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[NPU] Add GitHub test summary and deduplicate test code. Part 1 (#23835)

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Co-authored-by: Elizaveta Martirosian <elizaveta.martirosian@gmail.com>
Co-authored-by: root <root@localhost.localdomain>
Co-authored-by: Elizaveta Martirosian <you@example.com>
Co-authored-by: ronnie_zheng <zl19940307@163.com>
This commit is contained in:
Elizaveta Martirosian
2026-05-02 14:18:18 +03:00
committed by GitHub
co-authored by Elizaveta Martirosian root Elizaveta Martirosian ronnie_zheng
parent 3259a2c789
commit ebbaab5597
8 changed files with 327 additions and 326 deletions
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python/sglang/test/ascend/gsm8k_ascend_mixin.py
+70 -35
View File
@@ -1,19 +1,22 @@
import os
import subprocess
from abc import ABC
from types import SimpleNamespace
from sglang.srt.utils import kill_process_tree
from sglang.test.ascend.test_ascend_utils import write_results_to_github_step_summary
from sglang.test.few_shot_gsm8k import run_eval
from sglang.test.test_utils import (
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
DEFAULT_URL_FOR_TEST,
popen_launch_server,
write_github_step_summary,
)
class GSM8KAscendMixin(ABC):
model = ""
accuracy = 0.00
timeout_for_server_launch = DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH
other_args = [
"--trust-remote-code",
@@ -23,48 +26,80 @@ class GSM8KAscendMixin(ABC):
"ascend",
"--disable-cuda-graph",
]
server_cmd = ""
gsm8k_num_shots = 5
num_questions = 200
env = {
**os.environ,
"PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
"ASCEND_MF_STORE_URL": "tcp://127.0.0.1:24666",
"HCCL_BUFFSIZE": "200",
"SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "24",
"USE_VLLM_CUSTOM_ALLREDUCE": "1",
"HCCL_EXEC_TIMEOUT": "200",
"STREAMS_PER_DEVICE": "32",
"SGLANG_ENBLE_TORCH_COMILE": "1",
"AUTO_USE_UC_MEMORY": "0",
"P2P_HCCL_BUFFSIZE": "20",
}
@classmethod
def setUpClass(cls):
cls.base_url = DEFAULT_URL_FOR_TEST
os.environ["PYTORCH_NPU_ALLOC_CONF"] = "expandable_segments:True"
os.environ["ASCEND_MF_STORE_URL"] = "tcp://127.0.0.1:24666"
os.environ["HCCL_BUFFSIZE"] = "200"
os.environ["SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK"] = "24"
os.environ["USE_VLLM_CUSTOM_ALLREDUCE"] = "1"
os.environ["HCCL_EXEC_TIMEOUT"] = "200"
os.environ["STREAMS_PER_DEVICE"] = "32"
os.environ["SGLANG_ENBLE_TORCH_COMILE"] = "1"
os.environ["AUTO_USE_UC_MEMORY"] = "0"
os.environ["P2P_HCCL_BUFFSIZE"] = "20"
env = os.environ.copy()
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=cls.timeout_for_server_launch,
other_args=cls.other_args,
env=env,
)
try:
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=cls.timeout_for_server_launch,
other_args=cls.other_args,
env=cls.env,
)
cls.server_cmd = subprocess.list2cmdline(cls.process.args)
except Exception as e:
write_github_step_summary(f"Failed to launch server for {cls.model}: {e}")
raise AssertionError(f"Test failed for {cls.model}: {e}")
@classmethod
def tearDownClass(cls):
kill_process_tree(cls.process.pid)
def test_gsm8k(self):
args = SimpleNamespace(
num_shots=self.gsm8k_num_shots,
data_path=None,
num_questions=200,
max_new_tokens=512,
parallel=128,
host="http://127.0.0.1",
port=int(self.base_url.split(":")[-1]),
)
metrics = run_eval(args)
self.assertGreaterEqual(
metrics["accuracy"],
self.accuracy,
f'Accuracy of {self.model} is {str(metrics["accuracy"])}, is lower than {self.accuracy}',
)
accuracy_threshold = getattr(self, "accuracy", 0.00)
output_throughput_threshold = getattr(self, "output_throughput", 0.00)
model_metrics = {
"server": self.server_cmd,
"client": "few_shot_gsm8k",
"accuracy_threshold": getattr(self, "accuracy", "N/A"),
"output_throughput_threshold": getattr(self, "output_throughput", "N/A"),
}
try:
args = SimpleNamespace(
num_shots=self.gsm8k_num_shots,
data_path=None,
num_questions=self.num_questions,
max_new_tokens=512,
parallel=128,
host="http://127.0.0.1",
port=int(self.base_url.split(":")[-1]),
)
metrics = run_eval(args)
model_metrics["accuracy"] = metrics["accuracy"]
model_metrics["output_throughput"] = metrics["output_throughput"]
self.assertGreaterEqual(
metrics["accuracy"],
accuracy_threshold,
f'Accuracy of {self.model} is {str(metrics["accuracy"])}, is lower than {accuracy_threshold}',
)
self.assertGreaterEqual(
metrics["output_throughput"],
output_throughput_threshold,
f'Output throughput of {self.model} is {str(metrics["output_throughput"])}, is lower than {output_throughput_threshold}',
)
except Exception as e:
model_metrics["error"] = e
self.fail(f"Test failed for {self.model}: {e}")
finally:
write_results_to_github_step_summary({self.model: model_metrics})
python/sglang/test/ascend/test_ascend_utils.py
+48
View File
@@ -24,7 +24,9 @@ from sglang.test.test_utils import (
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
DEFAULT_URL_FOR_TEST,
auto_config_device,
is_in_ci,
popen_launch_server,
write_github_step_summary,
)
# Model weights storage directory
@@ -90,6 +92,9 @@ LLAMA_3_2_1B_WEIGHTS_PATH = os.path.join(MODEL_WEIGHTS_DIR, "LLM-Research/Llama-
LLAMA_4_SCOUT_17B_16E_INSTRUCT_WEIGHTS_PATH = os.path.join(
MODEL_WEIGHTS_DIR, "meta-llama/Llama-4-Scout-17B-16E-Instruct"
)
LLaDA2_0_MINI_WEIGHTS_PATH = os.path.join(
MODEL_WEIGHTS_DIR, "inclusionAI/LLaDA2.0-mini"
)
META_LLAMA_3_1_8B_INSTRUCT = os.path.join(
MODEL_WEIGHTS_DIR, "LLM-Research/Meta-Llama-3.1-8B-Instruct"
)
@@ -555,3 +560,46 @@ def run_bench_serving(
assert res["completed"] == num_prompts
return res
HEADER = """
### Models
| Model | Server | Client | Output Throughput | Expected Output Throughput | Latency | Expected Latency | Accuracy | Expected Accuracy | Status |
| ----- | ------ | ------ | -------- | ------------------ | ------- | ---------------- | -------- | --------- | ------ |
"""
def write_results_to_github_step_summary(results: dict):
if not is_in_ci():
return
write_github_step_summary_once(HEADER)
get_float = lambda metrics, item, precision: (
f"{metrics[item]:.{precision}f}"
if isinstance(metrics.get(item, "-"), (int, float))
else metrics.get(item, "-")
)
summary = ""
for model, metrics in results.items():
model = model.replace(MODEL_WEIGHTS_DIR, "").replace(HF_MODEL_WEIGHTS_DIR, "")
output_throughput = get_float(metrics, "output_throughput", 2)
output_throughput_threshold = metrics.get("output_throughput_threshold", "N/A")
accuracy = get_float(metrics, "accuracy", 4)
accuracy_threshold = metrics.get("accuracy_threshold", "N/A")
latency = get_float(metrics, "latency", 4)
latency_threshold = metrics.get("latency_threshold", "N/A")
server = metrics.get("server", "N/A")
client = metrics.get("client", "N/A")
error = metrics.get("error", "")
status = "✅" if error == "" else "❌ " + str(error)
summary += f"| {model} | {server} | {client} | {output_throughput} | {output_throughput_threshold} | {latency} | {latency_threshold} | {accuracy} | {accuracy_threshold} | {status} |\n"
write_github_step_summary(summary)
def write_github_step_summary_once(summary: str):
if getattr(write_github_step_summary_once, "has_written", False):
return
write_github_step_summary_once.has_written = True
write_github_step_summary(summary)
python/sglang/test/ascend/test_mmlu.py
+37
View File
@@ -0,0 +1,37 @@
import subprocess
from types import SimpleNamespace
from sglang.test.ascend.test_ascend_utils import write_results_to_github_step_summary
from sglang.test.run_eval import run_eval
class TestMMLU:
def test_mmlu(self):
accuracy_mmlu_threshold = getattr(self, "accuracy_mmlu", 0.00)
model_metrics = {
"server": getattr(
self, "server_cmd", subprocess.list2cmdline(map(str, self.other_args))
),
"client": "simple_eval_mmlu",
"accuracy_threshold": getattr(self, "accuracy_mmlu", "N/A"),
}
try:
args = SimpleNamespace(
base_url=self.base_url,
model=self.model,
eval_name="mmlu",
num_examples=128,
num_threads=32,
)
print("Starting mmlu test...")
metrics = run_eval(args)
model_metrics["accuracy"] = metrics["score"]
self.assertGreater(metrics["score"], accuracy_mmlu_threshold)
except Exception as e:
model_metrics["error"] = e
self.fail(f"Test failed for {self.model}: {e}")
finally:
write_results_to_github_step_summary({self.model: model_metrics})
python/sglang/test/ascend/vlm_utils.py
+18 -2
View File
@@ -4,6 +4,7 @@ import os
import subprocess
from sglang.srt.utils import kill_process_tree
from sglang.test.ascend.test_ascend_utils import write_results_to_github_step_summary
from sglang.test.test_utils import (
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
DEFAULT_URL_FOR_TEST,
@@ -96,6 +97,8 @@ class TestVLMModels(CustomTestCase):
timeout=3600,
)
return subprocess.list2cmdline(cmd) # Return the command for logging purposes
def _run_vlm_mmmu_test(
self,
output_path="./logs",
@@ -115,8 +118,15 @@ class TestVLMModels(CustomTestCase):
"""
print(f"\nTesting model: {self.model}{test_name}")
model_metrics = {
"server": subprocess.list2cmdline(map(str, self.other_args)),
"client": "mmmu_eval",
"accuracy_threshold": self.mmmu_accuracy,
}
process = None
server_output = ""
mmmu_accuracy = None
try:
# Prepare environment variables
@@ -143,8 +153,10 @@ class TestVLMModels(CustomTestCase):
),
)
model_metrics["server"] = subprocess.list2cmdline(process.args)
# Run evaluation
self.run_mmmu_eval(self.model, output_path, limit)
model_metrics["client"] = self.run_mmmu_eval(self.model, output_path, limit)
# Get the result file
result_file_path = glob.glob(f"{output_path}/*.json")[0]
@@ -163,6 +175,8 @@ class TestVLMModels(CustomTestCase):
if capture_output and process:
server_output = self._read_output_from_files()
model_metrics["accuracy"] = mmmu_accuracy
# Assert performance meets expected threshold
self.assertGreaterEqual(
mmmu_accuracy,
@@ -173,10 +187,12 @@ class TestVLMModels(CustomTestCase):
return server_output
except Exception as e:
model_metrics["error"] = e
print(f"Error testing {self.model}{test_name}: {e}")
self.fail(f"Test failed for {self.model}{test_name}: {e}")
finally:
write_results_to_github_step_summary({self.model: model_metrics})
# Ensure process cleanup happens regardless of success/failure
if process is not None and process.poll() is None:
print(f"Cleaning up process {process.pid}")