Files
sglang/test/registered/4-gpu-models/test_deepseek_v3_cutedsl_4gpu.py

165 lines
4.7 KiB
Python

import os
import unittest
from types import SimpleNamespace
from sglang.srt.utils import kill_process_tree
from sglang.test.ci.ci_register import register_cuda_ci
from sglang.test.few_shot_gsm8k import run_eval as run_eval_few_shot_gsm8k
from sglang.test.test_utils import (
DEFAULT_DEEPSEEK_NVFP4_MODEL_FOR_TEST,
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
DEFAULT_URL_FOR_TEST,
CustomTestCase,
popen_launch_server,
try_cached_model,
)
register_cuda_ci(est_time=1800, suite="stage-c-test-4-gpu-gb200")
class TestDeepseekR1Nvfp4CuteDSLDeepEP(CustomTestCase):
@classmethod
def setUpClass(cls):
cls.model = try_cached_model(DEFAULT_DEEPSEEK_NVFP4_MODEL_FOR_TEST)
cls.base_url = DEFAULT_URL_FOR_TEST
other_args = [
"--trust-remote-code",
"--disable-radix-cache",
"--mem-fraction-static",
"0.8",
"--max-prefill-tokens",
"16384",
"--max-running-requests",
"256",
"--chunked-prefill-size",
"1024",
"--tp",
"4",
"--dp",
"4",
"--ep",
"4",
"--moe-dense-tp-size",
"1",
"--enable-dp-attention",
"--quantization",
"modelopt_fp4",
"--attention-backend",
"trtllm_mla",
"--moe-a2a-backend",
"deepep",
"--moe-runner-backend",
"flashinfer_cutedsl",
"--deepep-mode",
"low_latency",
]
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=other_args,
env={
**os.environ,
"SGLANG_DEEPEP_BF16_DISPATCH": "1",
"SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "256",
"SGLANG_MOE_NVFP4_DISPATCH": "0",
},
)
@classmethod
def tearDownClass(cls):
kill_process_tree(cls.process.pid)
def test_gsm8k(self):
args = SimpleNamespace(
num_shots=5,
data_path=None,
num_questions=512,
parallel=512,
max_new_tokens=512,
host="http://127.0.0.1",
port=int(self.base_url.split(":")[-1]),
)
metrics = run_eval_few_shot_gsm8k(args)
print(f"Eval accuracy of GSM8K: {metrics=}")
self.assertGreater(metrics["accuracy"], 0.92)
class TestDummyWithSBO(CustomTestCase):
@classmethod
def setUpClass(cls):
cls.model = try_cached_model(DEFAULT_DEEPSEEK_NVFP4_MODEL_FOR_TEST)
cls.base_url = DEFAULT_URL_FOR_TEST
other_args = [
"--trust-remote-code",
"--disable-radix-cache",
"--mem-fraction-static",
"0.05",
"--max-prefill-tokens",
"16384",
"--max-running-requests",
"256",
"--chunked-prefill-size",
"1024",
"--cuda-graph-bs",
"64",
"--tp",
"4",
"--dp",
"4",
"--ep",
"4",
"--moe-dense-tp-size",
"1",
"--enable-dp-attention",
"--quantization",
"modelopt_fp4",
"--attention-backend",
"trtllm_mla",
"--moe-a2a-backend",
"deepep",
"--moe-runner-backend",
"flashinfer_cutedsl",
"--deepep-mode",
"low_latency",
"--json-model-override-args",
'{"num_hidden_layers": 1, "first_k_dense_replace": 0, "n_routed_experts": 24}',
"--enable-single-batch-overlap",
"--load-format",
"dummy",
]
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=other_args,
env={
**os.environ,
"SGLANG_DEEPEP_BF16_DISPATCH": "1",
"SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "256",
"SGLANG_MOE_NVFP4_DISPATCH": "0",
},
)
@classmethod
def tearDownClass(cls):
kill_process_tree(cls.process.pid)
def test_gsm8k(self):
args = SimpleNamespace(
num_shots=0,
data_path=None,
num_questions=512,
parallel=512,
max_new_tokens=16,
host="http://127.0.0.1",
port=int(self.base_url.split(":")[-1]),
)
metrics = run_eval_few_shot_gsm8k(args)
print(f"Eval accuracy of GSM8K: {metrics=}")
if __name__ == "__main__":
unittest.main()