import unittest from types import SimpleNamespace import requests from sglang.srt.utils import kill_process_tree from sglang.test.ci.ci_register import register_amd_ci, register_cuda_ci from sglang.test.run_eval import run_eval from sglang.test.test_utils import ( DEFAULT_MODEL_NAME_FOR_TEST, DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH, DEFAULT_URL_FOR_TEST, CustomTestCase, popen_launch_server, ) register_cuda_ci(est_time=66, suite="stage-b-test-small-1-gpu") register_amd_ci(est_time=66, suite="stage-b-test-small-1-gpu-amd") class TestPyTorchSamplingBackend(CustomTestCase): @classmethod def setUpClass(cls): cls.model = DEFAULT_MODEL_NAME_FOR_TEST cls.base_url = DEFAULT_URL_FOR_TEST cls.process = popen_launch_server( cls.model, cls.base_url, timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH, other_args=["--sampling-backend", "pytorch", "--disable-radix-cache"], ) @classmethod def tearDownClass(cls): kill_process_tree(cls.process.pid) def test_mmlu(self): args = SimpleNamespace( base_url=self.base_url, model=self.model, eval_name="mmlu", num_examples=64, num_threads=32, temperature=0.1, ) metrics = run_eval(args) self.assertGreaterEqual(metrics["score"], 0.65) def test_greedy(self): first_text = None # ensure the answer is identical across single response for _ in range(5): response_single = requests.post( self.base_url + "/generate", json={ "text": "The capital of Germany is", "sampling_params": { "temperature": 0, "max_new_tokens": 32, }, }, ).json() text = response_single["text"] if first_text is None: first_text = text self.assertEqual(text, first_text) first_text = None response_batch = requests.post( self.base_url + "/generate", json={ "text": ["The capital of Germany is"] * 10, "sampling_params": { "temperature": 0, "max_new_tokens": 32, }, }, ).json() # ensure the answer is identical among the batch for i in range(10): text = response_batch[i]["text"] if first_text is None: first_text = text self.assertEqual(text, first_text) if __name__ == "__main__": unittest.main()