76 lines
2.0 KiB
Python
76 lines
2.0 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.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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class TestW8A8(CustomTestCase):
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@classmethod
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def setUpClass(cls):
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cls.model = "neuralmagic/Meta-Llama-3-8B-Instruct-quantized.w8a8"
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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=["--quantization", "w8a8_int8"],
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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(args)
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print(metrics)
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self.assertGreater(metrics["accuracy"], 0.7)
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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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},
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"ignore_eos": True,
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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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max_tokens = 256
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tic = time.time()
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res = self.run_decode(max_tokens)
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tok = time.time()
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print(res["text"])
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throughput = max_tokens / (tok - tic)
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print(f"Throughput: {throughput} tokens/s")
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assert throughput >= 140
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if __name__ == "__main__":
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unittest.main()
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