66 lines
1.9 KiB
Python
66 lines
1.9 KiB
Python
import os
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import unittest
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from types import SimpleNamespace
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from sglang.srt.utils import kill_process_tree
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from sglang.test.run_eval import run_eval
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from sglang.test.test_utils import (
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DEFAULT_MODEL_NAME_FOR_TEST,
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DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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DEFAULT_URL_FOR_TEST,
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CustomTestCase,
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popen_launch_server,
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)
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class TestDoubleSparsity(CustomTestCase):
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@classmethod
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def setUpClass(cls):
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cls.model = DEFAULT_MODEL_NAME_FOR_TEST
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cls.base_url = DEFAULT_URL_FOR_TEST
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dirpath = os.path.dirname(__file__)
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config_file = os.path.join(
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dirpath, "double-sparsity-config-Llama-3.1-8B-Instruct.json"
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)
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# NOTE: Generate the config file by running https://github.com/andy-yang-1/DoubleSparse/blob/main/evaluation/group_channel_config.py
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cls.process = popen_launch_server(
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cls.model,
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cls.base_url,
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timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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other_args=[
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"--enable-double-sparsity",
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"--ds-channel-config-path",
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config_file,
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"--ds-heavy-channel-num",
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"32",
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"--ds-heavy-channel-type",
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"k",
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"--ds-heavy-token-num",
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"512",
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"--ds-sparse-decode-threshold",
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"0",
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"--max-total-tokens",
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"200000",
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],
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)
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@classmethod
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def tearDownClass(cls):
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kill_process_tree(cls.process.pid)
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def test_mmlu(self):
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args = SimpleNamespace(
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base_url=self.base_url,
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model=self.model,
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eval_name="mmlu",
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num_examples=64,
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num_threads=32,
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)
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metrics = run_eval(args)
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self.assertGreaterEqual(metrics["score"], 0.65)
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if __name__ == "__main__":
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unittest.main()
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