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test_mla_flashinfer.py 2.96 KB
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Baizhou Zhang's avatar
[Feature]Support ragged prefill in flashinfer mla backend (#3967)  
Baizhou Zhang committed
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import unittest
from types import SimpleNamespace

import torch

from sglang.srt.utils import kill_process_tree
from sglang.test.few_shot_gsm8k import run_eval as run_eval_few_shot_gsm8k
from sglang.test.run_eval import run_eval
from sglang.test.test_utils import (
    DEFAULT_MLA_MODEL_NAME_FOR_TEST,
    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
    DEFAULT_URL_FOR_TEST,
    popen_launch_server,
)


class TestFlashinferMLA(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.model = "sgl-project/sglang-ci-dsv3-test"
        cls.base_url = DEFAULT_URL_FOR_TEST
        other_args = ["--trust-remote-code"]
        if torch.cuda.is_available() and torch.version.cuda:
            other_args.extend(
                [
                    "--enable-torch-compile",
                    "--cuda-graph-max-bs",
                    "2",
                    "--enable-flashinfer-mla",
                ]
            )
        cls.process = popen_launch_server(
            cls.model,
            cls.base_url,
            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
            other_args=other_args,
        )

    @classmethod
    def tearDownClass(cls):
        kill_process_tree(cls.process.pid)

    def test_gsm8k(self):
        args = SimpleNamespace(
            num_shots=5,
            data_path=None,
            num_questions=200,
            max_new_tokens=512,
            parallel=128,
            host="http://127.0.0.1",
            port=int(self.base_url.split(":")[-1]),
        )
        metrics = run_eval_few_shot_gsm8k(args)
        print(metrics)

        self.assertGreater(metrics["accuracy"], 0.62)


class TestFlashinferMLANoRagged(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.model = "sgl-project/sglang-ci-dsv3-test"
        cls.base_url = DEFAULT_URL_FOR_TEST
        other_args = ["--trust-remote-code"]
        if torch.cuda.is_available() and torch.version.cuda:
            other_args.extend(
                [
                    "--enable-torch-compile",
                    "--disable-cuda-graph",
                    "--cuda-graph-max-bs",
                    "2",
                    "--enable-flashinfer-mla",
                    "--flashinfer-mla-disable-ragged",
                ]
            )
        cls.process = popen_launch_server(
            cls.model,
            cls.base_url,
            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
            other_args=other_args,
        )

    @classmethod
    def tearDownClass(cls):
        kill_process_tree(cls.process.pid)

    def test_gsm8k(self):
        args = SimpleNamespace(
            num_shots=5,
            data_path=None,
            num_questions=200,
            max_new_tokens=512,
            parallel=128,
            host="http://127.0.0.1",
            port=int(self.base_url.split(":")[-1]),
        )
        metrics = run_eval_few_shot_gsm8k(args)
        print(metrics)

        self.assertGreater(metrics["accuracy"], 0.62)


if __name__ == "__main__":
    unittest.main()