[CI] Fix test cases (#2137)

python/sglang/srt/layers/attention/triton_ops/decode_attention.py

@@ -24,6 +24,8 @@ import triton.language as tl
 
 from sglang.srt.utils import is_hip
 
+is_hip_ = is_hip()
+
 
 @triton.jit
 def tanh(x):
@@ -501,7 +503,7 @@ def _decode_grouped_att_m_fwd(
     num_warps = 4
 
     extra_kargs = {}
-    if is_hip():
+    if is_hip_:
         # https://rocm.docs.amd.com/en/docs-6.2.0/how-to/llm-fine-tuning-optimization/optimizing-triton-kernel.html
         # https://github.com/triton-lang/triton/blob/main/third_party/amd/backend/compiler.py
         extra_kargs = {"waves_per_eu": 4, "matrix_instr_nonkdim": 16, "kpack": 2}
@@ -557,7 +559,7 @@ def _decode_grouped_softmax_reducev_fwd(
     BLOCK_DMODEL = triton.next_power_of_2(Lv)
 
     extra_kargs = {}
-    if is_hip():
+    if is_hip_:
         # https://rocm.docs.amd.com/en/docs-6.2.0/how-to/llm-fine-tuning-optimization/optimizing-triton-kernel.html
         # https://github.com/triton-lang/triton/blob/main/third_party/amd/backend/compiler.py
         extra_kargs = {"waves_per_eu": 4, "matrix_instr_nonkdim": 16, "kpack": 2}

python/sglang/srt/layers/attention/triton_ops/extend_attention.py

@@ -29,6 +29,8 @@ is_cuda_available = torch.cuda.is_available()
 if is_cuda_available:
     CUDA_CAPABILITY = torch.cuda.get_device_capability()
 
+is_hip_ = is_hip()
+
 
 @triton.jit
 def tanh(x):
@@ -311,7 +313,7 @@ def extend_attention_fwd(
     num_stages = 1
 
     extra_kargs = {}
-    if is_hip():
+    if is_hip_:
         extra_kargs = {"waves_per_eu": 4, "matrix_instr_nonkdim": 16, "kpack": 2}
 
     _fwd_kernel[grid](

python/sglang/srt/model_executor/model_runner.py

@@ -242,15 +242,17 @@ class ModelRunner:
             )
             return get_model(vllm_config=vllm_config)
         except ImportError:
-            return get_model(
-                model_config=self.vllm_model_config,
-                load_config=self.load_config,
-                device_config=DeviceConfig(self.device),
-                parallel_config=None,
-                scheduler_config=None,
-                lora_config=None,
-                cache_config=None,
-            )
+            pass
+
+        return get_model(
+            model_config=self.vllm_model_config,
+            load_config=self.load_config,
+            device_config=DeviceConfig(self.device),
+            parallel_config=None,
+            scheduler_config=None,
+            lora_config=None,
+            cache_config=None,
+        )
 
     def get_model_config_params(self):
         sig = inspect.signature(VllmModelConfig.__init__)