Files
sglang/test/srt/test_hybrid_attn_backend.py
2025-07-28 11:42:29 +08:00

110 lines
3.2 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.few_shot_gsm8k import run_eval as run_eval_few_shot_gsm8k
from sglang.test.test_utils import (
DEFAULT_MODEL_NAME_FOR_TEST,
DEFAULT_MODEL_NAME_FOR_TEST_MLA,
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
DEFAULT_URL_FOR_TEST,
CustomTestCase,
popen_launch_server,
)
GSM_DATASET_PATH = None
# Default server arguments shared across all tests
DEFAULT_SERVER_ARGS = [
"--trust-remote-code",
"--cuda-graph-max-bs",
"8",
"--prefill-attention-backend",
"fa3",
"--decode-attention-backend",
"flashinfer",
]
@unittest.skipIf(get_device_sm() < 90, "Test requires CUDA SM 90 or higher")
class TestHybridAttnBackendBase(CustomTestCase):
model = DEFAULT_MODEL_NAME_FOR_TEST
base_url = DEFAULT_URL_FOR_TEST
accuracy_threshold = 0.65 # derived tests need to override this
speculative_decode = False
spec_decode_threshold = 1.0 # derived spec decoding tests need to override this
@classmethod
def get_server_args(cls):
"""Return the arguments for the server launch. Override in subclasses."""
return DEFAULT_SERVER_ARGS
@classmethod
def setUpClass(cls):
# disable deep gemm precompile to make launch server faster
# please don't do this if you want to make your inference workload faster
os.environ["SGL_JIT_DEEPGEMM_PRECOMPILE"] = "false"
os.environ["SGL_ENABLE_JIT_DEEPGEMM"] = "false"
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=cls.get_server_args(),
)
@classmethod
def tearDownClass(cls):
kill_process_tree(cls.process.pid)
def test_gsm8k(self):
requests.get(self.base_url + "/flush_cache")
args = SimpleNamespace(
num_shots=4,
num_questions=100,
max_new_tokens=512,
parallel=128,
host="http://127.0.0.1",
port=int(self.base_url.split(":")[-1]),
data_path=GSM_DATASET_PATH,
)
metrics = run_eval_few_shot_gsm8k(args)
print(f"{metrics=}")
# Use the appropriate metric key based on the test class
metric_key = "accuracy"
self.assertGreater(metrics[metric_key], self.accuracy_threshold)
if self.speculative_decode:
server_info = requests.get(self.base_url + "/get_server_info")
avg_spec_accept_length = server_info.json()["internal_states"][0][
"avg_spec_accept_length"
]
print(f"{avg_spec_accept_length=}")
self.assertGreater(avg_spec_accept_length, self.spec_decode_threshold)
class TestHybridAttnBackendMLA(TestHybridAttnBackendBase):
accuracy_threshold = 0.60
model = DEFAULT_MODEL_NAME_FOR_TEST_MLA
@classmethod
def get_server_args(cls):
return DEFAULT_SERVER_ARGS
class TestHybridAttnBackendTorchCompile(TestHybridAttnBackendBase):
accuracy_threshold = 0.65
@classmethod
def get_server_args(cls):
return DEFAULT_SERVER_ARGS + ["--enable-torch-compile"]
if __name__ == "__main__":
unittest.main()