[CI] Add Mistral Large 3 Eagle nightly performance test (#14525)

.github/workflows/nightly-test-nvidia.yml

@@ -432,6 +432,7 @@ jobs:
           SGLANG_ENABLE_JIT_DEEPGEMM: "0"
         run: |
           rm -rf test/performance_profiles_mistral_large3/
+          rm -rf test/performance_profiles_mistral_large3_eagle/
           cd test
           IS_BLACKWELL=1 python3 nightly/test_mistral_large3_perf.py
 
@@ -442,6 +443,7 @@ jobs:
           GITHUB_RUN_NUMBER: ${{ github.run_number }}
         run: |
           python3 scripts/ci/publish_traces.py --traces-dir test/performance_profiles_mistral_large3
+          python3 scripts/ci/publish_traces.py --traces-dir test/performance_profiles_mistral_large3_eagle
 
       - name: Run DeepSeek v3.1 nightly performance test
         if: always()

test/nightly/test_mistral_large3_perf.py

@@ -17,6 +17,7 @@ from sglang.test.test_utils import (
 register_cuda_ci(est_time=600, suite="nightly-8-gpu-b200", nightly=True)
 
 MISTRAL_LARGE3_MODEL_PATH = "mistralai/Mistral-Large-3-675B-Instruct-2512"
+MISTRAL_LARGE3_EAGLE_MODEL_PATH = "mistralai/Mistral-Large-3-675B-Instruct-2512-Eagle"
 PROFILE_DIR = "performance_profiles_mistral_large3"
 
 
@@ -101,5 +102,103 @@ class TestNightlyMistralLarge3Performance(unittest.TestCase):
             kill_process_tree(process.pid)
 
 
+class TestNightlyMistralLarge3EaglePerformance(unittest.TestCase):
+    """Test Mistral Large 3 with Eagle speculative decoding."""
+
+    @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 with Eagle speculative decoding
+        # Eagle model is used as draft model for speculative decoding
+        cls.other_args = [
+            "--tp",
+            "8",
+            "--attention-backend",
+            "trtllm_mla",
+            "--speculative-algorithm",
+            "EAGLE",
+            "--speculative-draft-model-path",
+            MISTRAL_LARGE3_EAGLE_MODEL_PATH,
+            "--speculative-num-steps",
+            "3",
+            "--speculative-eagle-topk",
+            "1",
+            "--speculative-num-draft-tokens",
+            "4",
+            "--kv-cache-dtype",
+            "auto",
+            "--model-loader-extra-config",
+            '{"enable_multithread_load": true}',
+            "--chat-template",
+            "mistral",
+        ]
+
+        cls.runner = NightlyBenchmarkRunner(
+            "performance_profiles_mistral_large3_eagle", 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_eagle_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} with Eagle. Check the logs for details."
+            )
+
+    def test_eagle_accuracy_mgsm(self):
+        """Run MGSM accuracy evaluation for Mistral Large 3 with Eagle."""
+        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} with Eagle: {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()