57 lines
1.8 KiB
Python
57 lines
1.8 KiB
Python
import unittest
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from sglang.test.accuracy_test_runner import AccuracyTestParams
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from sglang.test.ci.ci_register import register_cuda_ci
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from sglang.test.performance_test_runner import PerformanceTestParams
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from sglang.test.run_combined_tests import run_combined_tests
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from sglang.test.test_utils import ModelLaunchSettings
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# Runs on both H200 and B200 via nightly-8-gpu-common suite
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register_cuda_ci(est_time=1800, suite="nightly-8-gpu-common", nightly=True)
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LLAMA4_MODEL_PATH = "meta-llama/Llama-4-Scout-17B-16E-Instruct"
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@unittest.skip("Blocked: Missing HF token permission for Llama 4 model")
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class TestLlama4(unittest.TestCase):
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"""Unified test class for Llama-4-Scout performance and accuracy.
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Llama4 has local attention mechanism with hybrid sliding window attention.
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Single variant with TP=8 configuration.
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Runs BOTH:
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- Performance test (using NightlyBenchmarkRunner)
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- Accuracy test (using run_eval with gsm8k)
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"""
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def test_llama4(self):
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"""Run performance and accuracy for Llama-4-Scout."""
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base_args = [
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"--tp=8",
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"--trust-remote-code",
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"--chat-template=llama-4",
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"--mem-fraction-static=0.8",
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"--context-length=1000000",
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]
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variants = [
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ModelLaunchSettings(
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LLAMA4_MODEL_PATH,
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tp_size=8,
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extra_args=base_args,
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variant="TP8",
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),
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]
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run_combined_tests(
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models=variants,
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test_name="Llama-4-Scout",
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accuracy_params=AccuracyTestParams(dataset="gsm8k", baseline_accuracy=0.9),
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performance_params=PerformanceTestParams(
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profile_dir="performance_profiles_llama4",
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),
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)
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if __name__ == "__main__":
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unittest.main()
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