Use swa radix cache and memory pool for gpt-oss model

python/sglang/srt/configs/model_config.py

@@ -313,8 +313,7 @@ def _derive_hybrid_model(self):
             self.swa_attention_layer_ids, self.full_attention_layer_ids = (
                 get_hybrid_layer_ids(
                     self.hf_config.architectures,
-                    self.hf_text_config.num_hidden_layers,
-                    getattr(self.hf_text_config, "hybrid_layer_pattern", None),
+                    self.hf_text_config,
                 )
             )
 
@@ -1197,6 +1196,7 @@ def is_hybrid_swa_model(model_architectures: List[str]):
 
     hybrid_swa_archs = {
         "Llama4ForConditionalGeneration",
+        "GptOssForCausalLM",
         "MiMoV2FlashForCausalLM",
         "MiMoV2MTP",
     }
@@ -1205,17 +1205,26 @@ def is_hybrid_swa_model(model_architectures: List[str]):
 
 def get_hybrid_layer_ids(
     model_architectures: List[str],
-    num_hidden_layers: int,
-    hybrid_layer_pattern: Optional[List[int]] = None,
+    hf_text_config: PretrainedConfig,
 ):
+    num_hidden_layers = hf_text_config.num_hidden_layers
     if "Llama4ForConditionalGeneration" in model_architectures:
         swa_attention_layer_ids = [
             i for i in range(num_hidden_layers) if (i + 1) % 4 != 0
         ]
         full_attention_layer_ids = [
             i for i in range(num_hidden_layers) if (i + 1) % 4 == 0
         ]
+    elif "GptOssForCausalLM" in model_architectures:
+        layer_types = getattr(hf_text_config, "layer_types", None)
+        swa_attention_layer_ids = [
+            i for i, x in enumerate(layer_types) if x == "sliding_attention"
+        ]
+        full_attention_layer_ids = [
+            i for i, x in enumerate(layer_types) if x == "full_attention"
+        ]
     elif "MiMoV2FlashForCausalLM" in model_architectures:
+        hybrid_layer_pattern = getattr(hf_text_config, "hybrid_layer_pattern", None)
         swa_attention_layer_ids = [
             i for i in range(num_hidden_layers) if hybrid_layer_pattern[i] == 1
         ]

python/sglang/srt/models/utils.py

@@ -24,6 +24,7 @@
 from sglang.jit_kernel.norm import can_use_fused_inplace_qknorm, fused_inplace_qknorm
 from sglang.srt.environ import envs
 from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.mem_cache.swa_memory_pool import SWAKVPool
 from sglang.srt.model_executor.cuda_graph_runner import get_is_capture_mode
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.utils import get_current_device_stream_fast, is_cuda
@@ -109,6 +110,7 @@ def enable_fused_set_kv_buffer(forward_batch: ForwardBatch):
         _is_cuda
         and hasattr(forward_batch.token_to_kv_pool, "dtype")
         and forward_batch.token_to_kv_pool.dtype == torch.bfloat16
+        and not isinstance(forward_batch.token_to_kv_pool, SWAKVPool)
     )
 
 

python/sglang/srt/server_args.py

@@ -1261,6 +1261,16 @@ def _handle_model_specific_adjustments(self):
                 f"- Decode: {decode_attn_backend}\n"
             )
 
+            if (
+                prefill_attn_backend == "trtllm_mha"
+                or decode_attn_backend == "trtllm_mha"
+            ):
+                # TODO: support swa kv indices translation for trtllm_mha attention backend
+                self.swa_full_tokens_ratio = 1.0
+                logger.warning(
+                    "Set swa_full_tokens_ratio to 1.0 for GPT-OSS model with trtllm_mha attention backend."
+                )
+
             quant_method = get_quantization_config(hf_config)
             is_mxfp4_quant_format = quant_method == "mxfp4"
             if is_mxfp4_quant_format:
@@ -1288,7 +1298,6 @@ def _handle_model_specific_adjustments(self):
                 assert (
                     self.ep_size == 1
                 ), "Triton kernel MoE is only supported when ep_size == 1"
-            self.disable_hybrid_swa_memory = True
 
         elif "MiMoV2FlashForCausalLM" in model_arch:
             if self.speculative_algorithm == "EAGLE":