129 lines
4.3 KiB
Python
129 lines
4.3 KiB
Python
import unittest
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from nightly_utils import NightlyBenchmarkRunner
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from sglang.test.test_utils import DEFAULT_URL_FOR_TEST, _parse_int_list_env
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DEEPSEEK_V32_MODEL_PATH = "deepseek-ai/DeepSeek-V3.2-Exp"
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PROFILE_DIR = "performance_profiles_deepseek_v32"
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class TestNightlyDeepseekV32Performance(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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cls.model = DEEPSEEK_V32_MODEL_PATH
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cls.base_url = DEFAULT_URL_FOR_TEST
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cls.batch_sizes = [1, 1, 8, 16, 64]
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cls.input_lens = tuple(_parse_int_list_env("NIGHTLY_INPUT_LENS", "4096"))
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cls.output_lens = tuple(_parse_int_list_env("NIGHTLY_OUTPUT_LENS", "512"))
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# Define variant configurations
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cls.variants = [
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{
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"name": "basic",
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"other_args": [
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"--trust-remote-code",
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"--tp",
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"8",
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"--dp",
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"8",
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"--enable-dp-attention",
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"--model-loader-extra-config",
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'{"enable_multithread_load": true}',
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],
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},
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{
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"name": "mtp",
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"other_args": [
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"--trust-remote-code",
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"--tp",
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"8",
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"--dp",
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"8",
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"--enable-dp-attention",
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"--speculative-algorithm",
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"EAGLE",
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"--speculative-num-steps",
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"3",
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"--speculative-eagle-topk",
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"1",
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"--speculative-num-draft-tokens",
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"4",
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"--mem-frac",
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"0.7",
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"--model-loader-extra-config",
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'{"enable_multithread_load": true}',
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],
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},
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{
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"name": "nsa",
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"other_args": [
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"--trust-remote-code",
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"--tp",
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"8",
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"--dp",
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"8",
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"--enable-dp-attention",
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"--attention-backend",
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"nsa",
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"--nsa-prefill-backend",
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"flashmla_sparse",
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"--nsa-decode-backend",
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"flashmla_kv",
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"--model-loader-extra-config",
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'{"enable_multithread_load": true}',
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],
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},
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{
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"name": "pure_tp",
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"other_args": [
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"--trust-remote-code",
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"--tp",
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"8",
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"--attention-backend",
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"nsa",
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"--nsa-prefill-backend",
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"flashmla_sparse",
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"--nsa-decode-backend",
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"flashmla_kv",
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"--model-loader-extra-config",
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'{"enable_multithread_load": true}',
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],
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},
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]
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cls.runner = NightlyBenchmarkRunner(PROFILE_DIR, cls.__name__, cls.base_url)
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cls.runner.setup_profile_directory()
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def test_bench_one_batch(self):
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failed_variants = []
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try:
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for variant_config in self.variants:
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with self.subTest(variant=variant_config["name"]):
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results, success = self.runner.run_benchmark_for_model(
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model_path=self.model,
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batch_sizes=self.batch_sizes,
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input_lens=self.input_lens,
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output_lens=self.output_lens,
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other_args=variant_config["other_args"],
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variant=variant_config["name"],
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)
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if not success:
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failed_variants.append(variant_config["name"])
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self.runner.add_report(results, variant=variant_config["name"])
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finally:
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self.runner.write_final_report()
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if failed_variants:
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raise AssertionError(
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f"Benchmark failed for {self.model} with the following variants: "
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f"{', '.join(failed_variants)}"
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)
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if __name__ == "__main__":
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unittest.main()
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