116 lines
4.4 KiB
Python
116 lines
4.4 KiB
Python
import json
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import unittest
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import warnings
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from types import SimpleNamespace
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from sglang.srt.utils import kill_process_tree
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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_NIGHTLY_EVAL_FP8_TP1,
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DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP2,
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DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP1,
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DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP2,
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DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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DEFAULT_URL_FOR_TEST,
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check_evaluation_test_results,
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parse_models,
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popen_launch_server,
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write_results_to_json,
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)
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MODEL_SCORE_THRESHOLDS = {
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"meta-llama/Llama-3.1-8B-Instruct": 0.82,
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"mistralai/Mistral-7B-Instruct-v0.3": 0.58,
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"deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct": 0.85,
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"google/gemma-2-27b-it": 0.91,
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"meta-llama/Llama-3.1-70B-Instruct": 0.95,
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"mistralai/Mixtral-8x7B-Instruct-v0.1": 0.616,
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"Qwen/Qwen2-57B-A14B-Instruct": 0.86,
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"neuralmagic/Meta-Llama-3.1-8B-Instruct-FP8": 0.83,
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"neuralmagic/Mistral-7B-Instruct-v0.3-FP8": 0.54,
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"neuralmagic/DeepSeek-Coder-V2-Lite-Instruct-FP8": 0.835,
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"zai-org/GLM-4.5-Air-FP8": 0.75,
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# The threshold of neuralmagic/gemma-2-2b-it-FP8 should be 0.6, but this model has some accuracy regression.
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# The fix is tracked at https://github.com/sgl-project/sglang/issues/4324, we set it to 0.50, for now, to make CI green.
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"neuralmagic/gemma-2-2b-it-FP8": 0.50,
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"neuralmagic/Meta-Llama-3.1-70B-Instruct-FP8": 0.94,
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"neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8": 0.65,
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"neuralmagic/Qwen2-72B-Instruct-FP8": 0.94,
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"neuralmagic/Qwen2-57B-A14B-Instruct-FP8": 0.82,
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}
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# Do not use `CustomTestCase` since `test_mgsm_en_all_models` does not want retry
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class TestNightlyGsm8KEval(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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cls.model_groups = [
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(parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP1), False, False),
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(parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP2), False, True),
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(parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP1), True, False),
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(parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP2), True, True),
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]
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cls.base_url = DEFAULT_URL_FOR_TEST
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def test_mgsm_en_all_models(self):
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warnings.filterwarnings(
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"ignore", category=ResourceWarning, message="unclosed.*socket"
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)
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is_first = True
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all_results = []
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model_count = 0
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for model_group, is_fp8, is_tp2 in self.model_groups:
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for model in model_group:
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model_count += 1
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with self.subTest(model=model):
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other_args = ["--tp", "2"] if is_tp2 else []
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if model == "meta-llama/Llama-3.1-70B-Instruct":
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other_args.extend(["--mem-fraction-static", "0.9"])
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process = popen_launch_server(
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model=model,
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other_args=other_args,
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base_url=self.base_url,
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timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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)
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args = SimpleNamespace(
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base_url=self.base_url,
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model=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(
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f"{'=' * 42}\n{model} - metrics={metrics} score={metrics['score']}\n{'=' * 42}\n"
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)
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write_results_to_json(model, metrics, "w" if is_first else "a")
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is_first = False
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# 0.0 for empty latency
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all_results.append((model, metrics["score"], 0.0))
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kill_process_tree(process.pid)
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try:
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with open("results.json", "r") as f:
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print("\nFinal Results from results.json:")
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print(json.dumps(json.load(f), indent=2))
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except Exception as e:
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print(f"Error reading results.json: {e}")
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# Check all scores after collecting all results
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check_evaluation_test_results(
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all_results,
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self.__class__.__name__,
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model_accuracy_thresholds=MODEL_SCORE_THRESHOLDS,
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model_count=model_count,
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)
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if __name__ == "__main__":
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unittest.main()
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