test_w8a8_quantization.py

import time
import unittest
from types import SimpleNamespace

import requests

from sglang.srt.utils import kill_process_tree
from sglang.test.few_shot_gsm8k import run_eval
from sglang.test.test_utils import (
    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
    DEFAULT_URL_FOR_TEST,
    popen_launch_server,
)


class TestW8A8(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.model = "neuralmagic/Meta-Llama-3-8B-Instruct-quantized.w8a8"
        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=["--quantization", "w8a8_int8"],
        )

    @classmethod
    def tearDownClass(cls):
        kill_process_tree(cls.process.pid)

    def test_gsm8k(self):
        args = SimpleNamespace(
            num_shots=5,
            data_path=None,
            num_questions=200,
            max_new_tokens=512,
            parallel=128,
            host="http://127.0.0.1",
            port=int(self.base_url.split(":")[-1]),
        )
        metrics = run_eval(args)
        print(metrics)

        self.assertGreater(metrics["accuracy"], 0.7)

    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):
        max_tokens = 256

        tic = time.time()
        res = self.run_decode(max_tokens)
        tok = time.time()
        print(res["text"])
        throughput = max_tokens / (tok - tic)
        print(f"Throughput: {throughput} tokens/s")
        assert throughput >= 140


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