Add Mistral Large 3 to nightly CI tests (#14459)

test/nightly/test_mistral_large3_perf.py

@@ -0,0 +1,105 @@
+import os
+import unittest
+from types import SimpleNamespace
+
+from nightly_utils import NightlyBenchmarkRunner
+
+from sglang.srt.utils import kill_process_tree
+from sglang.test.ci.ci_register import register_cuda_ci
+from sglang.test.run_eval import run_eval
+from sglang.test.test_utils import (
+    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+    DEFAULT_URL_FOR_TEST,
+    _parse_int_list_env,
+    popen_launch_server,
+)
+
+register_cuda_ci(est_time=600, suite="nightly-8-gpu-b200", nightly=True)
+
+MISTRAL_LARGE3_MODEL_PATH = "mistralai/Mistral-Large-3-675B-Instruct-2512"
+PROFILE_DIR = "performance_profiles_mistral_large3"
+
+
+class TestNightlyMistralLarge3Performance(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        # Set environment variable to disable JIT DeepGemm
+        os.environ["SGLANG_ENABLE_JIT_DEEPGEMM"] = "0"
+
+        cls.model = MISTRAL_LARGE3_MODEL_PATH
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.batch_sizes = [1, 1, 8, 16, 64]
+        cls.input_lens = tuple(_parse_int_list_env("NIGHTLY_INPUT_LENS", "4096"))
+        cls.output_lens = tuple(_parse_int_list_env("NIGHTLY_OUTPUT_LENS", "512"))
+
+        # Mistral-Large-3-675B requires TP=8 and trtllm_mla attention backend
+        cls.other_args = [
+            "--tp",
+            "8",
+            "--attention-backend",
+            "trtllm_mla",
+            "--model-loader-extra-config",
+            '{"enable_multithread_load": true}',
+            "--chat-template",
+            "mistral",
+        ]
+
+        cls.runner = NightlyBenchmarkRunner(PROFILE_DIR, cls.__name__, cls.base_url)
+        cls.runner.setup_profile_directory()
+
+    @classmethod
+    def tearDownClass(cls):
+        # Clean up environment variable
+        if "SGLANG_ENABLE_JIT_DEEPGEMM" in os.environ:
+            del os.environ["SGLANG_ENABLE_JIT_DEEPGEMM"]
+
+    def test_bench_one_batch(self):
+        results, success = self.runner.run_benchmark_for_model(
+            model_path=self.model,
+            batch_sizes=self.batch_sizes,
+            input_lens=self.input_lens,
+            output_lens=self.output_lens,
+            other_args=self.other_args,
+        )
+
+        self.runner.add_report(results)
+        self.runner.write_final_report()
+
+        if not success:
+            raise AssertionError(
+                f"Benchmark failed for {self.model}. Check the logs for details."
+            )
+
+    def test_accuracy_mgsm(self):
+        """Run MGSM accuracy evaluation for Mistral Large 3."""
+        process = popen_launch_server(
+            model=self.model,
+            base_url=self.base_url,
+            other_args=self.other_args,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+        )
+
+        try:
+            args = SimpleNamespace(
+                base_url=self.base_url,
+                model=self.model,
+                eval_name="mgsm_en",
+                num_examples=None,
+                num_threads=1024,
+            )
+            metrics = run_eval(args)
+            print(f"MGSM accuracy for {self.model}: {metrics['score']}")
+
+            # Placeholder threshold - adjust after first successful run
+            expected_threshold = 0.90
+            self.assertGreaterEqual(
+                metrics["score"],
+                expected_threshold,
+                f"MGSM accuracy {metrics['score']} below threshold {expected_threshold}",
+            )
+        finally:
+            kill_process_tree(process.pid)
+
+
+if __name__ == "__main__":
+    unittest.main()