Adding nightly tests for Kimi-K2-thinking, Qwen3, minimax-m2, GLM4.6 (#13890)

test/nightly/test_glm_4_6_perf.py

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+import unittest
+
+from nightly_utils import NightlyBenchmarkRunner
+
+from sglang.test.test_utils import DEFAULT_URL_FOR_TEST, _parse_int_list_env
+
+GLM_4_6_MODEL_PATH = "zai-org/GLM-4.6"
+PROFILE_DIR = "performance_profiles_glm_4_6"
+
+
+class TestNightlyGLM46Performance(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.model = GLM_4_6_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"))
+
+        # GLM-4.6 is a 357B MoE model
+        cls.other_args = [
+            "--tp",
+            "8",
+            "--trust-remote-code",
+        ]
+
+        cls.runner = NightlyBenchmarkRunner(PROFILE_DIR, cls.__name__, cls.base_url)
+        cls.runner.setup_profile_directory()
+
+    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."
+            )
+
+
+if __name__ == "__main__":
+    unittest.main()

test/nightly/test_kimi_k2_thinking_perf.py

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+import unittest
+
+from nightly_utils import NightlyBenchmarkRunner
+
+from sglang.test.test_utils import DEFAULT_URL_FOR_TEST, _parse_int_list_env
+
+KIMI_K2_THINKING_MODEL_PATH = "moonshotai/Kimi-K2-Thinking"
+PROFILE_DIR = "performance_profiles_kimi_k2_thinking"
+
+
+class TestNightlyKimiK2ThinkingPerformance(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.model = KIMI_K2_THINKING_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"))
+
+        # Kimi-K2-Thinking requires specific launch arguments
+        cls.other_args = [
+            "--tp",
+            "8",
+            "--trust-remote-code",
+            "--tool-call-parser",
+            "kimi_k2",
+            "--reasoning-parser",
+            "kimi_k2",
+        ]
+
+        cls.runner = NightlyBenchmarkRunner(PROFILE_DIR, cls.__name__, cls.base_url)
+        cls.runner.setup_profile_directory()
+
+    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,
+            extra_bench_args=["--trust-remote-code"],
+        )
+
+        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."
+            )
+
+
+if __name__ == "__main__":
+    unittest.main()

test/nightly/test_minimax_m2_perf.py

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+import unittest
+
+from nightly_utils import NightlyBenchmarkRunner
+
+from sglang.test.test_utils import DEFAULT_URL_FOR_TEST, _parse_int_list_env
+
+MINIMAX_M2_MODEL_PATH = "MiniMaxAI/MiniMax-M2"
+PROFILE_DIR = "performance_profiles_minimax_m2"
+
+
+class TestNightlyMiniMaxM2Performance(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.model = MINIMAX_M2_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"))
+
+        # MiniMax-M2 is a 230B MoE model with 10B active params
+        cls.other_args = [
+            "--tp",
+            "8",
+            "--trust-remote-code",
+        ]
+
+        cls.runner = NightlyBenchmarkRunner(PROFILE_DIR, cls.__name__, cls.base_url)
+        cls.runner.setup_profile_directory()
+
+    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."
+            )
+
+
+if __name__ == "__main__":
+    unittest.main()

test/nightly/test_qwen3_235b_perf.py

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+import unittest
+
+from nightly_utils import NightlyBenchmarkRunner
+
+from sglang.test.test_utils import DEFAULT_URL_FOR_TEST, _parse_int_list_env
+
+QWEN3_235B_MODEL_PATH = "Qwen/Qwen3-235B-A22B-Instruct-2507"
+PROFILE_DIR = "performance_profiles_qwen3_235b"
+
+
+class TestNightlyQwen3235BPerformance(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.model = QWEN3_235B_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"))
+
+        # Qwen3-235B requires TP=8 for 8 GPUs
+        cls.other_args = [
+            "--tp",
+            "8",
+            "--trust-remote-code",
+        ]
+
+        cls.runner = NightlyBenchmarkRunner(PROFILE_DIR, cls.__name__, cls.base_url)
+        cls.runner.setup_profile_directory()
+
+    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."
+            )
+
+
+if __name__ == "__main__":
+    unittest.main()