85 lines
2.3 KiB
Python
85 lines
2.3 KiB
Python
import time
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import unittest
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from types import SimpleNamespace
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import requests
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from sglang.srt.utils import kill_process_tree
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from sglang.test.ci.ci_register import register_amd_ci, register_cuda_ci
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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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is_in_amd_ci,
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popen_launch_server,
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)
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register_cuda_ci(est_time=144, suite="stage-b-test-large-1-gpu")
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register_amd_ci(est_time=1100, suite="stage-b-test-small-1-gpu-amd")
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class TestTorchCompile(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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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=["--enable-torch-compile", "--cuda-graph-max-bs", "4"],
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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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def run_decode(self, max_new_tokens):
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response = requests.post(
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self.base_url + "/generate",
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json={
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"text": "The capital of France is",
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"sampling_params": {
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"temperature": 0,
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"max_new_tokens": max_new_tokens,
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"ignore_eos": True,
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},
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},
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)
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return response.json()
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def test_throughput(self):
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# Warmup
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res = self.run_decode(16)
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max_tokens = 256
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tic = time.perf_counter()
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res = self.run_decode(max_tokens)
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tok = time.perf_counter()
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print(f"{res=}")
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throughput = max_tokens / (tok - tic)
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print(f"Throughput: {throughput} tokens/s")
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if is_in_amd_ci():
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self.assertGreaterEqual(throughput, 145)
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else:
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self.assertGreaterEqual(throughput, 152)
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if __name__ == "__main__":
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unittest.main()
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