test_torch_compile.py

import time
import unittest
from types import SimpleNamespace

import requests

from sglang.srt.utils import kill_process_tree
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,
    popen_launch_server,
)


class TestTorchCompile(unittest.TestCase):
    @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=["--enable-torch-compile", "--cuda-graph-max-bs", "4"],
        )

    @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,
        )

        metrics = run_eval(args)
        self.assertGreaterEqual(metrics["score"], 0.65)

    def run_decode(self, max_new_tokens):
        response = requests.post(
            self.base_url + "/generate",
            json={
                "text": "The capital of France is",
                "sampling_params": {
                    "temperature": 0,
                    "max_new_tokens": max_new_tokens,
                    "ignore_eos": True,
                },
            },
        )
        return response.json()

    def test_throughput(self):
        # Warmup
        res = self.run_decode(16)

        max_tokens = 256
        tic = time.time()
        res = self.run_decode(max_tokens)
        tok = time.time()
        print(f"{res=}")
        throughput = max_tokens / (tok - tic)
        print(f"Throughput: {throughput} tokens/s")
        self.assertGreaterEqual(throughput, 152)


if __name__ == "__main__":
    unittest.main()