_window_size(self.config)
 

python/sglang/srt/models/gemma3n_mm.py

@@ -501,20 +501,26 @@ class Gemma3nForConditionalGeneration(PreTrainedModel):
 
     def get_hidden_dim(self, module_name):
         # return input_dim, output_dim
-        if module_name in ["q_proj", "qkv_proj"]:
+        # TODO: the special handling of qkv will be addressed in #8940.
+        if module_name == "qkv_proj":
             return (
                 self.config.hidden_size,
-                self.config.head_dim * self.config.num_attention_heads,
+                None,  # qkv_proj is only used in LoRA A
             )
-        elif module_name in ["o_proj"]:
+        elif module_name == "kv_proj":
+            return (
+                None,  # kv_proj is only used in LoRA B
+                self.config.head_dim * self.config.num_key_value_heads,
+            )
+        elif module_name == "q_proj":
             return (
+                None,  # q_proj is only used in LoRA B
                 self.config.head_dim * self.config.num_attention_heads,
-                self.config.hidden_size,
             )
-        elif module_name in ["kv_proj"]:
+        elif module_name in ["o_proj"]:
             return (
+                self.config.head_dim * self.config.num_attention_heads,
                 self.config.hidden_size,
-                self.config.head_dim * self.config.num_key_value_heads,
             )
         elif module_name == "gate_up_proj":
             assert len(set(self.config.intermediate_size)) == 1, (

python/sglang/srt/models/granite.py

@@ -363,31 +363,6 @@ class GraniteForCausalLM(nn.Module):
         else:
             return self.pooler(hidden_states, forward_batch)
 
-    def get_hidden_dim(self, module_name):
-        # return input_dim, output_dim
-        if module_name in ["q_proj", "o_proj", "qkv_proj"]:
-            return self.config.hidden_size, self.config.hidden_size
-        elif module_name in ["kv_proj"]:
-            return self.config.hidden_size, self.config.hidden_size // (
-                self.config.num_attention_heads // 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()
-
-    def get_module_name(self, name):
-        params_mapping = {
-            "q_proj": "qkv_proj",
-            "k_proj": "qkv_proj",
-            "v_proj": "qkv_proj",
-            "gate_proj": "gate_up_proj",
-            "up_proj": "gate_up_proj",
-        }
-        return params_mapping.get(name, name)
-
     def get_module_name_from_weight_name(self, name):
         for param_name, weight_name, shard_id, num_shard in self.stacked_params_mapping:
             if weight_name in name:

python/sglang/srt/models/llama.py

@@ -532,31 +532,6 @@ class LlamaForCausalLM(nn.Module):
     def get_input_embeddings(self) -> nn.Embedding:
         return self.model.embed_tokens
 
-    def get_hidden_dim(self, module_name):
-        # return input_dim, output_dim
-        if module_name in ["q_proj", "o_proj", "qkv_proj"]:
-            return self.config.hidden_size, self.config.hidden_size
-        elif module_name in ["kv_proj"]:
-            return self.config.hidden_size, self.config.hidden_size // (
-                self.config.num_attention_heads // 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()
-
-    def get_module_name(self, name):
-        params_mapping = {
-            "q_proj": "qkv_proj",
-            "k_proj": "qkv_proj",
-            "v_proj": "qkv_proj",
-            "gate_proj": "gate_up_proj",
-            "up_proj": "gate_up_proj",
-        }
-        return params_mapping.get(name, name)
-
     def get_module_name_from_weight_name(self, name):
         for param_name, weight_name, shard_id, num_shard in self.stacked_params_mapping:
             if weight_name in name:

python/sglang/srt/models/qwen3.py

@@ -330,30 +330,6 @@ 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/srt/models/torch_native_llama.py

@@ -416,30 +416,6 @@ class TorchNativeLlamaForCausalLM(nn.Module):
             input_ids, hidden_states, self.lm_head, forward_batch
         )
 
-    def get_hidden_dim(self, module_name):
-        if module_name in ["q_proj", "o_proj", "qkv_proj"]:
-            return self.config.hidden_size, self.config.hidden_size
-        elif module_name in ["kv_proj"]:
-            return self.config.hidden_size, self.config.hidden_size // (
-                self.config.num_attention_heads // 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()
-
-    def get_module_name(self, name):
-        params_mapping = {
-            "q_proj": "qkv_proj",
-            "k_proj": "qkv_proj",
-            "v_proj": "qkv_proj",
-            "gate_proj": "gate_up_proj",
-            "up_proj": "gate_up_proj",
-        }
-        return params_mapping.get(name, name)
-
     def get_module_name_from_weight_name(self, name):
         stacked_params_mapping = [
             # (param_name, shard_name, shard_id, num_shard)