ci: adding llama4 placeholder test to nightly (#16599)

test/registered/8-gpu-models/test_llama4.py

@@ -0,0 +1,55 @@
+import unittest
+
+from sglang.test.accuracy_test_runner import AccuracyTestParams
+from sglang.test.ci.ci_register import register_cuda_ci
+from sglang.test.performance_test_runner import PerformanceTestParams
+from sglang.test.run_combined_tests import run_combined_tests
+from sglang.test.test_utils import ModelLaunchSettings
+
+# Runs on both H200 and B200 via nightly-8-gpu-common suite
+register_cuda_ci(est_time=12000, suite="nightly-8-gpu-common", nightly=True)
+
+LLAMA4_MODEL_PATH = "meta-llama/Llama-4-Scout-17B-16E-Instruct"
+
+
+class TestLlama4Unified(unittest.TestCase):
+    """Unified test class for Llama-4-Scout performance and accuracy.
+
+    Llama4 has local attention mechanism with hybrid sliding window attention.
+    Single variant with TP=8 configuration.
+    Runs BOTH:
+    - Performance test (using NightlyBenchmarkRunner)
+    - Accuracy test (using run_eval with gsm8k)
+    """
+
+    def test_llama4(self):
+        """Run performance and accuracy for Llama-4-Scout."""
+        base_args = [
+            "--tp=8",
+            "--trust-remote-code",
+            "--chat-template=llama-4",
+            "--mem-fraction-static=0.8",
+            "--context-length=1000000",
+        ]
+
+        variants = [
+            ModelLaunchSettings(
+                LLAMA4_MODEL_PATH,
+                tp_size=8,
+                extra_args=base_args,
+                variant="TP8",
+            ),
+        ]
+
+        run_combined_tests(
+            models=variants,
+            test_name="Llama-4-Scout Unified",
+            accuracy_params=AccuracyTestParams(dataset="gsm8k", baseline_accuracy=0.9),
+            performance_params=PerformanceTestParams(
+                profile_dir="performance_profiles_llama4",
+            ),
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()