106 lines
3.4 KiB
Python
106 lines
3.4 KiB
Python
import os
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import unittest
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from types import SimpleNamespace
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from nightly_utils import NightlyBenchmarkRunner
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from sglang.srt.utils import kill_process_tree
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from sglang.test.ci.ci_register import 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_TIMEOUT_FOR_SERVER_LAUNCH,
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DEFAULT_URL_FOR_TEST,
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_parse_int_list_env,
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popen_launch_server,
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)
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register_cuda_ci(est_time=600, suite="nightly-8-gpu-b200", nightly=True)
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MISTRAL_LARGE3_MODEL_PATH = "mistralai/Mistral-Large-3-675B-Instruct-2512"
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PROFILE_DIR = "performance_profiles_mistral_large3"
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class TestNightlyMistralLarge3Performance(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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# Set environment variable to disable JIT DeepGemm
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os.environ["SGLANG_ENABLE_JIT_DEEPGEMM"] = "0"
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cls.model = MISTRAL_LARGE3_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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# Mistral-Large-3-675B requires TP=8 and trtllm_mla attention backend
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cls.other_args = [
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"--tp",
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"8",
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"--attention-backend",
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"trtllm_mla",
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"--model-loader-extra-config",
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'{"enable_multithread_load": true}',
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"--chat-template",
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"mistral",
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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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@classmethod
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def tearDownClass(cls):
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# Clean up environment variable
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if "SGLANG_ENABLE_JIT_DEEPGEMM" in os.environ:
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del os.environ["SGLANG_ENABLE_JIT_DEEPGEMM"]
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def test_bench_one_batch(self):
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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=self.other_args,
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)
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self.runner.add_report(results)
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self.runner.write_final_report()
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if not success:
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raise AssertionError(
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f"Benchmark failed for {self.model}. Check the logs for details."
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)
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def test_accuracy_mgsm(self):
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"""Run MGSM accuracy evaluation for Mistral Large 3."""
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process = popen_launch_server(
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model=self.model,
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base_url=self.base_url,
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other_args=self.other_args,
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timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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)
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try:
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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="mgsm_en",
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num_examples=None,
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num_threads=1024,
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)
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metrics = run_eval(args)
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print(f"MGSM accuracy for {self.model}: {metrics['score']}")
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# Placeholder threshold - adjust after first successful run
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expected_threshold = 0.90
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self.assertGreaterEqual(
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metrics["score"],
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expected_threshold,
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f"MGSM accuracy {metrics['score']} below threshold {expected_threshold}",
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)
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finally:
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kill_process_tree(process.pid)
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if __name__ == "__main__":
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unittest.main()
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