69 lines
2.0 KiB
Python
69 lines
2.0 KiB
Python
import unittest
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from types import SimpleNamespace
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from sglang.srt.utils import get_device_sm, kill_process_tree
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from sglang.test.ci.ci_register import register_cuda_ci
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from sglang.test.few_shot_gsm8k import run_eval
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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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# modelopt_fp4 requires SM 100+ (Blackwell)
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register_cuda_ci(est_time=300, suite="nightly-1-gpu", nightly=True)
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@unittest.skipIf(
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get_device_sm() < 100, "Test requires CUDA SM 100 or higher (Blackwell)"
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)
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class TestFlashinferTrtllmGenMoeBackend(CustomTestCase):
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@classmethod
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def setUpClass(cls):
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cls.model = "nvidia/Qwen3-30B-A3B-NVFP4"
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cls.base_url = DEFAULT_URL_FOR_TEST
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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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"--moe-runner-backend",
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"flashinfer_trtllm",
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"--quantization",
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"modelopt_fp4",
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"--trust-remote-code",
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"--disable-radix-cache",
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"--max-running-requests",
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"1024",
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"--chunked-prefill-size",
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"16384",
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"--mem-fraction-static",
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"0.89",
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"--max-prefill-tokens",
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"16384",
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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_gsm8k(self):
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args = SimpleNamespace(
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num_shots=8,
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data_path=None,
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num_questions=1319,
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max_new_tokens=512,
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parallel=1319,
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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(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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