EngineX-Hygon/sglang
5
0
Fork 0
Code Issues Pull Requests Actions 7 Projects Releases Wiki Activity

ci: unify the model launch method of nightly ci (#11230)

Browse Source
This commit is contained in:
Mick
2025-10-08 09:13:14 +08:00
committed by GitHub
parent f3764c26a3
commit 64d1505c0a
5 changed files with 192 additions and 153 deletions
Download Patch File Download Diff File Expand all files Collapse all files

59
test/srt/test_nightly_text_models_gsm8k_eval.py
View File

@@ -12,6 +12,7 @@ from sglang.test.test_utils import (
DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP2,
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
DEFAULT_URL_FOR_TEST,
ModelLaunchSettings,
check_evaluation_test_results,
parse_models,
popen_launch_server,
@@ -44,12 +45,19 @@ MODEL_SCORE_THRESHOLDS = {
class TestNightlyGsm8KEval(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.model_groups = [
(parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP1), False, False),
(parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP2), False, True),
(parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP1), True, False),
(parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP2), True, True),
]
cls.models = []
models_tp1 = parse_models(
DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP1
) + parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP1)
for model_path in models_tp1:
cls.models.append(ModelLaunchSettings(model_path, tp_size=1))
models_tp2 = parse_models(
DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP2
) + parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP2)
for model_path in models_tp2:
cls.models.append(ModelLaunchSettings(model_path, tp_size=2))
cls.base_url = DEFAULT_URL_FOR_TEST
def test_mgsm_en_all_models(self):
@@ -58,26 +66,24 @@ class TestNightlyGsm8KEval(unittest.TestCase):
)
is_first = True
all_results = []
model_count = 0
for model_group, is_fp8, is_tp2 in self.model_groups:
for model in model_group:
model_count += 1
with self.subTest(model=model):
other_args = ["--tp", "2"] if is_tp2 else []
for model_setup in self.models:
with self.subTest(model=model_setup.model_path):
other_args = list(model_setup.extra_args)
if model == "meta-llama/Llama-3.1-70B-Instruct":
other_args.extend(["--mem-fraction-static", "0.9"])
if model_setup.model_path == "meta-llama/Llama-3.1-70B-Instruct":
other_args.extend(["--mem-fraction-static", "0.9"])
process = popen_launch_server(
model=model,
other_args=other_args,
base_url=self.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
)
process = popen_launch_server(
model=model_setup.model_path,
other_args=other_args,
base_url=self.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
)
try:
args = SimpleNamespace(
base_url=self.base_url,
model=model,
model=model_setup.model_path,
eval_name="mgsm_en",
num_examples=None,
num_threads=1024,
@@ -85,14 +91,17 @@ class TestNightlyGsm8KEval(unittest.TestCase):
metrics = run_eval(args)
print(
f"{'=' * 42}\n{model} - metrics={metrics} score={metrics['score']}\n{'=' * 42}\n"
f"{'=' * 42}\n{model_setup.model_path} - metrics={metrics} score={metrics['score']}\n{'=' * 42}\n"
)
write_results_to_json(model, metrics, "w" if is_first else "a")
write_results_to_json(
model_setup.model_path, metrics, "w" if is_first else "a"
)
is_first = False
# 0.0 for empty latency
all_results.append((model, metrics["score"], 0.0))
all_results.append((model_setup.model_path, metrics["score"], 0.0))
finally:
kill_process_tree(process.pid)
try:
@@ -107,7 +116,7 @@ class TestNightlyGsm8KEval(unittest.TestCase):
all_results,
self.__class__.__name__,
model_accuracy_thresholds=MODEL_SCORE_THRESHOLDS,
model_count=model_count,
model_count=len(self.models),
)

172
test/srt/test_nightly_text_models_perf.py
View File

@@ -8,6 +8,7 @@ from sglang.srt.utils import kill_process_tree
from sglang.test.test_utils import (
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
DEFAULT_URL_FOR_TEST,
ModelLaunchSettings,
_parse_int_list_env,
is_in_ci,
parse_models,
@@ -21,14 +22,16 @@ PROFILE_DIR = "performance_profiles_text_models"
class TestNightlyTextModelsPerformance(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.model_groups = [
(parse_models("meta-llama/Llama-3.1-8B-Instruct"), False, False),
(parse_models("Qwen/Qwen2-57B-A14B-Instruct"), False, True),
# (parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP1), False, False),
# (parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP2), False, True),
# (parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP1), True, False),
# (parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP2), True, True),
]
cls.models = []
# TODO: replace with DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP1 or other model lists
for model_path in parse_models("meta-llama/Llama-3.1-8B-Instruct"):
cls.models.append(ModelLaunchSettings(model_path, tp_size=1))
for model_path in parse_models("Qwen/Qwen2-57B-A14B-Instruct"):
cls.models.append(ModelLaunchSettings(model_path, tp_size=2))
# (parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP1), False, False),
# (parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP2), False, True),
# (parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP1), True, False),
# (parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP2), True, True),
cls.base_url = DEFAULT_URL_FOR_TEST
cls.batch_sizes = [1, 1, 8, 16, 64]
cls.input_lens = tuple(_parse_int_list_env("NIGHTLY_INPUT_LENS", "4096"))
@@ -39,93 +42,86 @@ class TestNightlyTextModelsPerformance(unittest.TestCase):
def test_bench_one_batch(self):
all_benchmark_results = []
for model_group, is_fp8, is_tp2 in self.model_groups:
for model in model_group:
benchmark_results = []
with self.subTest(model=model):
process = popen_launch_server(
model=model,
base_url=self.base_url,
other_args=["--tp", "2"] if is_tp2 else [],
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
for model_setup in self.models:
benchmark_results = []
with self.subTest(model=model_setup.model_path):
process = popen_launch_server(
model=model_setup.model_path,
base_url=self.base_url,
other_args=model_setup.extra_args,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
)
try:
profile_filename = (
f"{model_setup.model_path.replace('/', '_')}_{int(time.time())}"
)
try:
profile_path_prefix = os.path.join(PROFILE_DIR, profile_filename)
json_output_file = f"results_{model_setup.model_path.replace('/', '_')}_{int(time.time())}.json"
profile_filename = (
f"{model.replace('/', '_')}_{int(time.time())}"
command = [
"python3",
"-m",
"sglang.bench_one_batch_server",
"--model",
model_setup.model_path,
"--base-url",
self.base_url,
"--batch-size",
*[str(x) for x in self.batch_sizes],
"--input-len",
*[str(x) for x in self.input_lens],
"--output-len",
*[str(x) for x in self.output_lens],
"--show-report",
"--profile",
"--profile-by-stage",
"--profile-filename-prefix",
profile_path_prefix,
f"--output-path={json_output_file}",
"--no-append-to-github-summary",
]
print(f"Running command: {' '.join(command)}")
result = subprocess.run(command, capture_output=True, text=True)
if result.returncode != 0:
print(
f"Error running benchmark for {model_setup.model_path} with batch size:"
)
profile_path_prefix = os.path.join(
PROFILE_DIR, profile_filename
)
json_output_file = (
f"results_{model.replace('/', '_')}_{int(time.time())}.json"
print(result.stderr)
# Continue to next batch size even if one fails
continue
# Load and deserialize JSON results
if os.path.exists(json_output_file):
import json
with open(json_output_file, "r") as f:
json_data = json.load(f)
# Convert JSON data to BenchmarkResult objects
for data in json_data:
benchmark_result = BenchmarkResult(**data)
all_benchmark_results.append(benchmark_result)
benchmark_results.append(benchmark_result)
print(
f"Loaded {len(benchmark_results)} benchmark results from {json_output_file}"
)
command = [
"python3",
"-m",
"sglang.bench_one_batch_server",
"--model",
model,
"--base-url",
self.base_url,
"--batch-size",
*[str(x) for x in self.batch_sizes],
"--input-len",
*[str(x) for x in self.input_lens],
"--output-len",
*[str(x) for x in self.output_lens],
"--show-report",
"--profile",
"--profile-by-stage",
"--profile-filename-prefix",
profile_path_prefix,
f"--output-path={json_output_file}",
"--no-append-to-github-summary",
]
# Clean up JSON file
os.remove(json_output_file)
else:
print(f"Warning: JSON output file {json_output_file} not found")
print(f"Running command: {' '.join(command)}")
result = subprocess.run(command, capture_output=True, text=True)
finally:
kill_process_tree(process.pid)
if result.returncode != 0:
print(
f"Error running benchmark for {model} with batch size:"
)
print(result.stderr)
# Continue to next batch size even if one fails
continue
# Load and deserialize JSON results
if os.path.exists(json_output_file):
import json
with open(json_output_file, "r") as f:
json_data = json.load(f)
# Convert JSON data to BenchmarkResult objects
for data in json_data:
benchmark_result = BenchmarkResult(**data)
all_benchmark_results.append(benchmark_result)
benchmark_results.append(benchmark_result)
print(
f"Loaded {len(benchmark_results)} benchmark results from {json_output_file}"
)
# Clean up JSON file
os.remove(json_output_file)
else:
print(
f"Warning: JSON output file {json_output_file} not found"
)
finally:
kill_process_tree(process.pid)
report_part = BenchmarkResult.generate_markdown_report(
PROFILE_DIR, benchmark_results
)
self.full_report += report_part + "\n"
report_part = BenchmarkResult.generate_markdown_report(
PROFILE_DIR, benchmark_results
)
self.full_report += report_part + "\n"
if is_in_ci():
write_github_step_summary(self.full_report)

41
test/srt/test_nightly_vlms_mmmu_eval.py
View File

@@ -1,6 +1,7 @@
import json
import unittest
import warnings
from functools import partial
from types import SimpleNamespace
from sglang.srt.utils import kill_process_tree
@@ -8,8 +9,8 @@ from sglang.test.run_eval import run_eval
from sglang.test.test_utils import (
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
DEFAULT_URL_FOR_TEST,
ModelDeploySetup,
ModelEvalMetrics,
ModelLaunchSettings,
check_evaluation_test_results,
popen_launch_server,
write_results_to_json,
@@ -17,25 +18,29 @@ from sglang.test.test_utils import (
MODEL_THRESHOLDS = {
# Conservative thresholds on 100 MMMU samples, especially for latency thresholds
ModelDeploySetup("deepseek-ai/deepseek-vl2-small"): ModelEvalMetrics(0.330, 56.1),
ModelDeploySetup("deepseek-ai/Janus-Pro-7B"): ModelEvalMetrics(0.285, 39.9),
ModelDeploySetup("Efficient-Large-Model/NVILA-Lite-2B-hf-0626"): ModelEvalMetrics(
0.305, 23.8
ModelLaunchSettings("deepseek-ai/deepseek-vl2-small"): ModelEvalMetrics(
0.330, 56.1
),
ModelDeploySetup("google/gemma-3-4b-it"): ModelEvalMetrics(0.360, 10.9),
ModelDeploySetup("google/gemma-3n-E4B-it"): ModelEvalMetrics(0.360, 15.3),
ModelDeploySetup("mistral-community/pixtral-12b"): ModelEvalMetrics(0.360, 16.6),
ModelDeploySetup("moonshotai/Kimi-VL-A3B-Instruct"): ModelEvalMetrics(0.330, 22.3),
ModelDeploySetup("openbmb/MiniCPM-o-2_6"): ModelEvalMetrics(0.330, 29.3),
ModelDeploySetup("openbmb/MiniCPM-v-2_6"): ModelEvalMetrics(0.270, 24.5),
ModelDeploySetup("OpenGVLab/InternVL2_5-2B"): ModelEvalMetrics(0.300, 14.0),
ModelDeploySetup("Qwen/Qwen2-VL-7B-Instruct"): ModelEvalMetrics(0.310, 83.3),
ModelDeploySetup("Qwen/Qwen2.5-VL-7B-Instruct"): ModelEvalMetrics(0.340, 31.9),
ModelDeploySetup("unsloth/Mistral-Small-3.1-24B-Instruct-2503"): ModelEvalMetrics(
0.310, 16.7
ModelLaunchSettings("deepseek-ai/Janus-Pro-7B"): ModelEvalMetrics(0.285, 40.3),
ModelLaunchSettings(
"Efficient-Large-Model/NVILA-Lite-2B-hf-0626"
): ModelEvalMetrics(0.305, 23.8),
ModelLaunchSettings("google/gemma-3-4b-it"): ModelEvalMetrics(0.360, 10.9),
ModelLaunchSettings("google/gemma-3n-E4B-it"): ModelEvalMetrics(0.360, 15.3),
ModelLaunchSettings("mistral-community/pixtral-12b"): ModelEvalMetrics(0.360, 16.6),
ModelLaunchSettings("moonshotai/Kimi-VL-A3B-Instruct"): ModelEvalMetrics(
0.330, 22.3
),
ModelDeploySetup("XiaomiMiMo/MiMo-VL-7B-RL"): ModelEvalMetrics(0.28, 32.0),
ModelDeploySetup("zai-org/GLM-4.1V-9B-Thinking"): ModelEvalMetrics(0.280, 30.4),
ModelLaunchSettings("openbmb/MiniCPM-o-2_6"): ModelEvalMetrics(0.330, 29.3),
ModelLaunchSettings("openbmb/MiniCPM-v-2_6"): ModelEvalMetrics(0.270, 24.5),
ModelLaunchSettings("OpenGVLab/InternVL2_5-2B"): ModelEvalMetrics(0.300, 14.0),
ModelLaunchSettings("Qwen/Qwen2-VL-7B-Instruct"): ModelEvalMetrics(0.310, 83.3),
ModelLaunchSettings("Qwen/Qwen2.5-VL-7B-Instruct"): ModelEvalMetrics(0.340, 31.9),
ModelLaunchSettings(
"unsloth/Mistral-Small-3.1-24B-Instruct-2503"
): ModelEvalMetrics(0.310, 16.7),
ModelLaunchSettings("XiaomiMiMo/MiMo-VL-7B-RL"): ModelEvalMetrics(0.28, 32.0),
ModelLaunchSettings("zai-org/GLM-4.1V-9B-Thinking"): ModelEvalMetrics(0.280, 30.4),
}

47
test/srt/test_nightly_vlms_perf.py
View File

@@ -8,6 +8,7 @@ from sglang.srt.utils import kill_process_tree
from sglang.test.test_utils import (
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
DEFAULT_URL_FOR_TEST,
ModelLaunchSettings,
_parse_int_list_env,
is_in_ci,
parse_models,
@@ -19,8 +20,13 @@ PROFILE_DIR = "performance_profiles_vlms"
MODEL_DEFAULTS = [
# Keep conservative defaults. Can be overridden by env NIGHTLY_VLM_MODELS
"Qwen/Qwen2.5-VL-7B-Instruct",
"google/gemma-3-27b-it",
ModelLaunchSettings(
"Qwen/Qwen2.5-VL-7B-Instruct",
extra_args=["--mem-fraction-static=0.7"],
),
ModelLaunchSettings(
"google/gemma-3-27b-it",
),
# "OpenGVLab/InternVL2_5-2B",
# buggy in official transformers impl
# "openbmb/MiniCPM-V-2_6",
@@ -33,9 +39,18 @@ class TestNightlyVLMModelsPerformance(unittest.TestCase):
warnings.filterwarnings(
"ignore", category=ResourceWarning, message="unclosed.*socket"
)
cls.models = parse_models(
os.environ.get("NIGHTLY_VLM_MODELS", ",".join(MODEL_DEFAULTS))
)
nightly_vlm_models_str = os.environ.get("NIGHTLY_VLM_MODELS")
if nightly_vlm_models_str:
cls.models = []
model_paths = parse_models(nightly_vlm_models_str)
for model_path in model_paths:
cls.models.append(
ModelLaunchSettings(model_path, extra_args=VLM_EXTRA_ARGS)
)
else:
cls.models = MODEL_DEFAULTS
cls.base_url = DEFAULT_URL_FOR_TEST
cls.batch_sizes = _parse_int_list_env("NIGHTLY_VLM_BATCH_SIZES", "1,1,2,8,16")
@@ -46,29 +61,31 @@ class TestNightlyVLMModelsPerformance(unittest.TestCase):
def test_bench_one_batch(self):
all_benchmark_results = []
for model in self.models:
for model_setup in self.models:
benchmark_results = []
with self.subTest(model=model):
with self.subTest(model=model_setup.model_path):
process = popen_launch_server(
model=model,
model=model_setup.model_path,
base_url=self.base_url,
other_args=["--mem-fraction-static=0.7"],
other_args=model_setup.extra_args,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
)
try:
# Run bench_one_batch_server against the launched server
profile_filename = f"{model.replace('/', '_')}"
profile_filename = f"{model_setup.model_path.replace('/', '_')}"
# path for this run
profile_path_prefix = os.path.join(PROFILE_DIR, profile_filename)
# JSON output file for this model
json_output_file = f"results_{model.replace('/', '_')}.json"
json_output_file = (
f"results_{model_setup.model_path.replace('/', '_')}.json"
)
command = [
"python3",
"-m",
"sglang.bench_one_batch_server",
f"--model={model}",
f"--model={model_setup.model_path}",
"--base-url",
self.base_url,
"--batch-size",
@@ -91,12 +108,14 @@ class TestNightlyVLMModelsPerformance(unittest.TestCase):
result = subprocess.run(command, capture_output=True, text=True)
if result.returncode != 0:
print(f"Error running benchmark for {model} with batch size:")
print(
f"Error running benchmark for {model_setup.model_path} with batch size:"
)
print(result.stderr)
# Continue to next batch size even if one fails
continue
print(f"Output for {model} with batch size:")
print(f"Output for {model_setup.model_path} with batch size:")
print(result.stdout)
# Load and deserialize JSON results