Files
sglang/test/manual/attention/test_local_attn.py
T

75 lines
1.9 KiB
Python

import os
import unittest
from types import SimpleNamespace
import requests
from sglang.srt.utils import get_device_sm, kill_process_tree
from sglang.test.run_eval import run_eval
from sglang.test.test_utils import (
DEFAULT_MODEL_NAME_FOR_TEST_LOCAL_ATTENTION,
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
DEFAULT_URL_FOR_TEST,
CustomTestCase,
popen_launch_server,
)
# Local attention with FA3 (requires SM 90+ / H100, tp=4)
@unittest.skipIf(get_device_sm() < 90, "Test requires CUDA SM 90 or higher")
class TestFlashAttention3LocalAttn(CustomTestCase):
model = DEFAULT_MODEL_NAME_FOR_TEST_LOCAL_ATTENTION
base_url = DEFAULT_URL_FOR_TEST
accuracy_threshold = 0.90
@classmethod
def get_server_args(cls):
return [
"--cuda-graph-max-bs-decode",
"2",
"--attention-backend",
"fa3",
"--tp",
"4",
"--context-length",
"1000000",
]
@classmethod
def setUpClass(cls):
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=cls.get_server_args(),
env=os.environ,
)
@classmethod
def tearDownClass(cls):
kill_process_tree(cls.process.pid)
def test_gsm8k(self):
requests.get(self.base_url + "/flush_cache")
args = SimpleNamespace(
base_url=self.base_url,
model=self.model,
eval_name="gsm8k",
api="completion",
max_tokens=512,
num_examples=100,
num_threads=128,
num_shots=4,
)
metrics = run_eval(args)
print(f"{metrics=}")
# Use the appropriate metric key based on the test class
metric_key = "score"
self.assertGreater(metrics[metric_key], self.accuracy_threshold)
if __name__ == "__main__":
unittest.main()