[VLM] Add VLM TP=4 per-commit CI test and improve MMMU eval prompt/parser (#21841)
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@@ -148,11 +148,19 @@ class MMMUVLMEval(Eval):
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options = None
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options = None
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# Build final textual prompt; include choices if MC
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# Build final textual prompt; include choices if MC
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prompt_text = f"Question: {question}\n\n"
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prompt_text = f"{question}\n"
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if options:
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if options:
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letters = [chr(ord("A") + i) for i in range(len(options))]
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letters = [chr(ord("A") + i) for i in range(len(options))]
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for letter, opt in zip(letters, options):
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for letter, opt in zip(letters, options):
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prompt_text += f"{letter}) {opt}\n"
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prompt_text += f"{letter}. {opt}\n"
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prompt_text += (
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"\nAnswer the following multiple-choice question. "
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"The last line of your response should be of the "
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"following format: 'Answer: $LETTER' (without quotes) "
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"where LETTER is one of the options. "
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"Think step by step before answering."
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)
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else:
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prompt_text += "\nAnswer: "
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prompt_text += "\nAnswer: "
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samples.append(
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samples.append(
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@@ -330,6 +338,14 @@ def _parse_multi_choice_response(
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response: str, all_choices: List[str], index2ans: dict
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response: str, all_choices: List[str], index2ans: dict
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) -> str:
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) -> str:
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# loosely adapted from benchmark mmmu eval
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# loosely adapted from benchmark mmmu eval
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# First, look for explicit "Answer: X" pattern (last occurrence)
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answer_matches = re.findall(r"[Aa]nswer\s*:\s*\*?\*?\s*\(?([A-Z])\)?", response)
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if answer_matches:
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candidate = answer_matches[-1]
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if candidate in all_choices:
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return candidate
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for char in [",", ".", "!", "?", ";", ":", "'"]:
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for char in [",", ".", "!", "?", ";", ":", "'"]:
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response = response.strip(char)
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response = response.strip(char)
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response = " " + response + " "
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response = " " + response + " "
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@@ -0,0 +1,82 @@
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"""
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VLM TP=4 per-commit test using Qwen3.5-27B with MMMU evaluation.
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"""
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import unittest
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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.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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CustomTestCase,
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popen_launch_server,
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)
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register_cuda_ci(est_time=200, suite="stage-c-test-4-gpu-h100")
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QWEN35_27B_MODEL = "Qwen/Qwen3.5-27B"
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MMMU_ACCURACY_THRESHOLD = 0.65
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MMMU_NUM_EXAMPLES = 32
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class TestVLMTP4(CustomTestCase):
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@classmethod
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def setUpClass(cls):
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cls.model = QWEN35_27B_MODEL
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cls.base_url = DEFAULT_URL_FOR_TEST
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cls.process = popen_launch_server(
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cls.model,
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cls.base_url,
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timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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other_args=[
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"--tp-size",
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"4",
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"--cuda-graph-max-bs",
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"32",
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"--mem-fraction-static",
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"0.8",
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"--trust-remote-code",
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"--mamba-scheduler-strategy",
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"extra_buffer",
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"--mamba-track-interval",
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"128",
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"--mamba-ssm-dtype",
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"bfloat16",
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"--chunked-prefill-size",
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"2048",
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"--max-running-requests",
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"128",
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],
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)
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@classmethod
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def tearDownClass(cls):
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if hasattr(cls, "process") and cls.process:
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kill_process_tree(cls.process.pid)
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def test_mmmu_accuracy(self):
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args = SimpleNamespace(
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model=self.model,
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eval_name="mmmu",
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num_examples=MMMU_NUM_EXAMPLES,
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num_threads=16,
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max_tokens=2048,
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chat_template_kwargs={"enable_thinking": False},
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base_url=self.base_url,
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host="http://127.0.0.1",
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port=int(self.base_url.split(":")[-1]),
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)
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metrics = run_eval(args)
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print(f"MMMU score: {metrics['score']}")
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self.assertGreaterEqual(
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metrics["score"],
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MMMU_ACCURACY_THRESHOLD,
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f"MMMU accuracy {metrics['score']:.4f} below threshold {MMMU_ACCURACY_THRESHOLD}",
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)
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if __name__ == "__main__":
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unittest.main()
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