sglang_v0.5.2/sglang/test/srt/test_double_sparsity.py

66 lines
1.9 KiB
Python

import os
import unittest
from types import SimpleNamespace
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,
CustomTestCase,
popen_launch_server,
)
class TestDoubleSparsity(CustomTestCase):
@classmethod
def setUpClass(cls):
cls.model = DEFAULT_MODEL_NAME_FOR_TEST
cls.base_url = DEFAULT_URL_FOR_TEST
dirpath = os.path.dirname(__file__)
config_file = os.path.join(
dirpath, "double-sparsity-config-Llama-3.1-8B-Instruct.json"
)
# NOTE: Generate the config file by running https://github.com/andy-yang-1/DoubleSparse/blob/main/evaluation/group_channel_config.py
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=[
"--enable-double-sparsity",
"--ds-channel-config-path",
config_file,
"--ds-heavy-channel-num",
"32",
"--ds-heavy-channel-type",
"k",
"--ds-heavy-token-num",
"512",
"--ds-sparse-decode-threshold",
"0",
"--max-total-tokens",
"200000",
],
)
@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)
if __name__ == "__main__":
unittest.main()