Remove hybrid_kvcache_ratio in server args (#16399)

python/sglang/srt/configs/model_config.py

@@ -94,9 +94,6 @@ class ModelConfig:
         quantization: Optional[str] = None,
         override_config_file: Optional[str] = None,
         is_draft_model: bool = False,
-        hybrid_kvcache_ratio: Optional[
-            float
-        ] = None,  # TODO: remove this, it is not a model config
         model_impl: Union[str, ModelImpl] = ModelImpl.AUTO,
         sampling_defaults: str = "openai",
         quantize_and_serve: bool = False,
@@ -199,7 +196,7 @@ class ModelConfig:
         self._derive_model_shapes()
 
         # Update hybrid model
-        self._derive_hybrid_model(hybrid_kvcache_ratio)
+        self._derive_hybrid_model()
 
         # Verify quantization
         self._verify_quantization()
@@ -251,7 +248,6 @@ class ModelConfig:
             enable_multimodal=server_args.enable_multimodal,
             dtype=server_args.dtype,
             quantization=quantization,
-            hybrid_kvcache_ratio=server_args.hybrid_kvcache_ratio,
             model_impl=server_args.model_impl,
             sampling_defaults=server_args.sampling_defaults,
             quantize_and_serve=server_args.quantize_and_serve,
@@ -304,15 +300,11 @@ class ModelConfig:
             self.hf_config.architectures[0] = "Qwen3NextForCausalLMMTP"
             self.hf_config.num_nextn_predict_layers = 1
 
-    def _derive_hybrid_model(self, hybrid_kvcache_ratio: Optional[float] = None):
+    def _derive_hybrid_model(self):
         # Use self.context_len after it has been initialized to prevent using context_len which may be None.
-        self.is_hybrid_swa = is_hybrid_model(
-            self.hf_config.architectures,
-            hybrid_kvcache_ratio=hybrid_kvcache_ratio,
-            context_length=self.context_len,
-            attention_chunk_size=self.attention_chunk_size,
-        )
-        if self.is_hybrid_swa is not None:
+        self.is_hybrid_swa = is_hybrid_swa_model(self.hf_config.architectures)
+
+        if self.is_hybrid_swa:
             self.swa_attention_layer_ids, self.full_attention_layer_ids = (
                 get_hybrid_layer_ids(
                     self.hf_config.architectures,
@@ -1151,27 +1143,14 @@ def yarn_get_mscale(scale: float = 1, mscale: float = 1) -> float:
     return 0.1 * mscale * math.log(scale) + 1.0
 
 
-def is_hybrid_model(
-    model_architectures: List[str],
-    hybrid_kvcache_ratio: Optional[float],
-    context_length: Optional[int],
-    attention_chunk_size: Optional[int],
-):
-    if model_architectures[0] in [
+def is_hybrid_swa_model(model_architectures: List[str]):
+
+    hybrid_swa_archs = {
+        "Llama4ForConditionalGeneration",
         "MiMoV2FlashForCausalLM",
         "MiMoV2MTP",
-    ]:
-        return 1
-    if hybrid_kvcache_ratio is None:
-        return None
-    elif (
-        hybrid_kvcache_ratio > 0
-        and model_architectures[0] == "Llama4ForConditionalGeneration"
-        and context_length > attention_chunk_size
-    ):
-        return hybrid_kvcache_ratio
-    else:
-        return None
+    }
+    return any(arch in hybrid_swa_archs for arch in model_architectures)
 
 
 def get_hybrid_layer_ids(