72 lines
1.9 KiB
Python
72 lines
1.9 KiB
Python
from sglang.test.ci.ci_register import register_cuda_ci
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register_cuda_ci(est_time=500, suite="stage-b-test-large-1-gpu")
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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.few_shot_gsm8k import run_eval as run_eval_few_shot_gsm8k
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from sglang.test.test_utils import (
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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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"""
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Test dLLM batching capability on CUDA GPUs.
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As current dLLM batching performance is suboptimal to BS=1, this test only verifies correctness.
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The test will be removed once dLLM batching performance improves.
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"""
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class TestBatching(CustomTestCase):
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@classmethod
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def setUpClass(cls):
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cls.model = "inclusionAI/LLaDA2.0-mini"
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cls.base_url = DEFAULT_URL_FOR_TEST
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other_args = [
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"--trust-remote-code",
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"--mem-fraction-static",
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"0.9",
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"--max-running-requests",
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"4",
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"--attention-backend",
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"flashinfer",
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"--dllm-algorithm",
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"LowConfidence",
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]
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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=other_args,
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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_gsm8k(self):
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args = SimpleNamespace(
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num_shots=5,
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data_path=None,
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num_questions=200,
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max_new_tokens=512,
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parallel=128,
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host="http://127.0.0.1",
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port=int(self.base_url.split(":")[-1]),
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)
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metrics = run_eval_few_shot_gsm8k(args)
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print(f"{metrics=}")
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self.assertGreater(metrics["accuracy"], 0.88)
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if __name__ == "__main__":
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unittest.main()
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