222 lines
7.4 KiB
Python
222 lines
7.4 KiB
Python
import json
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import os
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import unittest
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import warnings
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from datetime import datetime
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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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is_in_ci,
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popen_launch_server,
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write_github_step_summary,
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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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"meta-llama/Llama-3.1-70B-Instruct": 0.95,
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"mistralai/Mixtral-8x7B-Instruct-v0.1": 0.64,
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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/Meta-Llama-3.1-70B-Instruct-FP8": 0.94,
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"neuralmagic/Qwen2-72B-Instruct-FP8": 0.94,
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"neuralmagic/Qwen2-57B-A14B-Instruct-FP8": 0.86,
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"neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8": 0.65,
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"google/gemma-2-27b-it": 0.91,
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"neuralmagic/DeepSeek-Coder-V2-Lite-Instruct-FP8": 0.84,
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}
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failing_models = {
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"neuralmagic/gemma-2-2b-it-FP8",
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}
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def remove_failing_models(model_str):
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models = model_str.split(",")
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filtered = [m for m in models if m not in failing_models]
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return ",".join(filtered)
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DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP1 = remove_failing_models(
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DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP1
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)
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DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP2 = remove_failing_models(
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DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP2
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)
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DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP1 = remove_failing_models(
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DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP1
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)
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DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP2 = remove_failing_models(
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DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP2
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)
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NO_MOE_PADDING_MODELS = {"neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8"}
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DISABLE_HF_XET_MODELS = {
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"Qwen/Qwen2-57B-A14B-Instruct",
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"neuralmagic/Qwen2-57B-A14B-Instruct-FP8",
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}
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TRITON_MOE_MODELS = {
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"neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8",
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"neuralmagic/DeepSeek-Coder-V2-Lite-Instruct-FP8",
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"mistralai/Mixtral-8x7B-Instruct-v0.1",
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"mistralai/Mistral-7B-Instruct-v0.3",
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}
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def parse_models(model_string):
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return [model.strip() for model in model_string.split(",") if model.strip()]
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def popen_launch_server_wrapper(base_url, model, is_tp2):
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other_args = ["--log-level-http", "warning", "--trust-remote-code"]
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if is_tp2:
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other_args.extend(["--tp", "2"])
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process = popen_launch_server(
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model,
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base_url,
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timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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other_args=other_args,
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)
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return process
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def write_results_to_json(model, metrics, mode="a"):
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result = {
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"timestamp": datetime.now().isoformat(),
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"model": model,
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"metrics": metrics,
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"score": metrics["score"],
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}
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existing_results = []
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if mode == "a" and os.path.exists("results.json"):
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try:
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with open("results.json", "r") as f:
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existing_results = json.load(f)
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except json.JSONDecodeError:
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existing_results = []
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if isinstance(existing_results, list):
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existing_results.append(result)
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else:
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existing_results = [result]
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with open("results.json", "w") as f:
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json.dump(existing_results, f, indent=2)
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def check_model_scores(results):
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failed_models = []
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summary = " | model | score | threshold |\n"
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summary += "| ----- | ----- | --------- |\n"
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for model, score in results:
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threshold = MODEL_SCORE_THRESHOLDS.get(model)
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if threshold is None:
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print(f"Warning: No threshold defined for model {model}")
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continue
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if score < threshold:
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failed_models.append(
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f"\nScore Check Failed: {model}\n"
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f"Model {model} score ({score:.4f}) is below threshold ({threshold:.4f})"
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)
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line = f"| {model} | {score} | {threshold} |\n"
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summary += line
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print(summary)
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if is_in_ci():
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write_github_step_summary(f"### TestNightlyGsm8KEval\n{summary}")
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if failed_models:
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raise AssertionError("\n".join(failed_models))
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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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for model_group, is_fp8, is_tp2 in self.model_groups:
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for model in model_group:
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with self.subTest(model=model):
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os.environ["SGLANG_MOE_PADDING"] = (
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"0" if model in NO_MOE_PADDING_MODELS else "1"
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)
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os.environ["HF_HUB_DISABLE_XET"] = (
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"1" if model in DISABLE_HF_XET_MODELS else "0"
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)
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os.environ["SGLANG_USE_AITER"] = (
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"0" if model in TRITON_MOE_MODELS else "1"
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)
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process = popen_launch_server_wrapper(self.base_url, model, is_tp2)
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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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# Allow retries, so flaky errors are avoided.
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threshold = MODEL_SCORE_THRESHOLDS.get(model)
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for attempt in range(3):
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try:
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metrics = run_eval(args)
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score = metrics["score"]
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if score >= threshold:
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break
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except Exception as e:
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print(f"Attempt {attempt + 1} failed with error: {e}")
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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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all_results.append((model, metrics["score"]))
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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_model_scores(all_results)
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if __name__ == "__main__":
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unittest.main()
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