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

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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
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172
test/srt/test_nightly_text_models_perf.py
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@@ -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)