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adapt to sglang v0.5.2rc1 on dcu

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maxiao
2025-09-04 15:56:33 +08:00
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test/srt/test_bench_serving.py Normal file
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import asyncio
import itertools
import unittest
import requests
from sglang.test.test_utils import (
DEFAULT_EAGLE_DRAFT_MODEL_FOR_TEST,
DEFAULT_EAGLE_TARGET_MODEL_FOR_TEST,
DEFAULT_MODEL_NAME_FOR_TEST,
DEFAULT_MODEL_NAME_FOR_TEST_FP8,
DEFAULT_MOE_MODEL_NAME_FOR_TEST,
DEFAULT_SMALL_VLM_MODEL_NAME_FOR_TEST,
CustomTestCase,
is_in_amd_ci,
is_in_ci,
run_bench_serving,
write_github_step_summary,
)
class TestBenchServing(CustomTestCase):
def test_offline_throughput_default(self):
res = run_bench_serving(
model=DEFAULT_MODEL_NAME_FOR_TEST,
num_prompts=500,
request_rate=float("inf"),
other_server_args=[],
)
if is_in_ci():
write_github_step_summary(
f"### test_offline_throughput_default\n"
f"Output throughput: {res['output_throughput']:.2f} token/s\n"
)
if is_in_amd_ci():
self.assertGreater(res["output_throughput"], 3050)
else:
self.assertGreater(res["output_throughput"], 3800)
def test_offline_throughput_non_stream_small_batch_size(self):
res = run_bench_serving(
model=DEFAULT_MODEL_NAME_FOR_TEST,
num_prompts=200,
request_rate=float("inf"),
other_server_args=["--max-running-requests", "10"],
dataset_name="sharegpt",
random_input_len=None,
random_output_len=None,
disable_stream=True,
need_warmup=True,
)
if is_in_ci():
write_github_step_summary(
f"### test_offline_throughput_non_stream_small_batch_size\n"
f"Output throughput: {res['output_throughput']:.2f} token/s\n"
)
if is_in_amd_ci():
self.assertGreater(res["output_throughput"], 1000)
else:
self.assertGreater(res["output_throughput"], 1050)
def test_offline_throughput_without_radix_cache(self):
res = run_bench_serving(
model=DEFAULT_MODEL_NAME_FOR_TEST,
num_prompts=500,
request_rate=float("inf"),
other_server_args=["--disable-radix-cache"],
)
if is_in_ci():
write_github_step_summary(
f"### test_offline_throughput_without_radix_cache\n"
f"Output throughput: {res['output_throughput']:.2f} token/s\n"
)
if is_in_amd_ci():
self.assertGreater(res["output_throughput"], 3050)
else:
self.assertGreater(res["output_throughput"], 3800)
def test_offline_throughput_without_chunked_prefill(self):
res = run_bench_serving(
model=DEFAULT_MODEL_NAME_FOR_TEST,
num_prompts=500,
request_rate=float("inf"),
other_server_args=["--chunked-prefill-size", "-1"],
)
if is_in_ci():
write_github_step_summary(
f"### test_offline_throughput_without_chunked_prefill\n"
f"Output throughput: {res['output_throughput']:.2f} token/s\n"
)
self.assertGreater(res["output_throughput"], 2600)
def test_offline_throughput_with_triton_attention_backend(self):
res = run_bench_serving(
model=DEFAULT_MODEL_NAME_FOR_TEST,
num_prompts=500,
request_rate=float("inf"),
other_server_args=[
"--attention-backend",
"triton",
"--context-length",
"8192",
],
)
if is_in_ci():
write_github_step_summary(
f"### test_offline_throughput_with_triton_attention_backend\n"
f"Output throughput: {res['output_throughput']:.2f} token/s\n"
)
if is_in_amd_ci():
self.assertGreater(res["output_throughput"], 3500)
else:
self.assertGreater(res["output_throughput"], 3700)
def test_offline_throughput_default_fp8(self):
res = run_bench_serving(
model=DEFAULT_MODEL_NAME_FOR_TEST_FP8,
num_prompts=500,
request_rate=float("inf"),
other_server_args=[],
)
if is_in_ci():
write_github_step_summary(
f"### test_offline_throughput_default_fp8\n"
f"Output throughput: {res['output_throughput']:.2f} token/s\n"
)
if is_in_amd_ci():
self.assertGreater(res["output_throughput"], 3500)
else:
self.assertGreater(res["output_throughput"], 4300)
def test_online_latency_default(self):
res = run_bench_serving(
model=DEFAULT_MODEL_NAME_FOR_TEST,
num_prompts=100,
request_rate=1,
other_server_args=[],
)
if is_in_ci():
write_github_step_summary(
f"### test_online_latency_default\n"
f"median_e2e_latency_ms: {res['median_e2e_latency_ms']:.2f} ms\n"
)
self.assertLess(res["median_e2e_latency_ms"], 11000)
if is_in_amd_ci():
self.assertLess(res["median_ttft_ms"], 115)
else:
self.assertLess(res["median_ttft_ms"], 86)
self.assertLess(res["median_itl_ms"], 10)
def test_vlm_offline_throughput(self):
res = run_bench_serving(
model=DEFAULT_SMALL_VLM_MODEL_NAME_FOR_TEST,
num_prompts=200,
request_rate=float("inf"),
other_server_args=[
"--mem-fraction-static",
"0.7",
],
dataset_name="mmmu",
)
if is_in_ci():
write_github_step_summary(
f"### test_vlm_offline_throughput\n"
f"Output throughput: {res['output_throughput']:.2f} token/s\n"
)
if is_in_amd_ci():
self.assertGreater(res["output_throughput"], 2000)
# TODO: not set yet, need AMD machine
else:
self.assertGreater(res["output_throughput"], 2500)
def test_vlm_online_latency(self):
res = run_bench_serving(
model=DEFAULT_SMALL_VLM_MODEL_NAME_FOR_TEST,
num_prompts=250,
request_rate=1,
other_server_args=[
"--mem-fraction-static",
"0.7",
],
dataset_name="mmmu",
)
if is_in_ci():
write_github_step_summary(
f"### test_vlm_online_latency\n"
f"median_e2e_latency_ms: {res['median_e2e_latency_ms']:.2f} ms\n"
)
self.assertLess(res["median_e2e_latency_ms"], 16500)
if is_in_amd_ci():
self.assertLess(res["median_ttft_ms"], 150)
# TODO: not set yet, need AMD machine
else:
self.assertLess(res["median_ttft_ms"], 100)
self.assertLess(res["median_itl_ms"], 8)
def test_lora_online_latency(self):
# TODO (lifuhuang): verify LoRA support in AMD.
if is_in_amd_ci():
pass
res = self._run_lora_latency_test(enable_background_task=False)
if is_in_ci():
write_github_step_summary(
f"### test_lora_online_latency\n"
f"median_e2e_latency_ms: {res['median_e2e_latency_ms']:.2f} ms\n"
f"median_ttft_ms: {res['median_ttft_ms']:.2f} ms\n"
)
self.assertLess(res["median_e2e_latency_ms"], 2400)
self.assertLess(res["median_ttft_ms"], 58)
def test_lora_online_latency_with_concurrent_adapter_updates(self):
# TODO (lifuhuang): verify LoRA support in AMD.
if is_in_amd_ci():
pass
res = self._run_lora_latency_test(enable_background_task=True)
if is_in_ci():
write_github_step_summary(
f"### test_lora_online_latency\n"
f"median_e2e_latency_ms: {res['median_e2e_latency_ms']:.2f} ms\n"
f"median_ttft_ms: {res['median_ttft_ms']:.2f} ms\n"
)
self.assertLess(res["median_e2e_latency_ms"], 4000)
self.assertLess(res["median_ttft_ms"], 80)
def _run_lora_latency_test(self, enable_background_task: bool):
"""
Run a latency test for LoRA with the specified background task setting.
"""
async def lora_loader_unloader_task(
base_url: str,
start_event: asyncio.Event,
stop_event: asyncio.Event,
):
"""
A background task that repeatedly loads and unloads a LoRA adapter.
"""
await start_event.wait()
path_cycler = itertools.cycle(
[
"pbevan11/llama-3.1-8b-ocr-correction",
"faridlazuarda/valadapt-llama-3.1-8B-it-chinese",
"philschmid/code-llama-3-1-8b-text-to-sql-lora",
]
)
load_url = f"{base_url}/load_lora_adapter"
unload_url = f"{base_url}/unload_lora_adapter"
num_updates = 0
while not stop_event.is_set():
# 1. Load the LoRA adapter
lora_path = next(path_cycler)
response = await asyncio.to_thread(
requests.post,
load_url,
json={"lora_name": lora_path, "lora_path": lora_path},
)
self.assertTrue(
response.ok, f"Failed to load LoRA adapter: {response.text}"
)
num_updates += 1
if stop_event.is_set():
break
# Yield control to allow other tasks to run.
await asyncio.sleep(1)
# 2. Unload the LoRA adapter
response = await asyncio.to_thread(
requests.post,
unload_url,
json={"lora_name": lora_path},
)
self.assertTrue(
response.ok, f"Failed to unload LoRA adapter: {response.text}"
)
num_updates += 1
# Yield control to allow other tasks to run.
await asyncio.sleep(1)
background_task = lora_loader_unloader_task if enable_background_task else None
res = run_bench_serving(
model=DEFAULT_MODEL_NAME_FOR_TEST,
num_prompts=400,
request_rate=8,
other_server_args=[
"--enable-lora",
"--max-loras-per-batch",
"1",
"--disable-radix-cache",
"--random-seed",
"42",
"--mem-fraction-static",
"0.8",
"--lora-paths",
"Nutanix/Meta-Llama-3.1-8B-Instruct_lora_4_alpha_16",
"--max-lora-rank",
"256",
],
dataset_name="random",
random_input_len=256,
random_output_len=256,
lora_name=["Nutanix/Meta-Llama-3.1-8B-Instruct_lora_4_alpha_16"],
background_task=background_task,
)
return res
def test_online_latency_eagle(self):
res = run_bench_serving(
model=DEFAULT_EAGLE_TARGET_MODEL_FOR_TEST,
num_prompts=300,
request_rate=8,
sharegpt_context_len=3072,
disable_ignore_eos=True,
dataset_name="sharegpt",
other_server_args=[
"--speculative-algorithm",
"EAGLE",
"--speculative-draft-model-path",
DEFAULT_EAGLE_DRAFT_MODEL_FOR_TEST,
"--speculative-num-steps",
"5",
"--speculative-eagle-topk",
"4",
"--speculative-num-draft-tokens",
"16",
"--mem-fraction-static",
"0.7",
],
need_warmup=True,
seed=42,
)
if is_in_ci():
write_github_step_summary(
f"### test_online_latency_eagle\n"
f"median_e2e_latency_ms: {res['median_e2e_latency_ms']:.2f} ms\n"
f"accept_length: {res['accept_length']:.2f} \n"
)
if is_in_amd_ci():
self.assertLess(res["median_e2e_latency_ms"], 1800)
else:
self.assertLess(res["median_e2e_latency_ms"], 900)
self.assertGreater(res["accept_length"], 3.0)
def test_moe_offline_throughput_default(self):
res = run_bench_serving(
model=DEFAULT_MOE_MODEL_NAME_FOR_TEST,
num_prompts=300,
request_rate=float("inf"),
other_server_args=["--tp", "2"],
)
if is_in_ci():
write_github_step_summary(
f"### test_moe_offline_throughput_default\n"
f"Output throughput: {res['output_throughput']:.2f} token/s\n"
)
if is_in_amd_ci():
self.assertGreater(res["output_throughput"], 2100)
else:
self.assertGreater(res["output_throughput"], 2200)
def test_moe_offline_throughput_without_radix_cache(self):
res = run_bench_serving(
model=DEFAULT_MOE_MODEL_NAME_FOR_TEST,
num_prompts=300,
request_rate=float("inf"),
other_server_args=["--tp", "2", "--disable-radix-cache"],
)
if is_in_ci():
write_github_step_summary(
f"### test_moe_offline_throughput_without_radix_cache\n"
f"Output throughput: {res['output_throughput']:.2f} token/s\n"
)
if is_in_amd_ci():
self.assertGreater(res["output_throughput"], 2100)
else:
self.assertGreater(res["output_throughput"], 2200)
def test_pp_offline_throughput_default_decode(self):
res = run_bench_serving(
model=DEFAULT_MOE_MODEL_NAME_FOR_TEST,
num_prompts=1000,
request_rate=float("inf"),
random_input_len=1,
random_output_len=1024,
other_server_args=["--pp", "2"],
need_warmup=True,
seed=42,
)
if is_in_ci():
write_github_step_summary(
f"### test_pp_offline_throughput_default_decode\n"
f"Output throughput: {res['output_throughput']:.2f} token/s\n"
)
self.assertGreater(res["output_throughput"], 6700)
def test_pp_long_context_prefill(self):
res = run_bench_serving(
model="meta-llama/Llama-3.3-70B-Instruct",
num_prompts=4,
request_rate=float("inf"),
random_input_len=128000,
random_output_len=1,
dataset_name="random",
other_server_args=[
"--quantization",
"fp8",
"--pp",
2,
],
need_warmup=False,
seed=42,
)
if is_in_ci():
write_github_step_summary(
f"### test_pp_long_context_latency_prefill\n"
f"input_throughput: {res['input_throughput']:.2f} ms\n"
)
self.assertGreater(res["input_throughput"], 4000)
if __name__ == "__main__":
unittest.main()