support Llama4 with non uniformed intermediate size across layers for… (#10047)

python/sglang/srt/lora/mem_pool.py

@@ -104,12 +104,18 @@ class LoRAMemoryPool:
             return all(_can_support(x) for x in config)
 
     def get_lora_A_shape(
-        self, module_name: str, base_model: torch.nn.Module, max_lora_dim: int
+        self,
+        module_name: str,
+        base_model: torch.nn.Module,
+        max_lora_dim: int,
+        layer_idx: int,
     ) -> Tuple[int]:
         """
         Given a module_name (might be a stacked name), return the hidden dims of modules' input and output.
         """
-        input_dim, _ = get_hidden_dim(module_name, self.base_hf_config, base_model)
+        input_dim, _ = get_hidden_dim(
+            module_name, self.base_hf_config, base_model, layer_idx
+        )
         c = get_stacked_multiply(module_name)
         if self.tp_size > 1 and module_name in ROW_PARALLELISM_LINEAR_LORA_NAMES:
             input_dim = divide(input_dim, self.tp_size)
@@ -120,12 +126,18 @@ class LoRAMemoryPool:
         )
 
     def get_lora_B_shape(
-        self, module_name: str, base_model: torch.nn.Module, max_lora_dim: int
+        self,
+        module_name: str,
+        base_model: torch.nn.Module,
+        max_lora_dim: int,
+        layer_idx: int,
     ) -> Tuple[int]:
         """
         Given a module_name (might be a stacked name), return the hidden dims of modules' input and output.
         """
-        _, output_dim = get_hidden_dim(module_name, self.base_hf_config, base_model)
+        _, output_dim = get_hidden_dim(
+            module_name, self.base_hf_config, base_model, layer_idx
+        )
         if self.tp_size > 1 and module_name not in ROW_PARALLELISM_LINEAR_LORA_NAMES:
             output_dim = divide(output_dim, self.tp_size)
         return (
@@ -140,19 +152,21 @@ class LoRAMemoryPool:
         def init_buffer(
             buffer: Dict[str, List[torch.Tensor]],
             target_modules: Set[str],
-            get_lora_shape_fn: Callable[[str, torch.nn.Module, int], Tuple[int]],
+            get_lora_shape_fn: Callable[[str, torch.nn.Module, int, int], Tuple[int]],
         ):
             for module_name in target_modules:
-                lora_shape = get_lora_shape_fn(
-                    module_name, base_model, self.max_lora_rank
-                )
                 buffer[module_name] = [
                     torch.empty(
-                        lora_shape,
+                        get_lora_shape_fn(
+                            module_name,
+                            base_model,
+                            self.max_lora_rank,
+                            idx,
+                        ),
                         dtype=self.dtype,
                         device=device,
                     )
-                    for _ in range(self.num_layer)
+                    for idx in range(self.num_layer)
                 ]
 
         init_buffer(

python/sglang/srt/lora/utils.py

@@ -48,14 +48,14 @@ def get_layer_id(name: str) -> int:
 
 
 def get_hidden_dim(
-    module_name: str, config: AutoConfig, base_model: torch.nn.Module
+    module_name: str, config: AutoConfig, base_model: torch.nn.Module, layer_idx: int
 ) -> Tuple[int]:
     """
     Given a module_name (might be a stacked name), return the hidden dims of modules' input and output.
     """
 
     if hasattr(base_model, "get_hidden_dim"):
-        return base_model.get_hidden_dim(module_name)
+        return base_model.get_hidden_dim(module_name, layer_idx)
     else:
         """
         WARNING: get_hidden_dim() is not defined,

python/sglang/srt/models/gemma3n_mm.py

@@ -499,7 +499,7 @@ class Gemma3nForConditionalGeneration(PreTrainedModel):
     def should_apply_lora(self, module_name: str) -> bool:
         return bool(self.lora_pattern.match(module_name))
 
-    def get_hidden_dim(self, module_name):
+    def get_hidden_dim(self, module_name, layer_idx):
         # return input_dim, output_dim
         if module_name == "qkv_proj":
             return (

python/sglang/srt/models/llama4.py

@@ -423,6 +423,12 @@ class Llama4DecoderLayer(nn.Module):
             return self.config.num_local_experts > 0
         return (layer_id + 1) % self.config.interleave_moe_layer_step == 0
 
+    def get_intermediate_size(self) -> int:
+        if isinstance(self.feed_forward, Llama4MoE):
+            return self.config.intermediate_size
+        else:
+            return self.config.intermediate_size_mlp
+
     def forward(
         self,
         positions: torch.Tensor,
@@ -540,6 +546,9 @@ class Llama4ForCausalLM(LlamaForCausalLM):
     def get_input_embeddings(self):
         return self.model.embed_tokens
 
+    def get_layers(self):
+        return self.model.layers
+
     def _init_model(
         self,
         config: Llama4TextConfig,

python/sglang/srt/models/mllama4.py

@@ -961,5 +961,30 @@ class Llama4ForConditionalGeneration(nn.Module):
     def set_embed(self, embed):
         return self.language_model.set_embed(embed)
 
+    def get_hidden_dim(self, module_name, layer_idx):
+        # return input_dim, output_dim
+        if module_name == "qkv_proj":
+            return (
+                self.config.hidden_size,
+                self.config.head_dim
+                * (
+                    self.config.num_attention_heads
+                    + self.config.num_key_value_heads * 2
+                ),
+            )
+        elif module_name == "o_proj":
+            return (
+                self.config.head_dim * self.config.num_attention_heads,
+                self.config.hidden_size,
+            )
+        elif module_name == "gate_up_proj":
+            return self.config.hidden_size, self.config.intermediate_size * 2
+        elif module_name == "down_proj":
+            decoder_layer = self.language_model.get_layers()[layer_idx]
+            intermediate_size = decoder_layer.get_intermediate_size()
+            return intermediate_size, self.config.hidden_size
+        else:
+            raise NotImplementedError()
+
 
 EntryClass = Llama4ForConditionalGeneration

test/srt/lora/test_lora_llama4.py

+import unittest
+from types import SimpleNamespace
+
+from sglang.srt.utils import kill_process_tree
+from sglang.test.test_utils import (
+    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+    DEFAULT_URL_FOR_TEST,
+    CustomTestCase,
+    popen_launch_server,
+)
+
+MODELS = [
+    SimpleNamespace(
+        model="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
+        tp_size=8,
+    ),
+]
+
+
+class TestLlama4LoRA(CustomTestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.base_url = DEFAULT_URL_FOR_TEST
+
+    def test_bringup(self):
+        for model in MODELS:
+            try:
+                process = popen_launch_server(
+                    model.model,
+                    self.base_url,
+                    timeout=3 * DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+                    other_args=[
+                        "--enable-lora",
+                        "--max-lora-rank",
+                        "64",
+                        "--lora-target-modules",
+                        "all",
+                        "--tp-size",
+                        str(model.tp_size),
+                        "--context-length",
+                        "1048576",
+                        "--attention-backend",
+                        "fa3",
+                    ],
+                )
+            except Exception as e:
+                print(f"Error testing {model.model}: {e}")
+                self.fail(f"Test failed for {model.model}: {e}")
+
+            finally:
+                # Ensure process cleanup happens regardless of success/failure
+                if process is not None and process.poll() is None:
+                    print(f"Cleaning up process {process.pid}")
+                    try:
+                        kill_process_tree(process.pid)
+                    except Exception as e:
+                        print(f"Error killing process: {e}")
+
+
+if __name__ == "__main__":
+    unittest.main()

test/srt/run_suite.py

@@ -136,6 +136,7 @@ suites = {
     "per-commit-8-gpu": [
         # Disabled because it hangs on the CI.
         # TestFile("ep/test_moe_ep.py", 181),
+        TestFile("lora/test_lora_llama4.py", 600),
         TestFile("test_disaggregation.py", 499),
         TestFile("test_disaggregation_different_tp.py", 155),
         TestFile("test_full_deepseek_v3.py", 333),